How do ecosystems collapse? How can we engineer solutions to environmental catastrophe? This 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.

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.

Algorithms are sets of instructions to execute tasks. The task may seem as simple as sorting alphabetically a set of words, or complex such as the reconstruction of genomes from biochemical experiments or the encryption of sensitive data for safety purposes.

There are several algorithms to accomplish the same task, but some algorithms are more efficient than others. The efficiency of an algorithm depends to the number of operations and the memory space required for its execution. If an algorithm is not efficient, it may require so much time or memory to execute that it becomes useless. The reading of genomes is an example of this fact. Reading genomes involves biochemical experiments to collect data, and algorithms to process this data on computers. The development of efficient algorithms to read genomes helped decrease its cost from billions to just hundreds of dollars.

Data structures refers on how the data is represented. Data structures go hand with algorithms. Different algorithms may require different data structures

This course is in algorithms, data structures and the programming language Python. It is recommended only for students should enjoy computer science and mathematics. After completing this course, students will understand how many real-world programs work, and will also be able to write their own programs in Python.

Pre-approved Topic List

1. Given an unsorted integer array, find a pair with the given sum in it.
 

2. Finding the longest subsequence present in given two sequences in the same order, i.e., find the longest sequence which can be obtained from the first original sequence by deleting some items and from the second original sequence by deleting other items.
 

3. Given an integer array, find a contiguous subarray within it that has the largest sum.
 

4. Given an unlimited supply of coins of given denominations, find the total number of distinct ways to get the desired change.
 

5. We are given a set of items, each with a weight and a value, and we need to determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.
 

6. Given a set of positive integers and an integer k, check if there is any non-empty subset that sums to k.
 

7. The Longest Palindromic Subsequence (LPS) problem is finding the longest subsequences of a string that is also a palindrome.
 

8. Matrix chain multiplication problem: Determine the optimal parenthesization of a product of n matrices.
 

9. The longest common substring problem is the problem of finding the longest string (or strings) that is a substring (or are substrings) of two strings.
 

10. Given a rod of length n and a list of rod prices of length i, where 1 <= i <= n, find the optimal way to cut the rod into smaller rods to maximize profit.
 

11. Word Break Problem: Given a string and a dictionary of words, determine if the string can be segmented into a space-separated sequence of one or more dictionary words.
 

12. The Levenshtein distance between two words is the minimum number of single-character edits (i.e., insertions, deletions, or substitutions) required to change one word into the other. Each of these operations has a unit cost. Develop an algorithm to compute the Levenshtein distance between two words.
 

13. Given a chessboard, find the shortest distance (minimum number of steps) taken by a knight to reach a given destination from a given source.
 

14. Given a set of positive integers, check if it can be divided into two subsets with equal sum.
 

15. 3-partition problem: Given a set S of positive integers, determine if it can be partitioned into three disjoint subsets that all have the same sum, and they cover S.
 

16. Find the minimum number of throws required to win a given Snakes and Ladders board game.
 

17. Given an integer array, find the largest subarray formed by consecutive integers. The subarray should contain all distinct values.
 

18. Given an array containing only 0’s, 1’s, and 2’s, sort it in linear time and using constant space.
 

19. Given a chessboard, print all sequences of moves of a knight on a chessboard such that the knight visits every square only once.
 

20. Given an N × N matrix of integers, find the maximum sum submatrix present in it.
 

21. Given a string, find the maximum length contiguous substring of it that is also a palindrome. For example, the longest palindromic substring of “bananas” is “anana”, and the longest palindromic substring of “abdcbcdbdcbbc” is “bdcbcdb”.

22. Given a list of tasks with deadlines and total profit earned on completing a task, find the maximum profit earned by executing the tasks within the specified deadlines. Assume that each task takes one unit of time to complete, and a task can’t execute beyond its deadline. Also, only a single task will be executed at a time.
 

23. The N–queens puzzle is the problem of placing N chess queens on an N × N chessboard so that no two queens threaten each other. Thus, the solution requires that no two queens share the same row, column, or diagonal.
 

24. Given an integer array, find the subarray that has the maximum product of its elements. The solution should return the maximum product of elements among all possible subarrays.
 

25. The Longest Repeating Subsequence (LRS) problem is finding the longest subsequences of a string that occurs at least twice.
 

