03 May
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WiML Virtual Social @ ICLR 2021

  • Virtual
  •  May 3, 2021   — May 3, 2021
  • 09:00 am — 11:00 am

WiML is hosting a virtual social, involving a panel discussion and socializing, at ICLR 2021 on Monday, May 3, 9.00am – 11.00am Eastern Time. The panel will take place in Zoom. After the panel, we will adjourn to the Icebreaker/Gatheround platform for socializing.

Event Format

Agenda (all times approximate)

9:00 – 9:05am ET – Meet in Zoom. Welcome and introductions
9:05 – 9:50am ET – Panel on “Starting and Navigating Careers Through COVID-19″
9:50 – 10:00am ET – Wrap-up and adjourn to Icebreaker/Gatheround platform
10:00 – 11:00am ET – Socializing in Icebreaker/Gatheround platform

What is the panel on? The panel, moderated by Ehi Nosakhare (Data Science Manager, Microsoft) with panelists:

  • Candace Ross (PhD student in Computer Science, MIT)
  • Christina Papadimitriou (Machine Learning Engineer, JPMorgan Chase)
  • Claire Vernade (Research Scientist, DeepMind)
  • Po-Ling Loh (Lecturer in the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge)
  • Sinead Williamson (Assistant Professor of Statistics, University of Texas at Austin)

is on the topic of “Starting and Navigating Careers Through COVID-19”. The panel features ML researchers at various career stages who will talk about their experience networking, job hunting, collaborating and/or starting a new position in a primarily online environment. Read more about the panelists below.

Joining Information

How to join: Everyone registered for ICLR is encouraged to attend! Event limited to 200 participants. You can find the Zoom link on the ICLR portal: https://iclr.cc/virtual/2021/social/4398 (ICLR registration required to access). The Icebreaker/Gatheround link will be shared in Zoom at the end of the panel. Icebreaker/Gatheround will ask you to give it permission to access your camera and microphone. Google Chrome browser recommended.

Participant instructions: During the panel, you can type questions for the panelists, so bring any questions on starting and navigating careers through COVID-19! If you will participate in the post-panel social, we suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss.

Additional opportunities: WiML is also offering two more opportunities at ICLR 2021 for women and/or non-binary individuals:

  • Thanks to ICLR’s DEI action fund (https://iclr.cc/public/DiversityInclusion) as well as WiML sponsors, WiML is able to fund registrations for eligible individuals to attend ICLR. If you are a student, postdoc, or early-career, underrepresented individual in machine learning, apply here by April 26: https://forms.gle/B2eJ4xWuPBodVeyGA
  • Regardless of whether you are attending ICLR or the WiML social, you can submit your resume to our WiML@ICLR 2021 resume book. The resume book will be shared with WiML sponsors. Submit here by May 1: https://forms.gle/ARs8BcnfgSyraPUDA

Questions? Email workshop@wimlworkshop.org. By joining the event, you agree to abide by the WiML Code of Conduct.

Panelists and Moderator bios

Candace Ross, MIT

Candace Ross is an EECS Phd Student in the InfoLab at MIT. She works on language grounding, particularly grounding in vision, and weakly supervised models for language acquisition. Outside of research, she plays lacrosse, participates in efforts for community diversity and inclusion, and enjoys traveling (which surprisingly can be done as a grad student)!

Christina Papadimitriou, JP Morgan

Christina Papadimitriou (she/her) is a Machine Learning Engineer on the Artificial Intelligence Acceleration team at JPMorgan Chase. Her team accelerates the adoption of AI into the firm’s products and services.  Christina is the co-chair of PRIDE, JPMorgan’s LGBTQ+ Business Resource Group in the NY Metro area, and she is the firm’s representative for OPEN Finance. She is also in the NY Leadership Team for Out in Tech, and she serves on the Board of Directors for WiML. Christina holds a Masters in Data Science from UC Berkeley, a Masters in Operations Research from Columbia University, and a Bachelor of Engineering in Chemical Engineering from the University of South Carolina.

Claire Vernade, DeepMind

Claire Vernade is a Research Scientist at DeepMind in London UK. She received her PhD from Telecom ParisTech in October 2017, under the guidance of Prof. Olivier Cappé. From January 2018-October 2018, she worked part-time as an Applied Scientist at Amazon in Berlin, while doing a postdoc with Alexandra Carpentier at the University of Magdeburg in Germany. She is involved in WiML-T, which connects women in Learning Theory and organizes social and career events at conferences like COLT and ALT. Her research is on sequential decision making. It mostly spans bandit problems, but Claire’s interest also extends to Reinforcement Learning and Learning Theory. While keeping in mind concrete problems — often inspired by interactions with product teams — she focuses on theoretical approaches, aiming for provably optimal algorithms.

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. She currently serves on the Board of Directors for WiML.

Professor Sinead Williamson, University of Texas at Austin

Sinead Williamson is an assistant professor of Statistics at the University of Texas at Austin. She works on Bayesian methods for machine learning, with particular interests in Bayesian nonparametrics, scalable sampling methods, and modeling structured data with complex dependency structures. Sinead has recently worked as a research scientist at Amazon and CognitiveScale, and served on the Board of Directors for WiML.

Ehi Nosakhare, Microsoft

Ehi Nosakhare is a Senior Data and Applied Science Manager at the Microsoft AI development and Acceleration Program (MAIDAP). She leads a team that designs, develops, and implements ML solutions in application projects across Microsoft’s products and services. Prior to joining Microsoft, she earned her PhD in Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). Her thesis work focused on using Latent Variable Modeling to uncover behavioral influences on mental health and well-being. She is deeply passionate about using ML to solve real-world problems and studying the ethical implications of ML/AI. She currently serves on the Board of Directors for WiML.





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