• STEM Course Dates and Deadlines

    You are currently viewing STEM courses.

    If you are searching for Social Science courses, please navigate to this page instead.

     

    Horizon Labs features flexible start dates. Students should apply at least one month prior to their preferred start date. Horizon Seminar has fixed term dates and a fixed application deadline.

     

    Applications for Horizon Seminar Fall 2021 are now closed, but applications for Horizon Labs remain open for the fall for students interested in beginning research 4 weeks after their date of application. Applications for Horizon Seminar Spring 2022 are now open.

     

    Horizon Seminar Term Dates

    Spring 2022: March 1 - June 30, 2022

    Application Deadline: February 4, 2022

     

    Summer 2022: June 24 - September 5, 2022

    Application Deadline: May 23, 2022

     

  • Horizon Seminar STEM Courses

    Horizon Academic offers two distinct kinds of research experiences: Horizon Labs, which is a one-on-one research mentorship program, and Horizon Seminar. Horizon Seminar allows high school students to complete a college-level research project under the guidance of a professor or lecturer with decades of teaching experience in their field. Students develop individualized research topics and attend small group classes, with an average class size of 4 and a maximum of 6 students. Our Senior Instructors lead 14 classes throughout the trimester, and our Teaching Assistants (who tend to be masters or PhD students in these fields) offer an additional 6 sessions for review, discussion, and feedback. Although Horizon Seminar classes meet in small groups of 4-6 students, all students complete their own individual research project and are not expected to agree upon a research topic with other students.

    Environmental Engineering Horizon Seminar STEM Course Icon: How do ecosystems collapse? How can we engineer solutions to environmental catastrophe? Dr. Truncer’s course explores how human society can react to environmental systems collapse. Students may examine and research a variety of sustainability issues with regard to agricultural production, urbanization, infrastructure, resource use, and modern day engineering innovations. Dr. Truncer has previously taught this course at Harvard University and Stanford University.

    How do ecosystems collapse? How can we engineer solutions to environmental catastrophe? Dr. Truncer’s course explores how human society can react to environmental systems collapse. Students may examine and research a variety of sustainability issues with regard to agricultural production, urbanization, infrastructure, resource use, and modern day engineering innovations. Dr. Truncer has previously taught this course at Harvard University and Stanford University.

    Theoretical Mathematics Horizon Seminar STEM Course Icon: Knot Theory is the mathematical branch of topology with many applications, including analyzing DNA structures. In this theoretical math course, we study knot theory with an eye for one of the most foundational uses of all: understanding causality and the relationship between events. We will analyze models of knots and links in 2+1-dimensional spacetimes and apply computable link invariants to study what invariants can plausibly enable us to detect causality between two points or events.
    (Only Available Summer Terms)

    Knot Theory is the mathematical branch of topology with many applications, including analyzing DNA structures. In this theoretical math course, we study knot theory with an eye for one of the most foundational uses of all: understanding causality and the relationship between events. We will analyze models of knots and links in 2+1-dimensional spacetimes and apply computable link invariants to study what invariants can plausibly enable us to detect causality between two points or events.

    Data Science & Machine Learning Horizon Seminar STEM Course Icon: The availability of computer power and massive data has propelled the growth of data science as a discipline and its application to numerous fields. In this course, we study machine learning techniques, the mathematics behind those techniques, and the computer language Python, to implement those techniques on real data. As a course project, the student will have the option of analyzing a real data set or exploring mathematical aspects of machine learning. The topics of the possible course projects are listed below.

    The availability of computer power and massive data has propelled the growth of data science as a discipline and its application to numerous fields. In this course, we study machine learning techniques, the mathematics behind those techniques, and the computer language Python, to implement those techniques on real data. As a course project, the student will have the option of analyzing a real data set or exploring mathematical aspects of machine learning. The topics of the possible course projects are listed below.

    Psychology & Emotion Regulation Horizon Seminar STEM Course Icon: The course will explain and illustrate research methods in psychology using current research on human emotions, emotion regulation, and emotional disorders. Students will become familiar with research methods and experimental designs in these areas. Students will also design a study on a current topic of their choice in one of these areas.
    (Not Available Fall Terms)

    The course will explain and illustrate research methods in psychology using current research on human emotions, emotion regulation, and emotional disorders. Students will become familiar with research methods and experimental designs in these areas. Students will also design a study on a current topic of their choice in one of these areas.

    Machine Learning and Social Media. Public social media platforms have become very popular avenues for many people to get news to share their thoughts, feelings, and worldviews. In turn, social media data feeds can provide invaluable insights and strong signals of emerging problems. For example, leveraging powerful machine learning tools and social media feeds, we can detect when a social media account is involved with spreading misinformation, fake news, and hate speech, or in engaging in cyberbullying or malicious “trolling.” Additionally, we can predict when a user might have indications of mental health decline such as depression.
    (Only available Spring and Summer terms)

    Public social media platforms have become very popular avenues for many people to get news to share their thoughts, feelings, and worldviews. In turn, social media data feeds can provide invaluable insights and strong signals of emerging problems. For example, leveraging powerful machine learning tools and social media feeds, we can detect when a social media account is involved with spreading misinformation, fake news, and hate speech, or in engaging in cyberbullying or malicious “trolling.” Additionally, we can predict when a user might have indications of mental health decline such as depression.

    Cybersecurity. Internet attacks are increasingly sophisticated and complex, and they can have huge impact on our everyday lives. Do you really know how the Internet works? Are you sure that your personal data is well protected? In this class, we will first build our background knowledge on how computers communicate with one another through the internet. Then, we'll use machine learning approaches to detect compromised machines through network traffic, denial of service attacks, and hijacking attacks.
    (Only available Summer terms)

    Internet attacks are increasingly sophisticated and complex, and they can have huge impact on our everyday lives. Do you really know how the Internet works? Are you sure that your personal data is well protected? In this class, we will first build our background knowledge on how computers communicate with one another through the internet. Then, we'll use machine learning approaches to detect compromised machines through network traffic, denial of service attacks, and hijacking attacks.

  • Horizon Labs STEM Courses

     

    Horizon Labs offers high school students the opportunity to work one-on-one with leading researchers and lecturers from some of the world's best known universities to develop highly specialized and unique research projects in interdisciplinary topics in the sciences and humanities. Horizon Labs allows students to get individualized mentorship from instructors who are on the front lines of PhD-level research, often who are in the process of completing their own PhD or postdoctoral research. These instructors are intimately acquainted with the latest studies, the most relevant data sets, and the most interesting perspectives being introduced in their respective fields. Through 20 hours of one-on-one mentorship with their instructors, Horizon Labs students can get access to useful and unique data sets, develop customized reading lists to enrich their writing, get individualized feedback about their paper drafts, and hear advice on publication opportunities from experts in their fields.

     

    Formulation Chemistry Horizon Labs STEM Course Icon: How is it that you can smell a shampoo fragrance hours after cleaning your hair? What is the purpose of the long list of ingredients in your favorite snack? How can you control the release of new therapeutic drugs in human bodies? This class begins with key concepts on formulation chemistry (emulsion preparation, system stability, encapsulation techniques, characterization methods) before studying concrete applications in filtration, food, paints & coatings, cosmetics or pharmaceutical industries. Projects may consist of applications of machine learning to predict chemical reactions or material properties, or extensive literature reviews on a specific scientific challenge at the intersection of formulation chemistry and Material Science.

    How is it that you can smell a shampoo fragrance hours after cleaning your hair? What is the purpose of the long list of ingredients in your favorite snack? How can you control the release of new therapeutic drugs in human bodies? This class begins with key concepts on formulation chemistry (emulsion preparation, system stability, encapsulation techniques, characterization methods) before studying concrete applications in filtration, food, paints & coatings, cosmetics or pharmaceutical industries. Projects may consist of applications of machine learning to predict chemical reactions or material properties, or extensive literature reviews on a specific scientific challenge at the intersection of formulation chemistry and Material Science.

    Machine Learning and Biotechnology Horizon Labs STEM Course Icon: Never before has there been so much available data about various diseases and possible genetic associations with them. At the same time, new machine learning tools and data science techniques are enabling researchers to identify patterns and linkages between genes and disease. In this course, we first begin by reviewing key concepts in data analysis and machine learning. From there, students may explore connections between genetics and cardiovascular disease, autism, heart disease, allergies or autoimmune diseases.

    Never before has there been so much available data about various diseases and possible genetic associations with them. At the same time, new machine learning tools and data science techniques are enabling researchers to identify patterns and linkages between genes and disease. In this course, we first begin by reviewing key concepts in data analysis and machine learning. From there, students may explore connections between genetics and cardiovascular disease, autism, heart disease, allergies or autoimmune diseases.

    Neuroscience Horizon Labs STEM Course Icon: Horizon Academic offers a wide range of 57 sub-topics in neuroscience, ranging from social neuroscience, neurobiology, and more than a dozen topics about neurodegeneration disorders. Originally created in collaboration with two of our instructors working at the Department of Physiology Anatomy & Genetics at Oxford, our neuroscience offerings have continued to grow in recognition of our students' diverse interests in this exciting field.

    Horizon Academic offers a wide range of 57 sub-topics in neuroscience, ranging from social neuroscience, neurobiology, and more than a dozen topics about neurodegeneration disorders. Originally created in collaboration with two of our instructors working at the Department of Physiology Anatomy & Genetics at Oxford, our neuroscience offerings have continued to grow in recognition of our students' diverse interests in this exciting field.

    Protein Biophysics Horizon Labs STEM Course Icon: What are proteins? How do enzymes speed up chemistry? What do the proteins in photosynthesis and the electron transport chain actually do to capture energy? What role do they play in the emerging antibiotic resistant bacteria which are increasing mortality in hospitals and how do they influence viral infection? Is there anything proteins can teach us about physics? In this course we will discuss these questions and more based on our own research and multiple biophysics courses we have taken from world leaders in these topics at Stanford.

    What are proteins? How do enzymes speed up chemistry? What do the proteins in photosynthesis and the electron transport chain actually do to capture energy? What role do they play in the emerging antibiotic resistant bacteria which are increasing mortality in hospitals and how do they influence viral infection? Is there anything proteins can teach us about physics? In this course we will discuss these questions and more based on our own research and multiple biophysics courses we have taken from world leaders in these topics at Stanford.

    Psychology Horizon Labs STEM Course Icon: Horizon Academic is thrilled to offer a full range of 72 subtopics in psychology, spanning key questions in clinical, social, developmental, and cognitive psychology. Our psychology program started out with a narrower focus on data science and pathology, but our psychology offerings have continued to grow as more instructors of diverse psychology backgrounds have joined our team. We invite you to have a closer look at the many diverse psychology sub-topics we offer.

    Horizon Academic is thrilled to offer a full range of 72 subtopics in psychology, spanning key questions in clinical, social, developmental, and cognitive psychology. Our psychology program started out with a narrower focus on data science and pathology, but our psychology offerings have continued to grow as more instructors of diverse psychology backgrounds have joined our team. We invite you to have a closer look at the many diverse psychology sub-topics we offer.

