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All events WiML Symposium @ ICML 2025 Vancouver, Canada July 16, 2025 9:30 AM - 5:00 PM The Women in Machine Learning Symposium @ ICML 2025 is an inclusive, community-centered event that is happening on Wednesday, July 16th, in person as part of the ICML 2025 conference in Vancouver, Canada. The program features invited talks, a panel discussion, poster sessions, mentoring round tables, breakout Q&As, and community networking opportunities. Additional information can be found here . Previous Next

< Back Jane Wang, PhD WiML Director (2020-2022) Visit my Profile

< Back Jenny Sy WiML Treasurer (2019-2022) Visit my Profile

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

Enhance the experience of women in machine learning. Toward this goal, we create opportunities for women to engage in substantive technical and professional conversations in a positive, supportive environment. We also work to increase awareness and appreciation of the achievements of women in machine learning. Our programs help women build their technical confidence and their voice so that their achievements are known in the community. Our Mission Enhance the experience of women in machine learning Increase the number of women in machine learning Help women in machine learning succeed professionally Increase the impact of women in machine learning in the community Our Mission Toward this goal, we create opportunities for women to engage in substantive technical and professional conversations in a positive, supportive environment (e.g. annual workshop, small events, mentoring program). We also work to increase awareness and appreciation of the achievements of women in machine learning (e.g. directory and profiles of women in machine learning). Our programs help women build their technical confidence and their voice, and our publicity efforts help ensure that women in machine learning and their achievements are known in the community. WiML is proud to support and promote all women in machine learning, regardless of nationality, ethnicity, race, religion, sexual orientation, or politics.

Women in Machine Learning (WiML) is a volunteer-run organization dedicated to supporting women in the male-dominated field of machine learning. Through events and programs, WiML fosters a positive and inclusive environment for professional and technical growth. For media inquiries, interview requests, and collaborations, please reach out to our press email. press@wimlworkshop.org About WiML Women in Machine Learning (WiML) is a volunteer-run organization dedicated to supporting women in the male-dominated field of machine learning. Through events and programs, WiML fosters a positive and inclusive environment for professional and technical growth. Our Mission The field of machine learning, while growing immensely in scale and scope in the last decade, has a major lack of gender diversity: only 14% of authors of ML papers are women and on average, only 20% of professors are female. WiML’s mission is to enhance the experience of women in machine learning; toward this goal, WiML aims to: WiML creates opportunities for women to engage in substantive technical and professional conversations in positive, supportive environments. WiML also works to increase awareness and appreciation of the achievements of women in the field (e.g. award nominations, invited speaker recommendations) to help ensure that women in machine learning and their achievements are known in the community. Our History and Achievements Since its founding in 2006, WiML has grown into a major force for inclusivity in machine learning. Some of our key milestones include: Annual WiML Workshop : Held alongside the NeurIPS Conference, our flagship workshop has grown from a small gathering to over 1,500 participants in 2021. Networking Events: We organize networking opportunities at ML conferences, fostering connections across the industry. Public Directory: Our directory helps conference organizers find women speakers and panelists in machine learning. Social Media Presence: With 17.2K followers on Twitter , we amplify the voices of women in ML. Mailing List: Nearly 7,000 subscribers receive job opportunities, event updates, and industry news. For media inquiries or interview requests, please reach out to our press email. Press Contact press@wimlworkshop.org

< Back Jessica Thompson, PhD WiML Secretary (2018-2020), Director (2016-2017) Visit my Profile

All events WiML Breakfast @ AAAI 2016 Phoenix, Arizona February 15, 2016 07:45 am — 08:45 am WiML is co-hosting a breakfast at AAAI 2016 in Phoenix, Arizona, “Breakfast with Champions: A Women’s Mentoring Event”. In this event, women students will get the opportunity to meet senior women in computer science and/or artificial intelligence. The organizers are: Amy McGovern, Kiri Wagstaff, Sarah Brown, and Marzyeh Ghassemi. The event is also sponsored by AI Journal. Date: Wednesday, 15 February 2016, 07:45-08:45 Venue: Remington, 2nd Floor, Hyatt Regency Phoenix Hotel Event details: http://www.aaai.org/Conferences/AAAI/2016/aaai16student.php Registration: Register during AAAI registration ( http://www.aaai.org/Conferences/AAAI/aaai16.php ) If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org SPONSORS Previous Next

< Back Shweta Khushu WiML Director Visit my Profile

< Back Po-Ling Loh, PhD WiML Treasurer (2023-2024), Director (2020-2023, 2024-2025) Visit my Profile

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

All events WiML Virtual Social @ ICLR 2021 Virtual May 3, 2021 9:00 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. SPONSORS -Platinum- Previous Next