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10th Annual Workshop for Women in Machine Learning (WiML 2015) Sunday, December 6 Co-Located with NIPS in Palais des Congrès de Montréal, Canada 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 Learning . 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 Education_(42).jpg 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 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. 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 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

15th Women in Machine Learning Workshop (WiML 2020) The 15th WiML Workshop is co-located with the virtual NeurIPS conference on December 9th, 2020. Logistics Program Call for Participation FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. 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 (WiML) Workshop, co-located with NeurIPS, is our flagship event. This day-long technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, exchange ideas and learn from each other. The workshop started at the 2006 Grace Hopper Celebration and moved to NeurIPS in 2008. A History of WiML poster was created in 2015 to celebrate the 10th workshop. 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. The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Location The workshop takes place virtually. See program for more details. PROGRAM The full program book for WiML 2020 is now available. Call for Participation Please fill out this form and register for NeurIPS if you would like to attend. All are welcome, and we look forward to your involvement! PLATINUM SPONSORS DIAMOND SPONSORS GOLD SPONSORS SILVER SPONSORS SILVER SPONSORS Back To Top

All events Black in AI-WiML Event: Speed Networking Sessions @ NeurIPS 2020 Virtual December 11, 2020 8:00 am - 9:00 am WiML is excited to announce the Black in AI-WiML Speed Networking Sessions @ NeurIPS 2020. Participants will be randomly paired up with another participant for 5-minute speed networking. Over the 1 hour session, you will receive several random pairings. We suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss. When: Friday, December 11, 2020. There will be two 1-hour sessions: Speed Networking Session 1: 8 – 9am PT Speed Networking Session 2: 2 – 3pm PT How to join: These sessions take place on the Glimpse platform; each session has its own link. Find the links on Black in AI’s neurips.cc page: https://neurips.cc/virtual/2020/protected/affinity_workshop_19449.html (NeurIPS registration required to access). You can join anytime during a session. Please enter your real name when you first access the link. NeurIPS registered attendees are welcome to attend. Organized by Black in AI 2020 workshop organizers. Questions? Email blackinai2020@googlegroups.com . Black in AI is also organizing other social events before and after these sessions. See here for more info. Previous Next

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3rd Women in Machine Learning Un-Workshop, ICML 2022 The 3rd WiML Un-Workshop is co-located with ICML on Monday, July 18th, 2022. Speakers Logistics Breakout Sessions 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 Learning, primarily intended to foster active participant engagement in the program. 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 3th year, the 2022 un-workshop is co-located with ICML . Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch or social at the AISTATS or AAAI conferences, 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 Emma Brunskill Emma Brunskill is an associate professor in the Computer Science Department at Stanford University. Her goal is to create AI systems that learn from few samples to robustly make good decisions, motivated by our applications to healthcare and education. Her work has been honored by early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research) received several best research paper nominations (CHI, EDMx3) and awards (UAI, RLDM, ITS). Celestine Mendler-Dünner Celestine Mendler-Dünner is a research group lead at the Max Planck Institute for Intelligent Systems in Tübingen. Her research focuses on the role of society in the study of computation, taking into account actions and reactions of individuals when analyzing and designing algorithmic systems. Prior to joining MPI-IS Celestine was a SNSF postdoctoral fellow at UC Berkeley, and a predoctoral researcher at IBM Research Zurich. She obtained her PhD from ETH Zurich where she was awarded the ETH medal and the Fritz Kutter prize for the academic as well as the industrial impact of her research. Yixin Wang Yixin Wang is an LSA Collegiate Fellow and an assistant professor of statistics at the University of Michigan. She works in the fields of Bayesian statistics, machine learning, and causal inference. Her research has received several awards, including the INFORMS data mining best paper award, Blackwell-Rosenbluth Award from the junior section of ISBA, student paper awards from ASA Biometrics Section and Bayesian Statistics Section, and the ICSA conference young researcher award. Location This workshop will be hybrid, co-located with ICML at the Baltimore Convention Center , Baltimore, Maryland USA. Type of registration required