    Gene Editing and CRISPR Technology Horizon Labs STEM Course Icon: How did life begin? What is the basis for human life and how are scientists learning to manipulate our genetic code? How can CRISPR allow use to control genetic expressions and human development? How is CRISPR being used in cutting edge diagnostic approaches and treatments? How can we theorize and understand the medical and social risks of CRISPR? This course allows students to interpret, understand, and perhaps build on leading scientific research on CRISPR and Gene Editing.

    How did life begin? What is the basis for human life and how are scientists learning to manipulate our genetic code? How can CRISPR allow use to control genetic expressions and human development? How is CRISPR being used in cutting edge diagnostic approaches and treatments? How can we theorize and understand the medical and social risks of CRISPR? This course allows students to interpret, understand, and perhaps build on leading scientific research on CRISPR and Gene Editing.

    Fluid Dynamics & Physics Horizon Labs STEM Course Icon: Fluid dynamics governs the water you drink, the air you breathe, and the blood running through you — even the plasma that makes up the stars. The intricacies of fluid motion are easily seen by watching phenomena such as the flame or smoke of a candle, the clouds moving overhead, or the ocean waves breaking against the shoreline — all fluids without a repeated pattern. The motion is constantly changing, sensitive to perturbations, and therefore difficult to predict. Fluid dynamics provides us the tools to better understand these complicated motions — through analytic, experimental, and computational study.

    Fluid dynamics governs the water you drink, the air you breathe, and the blood running through you — even the plasma that makes up the stars. The intricacies of fluid motion are easily seen by watching phenomena such as the flame or smoke of a candle, the clouds moving overhead, or the ocean waves breaking against the shoreline — all fluids without a repeated pattern. The motion is constantly changing, sensitive to perturbations, and therefore difficult to predict. Fluid dynamics provides us the tools to better understand these complicated motions — through analytic, experimental, and computational study.

    Applications of Machine Learning Horizon Labs STEM Course Icon: Machine learning and predictive analytics can be used in a stunning number of ways. From predicting the price of a stock you buy, to estimating the chances that your flight will be delayed, to estimating how well your favorite sports team might do next game, to even guessing the outcomes of a Supreme Court case, machine learning can help us predict the world around us. This course examines interesting and unlikely applications of machine learning that advance social goals, improve economic efficiency, or better understand the world around us.

    Machine learning and predictive analytics can be used in a stunning number of ways. From predicting the price of a stock you buy, to estimating the chances that your flight will be delayed, to estimating how well your favorite sports team might do next game, to even guessing the outcomes of a Supreme Court case, machine learning can help us predict the world around us. This course examines interesting and unlikely applications of machine learning that advance social goals, improve economic efficiency, or better understand the world around us.

    Organizational Behavior. How do organizations make good decisions, and why do they sometimes make bad ones? In what ways can team dynamics be improved? How can businesses foster creativity and innovation, and why are they important? This course examines the intersection of business and management studies, behavioral sciences, and psychology. Organizations, such as schools, startups, non-profits, corporations, and governments, are complex social systems that influence, and are influenced by, individual and group behavior.

    How do organizations make good decisions, and why do they sometimes make bad ones? In what ways can team dynamics be improved? How can businesses foster creativity and innovation, and why are they important? This course examines the intersection of business and management studies, behavioral sciences, and psychology. Organizations, such as schools, startups, non-profits, corporations, and governments, are complex social systems that influence, and are influenced by, individual and group behavior.

  • Research Questions by Each Course
     

    Below are the lists of pre-approved topics for each Horizon course. Please note that these lists are not restrictive or exhaustive: students at Horizon Academic often submit customized research topics or proposals. If a student wishes to research something else besides these questions but still related to the general course topic, then they should identify their proposed research question in their application. Prior to evaluating their application, we will consult with the course instructor to confirm whether the custom topic request is permitted.
     

  • Horizon Seminar

    Small Group Classes. Individualized Research Projects. Taught by Professors and Lecturers.

  • Environmental Problems in Human Society:

    Lessons from the Past, Engineering Future Solutions

    【 James Truncer 】

    How do ecosystems collapse? How can we engineer solutions to environmental catastrophe? Dr. Truncer’s course explores how human society can react to environmental systems collapse. Students may examine and research a variety of sustainability issues with regard to agricultural production, urbanization, infrastructure, resource use, and modern day engineering innovations. Dr. Truncer has previously taught his course at Harvard and Stanford University.

     

    Pre-approved Topic List

     

    1. What advantages does organic farming have over conventional farming? Can organic farms compete with conventional farms in feeding the world?

     

    2. How can cities and their infrastructure be designed for the predicted changes in climate? Provide specific examples in your response.

     

    3. The recent tremendous growth of urban areas has created a multitude of environmental problems and challenges. Choose one area of urban design that can improve the urban environment – what costs and benefits are involved?

     

    4. What are the latest advances in hydroponic and vertical farming? Are these the food production methods of the future? What are the costs?

     

    5. Are the economic benefits of dam building worth the environmental costs?

     

    6. Sea level rise is expected to impact many coastal cities and islands (e.g. Andaman Islands) in the coming years. What are the advantages or disadvantages of relocating an island settlement or city versus building dikes and protective barriers such as in the case of the Netherlands?

     

    7. Are genetically modified organisms (GMOs) significantly different from the variation produced through more traditional methods of cross-breeding and the creation of hybrids?

     

    8. Oceans are absorbing increasing amounts of carbon dioxide and are becoming more acidic. How will this affect marine ecosystems and thus human society? What policies might be implemented to make the public more aware of this looming environmental crisis and what incentives would encourage governments to take action?

     

    9. Money and research are now being poured into the technology of self-driving cars. Is maintaining the concept of “car” an efficient means of transportation, or are there better, more sustainable systems for the movement of people?

     

    10. Soil erosion is severe in many areas of the world. What farming methods and other activities are creating this erosion? What farming methods can not only reduce soil erosion but build nutrient-rich soil that enhances crop yields and lowers carbon emissions substantially? What policies might encourage soil conservation on farmland?

     

    11. Renewable energy sources are gaining more and more attention, and represent an increasingly larger percentage of energy production. What is the most promising type of renewable energy and why? Can modern society completely convert to renewable energy sources from a largely carbon-based system? What further advances or changes in lifestyle might be required?

     

    12. Most large farms rely on mechanization and need to add massive amounts of artificial fertilizer to produce high crop yields. How did this situation come about, and is this a sustainable practice? What are the carbon costs of such agriculture and are there feasible alternatives?

     

    13. Can sustainable practices be successfully incorporated into current business models? If not, what might need to change in order to create a better fit?

     

    14. Are United Nations treaties and resolutions an effective means to pass worldwide sustainability measures or is a different system necessary?

     

    15. Some architects are now designing “walkable” cities. What does this mean and what are the advantages and disadvantages of such an urban design? Illustrate your response with examples.

     

    16. Aquaculture, or fish farming, is increasingly providing a major source of food for a growing world population. What forms of aquaculture are most sustainable, and which forms are the least sustainable? Why? Provide specific examples of aquaculture in your analysis.

  • Theoretical Mathematics: Studying Knots, Links, Invariants to Prove Causality

    【 Vladimir Chernov 】

    Only available Summer Terms

     

    Prerequisites: Students will need to have completed Calculus 1 before beginning this course. This requirement would be satisfied by the completion of AP Calculus A/B, IB Math HL, or the equivalent offered in your school. Please note that Statistics and Pre-Calculus are not sufficient to satisfy this prerequisite.

     

    Knot Theory is the mathematical branch of topology with many applications, such as analyzing DNA structures. In this theoretical math course, we study knot theory with an eye for one of the most foundational uses of all: understanding causality and the relationship between events. Identifying cause and effect is foundational: to understanding the physics that govern our universe to functioning in daily life. Two points or events in a spacetime are causally related if one can get from one of them to the other without exceeding the speed of light. Mathematicians and physicists have related causality in spacetimes to the study of knots and links, and knot theorists often study simplified, flatted models of knots we encounter in everyday life, such as shoelaces tied together, to develop models for how we might understand causality and complex objects. We will analyze models of knots and links in 2+1-dimensional spacetimes and apply computable link invariants to study what invariants can plausibly enable us to detect causality between two points or events, drawing on the work in particular of Samantha Allen and Jacob Swenberg. Students will be free to select their own research topic relevant to knot theory, but Prof. Chernov is able to recommend particular racks or quandles that students can analyze. Student projects will examine which (of the many available) quandle invariants can be combined to the Alexander-Conway polynomial in order to plausibly detect causality in the toy models of the 2+1 dimensional spacetimes.

     

    Detailed Course Description

     

    Student projects will build on major developments in knot theory, culminating in their independent research topics. In order to reach this, students will examine the following theoretical developments:

    1. Robert Low (a student of this year Nobel Prize Winner Sir Roger Penrose) posed a conjecture relating causality in simplified, flatted models, called toy models, of (2+1)-dimensional spacetimes to the study of knots and links, essentially circular shoelaces tied together in different configurations. The Low conjecture was expanded on by Jose Natario and Paul Tod in the Legendrian Low conjecture to examine real world (3+1)-dimensional spacetimes and led to the question communicated by Penrose on the Vladimir Arnold Problem List. These conjectures and the questions were solved in the works of Stefan Nemirovski and Vladimir Chernov.

       

    2. In order to be able to apply these results to the real life problems, one needs to have computable invariants of links that completely determine causality. The work of Gage Martin, Ina Petkova and Vladimir Chernov show that the very powerful but computable Heegaard-Floer and Khovanov Homology Theories do solve this problem for the toy models of the (2+1)-dimensional spacetimes.

       

    3. The very recent work of Samantha Allen and Jacob Swenberg studied the question of whether the Alexander-Conway polynomial and the Jones polynomial are enough for this purpose. These polynomial invariants are obtained from the above homology theories by omitting much information, and the results of Allen and Swenberg suggest that the Jones polynomial is enough to detect causality, but the Alexander-Conway polynomial is likely not enough.

       

    4. Quandles and Racks are the classical and somewhat technical, but computable, link invariants that generalize the tri-coloring invariant. In this course, we will discuss all the theories mentioned above, and students will develop projects exploring which of the many Quandle invariants should be added to the Alexander-Conway polynomial so that it becomes plausible that they together completely detect causality in toy models of (2+1)-dimensional spacetimes. Examples of quandles that one can try to use are the Takasaki and more generally Alexander quandles, symplectic quandles, and one can also apply the theory of quandle cocycles that we will briefly discuss.

