Undergraduate research in statistics provides opportunities for gaining experience in data analysis, reading and writing about statistics, and collaboration with Statistics faculty mentors and their research teams. By doing an undergraduate research project, you will develop a deeper understanding of statistics, whether as a first/second year student considering a statistics major, or as a junior/senior considering graduate school and other career options.
The two largest programs for undergraduate research in statistics are the honors thesis, for juniors & seniors, and UROP, for first & second year students. In addition, some faculty research projects involve undergraduates not in either of these programs. Other statistics research-related activities involving undergraduates include the following:
- The annual Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS). MSSISS provides a forum for presenting completed research projects, and an opportunity to see the range and scope of statistical activity across the University of Michigan. Most of the research projects are carried out by graduate students, but undergraduates are welcome to participate and many have!
- The Statistics department occasionally runs a data mining competition.
- A relevant national forum is the free Electronic Undergraduate Statistics Research Conference, and the associated Undergraduate Statistics Project Competition.
- The Center for Statistics, Computing, and Analytics Research (CSCAR) sometimes employs undergraduates. Email email@example.com if you are interested in learning more about opportunities for involvement with CSCAR.
Writing an Honors Thesis in Statistics
The application process and requirements for the Statistics honors program are described on the department website. The primary requirement is writing an honors thesis under the supervision of a faculty member in the Statistics department. This is conducted during enrollment in the Senior Honors Seminar, STATS 499 (2-3 credits), usually preceded by a semester of guided reading and research, STATS 489 (2-3 credits). If you are interested in the honors program, a first step is to make an advising appointment to discuss possible supervisors. You can then approach any Statistics faculty member to ask them to supervise a project, based for example on studying the research areas on their websites. Students are encouraged to contribute their thesis to the archive of honors theses at the University of Michigan Library.
A primary goal of the honors program in Statistics is to ensure that successful students are well prepared for future graduate work in Statistics. However, it is also open to all those interested in going a step beyond the requirements for the Statistics major program.
Recent honors theses:
Undergraduate Research Opportunity Program (UROP)
UROP is a great way to get an introduction to research during the first two years at University of Michigan. See the UROP website for more information. For the most part, Statistics research projects require foundational preparation in statistics, mathematics and computer programming. Sometimes, first year students have sufficient preparation through AP courses and other experiences. Otherwise, it may be appropriate to take introductory statistics, computer programming and calculus courses in the first year to be ready for a second year UROP project.
Other Opportunities for Undergraduate Research in Statistics
It is possible to conduct undergraduate research that does not fall into either the honors program or UROP. If you find yourself interested in the research agenda of a Statistics faculty member, you can email to enquire about available options. This research can be carried out as part of Stats 489 [Independent Study in Statistics], as a paid position if one is available, or as an informal arrangement for neither course credit nor payment. Arrangements must be made on a case-by-case basis with the potential faculty superviser.
Faculty Supervising Undergraduate Research in Statistics
- Danny Almirall supervises undergraduate researchers with an interest in applied issues in causal inference, dynamic treatment regimens and sequential multiple assignment randomized trials (SMART). Projects include:
- Topics in design and analysis of clinical trials for adaptive treatment plans, by Hwanwoo Kim. Co-advised with Ed Ionides. 2nd prize winner in the national Undergraduate Statistics Project Competition.
- Adaptive intervention designs in substance use prevention, by Kelly Hall.
- Yves Atchade has supervised undergraduate projects including:
- Estimation of soil carbon stocks at contintntal scale in Africa, by Avery Wu
- A R/C++ package for large graphical lasso computations by Yusheng Jiang
- Moulinath Banerjee has supervised undergraduate projects including:
- Detecting Active Pathways in Gene Sets by Matthew Lomont
- Xuming He supervises UROP students and advanced undergraduate research in a broad area of statistics. Examples include:
- Monte Carlo evaluation of Value-at-Risk by Daniel Bendetson.
- Ordering of multivariate Data by Elisa Shibley
- Monte Carlo evaluation of Value-at-Risk by Daniel Bendetson.
- Al Hero has supervised undergraduate projects including:
- Dynamic distributed multidimensional scaling (MDS) for data visualization, by Adam Pacholski
- Spatio-temporal network anomaly detection in Abilene data streams, by Cheuk Chan
- Canonical correlation analysis for sunspot and coronal mass ejection image representation, by Jimmy Li
- Ed Ionides has supervised undergraduate projects including:
- Topics in design and analysis of clinical trials for adaptive treatment plans, by Hwanwoo Kim. Co-advised with Danny Almirall. 2nd prize winner in the national Undergraduate Statistics Project Competition.
- Modeling cholera as a stochastic process, by Murat Ahmed.
- Building POMP objects in R for a dynamic general stochastic equilibrium model, by Xiaoai Chai.
- Investigating sequential Monte Carlo methods for time series analysis, by Cong Zhang.
- Identification of insurance companies at risk of insolvency, by Xi Wu and Kelly Schmidt. Co-advised with Kristen Moore.
- Susan Murphy's lab involves how best to collect and analyze data to build mobile health interventions; see the websites of undergraduates currently working in our lab at Statistical Reinforcement Learning Lab Members. Former projects include:
- Dynamical Health Communication for the Twenty-First Century, by Kelly Hall.
- Sample Size Calculator in Micro-Randomized Trials by Ji Sun.
- Developing Learning Algorithms for Use in Mobile Health, by Zach Murray.
- Least Squares Policy Iteration (LSPI) and Greedy-GQ (GGQ) by Nick Meyer.
- Ed Rothman has supervised honors theses and other undergraduate research. The topics include the following recent activities, each designed to involve students in a topic found in graduate programs such as Functional Data Analysis.
- Motion and Emotion: detection of the emotional state by examining movement of a joint.
- Participation Rates in screening programs for breast cancer, colon cancer, prostate cancer etc.
- Car Sales in the US with attention to the Parable of the Boiled Frog
- A predictor of the auction price of art
- Defining Dialectic Density Code Switchers and Reading Performance
- Using FDA to Study Shape of Biological Organisms
- Kerby Shedden supervises undergraduate research with an emphasis on bioinformatics. Examples include:
- Statistical analysis of high frequency motion capture and muscle activity data: applications to assessing development of trunk postural control, by Raghav Haran.
- Sparsity in the distribution of correlation coefficients in molecular screening data, by Lo-Hua Yuan. Co-advised with Ji Zhu.
- Individual-specific and disease-specific factors in acquired copy number variations in cancer, by Christine Kim.
- Detection of DNA lesions in acute myelogenous leukemia, by Nick Parenti.
- Two-tiered false discovery rates, by Jia Jin Kee.
- Selective targeting of stem-cell-like cancer cell lines, by Newaj Abdullah. Co-advised with Gus Rosania.
- Ambuj Tewari has supervised undergraduate research projects and an honors theses. Former projects include:
- Development of an Android app for mobile health by Bingjie Xu
- Simulations comparing bandit algorithms by Scott Bommarito
- Development of HeartSteps, an Android app for encouraging physical activity by Cyrus Anderson. Co-advised with Predrag Klasnja
- Empirical evaluation of online learning algorithms (honors thesis) by Xinyan Han
- Numerical experiments with Lasso in high dimensional VAR models by Zifan Li