to attend Any type of in-person registration (tutorial / workshop / conference / all) grants you in-person access to the un-workshop. Also, an in-person registration includes access to the virtual one. Breakout Sessions Breakout Sessions During the day of the WiML Un-Workshop @ ICML 2022 there will be three different Breakout Sessions. We list the sessions, topics, and leaders. BreakoutGhoshehBreakout Session #1 (9.10AM - 10.10AM) IN-PERSON Breakout Sessions Machine learning real-time applications in health. Leader: Dania Humaidan, Co-leader: Cansu Sen. VIRTUAL Breakout Sessions Deep Generative Models for Electronic Health Records. Leader: Ghadeer Ghosheh, Co-leader: Tingting Zhu. Affective Computing: A Computational Perspective. Leader: Shreya Ghosh, Co-lead: Garima Sharma. Introducing geometry awareness in deep networks. Leader: Ankita Shukla. Breakout Session #2 (11.05AM - 12.05AM) IN-PERSON Breakout Sessions Challenges and opportunities in certified auditing of ML models. Leader: Chhavi Yadav. Robustness of Deep Learning Models to Distribution Shift. Leader: Polina Kirichenko, Co-leads: Shiori Sagawa, Sanae Lofti. VIRTUAL Breakout Sessions Knowledge Distillation through the lense of the capacity gap problem. Leader: Ibtihel Amara, Co-lead: Samrudhdhi Rangrej, Zahra Vaseqi. Improving AI Education. Leader: Mary Smart, Co-lead: Stefania Druga. Statistical Inference & Applications to Machine Learning. Leader: Lilian Wong, Co-lead: Po-ling Loh. Breakout Session #3 (15.25 - 16.25) IN-PERSON Breakout Sessions Robustness of Machine Learning. Leader: Yao Qin Towards efficient and robust deep learning training. Leader: Wenhan Xia. VIRTUAL Breakout Sessions Machine Learning for Physical Sciences. Leader: Taoli Cheng. Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement. Leader: Haoyu Du, Co-lead: Peiyuan Zhou, Annie Lee, Rainah Khan. Detection of Unseen Classes of different Domains using Computer Vision. Leader: Asra Aslam. PROGRAM PANELISTS IN-PERSON MENTORS VIRTUAL MENTORS POSTERS The program follows the following color scheme: talks , breakout sessions , poster sessions , mentoring sessions , program break , sponsor talks , panel discussion . All invited talk titles, and invited speaker/mentor/panelist names are *clickable*. The majority of the program will be streamed and occur synchronously in-person and virtually, except if marked as in-person/virtual only. You can find the zoom links and livestream on the WiML workshop page of the ICML website . 08:30 Introduction & Opening Remarks , Vinitra Swamy all-day Virtual Sponsor Booths , [DeepMind, D.E. Shaw Research, Home Depot, Microsoft Research] all-day In-Person Sponsor Booths , [DeepMind, Google, QuantumBlack] 08:45 Desiderata for Representation Learning: A Causal Perspective , Yixin Wang [Invited Talk] Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions capture. However, these desiderata are often intuitively defined and challenging to quantify or enforce. In this talk, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions, enabling metrics and algorithms that target efficient, non-spurious, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning. Joint work with Michael Jordan . 09:10 Breakout session [in-person only] Machine learning real-time applications in health (Leaders: Dania Humaidan, Cansu Sen) [hybrid] Introducing geometry awareness in deep networks (Leader: Ankita Shukla) [hybrid] Affective Computing: A Computational Perspective (Leaders: Shreya Ghosh, Garima Sharma) [hybrid] Deep Generative Models for Electronic Health Records (Leaders: Ghadeer Ghosheh) 10:10 Poster Session 10:40 Emma Brunskill [Invited Talk] 11:05 Breakout session [in-person only] Challenges and opportunities in certified auditing of ML models (Leader: Chhavi Yadav) [in-person only] Robustness of Deep Learning Models to Distribution Shift (Leaders: Polina Kirichenko, Shiori Sagawa) [hybrid] Knowledge Distillation through the Lens of the Capacity Gap Problem (Leaders: Ibtihel Amara, Samrudhdhi Rangrej, Zahra Vaseqi) [hybrid] Improving AI Education (Leaders: Mary Smart, Stefania Druga) [hybrid] Statistical Inference & Applications to Machine Learning (Leaders: Lilian Wong, Po-ling Loh) 12:05 Mentoring Roundtables [in-person only] /// Mentoring Panel [virtual only] Table 1: Choosing between academia and industry Amy Zhang & Lauren Gardiner Mentors: Jigyasa Grover , Ciara Pike-Burke, Nika Haghtalab, Po-Ling Loh, Hermina Petric Maretic Table 2: Finding mentors and taking on mentorship roles throughout your career / Celestine Mendler-Dünner & Cyril Zhang Moderator: Sinead Williamson Table 3: Establishing and maintaining collaborations Surbhi Goel & Max Simchowitz Table 4: Work-life Balance Ioana Bica & Kishore Kumar 13:05 Lunch Break, joint with NewInML [in-person only] /// Virtual Sponsor Booths [virtual only] 14:40 Harnessing the power of Hybrid Intelligence, Maria Olivia Lihn [QuantumBlack Sponsor Talk] 14:55 Building embodied agents that can learn from their environments and humans, Kavya Srinet [Meta Platforms Sponsor Talk] 15:10 Machine Learning at Apple, Tatiana Likhomanenko [Apple Sponsor Talk] 15:25 Breakout session [in-person only] Robustness of Machine Learning (Leader: Yao Qin) [in-person only] Distributionally robust