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Board of Directors Established in 2009, the goals of the Board of Directors are to: (i) facilitate long-term activities, such as multi-year funding grants; (ii) ensure the continuity of the workshop across years; (iii) organize activities such as a mentorship network and additional activities unrelated to the workshop. Current board members are listed below. Tatjana Chavdarova, PhD WiML President Read More Alessandra Tosi, PhD WiML Vice President of Programs Read More Michela Benedetti WiML Director Read More Luisa Cutillo, PhD WiML Director Read More Tiffany Ding WiML Director Read More Giulia Clerici, PhD WiML Director Read More Kairan Zhao WiML Director Read More Man Lou, PhD WiML Director Read More Natasa Tagasovska, PhD WiML Secretary Read More Arushi GK Majha, PhD WiML Treasurer Read More Eda Okur WiML Director Read More Laya Rafiee Sevyeri, PhD WiML Director Read More Shweta Khushu WiML Director Read More Claire Vernade, PhD WiML Director Read More Yolanne Lee WiML Director Read More Sophia Abraham, PhD WiML Director Read More Mandana Samiei WiML Vice President of Events Read More Erin Grant, PhD WiML Director Read More Judy Hanwen Shen WiML Director Read More Elizabeth Healey, PhD WiML Director Read More Nikita Saxena WiML Director Read More Irene Ballester WiML Director Read More Kimberly Ferguson-Walter, PhD WiML Director Read More

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Past Board of Directors: WiML would like to acknowledge the efforts of previous board members whose efforts made WiML into what it is today. Past Board of Directors WiML would like to acknowledge the efforts of previous board members whose efforts made WiML into what it is today. If you see any errors or omissions on this page please contact us at info@wimlworkshop.org . Jenn Wortman Vaughan, PhD WiML Co-Founder, Director (2009-2012, 2014-2018) Read More Sarah Osentoski, PhD WiML President (2019-2022), Director (2009-2015) Read More Bethany Edmunds, PhD WiML President (2022-2023), Vice President of Programs (2021-2022), Director (2020-2021) Read More Allison Chaney, PhD WiML Secretary (2017-2018), Vice President of Research & Policy (2018-2019), Director (2016) Read More Akiko I. Eriguchi, PhD WiML Treasurer Emeritus (2026-2027), Treasurer (2022-2026) Read More Jo-Anne Ting, PhD WiML Treasurer (2009-2012) Read More Jennifer Healey, PhD WiML Vice President of Events (2017-2019, 2020-2021), Director (2019-2020) Read More Jessica Montgomery WiML Vice President of Research & Policy (2020-2021), Director (2019-2020, 2021-2022) Read More Tamara Broderick, PhD WiML Director (2013-2019) Read More Sinead Williamson, PhD WiML Director (2015-2018, 2020-2021) Read More Christina Papadimitriou WiML Director (2021-2025) Read More Barbara Engelhardt, PhD WiML Director (2013-2016) Read More Ilene Cartright WiML Director (2019-2022) Read More Sasha Luccioni, PhD WiML Director (2022-2025) Read More Pallika Kanani, PhD WiML Director (2013-2015) Read More Brandie Nonnecke, PhD WiML Director (2019-2021) Read More Amy Zhang, PhD WiML Director (2020-2022) Read More Sarah Poole, PhD WiML Director (2020-2022) Read More Kristy Choi, PhD WiML Director (2022-2024) Read More Sara Jennings WiML Director (2021-2022) Read More S. Aga Lee WiML Director (2022-2023) Read More Hewitt Tusiime WiML Director (2024) Read More Gloria Namanya WiML Director (2023-2026) Read More Hanna Wallach, PhD WiML Co-Founder, President (2009-2012), Director (2012-2016) Read More Finale Doshi-Velez, PhD WiML President (2013-2015), Director (2009-2012, 2016-2018) Read More Ioana Bica, PhD WiML Secretary (2023-2025), Director (2021-2023) Read More Inmar Givoni, PhD WiML Secretary (2009-2012) Read More Po-Ling Loh, PhD WiML Treasurer (2023-2024), Director (2020-2023, 2024-2025) Read More Alice Zheng, PhD WiML Treasurer (2013-2015), Director (2012) Read More Jessica Schrouff, PhD WiML Vice President of Programs (2022-2023), Director (2020-2022, 2023-2024) Read More Nezihe Merve Gürel, PhD WiML Vice President of Events (2022-2023), Director (2021-2022) Read More Katherine Heller, PhD WiML Director (2012-2018) Read More Danielle Belgrave, PhD WiML Director (2019-2020, 2021-2024) Read