Join us for our another insightful webinar on the topic of leadership in the field of statistics and data science! The focus of this event will be to discuss how to successfully bridge the interests of academia and industry, and how statisticians and data scientists can build strong collaborations in both worlds. Statisticians and data scientists at any stage in their careers are invited to attend.
Speakers:
Barbara E Engelhardt, Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science
Natesh S. Pillai, Professor of Statistics, Harvard University
Moderator:
Lorin Crawford, Principal Researcher at Microsoft Research New England
About the COPSS-NISS Leadership Webinar Series
COPSS (Committee of the Presidents of Statistical Societies) and NISS have come together to organize and host a new webinar series focusing on leadership in statistics and data science. Plan to attend these webinars every month during the academic year! Keep an eye out for our Fall 2023 schedule - coming soon!
The COPSS-NISS Leadership Webinar Series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) Emerging Leaders in Statistics and the National Institute of Statistical Sciences (NISS). The purpose of the webinar series is to promote leadership skills for members of the statistical societies at any stage in their careers. The series features conversations with leaders throughout the discipline, including leaders from major academic and government institutions, and companies. Invited speakers share their leadership stories and answer questions about their experiences. Each webinar is moderated by a member of the COPSS Emerging Leaders in Statistics program.
Visit the COPSS-NISS Leadership Series Page for previous webinars.
About the Speakers
Barbara E Engelhardt is a Senior Investigator at Gladstone Institutes and Professor at Stanford University in the Department of Biomedical Data Science. She received her B.S. (Symbolic Systems) and M.S. (Computer Science) from Stanford University and her PhD from UC Berkeley (EECS) advised my Prof. Michael I Jordan. She was a postdoctoral fellow with Prof. Matthew Stephens at the University of Chicago. She was an Assistant Professor at Duke University from 2011-2014, and an Assistant, Associate, and then Full Professor at Princeton University in Computer Science from 2014-2022. She has worked at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc. In her career, she received an NSF GRFP, the Google Anita Borg Scholarship, the SMBE Walter M. Fitch Prize (2004), a Sloan Faculty Fellowship, an NSF CAREER, and the ISCB Overton Prize (2021). Her research is focused on developing and applying models for structured biomedical data that capture patterns in the data, predict results of interventions to the system, assist with decision-making support, and prioritize experiments for design and engineering of biological systems.
Natesh S. Pillai is a Professor in the Dept. of Statistics, Harvard University. He was born and brought up in Kerala, India. He obtained my Bachelors from the Indian Institute of Technology (IIT) Madras, Chennai. He went to graduate school at the Department of Statistical Science, Duke University and obtained my Ph.D. in statistics in 2008. After Duke, he was a Post Doctoral Research Fellow at the Center for Research in Statistical Methodology (CRiSM), University of Warwick, UK, during 2008-10. He has been a faculty at Harvard University since 2010. He was awarded the Young Statistical Scientist Award in Statistical Theory in 2018 by the International Indian Statistical Association and was elected as a Fellow of the Institute of Mathematical Statistics in 2021. He served as an associate editor for JASA and JRSS Series B. Currently, he is serving on the editorial boards of SIAM Journal on Mathematics of Data Science and Harvard Data Science Review. he has worked in a wide range of industries. He was an Amazon Scholar from May 2021-June 2023.
About the Moderator
Lorin Crawford is a Principal Researcher at Microsoft Research New England. He also maintained a faculty position in the School of Public Health as an Associate Professor of Biostatistics with an affiliation in the Center for Computational Molecular Biology at Brown University. The central aim of his research program is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. An overarching theme of the research done in the Crawford Lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. Some of his most recent work has landed himself a place on Forbes 30 Under 30 list and recognition as a member of The Root 100 Most Influential African Americans in 2019. He has also been awarded an Alfred P. Sloan Research Fellowship and a David & Lucile Packard Foundation Fellowship for Science and Engineering. Prior to joining both MSR and Brown, Crawford received a PhD from the Department of Statistical Science at Duke University where he was co-advised by Sayan Mukherjee and Kris C. Wood. As a Duke Dean’s Graduate Fellow and NSF Graduate Research Fellow, he completed his PhD dissertation entitled: "Bayesian Kernel Models for Statistical Genetics and Cancer Genomics." He also received a Bachelors of Science degree in Mathematics from Clark Atlanta University.
Access the Full COPSS-NISS Leadership Webinar Series
Full Playlist | COPSS-NISS Leadership Webinar Series: https://www.youtube.com/playlist?list=PLoRtupvDJTjvFukMcO6NfDr0GvxDsIj81
Jan 24, 2023 | COPSS-NISS Leadership in Academia: Click here to watch
Feb 28, 2023 | COPSS-NISS Social Justice and Community Leadership: Click here to watch
Mar 28, 2023 | COPSS-NISS Leadership in Statistical Research: Click here to watch
Apr 28, 2023 | COPSS-NISS Leadership in Government: Click here to watch
| Attachment | Size |
|---|---|
 October-flyer.pdf | 1.14 MB |
Event Type
- NISS Hosted