  • Machine Learning and Data Science

    【 Guillermo Goldsztein 】

    The availability of computer power and massive data has led to the incredible growth of data science as a discipline. Data science is now being applied to numerous fields. As a consequence, the demand for data scientists is very high. Leading universities have created new Data Science degrees at both the undergraduate and graduate level, and becoming a data scientist is considered the best career (or the career of the future) by many. In this course, students will learn machine learning techniques (the core of data science), the mathematics behind those techniques, and the computer language Python, to implement those techniques on real data. As a course project, the student will have the option of analyze a real data set or exploring mathematical aspects of machine learning. The topics of the possible course projects are listed below.

     

    Pre-approved Topic List

     

    Applications:

     

    1. Email Spam: Design an automatic spam detector that filters out spam to avoid clogging a user's inbox, without diverting desirable emails.

     

    2. Prostate Cancer: Predict the chances a patient has or will contract prostate cancer by utilizing clinical data.

     

    3. Handwritten Digit Recognition: Develop a computer program that automatically reads handwritten ZIP codes.

     

    4. DNA Expression Microarrays: Detect reads in DNA that are prevalent in certain cancers to enable a better understanding of cancer risks based on a patient's DNA.

     

    5. South African Heart Disease: Understand risk factors that lead to heart disease.

     

    6. Speech recognition: Develop a computer program that automatically detects different sounds and/or speech.

     

    7. Image recognition: Develop a computer program that recognizes subjects in an image.

     

    8. Dynamics of a galaxy: Use data from the motion of stars within the galaxy to understand its dynamics.

     

    9. Applications in sports: Analyze sports data that is of interest to the student.

     

    10. Income from demographics: Understand how different demographic factors are related to the income of an individual.

     

    Mathematics and methods:

     

    For the project, the student will have the option to learn and explore topics we did not have time to learn during the lectures. Some of these topics are likely to be:

     

    1. Some advanced aspects of Neural Networks. (We will cover Neural Networks in the course, but this is an expansive topic, so students will do a significant amount of independent work if they select this topic)

     

    2. Unsupervised learning

     

    3. Reinforcement Learning

     

    4. Advanced optimization techniques

     

    5. Advanced linear algebra

     

    6. Advanced Statistics

  • Clinical Psychology and Emotion Regulation

    Only available Spring and Summer terms

    The course will explain and illustrate research methods in psychology using current research on human emotions, emotion regulation, and emotional disorders. Students will become familiar with research methods and experimental designs in these areas. Students will also design a study on a current topic of their choice in one of these areas.

     

    Pre-approved Topic List

     

    1. Are some emotions more basic than others?

     

    2. Do emotions require cognitions?
     

    3. How do moods and emotions influence information processing and decision making?
     

    4. How are emotions experienced and are they always experienced?
     

    5. How can moods and emotions be measured and manipulated?
     

    6. Why do people want to regulate their emotions and how do cognitive emotion regulation strategies work?
     

    7. How are emotions and cognitions linked to goals and self-regulation?
     

    8. How do people differ in their cognitions and emotions and are there “emotion experts”?
     

    9. What are the implications of cognitive approaches towards emotions for our understanding and treatment of emotional disorders.
     

    10. What are the implications for theories about psychological resilience and well-being?
     

    11. What is depression, exactly? Is it one syndrome, or is it a collection of different syndromes that we grouped under the same name?

  • Leveraging Machine Learning and Social Media to Detect Fake News, Understand Mental Health, and Combat Cybercrime

    【 Maria Konte 】

    (Only Available Spring and Summer Terms)

    Public social media platforms have become very popular avenues for many people to get news to share their thoughts, feelings, and worldviews. In turn, social media data feeds can provide invaluable insights and strong signals of emerging problems. For example, leveraging powerful machine learning tools and social media feeds, we can detect when a social media account is involved with spreading misinformation, fake news, and hate speech, or in engaging in cyberbullying or malicious “trolling.” Additionally, we can predict when a user might have indications of mental health decline such as depression. Finally, we can detect when accounts on social media are being misused or abused for malicious purposes such as spamming, participating in cyberattacks, or proliferating malicious software. In this class, we will work with real world social media datasets (Twitter, Reddit, etc.) and applied machine learning techniques to develop models that indicate when a problem is under the way.

     

    Pre-approved Topic List

     

    1. Digital Epidemiology: Mental health and social media

     

    2. Predicting Depression using social media data

     

    3. Detecting fake news and misinformation

     

    4. Modeling the spread of information over social media

     

    5. Detecting hate speech

     

    6. Identifying cyberbullying on social media

     

    7. Applying graph analysis techniques on social media

     

    8. The formation of communities on social media

     

    9. Non-Coding Track: public policy and regulation of social media

  • Data Science Approaches to Internet Security

    【 Maria Konte 】

     

    (Only Available Summer Terms)

    Internet attacks are increasingly sophisticated and complex, and they can have huge impact on our everyday lives by disconnecting entire networks, disrupting food and gas supply chains, and leaking sensitive financial and personal information. As a result, the need for experts in all aspects in the Cybersecurity field is continuously increasing. In this class, we will first build our background knowledge on how computers communicate with one another, and how they work as parts of the Internet. Then we will learn about the indications that a device (computers, servers, handheld devices, and IoT / household devices connected to the Internet) is compromised or has atypical behavior. We use machine learning approaches to detect compromised machines through network traffic, denial of service attacks, and hijacking attacks. The course also features a non-coding track for students who are interested in the public policy and regulatory aspects of Cybersecurity. Skills we will focus on include Data science, Machine Learning, Network Traffic analysis, Internet Policies.

     

    We will work with real datasets on cross disciplinary projects.

     

     

    Pre-approved Topic List

     

     

    1. How does the Internet work?

     

    2. How to detect compromised devices?

     

    3. Hands-on Internet Security: Network Traffic Analysis

     

    4. What could bring the Internet down? Introduction to Security and overview of Internet Attacks.

     

    5. Hands-on Internet Security: Denial of Service Attacks (DoS)

     

    6. Hands-on Internet Security: Hijacking attacks

     

    7. How global physical and political events impact the Internet?

     

    8. Non-Coding Track: Public Policies and the Internet

  • Horizon Labs

    One on One Mentorship. Specialized Research Topics. Flexible Timing.

  • Formulation Chemistry

    【 David Brossault 】|【Sam Haddad】|【Nikzad Falahati】| 【Tom M.】

    When Formulation Chemistry Meets Scientific Challenges

     

    Have you ever been curious about the chemistry in the products you use everyday? How is it that you can smell a shampoo fragrance hours after cleaning your hair? What is the purpose of the long list of ingredients printed on your favorite drink or snack? How can you control the release of new therapeutic drugs in human bodies? If so, this program on formulation chemistry is made for you. In this class, we answer these questions and more. We first examine the main concepts on formulation chemistry (emulsion preparation, system stability, encapsulation techniques, characterization methods) before studying concrete applications in food, paints & coatings, cosmetics or pharmaceutical industries. The research project will then consist of an extensive literature review on a specific scientific challenge at the intersection of Formulation Chemistry and Material Science. The course taps into our instructors' research on chemistry at the University of Cambridge and at leading pharma companies like Sanofi.

     

    Pre-Approved Applications and Topics

     

    1. Food industry (e.g. Preparation of fat-free products with preserved textural properties)

     

    2. Cosmetics (e.g. Preparation of shampoos with sustained release of active principles)

     

    3. Plastics (e.g. Comparison of Plant-based vs oil-based materials)

     

    4. Paints & Coatings (e.g. Preparation of non-toxic paintings with enhanced drying and resistance properties)

     

    5. Pharmaceutical industry (e.g. Targeted drug delivery systems for controlled release of therapeutic compounds)

     

    6. Agriculture (e.g. Controlled release of herbicides on crops)

     

    7. Environmental applications (e.g. Composite materials for water pollutant removal)

     

    8. Buildings and roads (e.g. Development of self-healing concrete)

     

    9. Engine oil formulations (e.g. extending engine lifetimes and reducing CO2 emissions)

     

    10. Nanoparticles (e.g. imbuing formulations with countless properties, ie. conductivity, magnetism, catalysis and more)

     

    Topics in Analytical Chemistry

     

    The ways by which scientists elucidate the world around us rely on a complex but fascinating array of instrumental techniques and methodologies. Analytical chemistry stands at the cutting edge of scientific discovery, and this course will shine light (literally) on the many techniques that scientists use to solve and understand real-world problems. If you’re interested in the ways by which blood samples are analyzed, pollutants in the atmosphere are detected or how food and cosmetics pass the strict measures for quality control, then analytical chemistry is a key topic in your development as a scientist or entrepreneur. In your one-on-one lessons, you will be guided through the foundational theory that underpins the core techniques of analytical chemistry, and learn how to interpret actual data generated from real chemical systems. You will then apply these knowledge and skills as you explore a unique, scientific problem of your choosing, and produce a comprehensive literature review on the topic.

     

    1. Atomic and molecular spectroscopy involves using light (of varying properties) to identify and quantify chemical species. Therefore, spectroscopy can be implemented for an enormous host of applications such as impurity or contaminant detection in food and pharmaceuticals, measuring amounts of toxins and pollutants in the environment and atmosphere, analyzing the properties of objects in astronomy, or increasing agricultural production.

     

    2. Microscopy techniques are exploited when important phenomena are occurring far beyond the resolution of our eyes. From analyzing blood and tissue samples, to crime scene evidence, and then as far down as nanomaterials and single atoms, we gain insight into a myriad of systems through the art and ingenuity of microscopy.

     

    3. Diffraction and scattering techniques allow us to use various forms of radiation such as light X-rays and neutrons to determine the size, shape and properties of particles and materials. If you want to characterize nanoparticles, elucidate crystal structures with sub-nanometer resolution or analyze protein deformation over time, scattering and diffraction techniques may hold the key.

     

    4. Separations techniques allow scientists to purify and isolate single components from complex chemical mixtures. This may be for the purification of a new drug, to separate minerals from ore deposits or to remove pollutants from industrial effluent. Therefore, optimizing and innovating separations techniques is vital for economic and ecological benefits.

     

    Topics in Physical Chemistry

     

    Physical chemistry addresses the fundamental basis for the countless physicochemical phenomena exhibited by chemical systems. The mechanisms that underpin cloud formation, why water beads up on your living room window and how oil and water can be made to mix using emulsifiers, can all be explained within the grounds of physical chemistry. These lessons will rationalize the physical basis for these processes by describing and understanding the many interactions that take place between chemical species. Throughout the lessons, different chemical systems from particle dispersions to foamy fluids will be considered to aid in conceptualizing the complex interplay of forces involved in stabilizing these systems. This course is recommended for students that would like a challenge and are interested in consolidating their understanding of chemical systems with the relevant mathematical background. The mentor will also aid the student in conducting an extensive literature review on a related topic that the student desires to investigate.

     

    1. Chemical thermodynamics concerns the transfer of energy between chemical species and justifies when a reaction (or interaction) is favored to occur.

     

    2. Chemical kinetics concerns the timeframes of physicochemical phenomena and reactions.

     

    3. Interfaces are represented as the surface or boundary between two distinct phase regimes (ie. solid and liquid, oil and water, water and air). How does chemistry change at interfaces and how do these processes influence the world we experience?