More Elena Glassman, PhD WiML Director (2009-2012) Read More Diana Cai, PhD WiML Director (2019-2022) Read More Keren Gu WiML Director (2019-2022) Read More Svitlana Volkova, PhD WiML Director (2016-2018) Read More Claire Monteleoni, PhD WiML Director (2010-2012) Read More Meghana Bhimarao WiML Director (2019-2021) Read More Jane Wang, PhD WiML Director (2020-2022) Read More Sarah Aerni, PhD WiML Director (2021-2023) Read More Abigail Jacobs, PhD WiML Director (2018-2019) Read More Belén Saldías WiML Director (2022-2023) Read More Bahare Fatemi, PhD WiML Director (2023-2024) Read More Caroline Weis, PhD WiML Director (2022-2026) Read More Diane Oyen, PhD WiML Director (2025-2026) Read More Sarah Tan, PhD WiML President (2023-2026), Vice President of Events (2019-2020), Director (2018-2019, 2020-2023) Read More Katherine M. Kinnaird, PhD WiML President (2016-2019), Director (2014-2015) Read More Jessica Thompson, PhD WiML Secretary (2018-2020), Director (2016-2017) Read More Alicia Yi-Ting Tsai, PhD WiML Secretary (2020-2023) Read More Sarah Brown, PhD WiML Treasurer (2016-2019) Read More Jenny Sy WiML Treasurer (2019-2022) Read More Been Kim, PhD WiML Vice President of Research & Policy (2019-2020), Director (2016-2018) Read More Audrey Durand, PhD WiML Vice President of Events (2021-2022) Read More Emma Brunskill, PhD WiML Director (2011-2016) Read More Raia Hadsell, PhD WiML Director (2017-2021) Read More Nevena Lazic, PhD WiML Director (2009-2012) Read More Feryal Behbahani, PhD WiML Director (2019-2022) Read More Ehi Nosakhare, PhD WiML Director (2021-2024) Read More Marzyeh Ghassemi, PhD WiML Director (2016-2018) Read More Kate Niehaus, PhD WiML Director (2018-2020) Read More Savannah Thais, PhD WiML Director (2019-2021) Read More Ramya Ramakrishnan, PhD WiML Director (2020-2022) Read More Catherine Wah, PhD WiML Director (2022-2024) Read More Rachel Thomas, PhD WiML Director (2019-2020) Read More Linh Tran, PhD WiML Director (2022-2023) Read More Audrey Chang, PhD WiML Director (2022) Read More Archana Vaidheeswaran WiML Director (2023-2026) Read More Tiffany Vlaar, PhD WiML Director (2025-2026) Read More

All events WiML Social @ ICLR 2026 Rio de Janeiro April 24, 2026 12:00 PM – 3:00 PM Date: April 24, 2026 Time: 12:00 PM – 3:00 PM Location: Room 203C, Centro de Convenções / Convention Center The Women in Machine Learning (WiML) Social at ICLR 2026 is an opportunity to connect with members of the WiML community in an informal and welcoming setting. The event will bring together researchers and practitioners from academia and industry to foster meaningful conversations, exchange experiences, and build new connections. This year’s social will feature a panel discussion on: There’s no single path: Navigating careers in academia, industry, and beyond. The panel will explore different career trajectories in machine learning, highlighting transitions across academia, industry, and other paths. Panelists will share their experiences, challenges, and perspectives on building a career in ML, followed by an open discussion with the audience. Program 12:00 PM – 12:10 PM Opening Remarks 12:10 PM – 12:30 PM Icebreaker Game 12:30 PM – 1:45 PM Networking & Lunch 1:45 PM – 2:45 PM Panel Discussion 2:50 PM – 2:55 PM Closing Remarks Panelists Aleksandra Faust Aleksandra Faust is a Director of Research at Google DeepMind, where she leads Frontier AI Health efforts. Her research focuses on foundation models and world models for complex adaptive systems, treating the AI design pipeline as a learnable, sequential, and self-improving decision-making process. This methodology has driven state-of-the-art improvements across drug discovery, robotics, autonomous driving, and web agents, and led to her founding the field of Automated Reinforcement Learning (AutoRL). Notably, she co-authored the seminal "Levels of AGI" framework and led the Gemini Self-improvement research team, developing the reinforcement learning methods behind the Gemini model family. Previously, Aleksandra served as Chief AI Officer at Genesis Molecular AI and held foundational leadership roles at Google Brain, Google Robotics, and Waymo/X. Earlier in her career, she was a Senior R&D Engineer at Sandia