Reinforcement Learning (Leaders: Laixi Shi, Mengdi Xu) [hybrid] Machine Learning for Physical Sciences (Leader: Taoli Cheng) [hybrid] Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement (Leaders: Haoyu Du, Peiyuan Zhou, Annie Lee, Rainah Khan) [hybrid] Detection of Unseen Classes of different Domains using Computer Vision (Leader: Asra Aslam) 16:30 Poster Session, joint with LXAI 17:00 Social dynamics in prediction, Celestine Mendler-Dünner [Invited Talk] Abstract: Algorithmic predictions inform consequential decisions, incentivize strategic actions, and motivate precautionary measures. As such, predictions used in societal systems not only describe the world they aim to predict, but they have the power to change it; a prevalent phenomenon often neglected in theories and practices of machine learning. In this talk, I will introduce a risk minimization framework, called performative prediction, that conceptualizes this phenomenon by allowing the predictive model to influence the distribution over future data. This problem formulation elucidates different algorithmic solution concepts, optimization challenges, and offers a new perspective on prediction. In particular, I will discuss how performative prediction allows us to articulate the difference between learning from a population and steering a population through predictions, facilitating an emerging discourse on the topic of power of predictive systems in digital economies. 17:25 Best Practices for Research: Increasing Efficiency and Research Impact, and Navigating Hybrid Collaborations [Panel] Panelists: Amy Zhang , Surbhi Goel , Agni Kumar Moderator: Ioana Bica 18:25 Closing Remarks, Tatjana Chavdarova Note: Please navigate the 'Program' menu in the slidebar at the top to find more details about speakers, panelist and mentors. Surbhi Goel Surbhi Goel is currently a postdoctoral researcher at Microsoft Research NYC. In Spring 2023, she will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Prior to this, she received her Ph.D. from the Department of Computer Science at the University of Texas at Austin where she was advised by Adam Klivans. Her work lies at the intersection of machine learning and theoretical computer science, with a focus on developing the statistical and computational foundations of modern machine learning paradigms. Among other honors, she is a recipient of UT Austin's Bert Kay Dissertation award, a J.P. Morgan AI PhD fellowship, and a Simons-Berkeley research fellowship. She has been recognized as a Rising Star in ML by University of Maryland and in EECS by UIUC. She is actively involved in service and outreach through her role as the co-founder of Learning Theory Alliance (LeT-All), a community building and mentorship initiative for the learning theory community. Amy Zhang Amy is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research, and is starting as an assistant professor at UT Austin in the ECE department in Spring 2023. She works on state abstractions, model-based reinforcement learning, representation learning, and generalization in RL. She did her PhD at McGill University and Mila - Quebec AI Institute, co-supervised by Joelle Pineau and Doina Precup. She also has an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT. Agni Kumar Agni is an Applied Research Scientist on Apple’s Health AI team. She studied at MIT, graduating with an M.Eng. in Machine Learning and B.S. degrees in Mathematics and Computer Science. Her thesis on modeling the spread of healthcare-associated infections led to joining projects at Apple with applied health focuses, specifically on understanding cognitive decline from device usage data and discerning respiratory rate from wearable microphone audio. She has published hierarchical reinforcement learning research and predictive modeling work in conferences and journals, including CHIL, EMBC, PLOS Computational Biology, and Telehealth and Medicine Today. She was a workshop organizer for ICML’s first “Computational Approaches to Mental Health” workshop in 2021. She has also volunteered at WiML workshops and served as a reviewer for NeurIPS. For joy, Agni leads an Apple-wide global diversity network about encouraging mindfulness to find peace each day. Ioana Bica (Moderator) Ioana is a rising fifth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. Her PhD research focuses on building machine learning methods for improving and understanding decision making. To achieve this, she have worked on developing causal inference methods capable of estimating the individualized effect of interventions (e.g. actions or treatments) from observational data. Her research experience also includes an internship at DeepMind where she has been working with Jovana Mitrović on self-supervised learning and causality with the aim of learning better representations for objects in images. Prior to her PhD, she completed a Bachelor’s degree and a Master’s degree in Computer Science at the University of Cambridge where she worked with Prof. Pietro Liò on multi-modal data integration and unsupervised learning for genomics data. During this time, she has also interned at Google four times. Amy Zhang Amy is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research, and