     

    4. Chemical interactions dictate the overall behavior and reactivity of chemical systems. Understanding these complex interparticle forces is key in controlling system stability.

     

    5. Surfactants or ‘detergents’ are a widely used class of molecules that are commonly found in cosmetic and cleaning formulations. Why are these molecules so good at their job? What chemistry underpins the way they behave in solutions and at interfaces?

     

    6. Emulsions are formed when you achieve mixing of two liquids that would not normally mix (what?). Common examples include milk, mayonnaise and ice cream, and their formation and stability is owed to a unique array of interactions and processes.

     

    7. Foams and aerosols are the gas/liquid opposites of each other, and both find countless uses and manifestations in industry and nature. How is it that gas molecules can be trapped in a liquid and how is it that aerosol droplets can appear out of thin air?

     

    8. Liquid crystals are molecular assemblies that have the ordered structure of a crystal, but also the fluid/deformable properties of a liquid. Why and how do these unique structures emerge? What can they be used for?

  • Machine Learning and Biotechnology

    【Parsa A.】| 【Perman J.】|【Patrick Emedom-Nnamdi】|【 Rida Assaf 】|【 Emma R. 】|【 Angelina W. 】|【 Alex T. 】|【 Christa C. 】|【 Daniel K. 】|【 Lasya Sreepada 】

    Never before has there been so much collaboration between health researchers and computer scientists. This course examines applications of machine learning and predictive analytics in key areas of biology and health sciences. In this course, we first begin by reviewing key concepts in data analysis and machine learning and examine innovations in biotechnology enabled by machine learning. From there, we work with students in conducting original and novel analysis on large and complex data sets relating to a topic in health sciences such as cardiovascular disease, cancer diagnosis, epidemiological modelling, or drug discovery.

     

    Pre-approved Topic List

     

    Data Analysis and Statistics Based

     

    Please note that topics offered by Dr. Parsa A. are marked as "A". Those offered by Mr. Perman J. are marked as "J". Those offered by Mr. Patrick Emedom-Nnamdi are marked as "N". Those offered by Mr. Rida Assaf are marked as "R". Those offered by Mr. Alex T. are marked as "T". Those offered by Ms. Emma R. are marked as "E". Those offered by Ms. Angelina W. are marked as "W". Those offered by Ms. Christa C. are marked as "C". Those offered by Mr. Daniel K. are marked as "K". Those offered by Ms. Lasya Sreepada are marked as "L".

     

    1. Utilizing predictive machine learning models to learn more about cardiovascular diseases such as stroke and heart disease. [A, R, T, E, W, C, K, G, L]

     

    2. Utilizing predictive machine learning models to learn more about cancer prognosis and diagnosis. [A, J, N, R, E, W, C, K, G, L]

     

    3. Applications of machine learning in modeling the spread of infectious diseases such as COVID-19 and Ebola. [A, J, N, R, E, C, K, L]

     

    4. Applications of machine learning training a 'convolutional neural network' to predict skin lesions which are either benign or indicative of skin cancer. [A, R, T, E, W, C, L]

     

    5. Applications of Natural Language Processing in the health sector [R, C]

     

    Literature Review Based:

     

    6. Is the drug industry going bust? A review of the scientific literature and economic data. [A, T, E, K]

     

    7. A review of supervised and unsupervised Machine Learning models. [A, J, N, R, T, E, W, C, K, G, L]

     

    8. A review on computer-aided drug design and discovery [A, J, N, R, T, E, C, K, L]

  • Topics in Neuroscience

    Marta Madureira 】| 【 Patrick Liu 】| 【 Andy S.】|【 Carolina C.R. 】|【 Grace H.】| 【 Ellen R.】|【 Zeynep Ozturk 】|【 Paras Minhas 】|【 Natalya S. 】|【 Christa C.】|【 Shivang 】|【 ChiChi M.】|【 Kenneth K.】|【 Paula Martorell 】|【 Alex R. 】|【 Rui He 】|【 Eshaan R. B. 】|【 Benjamin H. 】|【 Mason 】

    Our neuroscience courses examine a variety of different aspects of the brain and the nervous system. Despite the incredible complexity of human behavior, we are able to take a deconstructive analysis to break down and better understand the various facets of behavior. Some of our instructors focus on integrative neuroscience, combining insights from psychology, data science, and philosophy together with traditional neuroscience, to better understand and reckon with deep questions about the nature of consciousness, perception, and memory. Some of our instructors focus specifically on neurodegenerative diseases and techniques that can be used to better understand and cure diseases like Alzheimer's and Parkinson's.

     

    Pre-approved Topic List

     

    Please note that topics offered by Mr. Patrick Liu are marked as "P". Those offered by Mr. Andy S. are marked as "S". Those offered by Ms. Marta Madureira are marked as "M". Those offered by Ms. Carolina C.R. are marked as "R". Those offered by Ms. Grace H. are marked as "G". Those offered by Ms. Natalya S. are marked as "N". Those offered by Ms. Ellen R. are marked "E". Those offered by Ms. Zeynep Ozturk are marked "Z". Those offered by Mr. Paras Minhas are marked "I". Those offered by Ms. Christa C. are marked "C". Those offered by Mr. Shivang are marked "H". Those offered by Ms. ChiChi M.are marked "CH". Those offered by Mr. Kenneth K. are marked "K". Those offered by Ms. Paula Martorell are marked "U". Those offered by Ms. Alex R. are marked "X". Those offered by Mr. Eshaan R. B. are marked "A". Those offered by Ms. Rui He are marked "RH". Those offered by Mr. Benjamin H. are marked "B". Those offered by Mr. Mason are marked "O".

     

    1. A review of important neurobiology fundamentals. [P, S, M, R, I, H, CH, K, A, RH, B, O]

     

    2. How do genes and environment interact to shape who we are? How we determine their effects? Is one more important than the other? Where did our personality come from? [P, S, M, R, G, I, C]

     

    3. How can we study the brain? How do we know which brain regions are responsible for certain behaviors? What are the limitations to studying the brain? [P, S, M, R, N, E, I, H, X, A, RH, B]

     

    4. How have evolutionary timescales and pressures carved human behavior? How can understanding the cellular basis of neural circuits explain our movements and the behaviors we make? Is human behavior entirely unique? What can we learn from the behavior of other animals in the evolutionary tree? [P, S, H]

     

    5. How did consciousness evolve? How do consciousness and intellect connect? Is there a limit to human consciousness? [P, S, G]

     

    6. How does the brain process and store information? How does emotion affect the brain’s judgment? More broadly, how do cognition, emotion, and memory mutually influence each other? [P, S, E]

     

    7. Where do empathy and sympathy come from? What can we learn about these from brain disorders and psychopathology? Does altruism really exist? [P, S, E]

     

    8. Why do humans and animals sleep? What purpose does it serve? How is sleep regulated? [P, S, G, E, H, CH, K, RH]

     

    9. As complex as decision making can be, what do we already know about its underlying processes? [P, S, E, H, RH]

     

    10. How do we perceive? What happens between when light enters our eyes to when we see objects? What is the difference between sensing and perceiving? [R, N, E, H, RH, B, O]

     

    11. What is attention? How does attention control what we perceive? Do we have control over attention? [N, E]

     

    12. Why do we think about the brain as having "circuitry"? Are electrical circuits a good metaphor for how the brain works? [G, N, E, H, B, O]

     

    13. What are the limits of neuroscience in analyzing and understanding consciousness. [E]

     

    14. Neuroscience and Law: How can neuroscience influence our rules and policies? [A]

     

    15. Neuroscience and Gender: What can science tell us (and not tell us) about sex and gender? [G, E]

     

    16. How does the physical environment impact cognition? Are there cognitive benefits to green space in cities? [E, CH]

     

    17. How does the brain produce behaviors? Is computation local or is it spread out over large scale networks? What can we learn from human neuroimaging such as fMRI, EEG, and MEG? [E]

     

    18. How does the social environment influence the brain? What can we learn from Human studies? What can we learn from animal studies? [E, X]

     

    19. General overview of frameworks for thinking about interactions between the physical and social environment, the brain, and behavior. [E, CH, RH]

     

    20. What is neuroplasticity? How does it relate to learning and memory? [S, M, R, E, I, H, X, A, RH, B]

     

    21. Is there such a thing as instincts or innateness? Or is all of our knowledge acquired during development? [S, E]

     

    22. What is the relationship between anticipation, motivation, and pleasure? How are dopamine signals involved? How does this relate to addiction? [S, E, H, RH]

     

    23. How does the brain generate affective states and construct emotions? How are the brain stem and amygdala involved? [S, E, H]

     

    24. What role does the prefrontal cortex play in attention, self-control, and decision making? [S, E]

     

    25. How is the hippocampus and medial temporal lobe involved in episodic future thinking and knowing where you are in space? [S, E, I, H]

     

    26. How are music, dance, and language related by common neural processes? [S]

     

    27. How does the brain represent the semantics of language and process linguistic syntax? How does this relate to language disorders, such as aphasia? How can we understand the evolution of language by studying the brain? [S, E]

     

    28. How does the brain recognize faces? Why are people with prosopagnosia not able to recognize any faces at all? [S, N, E, B, O]

     

    29. How can we use neuroimaging methods to understand the function and structure of neural networks? [S, G, E, H, X, RH, A]

     

    30. How can we use electrophysiological methods to understand neurons and information processing? [S, N, E, H, A, B]

     

    31. What are neurotransmitters, neuromodulators, receptors, ion channels, and synapses? And why are they so fundamental to brain function? [S, R, M, G, N, E, I, H, CH, K, A, B]

     

    32. What are glial cells? Why are they so crucial to normal brain function? How are they implicated in many common diseases? [S, R, M, E, I, H, CH, K, A]

     

    33. How do special adaptations in the brain allow bats to echolocate, owls to hunt in complete darkness, and birds to sing? [S, R, H]

     

    34. One of the most important qualities of the brain is its ability to change over time. What are the cellular and molecular mechanisms involved in this process? How do they relate to memory and learning? [M, R, I, H, K, A, B]

     

    35. What are some common experimental techniques that can be used to study the brain? How do they work? [M, R, N, E, I, H, A, B]

     

    36. Of all the organs in the human body, the brain consumes most of the nutrients (glucose, oxygen, etc.) available at a given time. Why is the brain so metabolically demanding? [M, R, G, E, H, CH, K, A, B]

     

    37. What are the mechanisms of touch? How is touch processed along the neuraxis? How is texture encoded in the brain? [N, E, H, RH]

     

    38. How does the brain control movement? Are all movements encoded the same way? How do reflexes arise? [N, E, H]

     

    39. How can we restore senses such as hearing, vision, touch, and movement to individuals who have lost them? What are the current technologies that exist and what are their limitations? [N, H]

     