National Laboratories. Faust holds a Ph.D. in Computer Science with distinction from the University of New Mexico and an M.S. from the University of Illinois at Urbana-Champaign. She is an IEEE Fellow and a recipient of the IEEE RAS Early Career Award for Industry and the Tom L. Popejoy Dissertation Award, and was named a Distinguished Alumna of the UNM School of Engineering. Her work has been featured in The New York Times, The Economist, and Forbes, and has received multiple Best Paper Awards at premier robotics, machine learning, and systems architecture venues. https://www.afaust.info/ Franziska Boenisch Franziska Boenisch is a tenure-track faculty at the CISPA Helmholtz Center for Information Security, where she co-leads the SprintML lab. Her research focuses on private and trustworthy machine learning; during her Ph.D. at Freie Universität Berlin and Fraunhofer AISEC she pioneered the notion of individualized privacy in ML. Before joining CISPA, she was a Postdoctoral Fellow at the University of Toronto and the Vector Institute. She received an ERC Starting Grant in 2025 for research on privacy in foundation models and has been recognized with the Fraunhofer ICT Dissertation Award (2023), a GI Junior Fellowship (2024), and a Werner‑von‑Siemens Fellowship (2025). https://franziska-boenisch.de/ Noa Garcia Noa Garcia is an Associate Professor at the Institute for Advanced Co-Creation Studies and D3 Center, The University of Osaka (Japan). She earned her Ph.D. in Computer Science from Aston University (UK), specializing in multimodal retrieval and instance-level recognition. She moved to Japan in 2018 as a postdoc, and has been conducting research at The University of Osaka since then. Her research sits at the intersection of computer vision, natural language processing, fairness, and art. Her recent work includes investigating demographic bias in computer vision, analyzing visual datasets, and exploring how generative models can reinforce social stereotypes. https://www.noagarciad.com/ Valentina Pyatkin Valentina Pyatkin is a postdoctoral researcher at the Allen Institute for AI and the University of Washington. Additionally, she is affiliated with the ETH AI Center, where she mentors students and works on post-training for the Swiss AI Initiative. She obtained her PhD in Computer Science from Bar Ilan University. Her work has been awarded an ACL Outstanding Paper Award and the ACL Best Theme Paper Award, and has been supported by a Schmidt Sciences Postdoctoral Award. During her doctoral studies, she conducted research internships at Google and the Allen Institute for AI, where she received the AI2 Outstanding Intern of the Year Award. https://valentinapy.github.io/ Moderator Karen Ullrich Karen is a research scientist at FAIR NY and she is actively collaborating with researchers from the Vector Institute and the University of Amsterdam. Her main research focus lies in the intersection of information theory and probabilistic machine learning / deep learning. She completed her PhD under the supervision of Prof. Max Welling. Prior to that, she worked at the Austrian Research Institute for AI, Intelligent Music Processing and Machine Learning Group lead by Prof. Gerhard Widmer. https://karenullrich.info/ WiML Social Organizers Ivona Najdenkoska Ivona is a postdoctoral researcher at the University of Amsterdam. Her research focuses on multimodal foundation models and generative AI, with an emphasis on scalable vision–language understanding and generation. She also works on AI-generated image detection, developing robust methods for distinguishing synthetic from real content. She recently completed her PhD at the University of Amsterdam under the supervision of Marcel Worring and Yuki Asano, where her work focused on learning from context with multimodal foundation models. During her PhD, she spent time at Meta working on context-aware image generation. She is also a member of the ELLIS Society, and her research has been published at leading AI conferences, such as ICLR and ECCV. Paula Feldman Paula is a postdoctoral researcher working at the intersection of AI and medical imaging at Weill Cornell Medicine. Her work focuses on generative AI and cardiovascular