Strategic Plan

Summary: Vision Statement

The Department of Statistics aims to:

  1. Build upon its current faculty’s world-class research in statistical models and data analysis to solve 21st century problems, including research in massive (distributed, diverse) data sets in science, (e.g. fMRI, biology, forensic science), medicine (e.g., pharmocokinetic models, screening trials), and social science (e.g., psychology, sociology, economics);
  2. Provide students at all levels a firm foundation in theory and applications, enabling undergraduate majors and graduate students to conduct research in statistics and to solve real-world problems;
  3. Support the research and teaching missions of UVA:

(i)   Provide undergraduate and graduate service courses;

(ii)  Teach courses in theory and practice for undergraduate majors and graduate students

(iii)  Identify research collaborations within Arts & Sciences and across Schools (e.g., forensic science, biostatistics);

(iv)   Offer statistical support to the UVA research community;

(v)   Host research colloquia and “career seminars” for faculty and students;

(vi) Coordinate with other initiatives involving statistical research, teaching, or service (e.g., Quantitative Collaboration (QC), UVA Library’s StatLab, Data Science Institute).

As student enrollments and declared undergraduate majors continue to climb, the hiring plan for the department (separate document) aims to attract future faculty and lecturers to advance this mission, with as much diversity as possible. To date, the de- partment includes three professors (two tenured track; one general faculty), two associate Professors, two assistant arofessors, two lecturers, and two post-docs. (The eleven mem- bers include five women: 1 professor, 1 assistant professor, 2 lecturers, and 1 post-doc.) Primary considerations in future hires include research expertise in development of an- alytical methods appropriate for large, distributed data sets (e.g., large-scale inference, feature selection, models for high-dimensional data).As student enrollments and declared undergraduate majors continue to climb, the hiring plan for the department (separate document) aims to attract future faculty and lecturers to advance this mission, with as much diversity as possible. To date, the de- partment includes three professors (two tenured track; one general faculty), two associate Professors, two assistant arofessors, two lecturers, and two post-docs. (The eleven mem- bers include five women: 1 professor, 1 assistant professor, 2 lecturers, and 1 post-doc.) Primary considerations in future hires include research expertise in development of an- alytical methods appropriate for large, distributed data sets (e.g., large-scale inference, feature selection, models for high-dimensional data).

  1. Faculty Expertize: Research and Teaching (support)

                        •    Chao Du (Assistant Professor, 2014): Statistical research in Bayesian statis- tics and nonparametric high-dimensional density estimation; interdisciplinary research in biophysics and systems biology, including stochastic modeling and
                        statistical inference of dynamical cellular and molecular systems such as single molecule enzymatic reaction and gene regulatory networks.

    •    Jeff Holt (Professor, 1999; 67% stat/33% math): Research in ecological sam- pling, developments in undergraduate education and educational resources, including degree programs in statistics (B.A., M.S., Ph.D.), and online edu- cational
    resources (e.g., WeBWorK ) (NSF).

    •    Karen Kafadar (Professor, 2014): Statistical research in robust methods and exploratory data analysis; statistical methodology for applications in physical, chemical, biological, and forensic sciences; computational and graph-

    ical statistics for massive and streaming data sets; design and analysis of data from randomized screening trials (NSF, Army Research Office, NIH).

    •    Daniel Keenan (Professor, 1989): Statistical modeling of problems in sci- ence and medicine, specifically in computer vision and artificial intelligence (1980-95); physiology and endocrinology (1996-2010) and brain (2011-) (NIH).

    •    Dan Spitzner (Associate Professor, 2007): Research interests include hy- pothesis testing in functional data analysis; Bayesian approaches to hypoth- esis testing, including variable selection and clustering; consequences of mul
    tiplicity; general Bayesian inference; applications in the social sciences.

    •    Tingting Zhang (Assistant Professor, 2009): Statistical research in func- tional data analysis, Bayesian statistics, and high-dimensional variable se- lection; statistical methodology development with applications to the mul-
    tidisciplinary field of human brain mapping, including brain activation and brain network studies (NSF; Virginia Affective Neuroscience Laboratory, with J. Coan, UVA-Psychology).

    •    Jianhui Zhou (Associate Professor, 2005): Research interests in dimen- sion reduction, feature selection, structural identification for high dimensional complex data, robust statistics, quantile regression; applications of longitudi- nal and functional methods on growth data on malnutritioned individuals to find interventions leading to improved health (NSF, Gates).

    •    Jessica Curran (Lecturer, 2014): Research interests in late phase clinical trials; Teaching interest in introductory statistical methods in the biological and medical sciences.

    •    Gretchen Martinet (Lecturer, 2014): Research interests in sample surveys, special cases calibration method adjustment for nonresponse; practical applications of sampling and survey methods. Teaching interests in introductory statistics with a focus on preparing students for careers in industry and con- necting them to the broader statistical community.

    •    Amber Tomas (Post-Doc, 2013): Research interests in the area of quanti- tative social science, including dynamic modeling and survey analysis using techniques from pattern recognition and social network analysis.

    •    Jeffrey Woo (Post-Doc, 2013) Research interests include statistical disclo- sure control, including misclassification of categorical variables in generalized
    linear models, and convex optimization issues in application of disclosure control methods to official statistics.

  2. Teaching and Student Activities
    Directors of Undergraduate Programs and Graduate Studies: Jeff Holt (DUP), Dan Spitzner (DGS)

  3. Colloquia and Career Seminars

  4. Interface with other UVa units

    •    A&S Quantitative Collaboration: Dan Spitzner

    •    A&S Psychology department: T. Zhang (J. Coan)

    •    A&S Mathematics department: J. Holt (67% appointment)

    •    School of Medicine: J. Zhou (J. Ma, Biostat; W. Petri, infectious disease)

    •    New courses in other departments (biology, economics): Kafadar, Curran

    •    Computational Science major, Data Science Initiative: KK, Spitzner, Ho

  5. Future Plans:

    1.     “Stat Literacy” class to satisfy undergraduate quantitative core requirement (on the agenda for approval)

    2.    More advanced “Stat Literacy” class for more quantitative-oriented students? (Holt has had conversations along these lines with Hawley.)

    3.    Coordinate the Computational Sciences major and the Data Science Institute’s academic objectives

    4.    Coordinate with StatLab: Statistics & Data Analysis Consulting (statlab.library.virginia.edu): “We provide advice and training in data analysis and statistical methods to UVA

    researchers through individual consulting, workshops, and online tutorials.”)

    5.    Identify opportunities for teaching and research in other departments (KK; all)