    40. What do neural networks and neuroscience have in common? How are neural networks used to answer questions about the brain and its function? What are the limitations of these and other machine learning approaches? [N, E, C, H, B, O]

     

    41. A neuron within a neuron: importance of endoplasmic reticulum in neurons. [Z, K]

     

    42. Organelle contact sites: a new intracellular communication and their importance in neurons. [Z, K]

     

    43. How is pain perceived by the brain? Why do different moods/states change the amount of pain we feel? [H, RH]

     

    44. What are neuromodulators and how do they work? [H, X]

     

    45. What are drugs of abuse? How do they act on our neural circuits? From an evolutionary perspective, why do we have neural circuits that can be targeted by drugs of abuse? [H, B]

     

    46. What are the problems associated with consumption of opioids? Nicotine? Cocaine? [H]

     

    47. What kind of signal processing occurs in the retina? [O]


    48. How do we integrate information over time in low-light conditions? [O]


    49. Why do optical illusions work the way they do? [O]


    50. What visual cues influence where we deploy our attention? [O]


    51. How can we model content-addressable memory? [O]

     

    52. How can the local learning that takes place between pairs of neurons scale up to organize organism-wide behavior? [O]

     

    Topics in Neurodegenerative Disorders
     

    1. In a world where life expectancy has greatly increased, the population is aging. Why are neurodegenerative disorders linked to ageing? [M, G, I, H, K, U, A]

     

    2. What are the most common neurodegenerative disorders and are there available treatments? [M, R, I, H, CH, K, U, A]

     

    3. Why are certain areas of the brain susceptible to neurodegeneration? How do neurodegenerative disorders affect other parts of the human body? [M, I, H, CH, K, U, A]

     

    4. What are the mechanisms involved in neurodegenerative disorders such as Alzheimer’s Disease or Parkinson’s Disease? [M, R, G, I, H, CH, K, U, A]

     

    5. Are there models to study AD and PD? What are their advantages? What are their caveats? [M, I, H, K, U, A]

     

    6. Can we use gene editing to study neurodegeneration? Can CRISPR be used as genetic therapy? [M, I, H, K, U, A]

     

    7. Why are current treatments considered ineffective? How can we develop new therapies for these disorders? [M, R, I, H, CH, K, U, A]

     

    8. What are the genetic factors contributing to neurodegeneration? Are environmental factors also contributing? [M, G, I, H, CH, K, U, A]

     

    9. Can we “predict” who is going to develop dementia? What are some of the approaches to tackle this problem? [M, I, H, K, U, A]

     

    10. Will personalized medicine fulfill its potential for neurodegeneration in the clinic? [M, G, I, H, K, U, A]

     

    11. Can we revert neurodegeneration? What would be some of the strategies for this? [M, G, I, H, K, U, A]

     

    12. Is adult neurogenesis the answer? [M, R, I, H, K, U, A]

     

    13. iPSC-derived neurons as a model for Alzheimer’s and Parkinson’s. [M, I, H, K, U, A]

     

    14. LRRK2 is one of the most commonly mutated genes in Parkinson’s disease. But why is the role of LRRK2 still unknown? [M, I, K, U, A]

     

    15. What is autophagy and why is it so important for neurons? [M, I, H, K, U, A]

     

    16. The curious case of a sleeping disorder that “predicts” Parkinson’s Disease. [M, K, U, A]

     

    17. What is adult neurogenesis and why is it so unique? How is it maintained and regulated? Provide three good reasons to study this process. [R, H, K, U, A]

     

    18. What are some of the current debates in the field of adult neurogenesis? Provide a description of each side of the debate and highlight conclusions that can be drawn from each side. [R, H, K, U, A]

     

    19. Neuro-inflammation is one of the primary consequences of neurodegenerative diseases such as AD. What is neuro-inflammation? What cells participate in this process and how do they work? [M, R, G, I, H, K, U, A]

     

    20. What are the neurological mechanisms underlying the symptoms of Parkinson’s disease? Describe the neural circuit that is affected and which drugs are currently used to remedy this disease. How do these drugs work? [M, R, I, H, K, U, A]

     

    21. What is HIF-1alpha and how does it work? Why is it so important? What is its role in the brain? [R, I, H, K, U, A]

     

    22. What is senescence and does it play a role in neurodegenerative diseases? Which cell types tend to senescence in the central nervous system? [U, A]

     

    23. Does genetic variation in inflammatory genes contribute to the risk of getting neurodegenerative diseases? [U, A]

     

    24.What is the physiological role of Tau protein and what are tauopathies? [U, A]

     

    25. Does neuroinflammation fit into the amyloid cascade hypothesis in Alzheimer’s disease? [U, A]

     

    26. What triggers neuroinflammation in neurodegenerative diseases? When does inflammation begin? [U, A]

     

    27. What are the effects of the different genetic variants of Apolipoprotein E in Alzheimer’s disease? [U, A]

     

    28. What do single-cell RNA sequencing analysis of different cell types tell us in the context of neurodegenerative diseases? [U, A]

     

    29. What is the role of TREM2 and why is it relevant to Alzheimer’s disease? [U, A]

    1. Protein Biophysics

      【 Jared Weaver】| 【 Jacob Kirsh】

      Proteins are all over most introductions to biology and with good reason. Proteins are a, if not the, major result of the genetic code. Much of modern chemistry looks to proteins throughout biology as the “master chemists” to better understand the millions of chemical reactions they direct inside the bodies of all organisms, from humans to bacteria. But what are proteins and what do they really look like? How do enzymes speed up chemistry? What do the proteins in photosynthesis and the electron transport chain actually do to capture energy? What role do they play in the emerging antibiotic resistant bacteria which are increasing mortality in hospitals and how do they influence viral infection? Is there anything proteins can teach us about physics? In this course we will discuss these questions and more based on our own research and multiple biophysics courses we have taken from world leaders in these topics at Stanford. Biophysics is the application of physics to understand biology, and we aim to focus on its study of proteins for simplicity. We will begin by focusing on what proteins are, basic types of chemistry they perform, and the physical principles they use to control chemistry. From there we will review a few of the most common tools in biophysics and will introduce students to a variety of current topics of the field. We will focus on the topic our students are most interested in, the focus of their research project. The following (as well as those listed above) are examples. NOTE: AP Chemistry (or the equivalent since as IB HL Chemistry) is a prerequisite for this course. If your school does not offer AP or IB Chemistry, you will need to demonstrate extensive knowledge of chemistry through some other avenue to verify that you are familiar with the concepts covered in the AP Chemistry curriculum.

       

      Pre-Approved Topics

       

      1. Is it possible to build better enzymes than those found in biology?

       

      2. What role do proteins play in the pharmaceutical industry?

       

      3. How are current computational efforts used to understand proteins and with them, chemistry as a whole?

       

      4. We produce many new industrial biological catalysts and drugs primarily by relying on methods which work but are random in nature and poorly understood. Could we ever understand these processes ourselves? What governs protein evolution? How does nature evolve on the molecular level?

       

      5. All proteins are made of essentially the same 20 building blocks, but could that change and were these 20 building blocks just a coincidence?

       

      6. What might we consider if we are interested in creating new proteins from a novel DNA sequence?

       

      7. How does biology produce light? How much can it control the color of the different organisms throughout biology?

       

      8. Why are plants green and other algae brown? Is there any reason for the colors we see all around us?

       

      9. Green fluorescent protein and other fluorescent proteins are used ubiquitously throughout biology, but why do they exist in the first place?

       

      10. What controls the muscles in your arm? Why does your bicep bulge when you flex and what do proteins have to do with that?

       

      11. What can we see on the nanoscopic level (and smaller)? How fast do proteins and chemicals in general move?

       

      12. Many proteins are masters at producing electric fields to control chemistry, but how do they do so? Can we measure fields at such a tiny scale?

    2. Research Topics in Psychology

      【 Sori Baek 】|【 Brian E. 】|【 Megha C.】|【 Andy S.】|【 Erik N. 】|【Ellen R.】|【Joanna S.】|【Ana Maria Pereira de Souza】|【 Christa C.】|【 Jiyoung 】|【 ChiChi M. 】|【 Aliza 】|【 Alex R. 】|【 Jackie Katzman 】|【 Alexander Jay 】|【 Karly D. 】

      Please note that topics offered by Ms. Sori Baek are marked with "B" next to them. Those offered by Mr. Erik N. are marked as "N". Those offered by Mr. Brian E. are marked as "E". Those offered by Ms. Ellen R. are marked as "R". Those offered by Ms. Joanna S. are marked as "J". Those offered by Ms. Megha C. are marked as "M". Those offered by Ms. Ana Maria Pereira de Souza are marked as "A". Those offered by Mr. Andy S. are marked as "S". Those offered by Ms. Christa C. are marked as "C". Those offered by Jiyoung are marked as "I". Those offered by Ms. ChiChi M. are marked as "H". Those offered by Ms. Aliza are marked as "L". Those offered by Ms. Alex R. are marked as "X". Those offered by Ms. Jackie Katzman are marked as "K". Those offered by Mr. Alexander Jay are marked as "D". Those offered by Ms. Karly D. are marked as "Y".

       

      Topics in Cognitive Psychology

       

      1. How do people learn a new language? Is it different for adults and kids? [B, R, M]

       

      2. What helps a memory stick? What helps us remember things better? [B, R, J, M, A, S, L, Y]

       

      3. What makes memories become more accurate or inaccurate? What does this mean for eyewitness testimonies? [B, J, M, A, S, L, K, Y]

       

      4. Why are we so good at seeing “faces” from objects, like an outlet or a smiley face [ :) ]? Does this have an evolutionary reason? [B, R, M, A, S]

       

      5. Carrying a heavier backpack can make a hill look bigger. What are some other ways in which things change our perception? [B, R, S, Y]

       

      6. What affects our attention, and what distracts us? How do we select what we pay attention to? [B, M, A, S]

       

      7. How do other people affect how we think? How do opinions of others change our own opinions? [B, N, R, M, A, S, L, X, Y]

       

      8. Why are we so captivated by surprising and unexpected things, like magic? Does this have an evolutionary reason? [B, S]

       

      9. How do optical illusions work? How do they trick our brains? [B]

       

      10. What happens in our brain when we make predictions that turn out to be wrong? How does this experience help us learn? [B, S, X, Y]

       

      11. We’re really good at hearing our name, even if it’s said by someone standing really far away in a loud room. Why does this happen? [B, R, M, A, S]

       

      12. Are babies’ brains as good as adults’ brains? In what way? [B, R, H]

       

      13. What do babies do to learn? Are they good learners? [B, R, H, L]

       

      14. Can newborn babies tell their mothers apart from other people? In what way? [B, R, H, L, Y]

       

      15. A lot of toys are marketed to be good for the brain. Is this true? Which toys? Why or why not? [B, H, Y]

       

      16. What is our brain doing when we form memories and remember things from the past? [B, S, X, Y]

       

      17. What is our brain doing when we see numbers and do math? [B]

       

      18. What is our brain doing when we see alphabets and read a sentence? [B]

       

      19. What is our brain doing when we’re not paying attention in class? [B, S]

       

      20. How does the brain change when we learn a new skill and become better at it? [B, M, A, S, Y]

       

      21. What factors lead to differences in intelligence? Is IQ a good measure of how intelligent someone is? [R, M, A, C, L, Y]

       

      22. What makes different education styles work better than others? What does it mean to be a certain type of learner? [L, Y]

       

      23.What are the differences between short and long term memory? How do attention and memory interact? [J, M, A, L, Y]

       

      24. What role does memory play in eating behaviors? Can we use memories to help us lose weight? [J]

       

      25. What are the barriers to access to mental health services between different racial and ethnic groups? [H, L, Y]

       

      26. Are there differences in how mental illness is perceived between different racial and ethnic groups? [H, L, Y]

       

      27. Are there differences in perception of mental illness between men and women, and does this have long term consequences? [H, L, Y]

       

      Topics in Clinical Psychology

       

      Clinical psychology is concerned with identifying, understanding, and treating psychological disorders. This course will explore questions such as how we differentiate sadness from depression, why some people develop mental disorders while others don’t, what the best treatments for anxiety disorders are, and more. Students will have the option of focusing on specific mental disorders or studying basic psychological mechanisms that have clinical relevance.