applications, collaborating closely with clinical teams as part of the group led by Mert Sabuncu. She completed her PhD at Universidad Torcuato Di Tella and Universidad Nacional del Sur under the supervision of Emmanuel Iarussi and Claudio Delrieux, where her research focused on deep generative modeling of 3D vascular structures. Her research has been published in leading conferences and journals in the field, including venues such as MICCAI and the journal Medical Image Analysis. More broadly, she is interested in multimodal learning and synthetic data generation. Mila Soares de Oliveira de Souza Mila de Oliveira is a research software engineer currently working on AI applications to address public health challenges in Brazil. She completed her MSc in Electrical Engineering (Computer Vision) at the Universidade Federal do Rio de Janeiro, where she developed baseline methodologies for real-time detection of breeding sites of Aedes aegypti (primary vector of dengue, the most pressing public health threat in Brazil) under the supervision of Eduardo Antonio Barros da Silva and Sergio Lima Netto. She has extensive experience in software engineering for AI products in industry, having previously worked at Apple and Microsoft Advanced Technology Labs. Her current interests include GPU programming, machine learning compilers and formal verification of algorithms using proof assistants, as well as broader applications of ML in healthcare and science. We welcome all ICLR attendees to join us for an afternoon of discussion, networking, and community building. Additionally, we are collecting CVs to share with our sponsors who are actively recruiting: 👉 https://forms.gle/4FasmbmiLBh5zqyu6 Thanks to our sponsors Previous Next

WIML Events Filter items by Event Type. Endorsed Events Mentorship Program Socials & Networking Symposium Workshops WiML Social @ ICLR 2026 Rio de Janeiro April 24, 2026 Read More WiML @ Data & AI Careers Festival 2026 Woodhouse, England March 24, 2026 Read More WiCS AI Research Day @ SFU SFU Burnaby Campus, Burnaby, British Columbia, Canada February 20, 2026 Read More WiML Social @ EurIPS 2025 Treehouse, Bella Center, Copenhagen December 3, 2025 Read More WiML Workshop @ NeurIPS San Diego 2025 San Diego, CA, USA. December 2, 2025 Read More WiML Reception @ NeurIPS Mexico City 2025 Ciudad de México, México November 30, 2025 Read More 15 Page 1

Past Mentors Here we highlight Past Mentors of WiML Sharmita Dey Postdoctoral Researcher at ETH Zurich Read More Love Allen Chijioke Ahakonye Senior Researcher at Kumoh National Institute of Technology Read More Sandhya Prabhakaran Applied Research Scientist at Moffitt Cancer Center, Tampa, Florida Read More Clarissa Guevara Gomez Professor at Tec de Monterrey Read More Sidhika Balachandar PhD Student at UC Berkeley Read More

< Back Hanna Wallach, PhD WiML Co-Founder, President (2009-2012), Director (2012-2016)

All events WiML Partner Event: Virtual Women in Science Fireside Chat with Amazon Virtual December 9, 2020 11:00 am - 12:30 pm WiML is excited to announce the Virtual Women in Science Fireside Chat by WiML Partner Amazon. Join some of Amazon’s top leaders and researchers in Machine Learning to learn about the valuable knowledge and experience of successful Women in Research at Amazon. These remarkable leaders from Alexa, Consumer and AWS will engage in a casual discussion on innovation, their career path and a Q&A. They are: Na Zhang, ML Science Manager, Customer Trust & Partner Support Vanessa Murdock, Manager Applied Science, Alexa Shopping Nashlie Sephus, Applied Science Manager, Rekognition and Video Katrin Kirchhoff, Senior Manager, Applied Science AWS Transcribe Priya Ponnapalli, Senior Manager, Data Science, AWS ML Solutions Lab These women will share their insights and experiences as women in science and will be covering topics such as navigating your career path, finding a mentor and work life balance. When: Wednesday, December 9th, 2020, 11am – 12.30pm PT Where: Virtual Registration: https://register.gotowebinar.com/register/4812705742642619918 Organized by Amazon. Amazon is a WiML Diamond Partner. Previous Next

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< Back Danielle Belgrave, PhD WiML Director (2019-2020, 2021-2024) Visit my Profile

