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Women in Machine Learning
Xinyi Chen · Sinead Williamson · Diana Cai · Erin Grant · Kristy Choi · Kristy Choi · Krystal Maughan · Xenia Miscouridou · Hanwen Shen · Raquel Aoki · Belén Saldías · Mel Woghiren · Elizabeth Wood

Wed Dec 09 01:40 AM -- 06:00 PM (PST) @ None
Event URL: https://wimlworkshop.org/neurips2020/ »

The Women in Machine Learning (WiML) workshop started in 2006 as a way of creating connections within the small community of women working in machine learning in order to encourage mentorship, networking, and interchange of ideas. The workshop has attracted representatives from both academia and industry, whose talks showcase some of the cutting edge research being done by women. In addition to technical presentations and discussion, the workshop aims to incite debate on prospective research avenues and career choices for machine learning professionals. More information about WiML’s history and past events can be found at www.wimlworkshop.org. WiML workshop is overseen by the WiML Board of Directors, who select and support the organizing committee for each year’s workshop.

Wed 1:40 a.m. - 2:40 a.m. [iCal]
Informal pre-workshop social (break)
Wed 2:40 a.m. - 2:50 a.m. [iCal]
Opening remarks from the WiML 2020 Organizers (remarks)
Xinyi Chen, Erin Grant
Wed 2:50 a.m. - 3:00 a.m. [iCal]
Remarks from the WiML 2020 Diversity & Inclusion Chairs (remarks)
Danielle Belgrave, Meire Fortunato
Wed 3:00 a.m. - 3:02 a.m. [iCal]
Introduction to invited speaker, Mihaela van der Schaar (remarks)
Kristy Choi, Krystal Maughan
Wed 3:02 a.m. - 3:32 a.m. [iCal]
Interpretable AutoML: Powering the machine learning revolution in healthcare in the era of Covid-19 and beyond (invited talk)
Mihaela van der Schaar
Wed 3:32 a.m. - 3:40 a.m. [iCal]
Q&A for invited speaker, Mihaela van der Schaar (question period)
Mihaela van der Schaar
Wed 3:40 a.m. - 3:50 a.m. [iCal]
Learning rich observation models in factor graphs (contributed talk)
Paloma Sodhi
Wed 3:50 a.m. - 4:00 a.m. [iCal]
Rebounding bandits for modeling satiation effects (contributed talk)
Leqi Liu
Wed 4:00 a.m. - 5:00 a.m. [iCal]
Poster session #1 (poster session)
Wed 5:00 a.m. - 11:00 a.m. [iCal]
Sponsor expo (break)
Wed 11:00 a.m. - 11:02 a.m. [iCal]
Introduction to invited speaker, Fernanda Viégas (remarks)
Kristy Choi, Krystal Maughan
Wed 11:02 a.m. - 11:32 a.m. [iCal]
Communicating imperfection (invited talk)
Fernanda Viegas
Wed 11:32 a.m. - 11:40 a.m. [iCal]
Q&A for invited speaker, Fernanda Viégas (question period)
Fernanda Viegas
Wed 11:40 a.m. - 11:50 a.m. [iCal]
Accelerating 3D deep learning with PyTorch3D (contributed talk)
Nikhila Ravi
Wed 11:50 a.m. - 12:00 p.m. [iCal]
Why language models can help with downstream tasks: A mathematical approach (contributed talk)
Sadhika Malladi
Wed 12:30 p.m. - 2:00 p.m. [iCal]
Mentorship roundtables session (roundtables session)
Wed 2:00 p.m. - 2:02 p.m. [iCal]
Introduction to invited speaker, Rediet Abebe (remarks)
Kristy Choi, Krystal Maughan
Wed 2:02 p.m. - 2:32 p.m. [iCal]
Roles for computing in social justice (invited talk)
Rediet Abebe
Wed 2:32 p.m. - 2:40 p.m. [iCal]
Q&A for invited speaker, Rediet Abebe (question period)
Rediet Abebe
Wed 2:40 p.m. - 2:50 p.m. [iCal]
Unexpected effects of online k-means clustering (contributed talk)
Michal Moshkovitz
Wed 2:50 p.m. - 3:00 p.m. [iCal]
Fairness under partial compliance (contributed talk)
Jessica Dai
Wed 3:00 p.m. - 4:00 p.m. [iCal]
Poster session #2 (poster session)
Wed 4:00 p.m. - 4:02 p.m. [iCal]
Introduction to invited speaker, Anca Dragan (remarks)
Kristy Choi, Krystal Maughan
Wed 4:02 p.m. - 4:32 p.m. [iCal]
Getting human-robot interaction strategies to emerge from first principles (invited talk)
Anca Dragan
Wed 4:32 p.m. - 4:40 p.m. [iCal]
Q&A for invited speaker, Anca Dragan (question period)
Anca Dragan
Wed 4:40 p.m. - 5:00 p.m. [iCal]
Concluding remarks from WiML's President (talk)
Sarah Osentoski
Wed 5:00 p.m. - 6:00 p.m. [iCal]
Informal post-workshop social (break)
Machine learning at Apple (sponsor talk) [ Video ]
Karla Vega
Women at DeepMind: Applying for technical roles (sponsor talk)
QuantumBlack: Revolutionising Pharma with Machine Learning (sponsor talk) [ Video ]
Diana Murgulet
Research at NVIDIA: New Core AI and Machine Learning Lab (sponsor talk) Anima Anandkumar
Google: Responsible AI for Healthcare (sponsor talk) [ Video ]
Jessica Schrouff
IBM Research (sponsor talk) [ Video ]
Kiran Kate
Machine Learning at Netflix Research (sponsor talk) [ Video ]
Sui Huang
Facebook (sponsor talk) [ Video ]
Sophia Yan

