A number of faculty members within the Statistics Department are available to provide research opportunities for undergraduate students. It is expected that most of these opportunities will be for students who are working towards Honors in the Major (in Statistics).
The faculty members listed below have directly expressed interest in working with undergraduate students and have provided information about the type of opportunities that could be available. It is possible that other faculty members in the Department might also be available under certain circumstances.
It should not be expected that any given faculty member listed below will automatically agree to every request for undergraduate research. Their willingness to participate will depend on how many other undergraduates are already working with them and other time constraints that they may have. Also, as noted, the highest priority will generally be given to those students working towards Honors in the Major.
As per L&S and campus policy, "The student and faculty member should prepare a study plan, determine the time and place for regular meetings, the number of credits to be earned, and how to enroll in the course. These agreements should be maintained in the department/program office." Students and faculty in Statistics are encouraged to use this template study plan, fill it out as appropriate, and send it to the Chair of the Statistics Undergraduate Committee.
Students working towards Honors in the Major need to be aware of the College and Department regulations regarding Honors. In particular, students are required to register for Stat 681 and Stat 682 while working on their research. Note that, as part of Stat 681 and Stat 682, there will be some “group” sessions each semester to discuss topics of common interest (e.g. strategies for presentation, ethics, reference sources).
Assuming that you will be doing your Honors research during the Fall and Spring of your senior year (which is recommended), usually the best time to approach a professor is during the semester before you intend to perform the research. In some circumstances you can make contact earlier than that. However, the stronger your training is in statistics, mathematics, and computer science, the more prepared you will be for a productive research experience.
Below is information provided by each of the faculty/staff members who expressed direct interest and also an email address for contact. If you have any general questions about research opportunities, feel free to contact Derek Bean (bean3 at wisc . edu).
My research focuses on statistical methods to answer questions in evolutionary biology. For most of what we do in my group, we use evolutionary trees, now inferred from very large molecular sequence data sets. We use and develop methods to answer questions about the molecular process of evolution, evolution of morphological traits, detection of natural selection, species delimitation, etc.
My research concerns on statistical problems in genetics and genomics, with a particular focus on mapping genetic loci contributing to complex diseases in model organisms (such as mice). My work has three parts: data analysis, development of improved statistical methods for analyzing data, and implementation of such methods in computer software. I also have a strong interest in data visualization.
My work is focused on developing, evaluating, and implementing statistical methods for analysis of large datasets collected in studies that aim to identify the genomic basis underlying complex diseases such as cancer and diabetes. I have an active research group that meets weekly to present progress and discuss new ideas. Although most of my work involves graduate students and postdocs, I have worked with many undergraduates over the years. Each of the students focused on a statistical problem that arose in one of the laboratories of my collaborators.
My research interests include statistical methods applied to biology in general and phylogenetics, the reconstruction of the evolutionary past from DNA sequences and other molecular data available in organisms today.
My current research interests include applications to various problems posed by researchers in the social sciences. I am also interested in research questions brought up in my teaching. Some of these include issues like robustness of various methods of inference in regression if some of the assumptions are not met or comparisons of various sampling strategies depending on the distributions of values of survey data by strata, cluster, etc.
My research interests include modeling massive data, complex system simulation, uncertainty quantification, design of experiments, the interface between statistics and optimization, statistical methods for information technology (nanotechnology, energy, and other high-tech industries), and experimental design for machine learning and data mining. Recently, I have developed an interest in designs motivated by Sudoku. I have worked with several undergraduates in the recent past. All of the research problems have been well-defined and are self-contained.
My research aims to provide tools to better understand networks (social, biological, communication, etc). There are *typically* three parts to the research; (a) motivation, this comes from a dialogue with domain experts; (b) fast algorithms, because we are in the age of big data, algorithms must be fast; and (c) statistical inference, because there is usually a bigger questions than "what does my data contain." Instead, the question is "what does the population or data generating mechanism look like."
My research interests include high-dimensional data analysis, variable selection and model selection, statistical learning and machine learning, longitudinal data analysis, survival data analysis and statistical modeling in medical sciences. I also have rich collaborations with doctors in medical school. Two main areas I am focusing on are orthopedics & rehabilitation and anesthesiology.
- Propose some simple but novel statistical methods for high-dimensional data analysis. It can be a simple or straightforward extension of the existing methods.
- Apply some existing statistical methods to some complicated and real scientific problem motivated datasets.
- Work on a real project collaborated with doctors in medical school. This project can be the data analysis or design of experiment.
My research areas are very diverse. On the application side: I have been conducting research on financial time series, time series forecasting, asset pricing and portfolio management, graphical models, clinical trials and medical statistics, computer simulation, optimization, climate models, etc. On the methodology side: I have been studying nonlinear multivariate analysis, nonlinear dependence measures, and nonlinear time series etc. On the theory side: I have been focusing on max-stable process and related topics.
- Studying the nonlinear dependence measures (the quotient correlation coefficients (QCC), the generalized measures of correlation (GMC)) introduced in my earlier work and applying them to some real problems such as identifying important genes in predicting patients' health status.
- Studying several nonlinear time series models introduced in my earlier work and applying them to financial time series data and weather time series data.
- Studying portfolio management using my extreme co-movement models.
- Studying independent component analysis and simulating max-independent component analysis.
- Studying and simulating the sufficient important difference (SID) and comparing it with the minimum important difference (MID) in clinical trials.
- Requires Stat 310 and part of Stat 456.
- Requires Stat 309, 310, and 349.
- Requires Stat 310, 349 and some knowledge in finance.
- Requires Stat 456.
- Requires Stat 310.