26. Given an unsorted integer array, find a triplet with a given sum in it.
 

27. The Shortest Common Supersequence (SCS) is finding the shortest supersequence Z of given sequences X and Y such that both X and Y are subsequences of Z.
 

28. Given an array containing positive and negative elements, find a subarray with alternating positive and negative elements, and in which the subarray is as long as possible.
 

29. 4-sum problem: Given an unsorted integer array, check if it contains four elements tuple (quadruplets) having a given sum.
 

30. In the k–partition problem, we need to partition an array of positive integers into k disjoint subsets that all have an equal sum, and they completely cover the set.
 

31. Given a set of positive integers S, partition set S into two subsets, S1 and S2, such that the difference between the sum of elements in S1 and S2 is minimized. The solution should return the minimum absolute difference between the sum of elements of two partitions.
 

32. Wildcard Pattern Matching: Given a string and a pattern containing wildcard characters, i.e., * and ?, where ? can match to any single character in the string and * can match to any number of characters including zero characters, design an efficient algorithm to check if the pattern matches with the complete string or not.
 

33. Consider an event where a log register is maintained containing the guest’s arrival and departure times. Given an array of arrival and departure times from entries in the log register, find the point when there were maximum guests present in the event.
 

34. Graph coloring (also called vertex coloring) is a way of coloring a graph’s vertices such that no two adjacent vertices share the same color. This post will discuss a greedy algorithm for graph coloring and minimize the total number of colors used.
 

35. The Longest Increasing Subsequence (LIS) problem is to find a subsequence of a given sequence in which the subsequence’s elements are in sorted order, lowest to highest, and in which the subsequence is as long as possible. This subsequence is not necessarily contiguous or unique.
 

36. There are two players, A and B, in Pots of gold game, and pots of gold arranged in a line, each containing some gold coins. The players can see how many coins are there in each gold pot, and each player gets alternating turns in which the player can pick a pot from either end of the line. The winner is the player who has a higher number of coins at the end. The objective is to “maximize” the number of coins collected by A, assuming B also plays “optimally”, and A starts the game.

37. Activity Selection Problem: Given a set of activities, along with the starting and finishing time of each activity, find the maximum number of activities performed by a single person assuming that a person can only work on a single activity at a time.
 

38. The longest alternating subsequence is a problem of finding a subsequence of a given sequence in which the elements are in alternating order and in which the sequence is as long as possible. In order words, we need to find the length of the longest subsequence with alternate low and high elements.
 

39. Given an integer array, find the length of the longest subsequence formed by the consecutive integers. The subsequence should contain all distinct values, and the character set should be consecutive, irrespective of its order.
 

40. Trapping rainwater problem: Find the maximum amount of water that can be trapped within a given set of bars where each bar’s width is 1 unit.
 

41. Given a list of jobs where each job has a start and finish time, and has profit associated with it, find a maximum profit subset of non-overlapping jobs.
 

42. The Longest Bitonic Subarray (LBS) problem is to find a subarray of a given sequence in which the subarray’s elements are first sorted in increasing order, then in decreasing order, and the subarray is as long as possible. Strictly ascending or descending subarrays are also accepted.
 

43. Suppose we are given n red and n blue water jugs, all of different shapes and sizes. All red jugs hold different amounts of water, as do the blue ones. Moreover, there is a blue jug for every red jug that holds the same amount of water and vice versa. The task is to efficiently group the jugs into pairs of red and blue jugs that hold the same amount of water. (Problem Source: CLRS)
 

44. Given a positive number n, find the total number of ways in which n hats can be returned to n people such that no hat makes it back to its owner.
 

45. Given a set of intervals, print all non-overlapping intervals after merging the overlapping intervals.
 

46. Write an efficient algorithm to find the longest common prefix (LCP) between a given set of strings.
 

47. Given a rod of length n, find the optimal way to cut the rod into smaller rods to maximize the product of each of the smaller rod’s price. Assume that each rod of length i has price i.
 

48. Given a set of rectangular 3D boxes (cuboids), create a stack of boxes as tall as possible and return the maximum height of the stacked boxes.
 

