Biostatistics Seminars and Working Groups

Throughout the academic year in the department, faculty, students, and collaborators meet to discuss various research projects in progress. These meetings and working groups are great opportunities for students to sit in and participate in research discussions in the areas of clinical trials, statistical genetics, and observational studies. Students gain firsthand experience in developing research with faculty and master- and PhD-level students. Additionally, there are opportunities for students to present on a developing research area. Students are encouraged to attend and take advantage of these opportunities.



The Biostatistics Seminar Series is designed to engage faculty and students in research projects happening within our department and outside of Boston University. The purpose of the seminars is to widen and deepen participants’ knowledge of research in the field of Biostatistics and encourage collaboration in the field. We invite speakers from diverse research backgrounds to present their latest findings. Attendees are encouraged to participate in discussions and provide feedback. Lunch will be served.

Time and Location

The seminars are held from 12:45 to 1:45pm at 801 Massachusetts Avenue, Crosstown Building, Room 305.

Upcoming Seminars

Interested speakers should contact Chanmin Kim, PhD. Please include abstract(s) in your correspondence. You can also email Dr. Kim to be added to our seminar mailing list.

Seminar Title
Thursday, Sep. 14, 2017
Bethany Hedt-Gauthier, Assistant Professor of Global Health and Social Medicine at Harvard Medical School
Biostatistics in Global Health: Approaches, Challenges, and Methodological Needs
Dr. Bethany Hedt-Gauthier is a biostatistician and Assistant Professor in the Department of Global Health and Social Medicine at Harvard Medical School. Her work over the last 15 years has focused on health disparities, particularly in sub-Saharan Africa, with eight years resident in Namibia, Malawi and Rwanda. In her talk, she will present  methods and results for her most recent work with a focus on surgical care in rural sub-Saharan Africa. She will discuss some of the challenges to the use of rigorous statistical methods in global health research and opportunities for individuals and departments to address these gaps.
Thursday, Oct. 5, 2017
Laura White, Associate Professor of Biostatistics at Boston University School of Public Health
Estimating Tuberculosis Transmission and Incidence
Tuberculosis (TB) is the leading cause of infectious disease death globally, yet our limited ability to track TB transmission and incidence is hindering our response. In this talk, I will discuss methods that are used to monitor and track infectious diseases. These approaches make use of routinely collected surveillance data and are intended to identify groups and locations that are most responsible for transmission. I will then discuss how these approaches might be applied to TB and our current work and future plans in this area.
Thursday, Nov. 9, 2017
Joseph Hogan, Professor of Biostatistics and Director of the Biostatistics Graduate Program at Brown University
Using electronic health records to model engagement and retention in HIV care
The HIV care cascade is a conceptual model describing the stages of care leading to long-term viral suppression of those with HIV. Distinct stages include case identification, linkage to care, initiation of antiviral treatment, and eventual viral suppression. After entering care, individuals are subject to disengagement from care, dropout, and mortality. Owing to the complexity of the cascade, evaluation of efficacy and cost effectiveness of specific policies has primarily relied on simulation-based approaches of mathematical models, where model parameters may be informed by multiple data sources that come from different populations or samples.  The growing availability of electronic health records and large-scale cohort data on HIV-infected individuals presents an opportunity for a more unified, data-driven approach using statistical models. We describe a statistical framework based on multi-state models that can be used for regression analysis, prediction and causal inferences. We illustrate using data from a large HIV care program in Kenya, focusing on comparisons between statistical and mathematical modeling approaches for inferring causal effects about treatment policies.
Thursday, Feb. 8, 2018
Hyonho Chun, Associate Professor of Mathematics and Statistics at Boston University
Nonlinearity and outliers in high-dimensional complex biological data

Modern high-throughput technologies provide information about high dimensional features in biomedical research.  These biological entities are often related to each other.   When characterized well, an inferred network can lead to useful insights to researchers.  However, a graph/network estimation problem becomes challenging since dependence can be nonlinear as well as data contains outliers.  In this talk, I will introduce recent network estimation approaches that address these challenges by modeling conditional medians via various non-parametric approaches.

Thursday, Mar. 8, 2018
Chris Gill, Associate Professor of Global Health, BUSPH
Thursday, Apr. 12, 2018
Soe Soe Thwin, Manager of SIS/Biostatistics and Data Management Group at the World Health Organization’s Department of Reproductive Health and Research
Research Profile of Reproductive Health and Research Department at World Health Organization, Geneva
Thursday, May 10, 2018
Elena Losina, Robert W. Lovett Professor of Orthopedic Surgery at Harvard Medical School, Brigham and Women’s Hospital’s Department of Orthopedic Surgery
Thursday, Jun. 14, 2018
Sebastien Haneuse, Associate Professor of Biostatistics at Harvard T.H. Chan School of Public Health

This methods group is co-organized with faculty in the Departments of Biostatistics; Epidemiology; Health Law, Policy & Management; and Global Health. The group will meet weekly for 2 hours during the Fall semester. Over the course of 10 meetings, we will discuss frontier topics in applied econometrics and their relevance to population health science and health services research. As an organizing text, we will cover Susan Athey and Guido Imbens’ 2016 review paper “The State of Applied Econometrics – Causality and Policy Evaluation”. Each session will cover a different topic and student participants will be asked to present on the topic and place it into context of the prior literature. The discussion will focus on understanding the methods, identifying questions for further inquiry, identifying population health and health services applications, and discussing how the methods might be implemented. As a final product, students and post-docs taking the course will prepare a brief research proposal to implement one of the discussed methods in a future research project, with potential for future mentorship.

Please contact Dr. Yorghos Tripodis (Biostatistics) or Dr. Jacob Bor (Global Health) for more information.

Click here for more information.

Interested speakers should contact Gheorghe Doros or Sandeep Menon. Please include abstract(s) in your correspondence. You may also email Dr. Doros or Dr. Menon to be added to the Working Group mailing list.

Lead Faculty: Gheorghe Doros

The Statistical Genetics Working Group meets regularly from 9:30 to 11 a.m. every other Friday at 801 Massachusetts Avenue, Crosstown Building, Room 305. The goal is to get to know each other, learn cutting-edge research, foster collaboration, and get help. The group brainstorms together in September to lay out the yearlong topics of interest for discussion. At each session, one person, group of participants, or invited outside speaker presents (formally/informally) the material, usually pertaining to his or her area of expertise, interest, or research, and leads the discussion. Our participants include a mix of Biostatistics and Bioinformatics students in addition to faculty members involved in genetics research. Please click here for more information.
Lead Faculty: Ching-Ti Liu.

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists and statistical geneticists to develop, evaluate, and compare statistical genetic methods. They are coordinated by the Southwest Foundation for Biomedical Research.

Monthly student luncheon followed by seminar, which features innovative speakers in the area of statistical genetics. More information. Contact: Haldan Smith