        1. Uncertainty is a core feature of our everyday lives, especially during current times. How do humans respond to uncertainty? How does it affect our cognition, emotions, and behavior? [N, M, A, L, Y]

         

        2. How does the psychological trait of intolerance of uncertainty increase risk for anxiety disorders? [N, M, L, Y]

         

        3. Does it make sense to think of mental disorders are discrete categories or as dimensions that we all vary on? [N, M, I, L, Y]

         

        4. How do cognitive factors like attention, memory, and interpretation contribute to depression? [N, M, L, Y]

         

        5. What is the difference between fear and anxiety? [N, M, A, L, Y]

         

        6. How do we regulate our emotions? How does emotion regulation go awry in psychopathology? [N, M, A, I, L, Y]

         

        7. Is worry adaptive? [N, M, L, Y]

         

        8. Rumination refers to repetitive negative thought about the past, and worry refers to repetitive negative thought about the future. Are these two processes fundamentally the same or different? [N, M, A, L, X, Y]

         

        9. Why are we not better at treating mental disorders? [N, M, L, X]

         

        10. Does it make more sense to call mental disorders (e.g., depression) a "brain disease"? Why or why not? [N, M, A, H, L, X, Y]

         

        11. What are the "active ingredients" in psychotherapies for emotional disorders? How do we know that these are really the mechanisms of change? [N, I, L, X, Y]

         

        12. What is depression, exactly? Is it one syndrome, or is it a collection of different syndromes that we've grouped under the same name? [N, M, I, H, L, X, Y]

         

        13. Are today's youth really more anxious and depressed than youth in the past? If so, what is contributing to this increase? [N, M, A, H, L, X, Y]

         

        14. What do we know—and what do we not know—about treatments for emotional disorders? [A, L, Y]

         

        15. How do scientists study treatments for mental health problems? What are empirically supported treatments, why are they useful, and what are their limitations? [A, I, L, X]

         

        16. How can mental health treatments be delivered? What are the advantages and disadvantages of certain delivery formats? [A, I, L]

         

        17. How can we increase access to mental health treatments? [I, L, Y]

         

        18. Are apps and internet programs effective treatments for common mental health problems? [I, L, Y]

         

        19. How have treatments been adapted for people in low- and middle-income countries? What strategies are used to ensure that treatments are effective and culturally appropriate? [H, Y]

         

        20. What is the research-implementation gap? How long does it take for research evidence to reach clinical practice? [I, L, Y]

         

        Topics in Pathology and Data Science

         

        What causes mental illness? Mr. Jones's course explores competing theories on the origins of emotional disorders such as depression, social anxiety, and post-traumatic stress disorder. We examine how complexity approaches in statistics and machine learning, such as network analysis, can help us understand the problem. Depending on their interests, students can focus on a substantive area of mental health or delve deeper into the computational aspects.

         

        1. The network theory of mental disorders states that mental disorders do not have a single underlying cause, but instead are the result of feedback loops in a complex system. How does this theory apply to depression? Anxiety? Trauma? Other psychological problems? [N, M, A, I]

         

        2. Why do mental disorder co-occur at such high rates? How can network analysis inform the comorbidity between them? [N, M, A, I, L]

         

        3. How can novel developments in data science (e.g., machine learning methods) contribute to the field of clinical psychology? [A, C, I, L]

         

        4. What can we learn from exploratory data analysis of mental disorder symptoms? What kinds of psychometric data analyses and visualizations are most helpful? [A, C, I, L]

         

        5. Why are rates of emotional disorders often observed to be more common in developed nations compared to less developed nations? [L, Y]

         

        6. One hallmark of anxiety disorders is avoidance. What factors lead people to avoid versus approach their fears? [N, M, A, L, X, Y]

         

        7. Rates of violence across the world have been steadily decreasing. If this is indeed the case, why are rates of post-traumatic stress disorder (PTSD) stagnant or even increasing? [N, Y]

         

        8. To what extent do mental disorders represent a "mismatch" between the modern world and our environment during evolution? What factors of modernity might influence mental illness? [E, M, A]

         

        9. Why do some individuals with PTSD seem to compulsively revisit their traumatic past? How does this square with research on avoidance? [N, M, A, L, Y]

         

        10. Are trigger warnings or safe spaces effective approaches to helping those with PTSD? Why or why not? [N, M, A, L, Y]

         

        11. Today, phones and devices capture a huge amount of data about individuals (e.g., location, movement, texts, phone calls, app usage). Can this data be used for good when it comes to mental health? How? [N, A, C, I, L]

         

        12. Can people really experience "post-traumatic growth" after a trauma? If so, what does this growth look like? [N, A, L, Y]

         

        13. What is idiographic science? How can we study one person at a time? [I]

         

        14. Can we personalize psychotherapy interventions for each person? [A, I, L, X]

         

        15. How can data science help us predict substance use for each person? [A, I]

         

        16. Can a single survey item capture enough information, or do we always need multiple items? [I, Y]

         

        17. How much can we generalize from group-level research? [I, Y]

         

        18. How can we best capture fluctuations in people's emotions? What are affective dynamics? [I, L]

         

        Topics in Psychology and Law:

         

        1. For cases in which juvenile offenders are transferred to adult court, do jurors take their developmental vulnerabilities into account when they make decisions about them? [K]

         

        2. Mistaken identification is the leading cause of wrongful conviction. What procedural best-practices can make eyewitness evidence more reliable? How can social psychological theory inform these practices? [K, D, Y]

         

        3. Do the demograhics of the people selected as jury members affect their ultimate verdict decisions? [K, D]

         

        4. What strategies can help jurors better understand complex evidence in the courtroom? [K, D]

         

        5. Most all criminal cases are adjudicated thorugh plea negotiation. how can social psychological theory help attorneys better advise their clients? [K, D]

         

        6. Racial disparities in the criminal justice system are well documented and widespread. How can we lessen racial bias in policing, prison populations, and participation on juries? [K, D, Y]

         

        7. Why do people make false confessions? What aspects of police interrogations might increase the rate of false confession? [D, Y]

         

        8. Why do innocent people plead guilty? What components of plea bargaining increase the odds an innocent person will plead guilty? [D]

         

        9. How do the racial characteristics of a criminal case impact jurors' decision-making? [D, Y]

         

        10. How do robust social cognitive processes, such as stereotyping, affect jurors' perceptions and decision-making in civil and criminal cases? [D, Y]

         

        11. What is criminal profiling, and does it resemble the crime shows on t.v.? What does the science say about criminal profiling? How is it practiced by law enforcement agencies, and does it work? [D, Y]

         

        12. Jurors are often presented with a lot of complex information presented in a disorganized fashion. How do jurors make sense of the evidence, and render their decisions? [D]

         

        13. How do jurors' emotions impact their decision-making? [D]

         

        14. How does pre-trial publicity impact jurors decision-making? [D]

         

        15. What is 'juror rehabilitation'? Can it successfully reduce jurors' biases? [D]

         

        16. Jurors are constitutionally required to be impartial at the outset of a trial, but are they? How effective are legal system safe-guards (e.g., voir dire and jury selection) at removing biases? What about implicit biases? [D]

         

        Additional Topics in Psychology:

         

        1. Is psychology really a science? Should we trust findings in psychology more or less than in other fields? What is the "replication crisis" in psychology? [E, R, J, M, A, S, L, K, D, Y]

         

        2. Some researchers believe that most published findings in psychology (and some other disciplines) are false alarms and so not reproducible. Why might they think that? Are they right? [E, R, J, M, S, L, K]

         

        3. How do psychologists use statistical information to infer the existence of invisible phenomena like psychological states or attributes? What are some of major problems with the way psychologists use statistics? [E, R, M, A, S, L, Y]

         

        4. What does it mean to falsify a finding in psychology? If Researcher A runs an experiment and gets result X, and you run the same experiment and don't get that result, have you disproved their finding? Have you falsified their hypothesis or theory? Why does any of this matter? [E, R, J, M, S, D, Y]

         

        5. What is the psychology of scientific communication -- and belief? Do people just believe whatever scientific findings they agree with morally? is belief in science politically polarized? What determines whether someone believes in climate science? Why do some people think vaccines are harmful? [E, R, M, S, Y]

         

        6. What does it mean to be "the same person" over time? Are you the same person as you were when you were a baby? If so, in what sense? What factors influence the perception that someone is "a completely different person" after some big change in their life (like becoming addicted to drugs, or undergoing a religious conversion)? [E, R, M, A, S]

         

        7. Does Alzheimer's disease change who you are? If you sign a contract before the disease sets in, is it still valid if you lose most of your memories? [E, A, S]

         

        8. What is the relationship between moral intuitions and psychological traits or disorders? If someone is willing to sacrifice the life of one person in order to save a greater number of people, for example, could this have something to do with the trait of psychopathy? [E, M, A, S, L]

         

        9. How does relational context influence moral judgments? Why are some things okay to do in one kind of relationship, but not okay in other relationships? What explains our moral intuitions about different actions? Is it all about causing harm, or are there other reasons for judging a behavior as wrong? [E, R, M, A, S]

         

        10. Where does our sense of right and wrong come from? Why do we judge some things to be morally okay, and other things as NOT morally okay? When people from different cultures disagree about moral questions, does at least one of them have to be wrong? [E, R, M, A, S, L]

         

        11. Does believing in free will make you a more moral person? Does encouraging a belief in determinism make people more likely to me immoral (e.g., cheat on an exam)? [E, M, A, S, L]

         

        12. What is gender? Is it the same thing as sex? Are there more than two genders? Is your gender a matter of having certain feelings or psychological properties? [E, R, M, A, H, Y]