is starting as an assistant professor at UT Austin in the ECE department in Spring 2023. She works on state abstractions, model-based reinforcement learning, representation learning, and generalization in RL Celestine Mendler-Dünner Celestine is a research group lead at the Max Planck Institute for Intelligent Systems in Tübingen. Her research focuses on the role of society in the study of computation, taking into account actions and reactions of individuals when analyzing and designing algorithmic systems. Surbhi Goel Surbhi is currently a postdoctoral researcher at Microsoft Research NYC. In Spring 2023, she will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Prior to this, she received her Ph.D. from the Department of Computer Science at the University of Texas at Austin where she was advised by Adam Klivans. Her work lies at the intersection of machine learning and theoretical computer science, with a focus on developing the statistical and computational foundations of modern machine learning paradigms. Ioana Bica Ioana is a rising fifth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. Her research focuses on building machine learning methods for improving and understanding decision making. Lauren Gardiner Lauren is Senior Applied Research Scientist in the Health AI team at Apple. Cyril Zhang Cyril is a Senior Researcher at Microsoft Research NYC. His research interests include sequential prediction and decision-making, the theory and practice of optimization (especially in deep learning), and the synthesis of these topics (especially in language models). Max Simchowitz Max is a postdoc in Russ Tedrake's group at MIT. His recent work has focused on the theoretical foundations of online control and reinforcement learning, with past research ranging broadly across topics in adaptive sampling, multi-arm bandits, complexity of convex and non-convex optimization, and fairness in machine learning. He is currently interested in developing rigorous, theoretical guarantees for nonlinear control, wherever possible. Kishore Kumar Kumar is a Data Science and Analytics Lead at Amazon Prime video. He strives to solve complex business problems using advanced Machine Learning Algorithms, and has 10+ years of overall experience across multiple sectors. Nika Haghtalab Nika is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She works broadly on the theoretical aspects of machine learning and algorithmic economics. Ciara Pike-Burke Ciara is a Lecturer in Statistics in the Department of Mathematics at Imperial College London. Her research is in the field of statistical machine learning, particularly interested in sequential decision making problems. Hermina Petric Maretic Hermina is an Applied Scientist at Amazon working on time series forecasting. Her research interests include optimal transport, graphical models, network inference and interpretability. Po-Ling Loh Po-ling is a Lecturer in the Statistical Laboratory in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. Her interests include high-dimensional statistics, optimization, network inference, and statistical applications to medical imaging and epidemiology. Jigyasa Grover Jigyasa is a Senior Machine Learning Engineer at Twitter working in the Online Ads Prediction & Ranking domain, where she is spearheading a variety of projects spanning ML model development, user tracking transparency remediations, and monetizing new Twitter products. Sinead Williamson (Moderator) Sinead is an Assistant Professor of Statistics at the University of Texas at Austin, in the Department of Statistics and Data Science. Her research interests include network analysis, scalable inference methods, and bayesian nonparametrics. Self-Similarity Priors: Neural Collages as Differentiable Fractal Representation s Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon [poster] Interpretable Adversarial Attacks using Frank Wolfe Tooba Imtiaz1, Morgan Kohler, Jared Miller, Octavia Camps, Mario Sznaier, Jennifer Dy [poster] Robust task-specific adaption of drug-target interaction models Emma Svensson, Pieter-jan Hoedt, Sepp Hochroiter, Gunter Klambauer [poster] Multi-modal Contrastive Learning with CLOOB Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter [poster] Mimicking Iterative Learning with Modern Hopfield Networks for Tabular Data Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter [poster] A Recurrent Neural Network Model of Travel Direction in Humans Lilian Cheng, Elizabeth R. Chrastil, Jeffrey Krichmar [poster] Automated Deep Lineage Tree Analysis Using Deep Learning with a Bayesian Single Cell Tracking Approach Kristina Ulicna, Giulia Vallardi, Guillaume Charras, Alan R. Lowe [poster] Prostate Cancer Malignancy Detection and Localization From MpMRI Using Auto-Deep Learning: One Step Closer to Clinical Utilization W. ZONG, E. CARVER, S. ZHU , E. SCHAFF, D. CHAPMAN, J. LEE, I. CHETTY, N. WEN [poster] Explaining Structure Activity Relationships Using Locally Faithful Surrogate Models Heta A. Gandhi, Andrew D. White [poster] Affects of Remote Learning on Academic Performance of High School Students Garima Giri, Robert M. Scott, Snigdha Chaturvedi [poster] Fourier-Based Strategies to Explore Ethnic Feature Generation during