13 April
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WiML-CWS Event: Community-Driven Mentoring Event and Panel @ AISTATS 2021

  • Virtual
  •  April 13, 2021   — April 13, 2021
  • 12:30 pm — 02:00 pm

WiML is excited to announce a joint event with the Caucus for Women in Statistics at AISTATS 2021. The event has two components: community-driven mentoring, and a panel. The event will be held on the Icebreaker.video platform on Tuesday, April 13, 2021, 12.30pm – 2pm PT.

Event Format

Agenda (all times approximate)
12:30 – 12:45pm PT – 1:1 mentor-mentee random pairings
12:45 – 1:10pm PT – Small group mentoring on time management tips and conducting research
1:10 – 1:45pm PT – Panel on publishing and reviewing
1:45 – 2pm PT – Small group debrief on panel

What is community-driven mentoring? It means anyone can be a mentor on a topic of their expertise! Upon entering the Icebreaker link, you will be asked to indicate if you want to be a mentor or mentee. The Icebreaker platform will distribute mentors among groups as much as possible. There will be a series of mentoring sessions, both 1:1s and in small groups. Read more about the mentoring prompts below.

Who can mentor? Mentoring topics will range from general life-work balance to general research questions, thus we encourage a larger number of participants, ranging from mid-PhD to senior levels, to participate as mentors. Mentors can be of any gender.

What is the panel on? The panel, moderated by Sinead Williamson (University of Texas at Austin) with panelists Bin Yu (UC Berkeley), Tomi Mori (St. Jude Children’s Research Hospital), Po-Ling Loh (University of Cambridge), Jessica Kohlschmidt (Ohio State University), is on the topic of “Reviewing and Publishing”. The rapid growth of the machine learning and statistics community has made the reviewing process of peer-reviewed conferences more challenging. Besides sharing their experiences, panelists will discuss publishing venues in ML and Statistics, as well as take questions from the audience. Read more about the panelists below.

Joining Instructions

How to join: You can find the Icebreaker link on the AISTATS portal: https://virtual.aistats.org/virtual/2021/affinityworkshop/2033 (AISTATS registration required to access). Event limited to 200 participants. You’ll be asked to sign in to Google, and give Icebreaker permission to access your camera and microphone. Google Chrome browser recommended. 

Participant instructions: Whether you will participate as a mentor or mentee, we suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss. During the panel, you can type questions for the panelists in Icebreaker chat, so bring any questions on reviewing and publishing! See below for more information on Icebreaker.

Questions? Email workshop@wimlworkshop.org or cws@cwstat.org. Note that this is a separate event from the AISTATS mentoring sessions. By joining the event, you agree to abide by the AISTATS Code of Conduct and WiML Code of Conduct.

Icebreaker how-to guide and mentoring prompts

Upon joining the platform, you will be given an option to join as either a “Mentee” or a “Mentor”. Select your preferred option, enter your full name, and click on “join event”. 

Each mentoring session has guided prompts in the form of cards:

For each mentoring session, you can choose if you want to participate or wait for the next one.

Panelists and Moderator bios

Professor Bin Yu, UC Berkeley

Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley. She leads the Yu Group which consists of 15-20 students and postdocs from Statistics and EECS. She was formally trained as a statistician, but her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics, and precision medicine. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice. She is a member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner. She is serving on the editorial board of Proceedings of National Academy of Sciences (PNAS) and the scientific advisory committee of the UK Turing Institute for Data Science and AI.

Professor Tomi Mori, St. Jude Children’s Research Hospital

Tomi Mori is a Member and Endowed Chair of the Department of Biostatistics at St. Jude Children’s Research Hospital in Memphis TN. She is an elected Fellow of the American Statistical Association and is currently President of the Caucus for Women in Statistics. Her statistical research interests include: designs of early phase clinical trial designs for drug combinations and precision oncology strategies, biomarker discovery and validation, predictive modeling, and risk stratification.

Professor Po-Ling Loh, University of Cambridge

Po-Ling Loh received her Ph.D. in Statistics from UC Berkeley in 2014. From 2014-2016, she was an Assistant Professor of Statistics at the University of Pennsylvania. From 2016-2018, she was an Assistant Professor of Electrical & Computer Engineering at UW-Madison, and from 2019-2020, she was an Associate Professor of Statistics at UW-Madison and a Visiting Associate Professor of Statistics at Columbia University. She began a position as a Lecturer in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge in January 2021. Po-Ling’s current research interests include high-dimensional statistics, robustness, and differential privacy. She is a recipient of an NSF CAREER Award, an ARO Young Investigator Award, the IMS Tweedie and Bernoulli Society New Researcher Awards, and a Hertz Fellowship.

Dr. Jessica Kohlschmidt, Ohio State University Comprehensive Cancer Center

Jessica Kohlschmidt is a Ph.D. Biostatistician at the Clara D. Bloomfield Center for Leukemia Outcomes Research at The Ohio State University Comprehensive Cancer Center.  Her research group looks retrospectively at patient data to try to determine what gene mutations and expression (or combinations) predict which patients will have better survival.  Jessica also teaches business analytics for the Fisher College of Business at The Ohio State University. She is a long time officer of the Caucus for Women in Statistics (CWS), serving for 10 years as Secretary and in 2018 became the first Executive Director and currently oversees the operations of CWS. Jessica is currently serving on the committee for the International Year of Women in Statistics and Data Science (IYWSDS) of ISI. She is also actively involved with the American Statistical Association (ASA) and is serving as Treasurer for the ASA Survey Research Methods Section, as well as President of the ASA Columbus Chapter and as Chair of the ASA History of Statistics Interest Group.

Professor Sinead Williamson, University of Texas at Austin

Sinead Williamson is an Assistant Professor of Statistics at the University of Texas at Austin, in the IROM Department and the Division of Statistics and Scientific Computation. She obtained her Ph.D. from the Computational and Biological Learning group at the University of Cambridge and spent two years as a postdoc in the SAILING laboratory at Carnegie Mellon University.

Copyright © Women in Machine Learning

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