All events WiML Luncheon @ ICML 2017 Sydney, Australia August 8, 2017 12:00 pm — 02:00 pm WiML is hosting a luncheon at ICML 2017 in Sydney, Australia. The goal of this event is to bring together female faculty members, research scientists, data scientists, and graduate students to meet, find mentorship, and learn from each other. Date: Tuesday, August 8, 2017, 12pm-2pm Venue: Grand Ballroom at The Westin Sydney, Sydney Registration: https://www.eventbrite.com/e/wiml-icml-luncheon-2017-tickets-36123495347# If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org SPONSORS -Gold- -Silver- Previous Next

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 11th Women in Machine Learning Workshop (WiML 2016) Monday, December 5, 2016 Co-Located with NIPS in Barcelona, Spain Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learnin g . This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 11th year, the 2016 workshop is co-located with NIPS in Barcelona, Spain on December 5, 2016. A History of WiML poster was created to celebrate the 10th workshop , held in 2015 in Montreal, Canada 2015. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as breakfast at ICML and AAAI conferences and local meetup events, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Jennifer Chayes Microsoft Research Maya Gupta Google Research Anima Anandkumar Amazon / UC Irvine Suchi Saria John Hopkins Univ Location The workshop takes place in Centre de Convencions Internacional Barcelona , located at Plaça de Willy Brandt, 11-14, 08019 Barcelona, Spain. PROGRAM RESEARCH ROUNDTABLES CAREER & ADVICE ROUNDTABLES CAREER & ADVICE ROUNDTABLES POSTERS Sunday, Dec 4 12.00 – 14.00 Registration desk open. Entrance Hall (enter from Entrance C) 14.00 – 19.00 Workshop on Effective Communication by Katherine Gorman of Talking Machines and Amazon (Optional). Invitation-only, RSVP required 16.00 – 18.00 Amazon Panel & Networking (Optional). Invitation-only, RSVP required 17.00 – 19.00 Facebook Lean-In Circles (Optional). Invitation-only, RSVP required 19.15 – 22.00 WiML Dinner (Optional). Separate registration required . Dedicated to Amazon 22.00 – 23.30 OpenAI Happy Hour (Optional). Invitation-only, RSVP required Monday, Dec 5 All events are held in Rooms 111 and 112, level P1, CCIB except for the poster session, which takes place in Area 5+6+7+8, level P0. 07.00 – 08.00 Registration and Breakfast. Dedicated to Microsoft and OpenAI. Registration desk at Entrance Hall (enter from Entrance C); Breakfast in Rooms 111 and 112, level P1 08.00 – 08.05 Opening Remarks 08.05 – 08.40 Invited Talk: Maya Gupta , Google Research. Designing Algorithms for Practical Machine Learning. [Abstract] [Video] 08.40 – 08.55 Contributed Talk: Maithra Raghu, Cornell Univ / Google Brain. On the Expressive Power of Deep Neural Networks. [Abstract] [Video] 08.55 – 09.10 Contributed Talk: Sara Magliacane, VU Univ Amsterdam. Ancestral Causal Inference. [Abstract] [Video] [Slides] 09.10 – 09.15 Break 09.15 – 10.15 Research Roundtables (Coffee served until 9.40am). Dedicated to Apple and Facebook 10.15 – 10.50 Invited Talk: Suchi Saria , John Hopkins Univ. Towards a Reasoning Engine for Individualizing Healthcare. [Abstract] [Video] 10.50 – 11.05 Contributed Talk: Madalina Fiterau, Stanford Univ. Learning Representations from Time Series Data through Contextualized LSTMs. [Abstract] [Video] 11.05 – 11.10 Break 11.10 – 11.25 Contributed Talk: Konstantina Christakopoulou, Univ Minnesota. Towards Conversational Recommender Systems. [Abstract] [Video] [Slides] 11.25 – 12.00 Invited Talk: Anima Anandkumar , Amazon / UC Irvine. Large-Scale Machine Learning through Spectral Methods: Theory & Practice. [Abstract] [Video] [Slides] 12.00 – 13.00 Career & Advice Roundtables 13.00 – 13.30 Lunch and Poster Setup. Dedicated to DeepMind and Google 13.30 – 15.30 Poster Session (Coffee served until 2pm). Open to WiML and NIPS attendees. Dedicated to our Silver Sponsors: Capital One, D.E. Shaw, Intel, Twitter. Area 5+6+7+8, level P0; Round 1: 1.40pm – 2.30pm; Round 2: 2.30pm – 3.20pm; Poster Removal: 3.20pm – 3.30pm 15.30 – 15.45 Raffle and WiML Updates : Tamara Broderick , MIT and Sinead Williamson , UT Austin. [Video] 15.45 – 16.00 Contributed Talk: Amy Zhang, Facebook. Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images. [Abstract] [Video] 16.00 – 16.35 Invited Talk: Jennifer Chayes , Microsoft Research. Graphons and Machine Learning: Estimation of Sparse Massive Networks. [Abstract] [Video] 16.35 – 16.40 Closing Remarks NIPS Main Conference (NIPS registration required) 17.00 NIPS Opening Remarks. Area 1 + 2, level P0 WiML 2016 Poster Session Monday, Dec 5, 1.30pm to 3:30pm, Area 5+6+7+8, level P0, open to WiML and NIPS attendees 200+ posters covering theory, methodology, and applications of machine learning will be presented in 2 rounds. Accepted posters Accepted posters (with abstracts) . Abstracts listed here are for archival purposes and do not constitute proceedings for this workshop. Information for poster presenters: Posters for both rounds should be setup 1-1.40pm and removed 3.20-3.30pm. Each poster board is shared by 2-3 presenters. Please check the program book for your round number and poster number. Look for that number in the poster room with ‘W’ appended to the front, e.g. W1, W2, etc. Poster size: up to 37.9 inches width and 35.8 inches height (or 96.3 cm x 91.0 cm), portrait or landscape. Research Roundtables 9.15 am - 10.15 am. Coffee served until 9.40 am. Table 1: Deep learning I – Katja Hofmann, Microsoft Research, Oriol Vinyals, DeepMind Table 2: Deep learning II – Junli Gu, Tesla, Sergio Guadarrama, Google Research, Niv Sundaram, Intel Table 3: Reinforcement learning – Emma Brunskill, Carnegie Mellon / Stanford, Yisong Yue, Caltech Table 4: Bayesian methods I – Barbara Engelhardt, Princeton, Lamiae Azizi, University of Sydney Table 5: Bayesian methods II – Ferenc Huszar, Twitter / Magic Pony Table 6: Graphical models – Margaret Mitchell, Google Research, Danielle Belgrave, Imperial College London Table 7: Learning theory – Cynthia Rush, Columbia University, Corinna Cortes, Google Research Table 8: Statistical inference and estimation – Katherine M. Kinnaird, Brown University, Alessandra Tosi, Mind Foundry, Oxford Table 9: Optimization – Anima Anandkumar, Amazon / UC Irvine, Puja Das, Apple Table 10: Neuroscience – Irina Higgins, DeepMind, Jascha Sohl-Dickstein, Google Brain Table 11: Robotics – Raia Hadsell, DeepMind, Julie Bernauer, NVIDIA Table 12: Natural language processing I – Catherine Breslin, Amazon, Olivia Buzek, IBM Watson Table 13: Natural language processing II – Pallika Kanani, Oracle Labs, Ana Peleteiro Ramallo, Zalando, Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil Table 14: Healthcare/biology applications – Tania Cerquitelli, Politecnico di Torino, Jennifer Healey, Intel Table 15: Music applications – Luba Elliott, iambicai, Kat Ellis, Amazon Music, Emilia Gomez, Universitat Pompeu Fabra, Barcelona Table 16: Social science applications – Allison Chaney, Princeton University, Isabel Valera, Max Planck Institute for Software Systems Table 17: Fairness, accountability, transparency in machine learning – Sarah Bird, Microsoft, Ekaterina Kochmar, University of Cambridge Table 18: Computational sustainability – Erin LeDell, H2O.ai, Jennifer Dy, Northeastern University Table 19: Computer vision – Judy Hoffman, Stanford University, Manohar Paluri, Facebook Table 20: Human-in-the-Loop Learning – Been Kim, Allen Institute for AI / Univ of Washington, Saleema Amershi, Microsoft Research Table 1: Machine Learning @Amazon: Jumpstarting your career in industry – Anima Anandkumar, Catherine Breslin, Enrica Maria Fillipi Table 2: Careers@Apple – Meriko Borogove, Anh Nguyen Table 3: Machine Learning @DeepMind: Research in industry vs. academia – Nando De Freitas, Viorica Patraucean, Kimberly Stachenfeld Table 4: Machine Learning @Facebook: Sponsorship vs. Mentorship Throughout Your Career – Angela Fan, Amy Zhang, Christy Sauper, Natalia Neverova, Manohar Paluri Table 5: Machine Learning @Google: Industrial Research and Academic Impact – Corinna Cortes, Google Table 6: Machine Learning and Deep Learning @Microsoft – Christopher Bishop, Mir Rosenberg, Anusua Trivedi Table 7: Delivering phenomenal customer experiences with Machine Learning @Capital One – Jennifer Hill, Marcie Apelt Table 8: Networking I – Olivia