Author Information

Xinyi Chen (Princeton University)
Sinead Williamson (UT Austin)
Diana Cai (Princeton University)

I am a PhD student at Princeton University. I work in the area of probabilistic modeling and Bayesian nonparametrics.

Erin Grant (UC Berkeley)
Kristy Choi (Stanford University)
Kristy Choi (Stanford University)
Krystal Maughan (University of Vermont)

I'm interested in the intersection of Programming languages, differential privacy/data privacy and machine learning

Xenia Miscouridou (University of Oxford)
Judy Hanwen Shen (Stanford)
Raquel Aoki (Simon Fraser University)
Belén Saldías (MIT)

Belén Saldías Fuentes is a second-year Ph.D. student at the Lab for Social Machines at MIT. Belén has worked in various research projects where she has contributed to multiple domains such as variational inference applications, human-computer interaction, online RCTs, and natural language processing. Before joining MIT, Belén worked at Harvard University as a research assistant with a focus on finding more efficient ways to create training sets, where she proposed a probabilistic graphical model to this end. Currently, she aims to understand and explore what child-aware NLP would entail.

Mel Woghiren (University of Alberta/EA)
Elizabeth Wood (Broad Institute)

Elizabeth Wood co-founded and co-runs JURA Bio, Inc., an early-stage therapeutics start up focusing on developing and delivering cell-based therapies for the treatment of autoimmune and immune-related neurodegenerative disease. Before founding JURA, Wood was a post-doc in the lab of Adam Cohen at Harvard, after completing her PhD studies with Angela Belcher and Markus Buehler at MIT, and Claus Helix-Neilsen at The Technical University of Denmark. She has also worked at the University of Copenhagen’s Biocenter with Kresten Lindorff-Larsen, integrating computational methods with experimental studies to understand how the ability of proteins to change their shape help modulate their function. Elizabeth Wood is a visiting scientist at the Broad Institute, where she serves on the steering committee of the Machine Inference Algorithm’s Initiative.

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