49. Given an integer array, find the maximum product of its elements among all its subsets.
 

50. Given a binary tree, find the size of the Maximum Independent Set (MIS) in it.

Additional Topics Available for Students Interested in Projects with a Higher Level of Difficulty

1. Applications of number theory and cryptography: Learn and implement in Python the RSA algorithm.

2. Applications in bioinformatics: Learn and implement in Python the algorithm used to reconstruct genomes.

The human brain, perhaps the most complex, sophisticated, and complicated learning system, controls virtually every aspect of our behavior. Neuroscience is the study of the brain, and computational neuroscience divides this study into three subspecialties: neural coding, biophysics of neurons, and neural networks. The course is primarily aimed at high school students that are interested in learning how the brain processes information. The course will start with a basic introduction to the structure and function of the central nervous system, and then include a study of the neurophysiology of the neuron, electrophysiological approaches to record from neurons, as well as mathematical and/or computer-based models that help explain existing biological data. The course will provide a simple introduction to basic computational methods of the brain from the cellular level and the network level with the aim of explaining what nervous systems do and how they function. Basic techniques of modeling biophysics, excitable membranes, small network and large-scale network systems will be introduced. The range of topics include simulations of electrical properties of membrane channels, single cells, neuronal networks, learning and memory models, and models of synaptic transmission, thereby providing a theoretical framework that encapsulates our emerging understanding of the sensory, motor, and cognitive functions of the brain. A main goal of this course also is to provide students with a broad overview of the many practical applications in the field of computational neuroscience and review neuroengineering methods and technologies that enable the study of and therapeutic solutions for diseases of the brain or damage to the CNS, particularly for research or clinical application in the neurosciences.

Pre-approved Topic List

1. How can the basic cellular and network-level organization of neurons in selected systems be defined?
 

2. How do the properties of cells that make up the nervous system, including the propagation of electrical signals used for cellular communication, relate to their function in organized neural circuits and systems?
 

3. How are biophysical models of neural systems that emulate electrical behavior of neurons constructed?
 

4. How can mathematical analyses of data recorded during neurophysiology experiments be performed to describe the principles of neural information coding in sensory and motor systems?

5. What hypotheses can be formulated by captured mathematical models, as possible explanations for observed relationships between experimental outcomes and manipulations?

6. What are the principles of electrophysiological techniques and imaging technologies?
 

7. What are the applications of neural engineering in sensory, motor, neurological and mental disorders?
 

8. What are the principles, methodologies and applications of the main engineering techniques used to study and interact with neural systems?
 

9. How can intracellular recordings be carried into the lab efficiently with all its components — from handing the animal to preparing solutions, slicing the brain and patching onto cells?

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

1. Applications of deep learning and reinforcement learning to train an autonomous agent to solve video game dynamics.
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.
3. Sentiment analysis techniques that can be used to understand public sentiment towards a policy, individual, or company using open social media platforms.
4. Modeling or predicting transportation patterns such as flight delays and traffic flows.
5. Modeling economic trends and fluctuations in real estate or stock markets.
6. Developing explanative or predictive models for the scores and performance of sports teams.
7. Using large data sets from public polling, censuses, and election results to understand the political geography of a country and assess the fairness of electoral maps
8. Using open source data to gain novel insights into the creation of "smart cities" or to improve the quality of life for urban residents (for example, by better understanding gentrification, the effects of development projects, the impact of housing initiatives, or the effects of particular transportation infrastructure approaches)
9. Understanding and modeling the ways in which algorithms can generate bias or ways to improve "data fairness", with applications in the criminal justice system, corporate hiring practices, and the health insurance industry
10. Applications of machine learning or automation in chemical synthesis
11. Applications of machine learning predicting chemical reactions
12. Designing and developing materials for CO2 capture and storage using Machine Learning
13. Predicting climate change impacts on crop yields and detecting climate change-induced drought.
14. Modeling or predicting the effects of climate change, such as extreme precipitation, wildfire risk, and the degradation of forests.
15. Data scaping techniques that can be used to create novel data sets on important socio-economic phenomena
16. Understanding the fundamentals of natural language processing as it applies to machine translation
17. Creating recommendation algorithms for personal entertainment such as movie, TV, or book suggestions
18. Machine Learning driven development of better materials (batteries, solar panels, etc.)
19. Overview of supervised and unsupervised machine learning algorithms with use cases in business (customer analysis, churn rate, etc.)
20. How do people react to recommender systems (e.g., Alexa, ChatGPT, Siri) making mistakes? How do mistakes -- and the type of mistake -- affect a user's trust and perception of the system? How does a user's trust evolve over time, and how rapidly do users learn the limitations of recommender systems? These systems are rarely 100% accurate, so when should they say "I don't know"?
21. How can a robot or "AI" system be a teammate instead of a tool? What makes an effective teammate? How can autonomous agents perceive the intentions of their human teammates and support their partners' goals?
22. How smart is "too smart"? How can robots and recommender systems apply what they think the user knows, to conduct high-order reasoning? Do people like systems capable of complex reasoning?
23. ML in Astronomy: Simulation of CMB (relic Big Bang radiation) to understand early conditions of the universe
24. ML in Astronomy: Using data from SDSS (an instrument) to analyze galaxy spectra and galactic formation
25. ML in Astronomy: Using Kepler data to understand exoplanet light curve behavior