         

        13. How does gender bias affect judgments about how much pain someone is in? Do stereotypes like 'boys don't cry' affect how we perceive the pain of others? [E, R, M, A, H, L, Y]

         

        14. Can your brain start processing visual information -- for example, people's faces -- prior to conscious awareness? Is there such a thing as "unconscious perception"? How can you study the unconscious mind? [E, M, A, S, L]

         

        15. What is sexual orientation? What determines the sexual orientation a person has? [E, R, M, A, L, Y]

         

        16. Is it possible to be addicted to love? [E, S, L]

         

        17. What is the reproducibility crisis in psychology? How can scientists work to make the field better? [R, J, M, A, S, I, L, K, D, Y]

         

        18. What are emotions? What theories do psychologists and philosophers have for how our feelings – a cornerstone of human experience – function? What issues are there with our theories of emotions, and how can we improve them? [N, R, M, A, S, I, L, Y]

         

        19. How do we best manage our emotions? What skills can people use to regulate what they’re feeling, and how can we make these skills most efficacious? [N, M, A, S, I, L, Y]

         

        20. How do emotions change across age? When do children and adolescents start to have certain emotional experiences, and what does this mean for their well-being? [N, A, S, L, X]

         

        21. How does language relate to emotion? Do people of different cultures have different emotional experiences, and what does this mean about the mind? Can changing what words we use to talk about our feelings change how we feel? [N, A, L]

         

        22. How does language relate to mental health? Can we use linguistic methods in verbal communication to learn things about how well someone is doing psychologically? Can we develop tools to intervene when people aren’t doing well? [N, A, L]

         

        23. How does the brain represent and regulate emotions? What brain regions are involved in these processes, and can we connect deregulations in brain functioning to mental health problems? [N, A, S, L, X, Y]

         

        24. How does the brain develop across childhood and adolescence, and what does this mean for the development of emotions, mental health, or social functioning? [N, S, L, X]

         

        25. It has long been the understanding of social psychologists that people do not intuitively use base-rate information when they make predictions. Is it possible to increase the relevancy of base-rate information? How? [K]

         

        26. Norm development is one of the most powerful vehicles for changing people's behvaior and beliefs. How do norms influence our behavior? How can they be developed? [K, Y]

         

        27. What are the dimensions underlying our social perceptions of others? How do these relate to stereotypes of people and groups? [D, Y]

         

        28. What causes stereotypes? Can stereotypes change? How do stereotypes impact a perceiver's emotional and behavioral reactions? Can they affect a perceiver's causal attributions for another behavior? [D, Y]

        Organizational Behavior

        【 Karly D. 】

        This course examines the intersection of business and management studies, behavioral sciences, and psychology. Organizations, such as schools, startups, non-profits, corporations, and governments, are complex social systems that influence, and are influenced by, individual and group behavior. How do organizations make good decisions, and why do they sometimes make bad ones? In what ways can team dynamics be improved? How can businesses foster creativity and innovation, and why are they important? How can an organizational leader change the culture of a company, and how else can organizational culture change? What makes a successful leader in an organization? What is the role of personality in receiving, maintaining, and excelling at a job? What predicts different levels of worker motivation and productivity? How do individual-level perceptions, attitudes, biases, and stereotypes shape an organization? What is at stake with contemporary discourse on diversity and inclusion? We will grapple with these questions, and you will develop an understanding of the antecedents and consequences of organizational behavior.

         

        1. Should organizations use personality tests to decide which job applicants to hire?

         

        2. How does personality affect job satisfaction and performance?

         

        3. What motivates workers? How can workers be motivated to perform better?

         

        4. How do cognitive biases influence decision making, such as who is hired?

         

        5. What is emotional intelligence? Why is it important?

         

        6. What causes ‘burn out’? How can well-being be improved at work?

         

        7. How do group norms develop at workplaces?

         

        8. Why do individuals conform to group norms?

         

        9. How can group work and group decisions be improved?

         

        10. Do groups make better decisions than individuals? What influences decision-making?

         

        11. Are diverse teams or cohesive teams more effective?

         

        12. Why do organizations have diversity statements and equal opportunity policies? Are they effective?

         

        13. What makes a good leader?

         

        14. Do men and women have different leadership styles?

         

        15. Why are there more men than women in leadership positions?

         

        16. What does creativity and innovation look like in organizations?

         

        17. What is the difference between creativity and innovation? Which is more important?

         

        18. How does organizational culture develop?

         

        19. How and why do different organizations have different work cultures?

         

        20. How and why do organizational cultures change?

         

        21. Does our work environment influence us and how we work? How?

         

        22. How and why is AI (Artificial Intelligence) biased against gender, race, and other social identities?

      1. Gene Editing and CRISPR Technology

        【 Erika DeBenedictis 】|【 Zeynep Ozturk 】|【 Erin Berlew 】|【 Nadia Nasreddin】|【 Merrick S. 】|【 Soufiane Aboulhouda 】|【 Ana Queiroz】|【 Everardo Hegewisch Solloa 】|【 Niki G. 】|【 Grace H. 】|【 Christa C. 】|【 William 】

        How did life begin? What is the basis for human life and how are scientists learning to manipulate our genetic code? How can CRISPR allow users to control genetic expressions and human development? What is CRISPR, how was it discovered, and how can it rapidly change our ability to understand and manipulate biology? how are CRISPR systems being applied to both detect and treat human disease? How do we find new CRISPR systems with ever expanding functionality? We examine these questions and more in this course, based on the sections of the Biological Engineering course at the Massachusetts Institute of Technology that our instructors teach.

         

        Pre-approved Topic List

         

        Please note that topics offered by Ms. Erika DeBenedictis are marked as "E". Those offered by Ms. Zeynep Ozturk are marked as "Z". Those offered by Ms. Erin Berlew are marked as "B". Those offered by Ms. Nadia Nadreddin are marked as "N". Those offered by Mr. Merrick S. are marked as "M". Those offered by Mr. Soufiane Aboulhouda are marked as "S". Those offered by Ms. Ana Queiroz are marked as "Q". Those offered by Mr. Everardo Hegewisch Solloa are marked as "H". Those offered by Ms. Niki G. are marked as "G". Those offered by Grace H. are marked as "R". Those offered by Ms. Christa C. are marked as "C". Those offered by Mr. William are marked as "W".

         

        1. How do CRISPR systems work on the molecular level? What was their original purpose? How did they evolve? [E, Z, B, N, M, S, Q, H, G, R, C]

         

        2. Why are CRISPR systems useful for modern genome engineering? How do they compare to other techniques such as zinc fingers? [E, Z, B, N, M, S, Q, H, G, R, C]

         

        3. CRISPR-based techniques rely on protein such as Cas12 or Cas9. Are some of the properties of these proteins undesirable? How might we engineer these proteins to work better? [E, Z, B, N, M, S, H, G, R]

         

        4. On a molecular level, what components in living organisms are used to implement the specific genetic code that exists? How can we modify these components to create new genetic codes? What benefits would different genetic codes have for engineering purposes? [E, B, M, S, H, G, C]

         

        5. What are recent developments in the field of CRISPR, such as CRISPR-guided base editors and prime editing? [E, Z, B, N, M, S, H, G, R, C]

         

        6. How can CRISPR systems be used to modify the genomes of entire wild populations using ‘gene drive’ constructs? What are possible applications of gene drives? What are the technical challenges to implementing gene drives safely? What are the ethical implications of using gene drives? [E, Z, B, M, H, R, C, W]

         

        7. Large-scale engineering projects require project management strategies. In biological engineering, what are good strategies for assessing the quality and feasibility of an idea? How should one go about rapidly de-risking and implementing a new engineering approach? [E, B, G, C]

         

        8. When our engineering goals require biomolecules with functions not found in nature, we can attempt to create these new components with rational or computational design approaches, with directed evolution, or both. How do these protein engineering techniques work? How do we assess which approach is likely to be successful in a particular situation? [E, B, M]

         

        9. How did life originate? How did the divide between genetic material (DNA) and functional biomolecules (proteins) come to exist? How did the genetic code come to exist? [E, Z, B, M, Q, H, R, C]

         

        10. Why is the universally conserved genetic code structured the way it is? In particular, why does it use three-base codons, why are the codons assigned to specific amino acids, why do some amino acids have more codons, and why were the specific 20 amino acids chosen? [E, Z, B, M, Q, H, G, R, C]

         

        11. What can directed evolution experiments teach us about how evolution works? Conversely, can evolution research of organisms in the wild guide best practices for directed evolution experiments in the laboratory? [E, B, H, C]

         

        12. If we want to add a new amino acid to the genetic code, or rearrange which codon encodes which amino acid, what engineering approaches are available to us? What are the strengths and weaknesses of these different approaches? [E, B, M, H, C]

         

        13. What inspiration can we take from computer science that may help us engineer biological systems? Do concepts like logic gates and abstraction exist in biology, and if not, how do we implement them? [E, M, C]

         

        14. Proteins are chemically complex, enabling proteins to perform diverse chemical functions in the cell, but be difficult to engineer and model. In contrast, DNA less chemically complex. How can we exploit the simplicity of DNA’s chemical structure to predict the shape that a strand of DNA will adopt? How do we use this predictive capability to engineer custom DNA shapes (like smiley faces), or processes (like an AND logic gate)? What are the limits of DNA nanotechnology? [E, B]

         

        15. CRISPR enzymes can have off-target effects that may have unintended side effects of a therapy. What are strategies that are used to identify these off-target effects. How are these off-targets avoided and how are CRISPR enzymes engineered to alleviate this problem? [E, Z, B, N, M, S, H, C]

         

        16. Some CRISPR systems don’t act on DNA but, instead, on RNA. What function do these proteins have and how are these interesting proteins being harnessed for treating human disease? [E, B, N, S, G, H, R, C]

         

        17. How can CRISPR systems be used to treat human disease outside of gene editing? How are CRISPR proteins being used to change the expression of genes and why would one want to do this? [E, B, Z, N, M, S, H, G, R, C]

         

        18. If you wanted to insert an entirely new gene into the genome, how would you achieve this? What current technologies are used for gene insertion, what are their limitations, and what new technologies on the horizon can transform this problem? [Z, B, N, M, S, H, G, R, C]

         

        19. How are CRISPR enzymes being used to treat humans today? What kinds of diseases are being treated, why were they chosen, and how are CRISPR enzymes critical to the success of the treatment? What are the limitations of CRISPR in the clinic that have limited its ability to treat more diseases? [Z, B, N, M, S, H, G, R, C]

         

        20. If you wanted to treat a genetic disease in a living human with CRISPR, how would you get the enzyme to the diseased tissue of interest? How and why are viruses commonly used to deliver CRISPR to cells? [Z, B, N, M, S, H, G, R, C]

         

