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All events WiML Luncheon @ COLT 2016 New York, New York June 24, 2016 12:30 pm — 02:30 pm WiML is hosting a luncheon at COLT 2016 in New York. The organizer is Kamalika Chaudhuri. Date: Friday, June 24, 2016, 12.30pm-2.30pm Venue: Columbia University, New York Event details: http://www.learningtheory.org/wp-content/uploads/handout.pdf Registration: Register during COLT registration ( www.learningtheory.org/colt2016/ ). If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org Previous Next

< Back Ramya Ramakrishnan, PhD WiML Director (2020-2022)

< Back Been Kim, PhD WiML Vice President of Research & Policy (2019-2020), Director (2016-2018)

< Back Sarah Osentoski, PhD WiML President (2019-2022), Director (2009-2015) Visit my Profile

All events WiML Luncheon @ CoRL 2019 Osaka, Japan November 1, 2019 12:00 pm — 01:30 pm WiML is hosting a luncheon at CoRL 2019 in Osaka, Japan to bring together women in machine learning from different research areas and across all stages of their careers to meet, find mentorship, and learn from each other. The invited speakers are Anca Dragan, Yukie Nagai, and Chelsea Finn. Date: Nov 1, 12-1:30pm Venue: Senri Hankyu Hotel Osaka Registration: https://www.eventbrite.com/e/wiml-at-corl-an-event-to-celebrate-the-women-in-the-corl-community-registration-77723345619 SPONSORS -Platinum- Previous Next

All events WiML Luncheon @ ICML 2016 New York, New York June 21, 2016 12:00 pm — 02:00 pm WiML is hosting a luncheon at ICML 2016 in New York, New York. This event gives female faculty, research scientists, data scientist, and graduate students in the machine learning community an opportunity to meet, exchange ideas and learn from each other. Date: Tuesday, June 21, 2016, 12pm-2pm Venue: Microsoft building (5th floor), 11 Times Square, New York Registration: https://www.eventbrite.com/e/wiml-icml-luncheon-2016-tickets-25415537557# If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org SPONSORS -Silver- -Bronze- Previous Next

< Back S. Aga Lee WiML Director (2022-2023)

All events WiML Virtual Un-Workshop @ ICML 2021 Virtual July 21, 2021 8:00 am- 6:00 pm The 2nd Women in Machine Learning virtual Un-Workshop is co-located with virtual ICML on Monday, July 21th, 2021. See the un-workshop website for details. The organizers are Olivia Choudhury, Vaidheeswaran Archana, Hadia Mohmmed Osman Ahmed Samil, Berivan Isik, Liyue Shen, Arushi Majha, Beliz Gokkaya and Wenshuo Guo. Previous Next

< Back Nezihe Merve Gürel, PhD WiML Vice President of Events (2022-2023), Director (2021-2022) Visit my Profile

WiML Statements and Calls serves as a repository for the organization's official statements on inclusivity and related matters, as well as announcements for leadership and employment opportunities within WiML. This page reflects WiML's dedication to promoting diversity, ethical standards, and active participation in leadership roles within the machine learning community.​ WiML+2 WiML Statements and Calls Statements on Inclusivity from WiML: First Statement from the Women In Machine Learning Executive Board on Inclusivity Second Statement from the Women In Machine Learning Executive Board on Inclusivity Black Lives Matter Statement for Dr. Timnit Gebru Calls for WiML Board of Directors: 2022 WIML Board of Directors–General 2021 WiML Board of Directors — General 2019 WiML Board of Directors — Policy and Research Committee 2019 WiML Board of Directors — Treasurer Call for WiML Full-time Employee: 2021 WiML Operations Administrator

All events WiML Un-Workshop @ ICML 2023 Honolulu, Hawai'i July 28, 2023 9:35 am - 10:50 am The 4th WiML Un-Workshop is co-located with ICML on Friday, July 28th, 2023. For more information or to register, please go here. Previous Next

All events WiML Social @ ICLR 2022 Virtual April 25, 2022 7:00 pm - 9:00 pm A one hour Virtual Panel session (45 min + 15 min Q&A from the audience) which will take place on April 25th, 19:00-20:00 GMT. The topic of the panel will be the interplay of academia and geographic location. During the Virtual Panel we hope to encourage discussions about geographically specific challenges experienced in academia. To apply for registration funding go to: https://forms.gle/A6zyMdXt21kHQZNSA Moderated by Dr. Caroline Weis Panelists Include: Akiko Eriguchi, Senior Researcher, Microsoft. Akiko is a Senior Researcher at Microsoft.　Her research interests lie in multilingual NLP and deep learning. With her position in the Microsoft Translator team, she has developed MT systems and multilingual NLP applications. Her work has been published in ACL, EMNLP, etc. She has served as a reviewer for ACL, EMNLP, NeurIPS, AAAI. She is also a 2021-2022 co-organizer of the Workshop on Asian Translation. Prior to joining Microsoft, she was a Research Fellow (DC1) at the Japan Society for the Promotion of Science. She obtained her doctoral degree from the University of Tokyo, Japan, where her PhD thesis received the sixth AAMT Nagao Student award from the Asia-Pacific Association for Machine Translation. Nora Hollenstein, Assistant Professor, University of Copenhagen. Nora is an assistant professor in NLP & Cognitive Science at the University of Copenhagen. Before joining the Center for Language Technology at the University of Copenhagen, Nora was a PhD candidate at DS3Lab at ETH Zurich working on cognitively inspired natural language processing. She was also a lecturer at the Institute of Computational Linguistics of the University of Zurich. The focus of her research lies in enhancing NLP applications with cognitive data such as eye-tracking and brain activity recordings. She is especially interested in multi-modal learning, learning from limited data, and the interpretability and cognitive plausibility of machine learning models. Jessica Schrouff, Senior Research Scientist, Google Research. Jessica is a Senior Research Scientist at Google Research working on machine learning for healthcare. Before joining Google in 2019, she was a Marie Curie post-doctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical predictions. Throughout her career, Jessica’s interests have lied not only in the technical advancement of machine learning methods, but also in critical aspects of their deployment such as their credibility, fairness, robustness or interpretability. Previous Next