STA 190S is a two-semester statistical research activity during which each student develops an individual, mentored research project with her/his Statistical Science faculty mentor/advisor throughout the year. Each member of the faculty is available to mentor one (or, sometimes, more than one) student each year, and research developments and reporting are guided by the faculty mentor. Students must complete a detailed written report by the end of the second semester. In some cases, this will comprise research advances that will eventually be publishable.

Prerequisites: Students must be working on statistical research with a Statistical Science faculty member. This pre-supposes relevant advanced course work in statistics and related areas. Most students should have prior courses to the level of at least STA 121 & 122.

Credit: 1 unit for STA 190S(A) in fall and 1 unit for STA 190(B)S in spring.

Planning for interested students: Contact me via email for general information and guidance on topics/faculty mentors, and explore the personal web pages of the Statistical Science faculty to get the flavour of their current research activities. Each professor has also provided one (or more) example topic, below. Students should contact professors directly for discussion and further information. Important: Please note that students sign-up fast with available professors. Some professors will typically become committed quickly so you should make sure to explore options and sign-up soon! See "Available/Potential Project Topics" in the table below. And of course professors are interested in hearing about your own project ideas!

Available/Potential Project Topics Professor   Gene selection in cancer classification     Artin Armagan    (On leave)     Jim Berger   Topic forthcoming ...     Sourish Das   Models for multiway contingency tables     Ian Dinwoodie   Statistical models for studying exposure disease relationships     David Dunson   Methods in environmental statistics     Elizabeth Shamseldin   Modelling and analysis for spatial data     Alan Gelfand   Simulation techniques for high-dimensional distributions     Mark Huber   Statistical problems in genetics and cancer risk assessment     Ed Iversen   (1) Geometry of high-dimensional data - applications to cancer genomics   (2) Filtering methods and volatility in the Black-Scholes model for options pricing     Sayan Mukherjee   Topics forthcoming soon ...     Fan Li   (1) Imputation of missing data in social surveys   (2) Protecting the confidentiality of social science data   (3) Issues in causal inference for observational data     Jerry Reitter   (1) Statistical estimation in scientific models: biology, chemistry, and physics   (2) Statistics in finance and decision making/portfolio theory   (3) Simulation and statistical inference for SDEs     Scott Schmidler   Statistical methods for the analysis of clinical trials and data in the health sciences     Dalene Stangl   (1) Biodiversity   (2) Spatial statistics   (3) Bayesian networks for decision support   (4) Copulas     Robert Wolpert Assigned Projects Professor   Student: Nihar Bhupalam - Statistical modelling in mortgage markets     Mike West   Student: Haolan Cai - Stochastic volatility modelling in financial times series     Mike West   Student: Annie Clark - Statistical genetics & genomics     Sayan Mukherjee   Student: Kiril Dimitrov - Statistical modelling of high-frequency financial data     Christian Macaro   Student: Ryan Gehring - Bayesian approaches to adversarial game theory and poker     David Banks   Student: Matt Tolson - Inference in experimental neuroscience     Merlise Clyde

Additional information: Students interested in research in statistical science and its applications should consider taking a relevant sequence of undergraduate courses. A one-year research project, developed under the mentorship of an ISDS faculty member and coupled with registration for STA 190S, is a requirement for the Major in Statistical Science. STA 190S as an elective counts towards the requirements for the Minor in Statistical Science.