Statistics and the Life Sciences: Creating a Healthier World is a one-day conference as part of the Boston University Dean’s Symposia series.
Overview
We are globally connected like never before, in nearly all aspects of our lives. While this fact has numerous implications, from the perspective of public health it leaves us uniquely poised to potentially overcome major challenges that have to date been out of reach. These include aging traits such as cognitive decline and Alzheimer’s disease, pulmonary disease such as COPD and asthma, and cardiovascular diseases. Significant progress on any and all of these problems will be data intensive, with statistics a key element at the core. The goal of this workshop is to stage the statistical challenges and progress towards solutions in a handful of emerging and mission-critical areas of the health sciences with global impact. Specifically, focus will be on the following three areas: digital health, machine learning in causal inference, and networks for public health. Ultimately, the idea is to bring together a gathering of representatives from statistics and related domain areas, in an agile and interactive format, and use a web-based dissemination platform to bring broad visibility to these topics.
Agenda
OPENING REMARKS
Sandro Galea, Dean and Robert A. Knox Professor, Boston University School of Public Health
Robert A. Brown, President, Boston University
Josée Dupuis, Professor and Chair, Department of Biostatistics, Boston University School of Public Health
PLENARY SPEAKERS
Susan Murphy, Professor of Statistics, Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University and, Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences
Joseph Lehar, VP Data Science, Oncology, The Janssen Pharmaceutical Companies of Johnson & Johnson, Boston University, Adjunct Assistant Professor, Bioengineering & Bioinformatics, Boston University
PART ONE: DIGITAL HEALTH: INTEGRATING WEARABLES, SOCIAL MEDIA, AND MORE
Keynote
Vadim Zipunnikov, Assistant Professor, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
Panelists:
Pei Wang, Associate Professor, Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai
Greg Hather, Senior Principal Statistician, Takeda Pharmaceuticals
Margrit Betke, Professor and Data Science Faculty Fellow, Department of Computer Science, Boston University
Moderator: Elaine Nsoesie, Assistant Professor and Data Science Faculty Fellow, Boston University School of Public Health
PART TWO: MACHINE LEARNING IN CAUSAL INFERENCE
Beth Ann Griffin, Senior Statistician, Co-Director RAND Center for Causal Inference, RAND Corporation
Susan Gruber, Principal, Putnam Data Sciences
Stefan Wager, Assistant Professor of Operations, Information, and Technology, Stanford Graduate School of Business
Laura Balzer, Assistant Professor of Biostatistics, Department of Biostatistics & Epidemiology, School of Public Health & Health Sciences, University of Massachusetts – Amherst
Moderator: Chanmin Kim, Assistant Professor, Boston University School of Public Health
PART THREE: NETWORKS FOR PUBLIC HEALTH
Key Lecture
David Dunson, Arts and Sciences Professor of Statistical Science, Duke University
Panelists
Tian Zheng, Professor, Department of Statistics, Columbia University
Neha Gondal, Assistant Professor, Department of Sociology, Boston University
Ali Shojaie, Associate Professor, Department of Biostatistics, University of Washington
Moderator: Jacob Bor, Assistant Professor, Boston University School of Public Health
CLOSING REMARKS
Bhramar Mukherjee, John D. Kalbfleisch Collegiate Professor of Biostatistics and Chair of Biostatistics, The University of Michigan School of Public Health
Eric Kolaczyk, Professor and Data Science Faculty Fellow, Department of Mathematics and Statistics, Boston University
Event Type
- Affiliate Award Fund Eligible
- NISS Sponsored
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