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Undergraduate Summer Research in Statistics

Students will engage in interdisciplinary research using statistical methods for data mining, causal inference, machine learning within the following disciplines: biological sciences, computational statistics or statistical learning/optimization.

Summer 2021 Application is Closed! Application Here

Application Deadline: February 22, 2021 (Monday)

Important: ***Research topics will be discussed between the faculty and the student. We list past research topics below to give an indication of the type of the research that students can expect to work on.***

Participating Faculty for 2021:

Past Project: We will do some reading and research about multivariate data, looking at visualization and estimation problems. We will also write a research paper about a related problem, where the student will participate e.g. by writing some code.

  • Assistant Professor Mike Baiocchi, Epidemiology and Population Health, by Courtesy, Statistics 

Past Project: We are interested in developing a framework for measuring how a behavioral-intervention changes how people think. Probably the most natural way to do this would be to ask people to respond to a situation, having them talk about it and think-aloud about what they would do. Our group is developing new methods for taking in free-text and doing rigorous causal inference (that is, figuring out how much something changed due to an intervention). A good candidate would know something about things like regression, but a great candidate will want to think very carefully about how behavior changes and how to measure that change. We work on educational intervention (e.g., reducing imposter-syndrome) and violence prevention programs (both here at Stanford and also in the slums of Nairobi, Kenya).

  • Assistant Professor Julia Palacios, Statistics and Biomedical Data Science

Past Project: We will explore different model-based optimization algorithms for summarizing posterior distributions. We will do some reading and research on estimation of distribution algorithms and apply them to problems in cancer and antibody repertoire evolution. We will write a research paper with members of the lab.

Past Project: Illegal, unreported and unregulated fishing (IUU) contributes 10-30% of seafood in the market, jeopardizing livelihood of 3 billion people who rely on fisheries while aggravating modern slavery problems. We have started understanding fishing activities through automated identification system (AIS), which provides locations of fishing vessels at high frequencies. However, many fishing vessels are undetectable — they can “go dark” by turning off the AIS device, and small fishing boats are not required to carry the device. Toward painting a comprehensive picture of the IUU landscape, we aim to characterize activities of fishing vessels off the radar using satellite imagery. The project involves analysis of port usage by small vessels and characterization of dark vessel behavior through image analysis in combination with AIS data.

  • Assistant Professor Julia Salzman, Biochemistry and Biomedical Data Science

Past Project: This project involves statistical analysis of millions of single-cell RNA sequencing profiles of human and mouse lemur cells. Goals include uncovering evolutionary divergence and conservation of tissue function and regulation across primates and identification of disease-relevant pathways

Past Project: Our research team has multiple projects involving population level and Single cell datasets (Transcriptomic, epigenetic and proteomic) regarding stem and progenitor cell function in health and musculoskeletal diseases. The aims for the potential summer projects would be to (a) develop statistical approaches to discern specific population subsets from the bulk population data to analyze how different cell populations change during disease pathogenesis, (b) optimize tools to stratify patients and (c) to correlate data from multiple tissues and develop predictive models. Please feel free to reach out to discuss in detail.

Past Project: Do sharks have friends? Using Social Network Graphs to Identify Patterns in Shark Aggregations
Recent advances in animal tagging and marine fish observation has resulted in new efforts to study the social behavior of sharks. New observations have shown that many species of sharks can form large aggregations at different times during their life history. For some species these aggregations are temporary, but for others, they are more persistent. One of those species for which aggregations appear to persist is the population of Sand Tiger sharks (Carcharias taurus), along the Eastern Coast of the US. We have two datasets that contain information about potential aggregations and interactions between individual sharks that can be explored more thoroughly to identify patterns in the networks of interactions. For a student with interest in animal behavior and/or network graphs and analysis, there is much that could be done using simulations to identify non-random associations between sharks, and identifying patterns in aggregations during their annual migratory behavior. The appropriate student would ideally be interested in social network analysis, computer simulations (although not necessary), and have some coding experience or willingness to learn

  • Dr. David Cade, Researcher, Hopkins Marine Station

  • Professor, Hua Tang, Genetics, by Courtesy, Statistics 

Current Project: Our group develops statistical and computational approaches for elucidating the genetic (and non-genetic) basis of human complex diseases, such as cardiovascular diseases. We are inviting motivated students to join us in tackling this challenging problem. In this project, we will simulate data under a range of disease models and evaluate the performance of a variety of machine learning approaches in identifying risk factors as well as in better predicting an individual’s disease risk. Insight gained from this research will guide us in developing novel analytic approaches that are suitable for specific diseases.

  • Or other Statistics affiliated faculty who agreed to supervise and mentor your work.

Funding is provided by VPUE and is offered to undergraduate students to support full-time research projects in Statistics. 

This program runs for 10 weeks starting in the Summer of 2021. 

This research opportunity is for Stanford University undergraduate students only. Learn more about student eligibility.

Previous research topics include:

  • Causal inference & evaluating behavioral programs
  • Data Mining Analysis
  • Computational Statistics & Multivariate Analysis
  • Analysis of multivariate microarray data
  • HAP map
  • Kaggle NASA image analysis
  • Human Microbiome studies

Summer research program requirements:

  • Have not conferred your undergraduate degree (including coterm students).
  • Proficiency in R (knowledge of C++, Julia or Java also a plus)
  • Applicants should have taken at least two of the following courses: Stats 191, 202, 208, 216, 217, 229, 290 before summer quarter.
  • Must be able to commit to full-time research (40 hours per week)

Application Materials:

  • CV/Resume with work history and relevant experience
  • Unofficial Transcript
  • Application form

VPUE's stipends for Full-Time Undergraduate Research Grants (including Major Grants, Chappell Lougee Scholarships, and stipends funded by VPUE Departmental and Faculty Grants) will be calculated for each individual student as the sum of three components. This change is intended to direct additional resources to the projects and students that need them most; no student participating in an on-campus project will receive less than in previous years, and most students with high financial need will receive more. Major Grant stipends start at $5,000, with a need-based supplement and location-based supplement for qualifying students. For details on the stipend structure please see VPUE's Constructing a Budget page.

Preference for successful faculty/student matches are given to Mathematical and Computational Science majors, however any Stanford undergraduate that meets our prerequisites may apply. 


Contact if you have any questions.