        21. How can CRISPR enzymes be used to diagnose disease? SHERLOCK and DETECTR are two platforms for detection of diseases and viruses. What are these tools, why are they increasingly gaining popularity as diagnostics, and how are these platforms being applied to detect viruses like COVID-19? [B, Q, C, W]

         

        22. New CRISPR enzymes are found every day from nature using computational tools. What are these computational tools, how do they work, and what new enzymes have been found using these techniques? [B, C]

         

        23. Next-generation sequencing is a transformative technology used by companies like 23andMe and Ancestry.com, by enabling rapid and inexpensive reading of DNA. How does next-generation sequencing work and how is it applied in research and in the clinic? [B, N, M, Q, H, G, R, C]

         

        24. Is it ethically appropriate to modify genomes including humans? What are the risks and how can we foresee the potential outcomes? [E, Z, B, N, M, S, Q, H, R, C]

         

        25. How can we use online genetic data in order to study genetic diseases and roles of genes in cell biology? [Z, B, N, M, H, G, R, C]

         

        26. Why is DNA sequencing important for scientific research? How does next generation sequencing compare with the previous sequencing methods, such as Sanger sequencing? And how are they simultaneously used in research? [N, M, Q, H, G, R, C]

         

        27. How has the next generation sequencing transformed scientific research? What is the 1000 genome and 100,000 genome projects? [N, M, Q, H, G, R, C]

         

        28. Most testing for COVID-19 is currently done on viral genetic material from nose and throat swabs, using reverse transcription polymerase chain reaction (RT-PCR). The next big goal is to develop a serological test. What are the molecular principles qPCR and PCR. What might be the issues associated with this diagnostic technique? [B, N, M, Q, H, G, C, W]

         

        29. All cancers arise as a result of changes that have occurred in the DNA sequence of the genomes of cancer cells, but not all mutations in cancer cells are involved in the development of cancer. What are driver and passenger mutations and why is it important to differentiate between them? How is the cancer genetic research revolutionizing treatment and management of cancer patients (with regards to cancer in general or a specific cancer type)? [B, N, H, G, R, C]

         

        30. Some RNA molecules fold into well-ordered structures. Given RNA sequences, how can these structures be predicted computationally? Are there useful applications for “riboswitches” which change their folds in response to a molecular signal? [E, M, C]

         

        31. How is epigenetic information written and read during the life cycles of cells and organisms? What kinds of epigenetic information are transferred between generations? How can CRISPR technology be used to alter the epigenome? [N, M, R, C]

         

        32. Why are stem cells special? What different kinds of stem cells are there? How can stem cells be used in research and therapeutics? [N, M, H, G, C, W]

         

        33. What are the various ways CRISPR systems are used to dissect fundamental biology and understand the function of genes? [B, N, S, Q, H, G, R, C]

         

        34. Can CRISPR be used to help edit RNA? What are the applications and benefits of RNA editing? [B, N, S, G, C, W]

         

        35. What are the various ways CRISPR is being used as a diagnostic, and what are the benefits of CRISPR based diagnostics? [B, S, Q, G, R, C, W]

         

        Additional Topics in Genomics:

         

        1. What are the limitations of genome-wide association studies (GWAS)? Given these limitations, how can we still use them to understand and treat disease? [N, R, C]

         

        2. How does the body use the same set of genes to produce cells with very different behavior, appearance and function? [N, R, C]

         

        3. What are the different kinds of ways a gene’s expression can be regulated? What are the advantages and disadvantages of each for an organism or a cell? [N, H, R, C]

         

        4. Gene imprinting: Why are ligers (the offspring of a male lion and a female tiger) much larger than lions OR tigers? Why are tigons (the reverse, an offspring of a female lion and a male tiger) much smaller? [H, R, C]

         

        5. How do our cells use ancient virus remnants to regulate gene expression? [H, R, C]

         

        6. How useful are the medical data you get when you do 23AndMe? [N, Q, H, R, C]

         

        7. What are the ethical implications of services like 23AndMe? How should we protect genetic privacy? What protections already exist for personal genetic data? [B, Q, H, R, C]

         

        8. Why is race a bad proxy for genetically-inherited risk for certain diseases? [H, R, C]

         

        9. What is the difference between self-reported race and genetic ancestry? [H, C]

         

        10. How can we use bioinformatic tools to understand genetic mutations and their affect in cells? [Z, H, C]

      2. Fluid Dynamics and Physics

        【 Haley Wohlever 】|【 William 】

        Fluid dynamics governs the water you drink, the air you breathe, and the blood running through you — even the plasma that makes up the stars. The intricacies of fluid motion are easily seen by watching phenomena such as the flame or smoke of a candle, the clouds moving overhead, or the ocean waves breaking against the shoreline. What makes fluids so fascinating is you can sit and watch these natural phenomena for hours without ever seeing a repeated pattern. The motion is constantly changing, sensitive to perturbations, and therefore difficult to predict. Fluid dynamics provides us the tools to better understand these complicated motions — through analytic, experimental, and computational study. Even without knowing the exact solution, insight to fluid motion has implications for world issues including predicting climate change, curing cancer, generating renewable energy sources, and producing clean water.

         

        Candidates should have completed one year of Calculus and one year of Physics coursework. Students who do not meet these requirements will still be considered, but they are encouraged to list a secondary course preference since their likelihood of admission is significantly lower. No programming experience is necessary.

         

        Pre-approved Topic List

         

        1. Oceanic or atmospheric waves (e.g. Impact of underwater boundaries on wave propagation in the ocean)


        2. Aerodynamic shape optimization (e.g. Reducing vehicle drag through geometry)


        3. Animal locomotion (e.g. Mechanisms animals have evolved to efficiently propel themselves through various fluids)
         

        4. Reusable energy (e.g. Production of energy through wind turbines)
         

        5. Vortices (e.g. Formation and propagation of vortices throughout the ocean or atmosphere)
         

        6. Stratification (e.g. Interaction between layers of flow with different properties)
         

        7. Environmental flows (e.g. Weather patterns and prediction)

      3. Applications of Machine Learning

        【 Parsa A. 】| 【 Patrick Emedom-Nnamdi 】|【 Perman J.】|【 Rida Assaf】|【 Derek S.】|【 Alex T.】|【 Emma R. 】|【 Angelina W. 】|【 Daniel K. 】|【 Jordan A. 】|【 Xiaoqi C. 】|【 Matthew G. 】|【 Lasya Sreepada 】

        Machine learning and predictive analytics can be used in a stunning number of ways. From predicting the price of a stock you buy, to estimating the chances that your flight will be delayed, to estimating how well your favorite sports team might do next game, to even guessing the outcomes of a Supreme Court case, machine learning can help us predict the world around us. This course examines interesting and unlikely applications of machine learning that advance social goals, improve economic efficiency, or better understand the world around us.

         

        Pre-approved Topic List

         

        Please note that topics offered by Dr. Parsa A. are marked as "A". Those offered by Mr. Patrick Emedom-Nnamdi are marked as "N". Those offered by Perman J. are marked as "J". Those offered by Mr. Rida Assaf are marked as "R". Those offered by Mr. Derek S. are marked as "D". Those offered by Mr. Alex T. are marked as "T". Those offered by Ms. Emma R. are marked as "E". Those offered by Ms. Angelina W. are marked as "W". Those offered by Mr. Daniel K. are marked as "K". Those offered by Mr. Jordan A. are marked as "O". Those offered by Mr. Xiaoqi C. are marked as "C". Those offered by Mr. Matthew G. are marked as "G". Those offered by Ms. Lasya Sreepada are marked as "L".

         

        1. Applications of deep learning and reinforcement learning to train an autonomous agent to solve video game dynamics. [A, N, R, T, E, O, C, G, S]

         

        2. Implementations of unsupervised learning to identify structure in social media data, for instance the structure of a group of followers of a popular Twitter account. [A, N, R, T, E, K, O, C]

         

        3. Developing convolutional neural networks to learn to scan images, with applications in image recognition and self-driving vehicles. [A, R, T, E, W, O, C, G, S, L]

         

        4. Applications of machine learning to predict socially important and economically useful outcomes such as:

        1)The probability of flight delays. [A, N, R, D, T, E, K, O, C, S, L]

        2)Stock market prices and fluctuations. [A, N, R, D, T, E, K, O, C, S, L]

        3)Studying and predicting real estate prices. [A, N, R, D, T, E, W, K, O, C, S, L]

        4)Predicting scores and performance of sports teams. [A, N, R, D, T, E, W, K, O, L]

        5)Incarceration, recidivism, and criminal justice outcomes; and ways that machine learning models can be deployed in this area to avoid replicating racial or socio-economic bias in the justice system. [A, N, R, D, T, E, K, O, L]

         

        5. Applications of machine learning or automation in chemical synthesis [J, O]

         

        6. Applications of machine learning predicting chemical reactions [J, O]

         

        7. Designing and developing materials for CO2 capture and storage using Machine Learning [J, O]

      4. Organizational Behavior

        【 Karly D. 】

        This course examines the intersection of business and management studies, behavioral sciences, and psychology. Organizations, such as schools, startups, non-profits, corporations, and governments, are complex social systems that influence, and are influenced by, individual and group behavior. How do organizations make good decisions, and why do they sometimes make bad ones? In what ways can team dynamics be improved? How can businesses foster creativity and innovation, and why are they important? How can an organizational leader change the culture of a company, and how else can organizational culture change? What makes a successful leader in an organization? What is the role of personality in receiving, maintaining, and excelling at a job? What predicts different levels of worker motivation and productivity? How do individual-level perceptions, attitudes, biases, and stereotypes shape an organization? What is at stake with contemporary discourse on diversity and inclusion? We will grapple with these questions, and you will develop an understanding of the antecedents and consequences of organizational behavior.

         

        1. Should organizations use personality tests to decide which job applicants to hire?

         

        2. How does personality affect job satisfaction and performance?

         

        3. What motivates workers? How can workers be motivated to perform better?

         

        4. How do cognitive biases influence decision making, such as who is hired?

         

        5. What is emotional intelligence? Why is it important?

         

        6. What causes ‘burn out’? How can well-being be improved at work?

         

        7. How do group norms develop at workplaces?

         

        8. Why do individuals conform to group norms?

         

        9. How can group work and group decisions be improved?

         

        10. Do groups make better decisions than individuals? What influences decision-making?

         

        11. Are diverse teams or cohesive teams more effective?

         

        12. Why do organizations have diversity statements and equal opportunity policies? Are they effective?

         

        13. What makes a good leader?

         

        14. Do men and women have different leadership styles?

         

        15. Why are there more men than women in leadership positions?

         

        16. What does creativity and innovation look like in organizations?

         

        17. What is the difference between creativity and innovation? Which is more important?

         

        18. How does organizational culture develop?

         

        19. How and why do different organizations have different work cultures?

         

        20. How and why do organizational cultures change?

         

        21. Does our work environment influence us and how we work? How?

         

        22. How and why is AI (Artificial Intelligence) biased against gender, race, and other social identities?

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