Topics in Image Recognition

1. Developing convolutional neural networks to learn to scan images, with applications in image recognition
2. Detecting image tampering, with applications in combatting fake news
3. Identifying objects and/or places in images
4. Creating stylized art/visual style transfer for other applications
5. Lensless imaging and Computational photography
6. Identifying words or bird calls in audio recordings
7. Low-power or low-light machine vision
8. Neuromorphic or "biologically plausible" machine learning
9. Data clustering
10. Multi-dimensional scaling and visualization

Topics in Medical Applications of Machine Learning

1. Utilizing predictive machine learning models to learn more about cardiovascular diseases such as stroke and heart disease
2. Utilizing predictive machine learning models to learn more about cancer prognosis and diagnosis
3. Applications of machine learning in modeling the spread of infectious diseases
4. Medical applications of machine learning training a 'convolutional neural network' to (for example, to predict skin lesions which are either benign or indicative of skin cancer)
5. Applications of Natural Language Processing in the health sector
6. A review on computer-aided drug design and discovery
7. Data and machine learning driven drug discovery with a case study on cancer, covid, or other diseases.

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". Those offered by Mr. Eoghan are marked as "A". Those offered by Mr. Paul Gehret are marked as "P". Those offered by Mr. Corrado Mazzaglia are marked as "O". Those offered by Ms. Angie are marked as "I". Those offered by Ms. Marta are marked as "T". Those offered by Mr. Lucas O. are marked as "L". Those offered by Ms. Nina H. are marked as "F".

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, A, O, F, L]

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, A, F, O, L]

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, F, N, M, S, H, G, R, O, L]

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, F, H, G, C, O, L]

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, O, F, L]

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, O, L, F]

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, O, L, F]

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, L, O, F]

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, O, L, I, F]

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, O, L, I]

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, L, C, O, I, F]

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, F, L, M, H, C, O, I]

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, O, L, F]

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, O, L]

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, O, L, F]

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, O, L, F]

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, L, G, R, F, C, O, I]

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, F, L ,B, N, M, S, H, G, R, C, O]

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, F, O, I, L]

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? [L, F, Z, B, N, M, S, H, G, R, C, O, I, X]

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, O, I, L]

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, L, C, O]

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, O, L]

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, O, I, L, F]

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

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, O, I, L]

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

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, O, L, I]

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, L, O, F, I]

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, L, M, C, O, I]

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, L, C, O, I]

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

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

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

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

36. Is gene therapy the future of cancer treatment and prevention? If so, how do we best deliver it? [L, Z, F, O, A, I] 

37. How can we unlock the potential of RNAs to treat disease? From MicroRNA to messangerRNA [L, F, Z, O, A] 

38. Viral or non-viral, that is the question-which is best for gene therapy and why? [L, Z, F, O, A, I]

39. Therapeutic Potential of Stem Cells: How can stem cells be used in regenerative medicine to repair damaged tissues and organs? [L, F]

40. Stem Cells in Disease Modelling: How do scientists use stem cells to model diseases in the laboratory? How are stem cells being used to study and potentially treat genetic disorders? What insights have stem cells provided in understanding complex diseases like cardiovascular diseases? [L, F]

41. Stem Cells in Cardiovascular Repair: How are stem cells contributing to advancements in cardiovascular medicine, particularly in heart regeneration? What are the successes and limitations seen in recent research? [L]

42. Ethical and Regulatory Aspects: What are the key ethical concerns and regulatory challenges associated with stem cell research? How do these issues impact the development and application of stem cell therapies? [L, F]

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, O, L]

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

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, O, F, L]