Buzek, IBM Watson, Jennifer Healey, Intel Table 9: Networking II – Pallika Kanani, Oracle Labs, Been Kim, Allen Institute for AI / Univ of Washington Table 10: Work/Life Balance (academia) – Namrata Vaswani, Iowa State University, Beka Steorts, Duke University Table 11: Work/Life Balance (industry) I – Yuanyuan Pao, Lyft, Antonio Penta, United Technologies Research Centre, Ireland Table 12: Work/Life Balance (industry) II – Kat Ellis, Amazon Music, Puja Das, Apple Table 13: Choosing between academia/industry I – Katherine M. Kinnaird, Brown University, Jascha Sohl-Dickstein, Google Brain Table 14: Choosing between academia/industry II – Sarah Bird, Microsoft, Oriol Vinyals, DeepMind Table 15: Life with Kids – Jenn Wortman Vaughan, Microsoft Research, Julie Bernauer, NVIDIA Table 16: Getting a job (academia) I – Jennifer Chayes, Microsoft Research, Yisong Yue, Caltech Table 17: Getting a job (academia) II – Tamara Broderick, MIT, Cynthia Rush, Columbia University Table 18: Getting a job (industry) I – Anne-Marie Tousch, Criteo, Sergio Guadarrama, Google Research Table 19: Getting a job (industry) II – Margaret Mitchell, Google Research, Erin LeDell, H2O.ai Table 20: Doing a postdoc – Cristina Savin, IST Austria / NYU, Judy Hoffman, Stanford University Table 21: Doing research in industry – Junli Gu, Tesla, Samy Bengio, Google Brain Table 22: Keeping up with academia while in industry – Irina Higgins, DeepMind, Alessandra Tosi, Mind Foundry, Oxford Table 23: Surviving graduate school – Allison Chaney, Princeton University, Viktoriya Krakovna, DeepMind Table 24: Seeking funding: fellowships and grants – Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil, Danielle Belgrave, Imperial College London Table 25: Establishing collaborations – Barbara Engelhardt, Princeton University, Ekaterina Kochmar, University of Cambridge Table 26: Joining startups – Alyssa Frazee, Stripe, Ferenc Huszar, Twitter / Magic Pony Table 27: Scientific communication – Katherine Gorman, Talking Machines, Ana Peleteiro Ramallo, Zalando Table 28: Building your professional brand – Luba Elliott, iambicai, Lamiae Azizi, The University of Sydney Table 29: Commercializing your research – Katherine Boyle, General Catalyst, Zehan Wang, Twitter / Magic Pony Table 30: Long-term career planning – Inmar Givoni, Kindred.ai, Jennifer Dy, Northeastern University Career & Advice Roundtables 12 pm - 1 pm Table 1: Machine Learning @Amazon: Jumpstarting your career in industry – Anima Anandkumar, Catherine Breslin, Enrica Maria Fillipi Table 2: Careers@Apple – Meriko Borogove, Anh Nguyen Table 3: Machine Learning @DeepMind: Research in industry vs. academia – Nando De Freitas, Viorica Patraucean, Kimberly Stachenfeld Table 4: Machine Learning @Facebook: Sponsorship vs. Mentorship Throughout Your Career – Angela Fan, Amy Zhang, Christy Sauper, Natalia Neverova, Manohar Paluri Table 5: Machine Learning @Google: Industrial Research and Academic Impact – Corinna Cortes, Google Table 6: Machine Learning and Deep Learning @Microsoft – Christopher Bishop, Mir Rosenberg, Anusua Trivedi Table 7: Delivering phenomenal customer experiences with Machine Learning @Capital One – Jennifer Hill, Marcie Apelt Table 8: Networking I – Olivia Buzek, IBM Watson, Jennifer Healey, Intel Table 9: Networking II – Pallika Kanani, Oracle Labs, Been Kim, Allen Institute for AI / Univ of Washington Table 10: Work/Life Balance (academia) – Namrata Vaswani, Iowa State University, Beka Steorts, Duke University Table 11: Work/Life Balance (industry) I – Yuanyuan Pao, Lyft, Antonio Penta, United Technologies Research Centre, Ireland Table 12: Work/Life Balance (industry) II – Kat Ellis, Amazon Music, Puja Das, Apple Table 13: Choosing between academia/industry I – Katherine M. Kinnaird, Brown University, Jascha Sohl-Dickstein, Google Brain Table 14: Choosing between academia/industry II – Sarah Bird, Microsoft, Oriol Vinyals, DeepMind Table 15: Life with Kids – Jenn Wortman Vaughan, Microsoft Research, Julie Bernauer, NVIDIA Table 16: Getting a job (academia) I – Jennifer Chayes, Microsoft Research, Yisong Yue, Caltech Table 17: Getting a job (academia) II – Tamara Broderick, MIT, Cynthia Rush, Columbia University Table 18: Getting a job (industry) I – Anne-Marie Tousch, Criteo, Sergio Guadarrama, Google Research Table 19: Getting a job (industry) II – Margaret Mitchell, Google Research, Erin LeDell, H2O.ai Table 20: Doing a postdoc – Cristina Savin, IST Austria / NYU, Judy Hoffman, Stanford University Table 21: Doing research in industry – Junli Gu, Tesla, Samy Bengio, Google Brain Table 22: Keeping up with academia while in industry – Irina Higgins, DeepMind, Alessandra Tosi, Mind Foundry, Oxford Table 23: Surviving graduate school – Allison Chaney, Princeton University, Viktoriya Krakovna, DeepMind Table 24: Seeking funding: fellowships and grants – Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil, Danielle Belgrave, Imperial College London Table 25: Establishing collaborations – Barbara Engelhardt, Princeton University, Ekaterina Kochmar, University of Cambridge Table 26: Joining startups – Alyssa Frazee, Stripe, Ferenc Huszar, Twitter / Magic Pony Table 27: Scientific communication – Katherine Gorman, Talking Machines, Ana Peleteiro Ramallo, Zalando Table 28: Building your professional brand – Luba Elliott, iambicai, Lamiae Azizi, The University of Sydney Table 29: Commercializing your research – Katherine Boyle, General Catalyst, Zehan Wang, Twitter / Magic Pony Table 30: Long-term career planning – Inmar Givoni, Kindred.ai, Jennifer Dy, Northeastern University Call for Participation The 11th WiML Workshop is co-located with NIPS in Barcelona, Spain on Monday, December 05, 2016. The workshop is a full-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, and research scientists for an opportunity to connect and exchange ideas. There will also be a panel discussion and a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will be female, all genders are invited to attend. This is a technical workshop with exciting technical talks. Important Dates August 29, 2016 11:59pm PST – Abstract submission deadline September 26, 2016 – Notification of abstract acceptance October 5, 2016 11:59pm PST- Travel grant/oral presentation application deadline October 15, 2016 – End of abstract editing period October 24, 2016 – Notification of travel grant/oral presentation acceptance November 1, 2016 (or before, if we run out of space) – Registration deadline December 4, 2016 – Pre-workshop dinner and events December 5, 2016 – Workshop Submission Instructions We strongly encourage female students, post-docs and researchers in all areas of machine learning to submit an abstract (500 words or less) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minutes oral presentations. Submission page: https://easychair.org/conferences/?conf=wiml2016 Evaluation criteria: Submissions will be peer reviewed. Abstracts will be evaluated on scientific merit and relevance to the community. To facilitate the peer review process, we encourage authors to sign up as reviewers when submitting abstracts. Examples of accepted abstracts from previous years. Note that despite the option to upload a paper in the submission system, this is not required. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract. Travel Scholarships Registration is free. Partial scholarships will be provided to female students and postdoctoral attendees with accepted abstracts to offset travel costs. GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTER Committee ORGANIZERS Diana Cai Statistics PhD student University of Chicago Deborah Hanus Computer Science PhD student Harvard University Sarah Tan Statistics PhD student Cornell University Isabel Valera Postdoctoral Fellow Max Planck Institute for Software Systems Rose Yu Computer Science PhD student University of Southern California AREA CHAIRS Danielle Belgrave (Imperial College London) Tamara Broderick (Massachusetts Institute of Technology) Allison Chaney (Princeton University) Deborah Hanus (Harvard University) Pallika Kanani (Oracle Labs) Katherine M. Kinnaird (Brown University) Lizhen Lin (University of Texas at Austin) Maria Lomeli (University of Cambridge) Konstantina Palla (University of Oxford) Sara Wade (University of Warwick) Sinead Williamson (University of Texas at Austin) Svitlana Volkova (Pacific Northwest National Laboratory) FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top