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, L, C, F, O]

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

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

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, O, L, F]

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

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

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

11. How can CRISPR be used to create patient-specific disease models and design targeted therapies for individuals with genetic disorders? [L]

12. Bioinformatics in Pathogen Genome Sequencing: Use bioinformatics tools to analyse the genomes of various pathogens from animal sources, looking for genetic markers of disease transmission or resistance. [T, L, F]

13. Comparative Genomic Studies for Animal-Derived Pathogens: Compare the genomes of pathogens isolated from animals with those from human infections to determine the evolution of virulence and resistance traits. [T, L]

14. Using Bioinformatics to Decode Bacterial Genomic Adaptations: Employ bioinformatics tools to understand how bacteria adapt their genomes in response to different environments. [L, T, F]

15. Impact of Bioinformatics on Vaccine Development Against Bacterial Pathogens : Examine how bioinformatics aids in identifying targets for vaccine development through genomic analysis. [L, T, F]

16. Machine Learning and AI in Predicting Bacterial Infection Outcomes: Analyse the role of machine learning and AI in predicting the outcomes of bacterial infections, supported by bioinformatics. [L, T]

17. How does bioinformatics enable the interpretation of vast amounts of data generated by next-generation sequencing? How can bioinformatics be utilised in uncovering biological foundations? [L]

18. How has next-generation sequencing revolutionized our understanding of genetics and molecular biology, and what are its major applications and implications in areas such as medical diagnostics, evolutionary biology, and personalized medicine? [L]

19. Fundamentals of Single Cell Sequencing: What is single cell sequencing and how does it differ from traditional bulk sequencing methods? What unique insights can we gain about cellular heterogeneity and function [L, F]

20. Single Cell Analysis in Embryonic Development: How is single cell sequencing transforming our understanding of early embryonic development? What can it reveal about the formation of different cell types and organogenesis? [L]

21. Integrating Single Cell Data with Other Omics: How can single cell sequencing data be integrated with other omics data (like genomics, proteomics) to provide a more comprehensive view of cellular function and disease states? [L]

22. What is enzyme replacement therapy? How can it be used to treat rare genetic diseases? [F, T, L]

Please note that topics offered by Ms. Sori Baek are marked with "B" next to them. 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". Those offered by Emily are marked as "Z".

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, Z]

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, Z]

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

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, Z]

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, Z]

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, Z]

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

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

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, Z]

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, Z]

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, Z]

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, Z]

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, Z]

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, Z]

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, Z]

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, Z]

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

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, Z]

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, Z]

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

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, Z]

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, Z]

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, Z]

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, Z]

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, Z]

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, Z]

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]

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. 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". Those offered by Mr. Joe Xiao are marked as "X".

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, X]

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

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, X]

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, X]

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

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, X]

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

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". Those offered by Ms. Emily are marked "EM". Those offered by Ms. Angie are marked "AG". Those offered by Ms. Marta are marked "T".

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

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, AG, T]

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, EM]

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, EM]

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, EM]

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, EM]

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, EM]

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

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, EM]

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

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, AG, T]

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, AG, T]

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, EM]

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, EM]

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, AG]

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

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, AG, T]

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

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, AG, T]

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

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

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

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

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, T]

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

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

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, T]

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, T]

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

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, T]

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]

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?

Bioindustry encapsulates the intersection of biological principles and technology, shaping the landscape of future industries. In this course, students will gain a comprehensive understanding of how this dynamic field extends its influence across diverse sectors. Utilizing biotechnology and other innovative life science methods, this industry creates, alters, and optimizes biological systems, living organisms, and their processes in an effort to harness the full value of biomass. Beyond its immediate applications, bioindustry plays a crucial role in sustainable practices, environmental stewardship, and the production of bio-based materials.

This course will navigate through the intricate threads of bioprocessing, illustrating its significance as a potential industrial revolution. Students can expect topics ranging from synthetic food, microbial biofuels, and cutting-edge therapies like CAR-T to emerging disciplinary directions such as algae bioproduction and enzymatic gold extraction from seawater. Possible research projects include the following:

Pre-approved Topic List

Please note that topics offered by Ms. Nadia Nadreddin are marked as "N". Those offered by Mr. Everardo Hegewisch Solloa are marked as "E". Those offered by Ms. Niki G. are marked as "G". Those offered by Ms. Christa C. are marked as "C". Those offered by Mr. Corrado Mazzaglia are marked as "M". Those offered by Ms. Angie are marked as "A". Those offered by Ms. Marta are marked as "T". Those offered by Dr. Rebeca R. are marked as "R". Those offered by Mr. Joaquin Caro-Astorga are marked as "J". Those offered by Dr. Lucas O. are marked as "O". Those offered by Dr. Nina H. are marked as "H".

1. Is bioprocessing the future industrial revolution? [G, O, H]

2. Synthetic food. Can we grow meat or milk in bioreactors to avoid growing animals? [G, T, M, J, O, H]

3. Beer and wine production. The origins of the bioindustry. [G, T, M, J, O, H]

4. Synthetic fuels. How can we produce biofuels using microorganisms? [G, T, J, O]

5. Future vaccines based on synthetic biology. [A, E, G, T, M, J, O, H]

6. Do you know the powder for the diswasher and laundry contains enzymes? Why? How are they produced? [J, T, O]

7. Degrading plastic with enzymes. Is it really industrially feasible? [J, T, O]

8. CAR-T cells theraphy. How to bring your T-cells to the lab to teach them how to fight your cancer cells before reintroducing them

back into your body. [J, M, C, E, G, O, H]

9. Bioproduction and display of enzymes to remove toxic compounds from wastewater. [J, O]

10. Wastewater treatment plants. The biggest bioindustrial process. Is it possible to improve these massive instalations? [J, T]

11. Bioproduction of algaes in photobioreactors. Capturing CO2 to converte it in high value molecules is the perfect business. [J, G, O, H]

12. The biggest source of gold in the world is in the sea water. Can we capture it using enzymes? [J, G]

13. Can we expect life in our galaxy? Where and what kind of life forms? [J, G, E, O, H]

14. Can we bioengineer life to be able to survive on Mars? [J, G, M, H]

15. Can we bioengineer life to be able to survive on Venus? [J, G, M, H]

16. Is there any other planet a real alternative for humans in case something terrible happens to Earth? [J, G, E, H]

Topics in Animal Biology

1. Epidemiology of Multi-Drug Resistant Organisms in Veterinary Medicine: Research the spread of multi-drug resistant organisms in veterinary settings and the implications for animal and human health. [T, E, O, H]

2. Role of Wildlife in the Ecology of Antibiotic Resistance: Study how wildlife populations act as reservoirs and vectors for antibiotic-resistant bacteria, affecting both animal and human populations. [T, E, G, H]

3. Impact of Agricultural Antibiotic Use on Ecosystem Health: Examine the broader ecological impacts of antibiotic use in agriculture, including effects on soil microbiomes and local wildlife. [T, E, H]

4. The Role of Livestock in the Spread of Antibiotic-Resistant Bacteria: Study the impact of antibiotic use in livestock on the emergence of resistant bacteria like MRSA. [T, E, G, H]

5. Phylogenetic Approaches to Understanding Bacterial Host Specificity: Use phylogenetics to study how bacteria like Staphylococcus aureus evolve host specificity, impacting both animal and human health. [T, E, H]

6. Spillover Events in Zoonotic Bacterial Infections: Analyse how zoonotic diseases cross species barriers from animals to humans, with a focus on bacterial infections. [T, E, G, O, H]

7. One Health Approach to Managing Spillover and Host Jumps: Apply the One Health framework to manage diseases at the intersection of human, animal, and environmental health. [T, E, G]

Topics in Rare Diseases and Cancer

1. Unlocking the power of the immune system: an in-depth analysis of cancer immunotherapy - How has cancer immunotherapy evolved over recent years, and what are its mechanisms and challenges? [R, O, H]

2. Personalised medicine: harnessing genomics to tailor cancer treatment - How is genomics reshaping the landscape of cancer treatment in the context of targeted therapies and predictive diagnostics? [R, H, O]

3. From bench to bedside: The odyssey of drug repurposing for rare diseases - how can drug repurposing help treat rare diseases? What are example success stories? The role of computational biology and AI in drug repurposing and what are some of the challenges in this space? [R, H, O]

4. Gene therapy for rare diseases: navigating the ethics - what are the ethical challenges and considerations in applying gene therapy for rare diseases? How are accessibility, potential unintended consequences, and ethical frameworks guiding these efforts? [R, H, O]