Visible-to-Thermal Facial Translation (Work-in-progress) Catherine Ordun, Edward Raff, Sanjay Purushotham [poster] Cross-modal contrastive learning of microscopy image and structure-based representations of molecules Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer [poster] CNN-based Emotion Recognition from Multimodal Peripheral Physiological Signals Sowmya Vijayakumar, Ronan Flynn, Peter Corcoran, Niall Murray [poster] Cancer Health Disparity with BERTopic and PyCaret Evaluation Mary Adewunmi, Saksham Kumar Sharma, Nistha Sharma, N Sudha Sharmaa, Bayangmbe Mounmo [poster] Bayesian Optimisation for Active Monitoring of Air Pollution Sigrid Passano Hellan, Christopher G. Lucas and Nigel H. Goddard [poster] Detecting Seen/Unseen Objects with Reducing Response Time for Multimedia Event Processing Asra Aslam [poster] Automated Adaptive Design in Real Time Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth [poster] [Talk] Early Identification of Tuta absoluta in Tomato Plants Using Deep Learning Lilian Mkonyi, Denis Rubanga, Baraka Maiseli, Dina Machuve [poster] Fast and Accurate Method for the Segmentation of Diabetic Foot UlcerImages Rehema Mwawado,Mussa Dida,Baraka Maiseli [poster] Deep Kernel Learning with Personalized Multi-task Gaussian Processes for Longitudinal Prediction in Alzheimer’s Disease Vasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos [poster] Learning to Solve PDE-constrained Inverse Problems with Graph Networks Qingqing Zhao, David Lindell, Gordon Wetzstein [poster] [Talk] Not All Poisons are Created Equal: Robust Training against Data Poisoning Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman [poster] Call for Participation WiML 3rd Un-Workshop @ ICML 2022 [submissions are now closed] The Women in Machine Learning will be organizing the third un-workshop at ICML 2022. The un-workshop is based on the concept of an un-conference, a form of discussion on a pre-selected topic that is primarily driven by participants. Different from the traditional workshop format, the un-workshop’s main focus is topical breakout sessions with short invited talks and casual, informal poster presentations. This is an event format to encourage more participant interaction and we are excited to be able to explore this format in-person for the first time! The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Students, postdocs and researchers in all areas of Machine Learning who are woman or non-binary are encouraged to submit a one-page proposal to lead a breakout session on a certain research topic. There are many ways to participate, see below! IMPORTANT DATES May 27th, 2022 -- Application Form opens June 17th 19th, 2022 -- Deadline (Anywhere on Earth ) to apply for a breakout session, poster, registration fee funding, facilitating or volunteering June 20th, 2022 -- Notification of acceptance for all of the above (midnight Anywhere on Earth ) July 18th, 2022 -- WiML Un-Workshop Day Various ways of participating in WiML un-workshop Lead a breakout session: submit a proposal to lead a breakout session on a certain research topic. Facilitate a breakout session: assist breakout session leaders by taking notes and encouraging participant interactions and taking attendance. Present a poster: present a poster in a casual, informal setting. Volunteer: help with technical setup and in-event needs. Attend: participate in breakout session discussions. 1. Breakout session proposals: A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage students, postdocs, and researchers who are women or nonbinary in all areas of machine learning to submit a proposal to lead a topical breakout session. A complete proposal consists of a 1 page blind PDF (example here) and the names and bios of leaders submitted separately in the application form . We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators can also be provided if known at submission time. Otherwise, the organizers will match facilitators to breakout sessions. WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below) and do not have any other sources of funding. Only one proposal submission per leader is allowed. If there are multiple leaders, only one leader needs to submit the proposal. There are no proceedings. Guidelines for and roles of leaders: Breakout session leaders must be women or nonbinary Point-out key characteristics of your topic and make connections with other topics. Describe the key challenges in this research area on a high-level. Describe the key approaches on a high-level to provide intuition. Highlight possible points of discussion/goals to achieve during the session. Use graphics/imagery and materials e.g. slides as needed Encourage inclusive (rather than unilateral) discussions Leaders should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Submission instructions for breakout sessions: Proposals must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be minimum 11 point font size and page margins must be minimum 0.75 inches (all sides). Your proposal should stand alone, without linking to a longer paper or supplement. You should provide a brief description of the topics you’d like to discuss, any relevant references, a plan for how you would organize the time (1 hour) allocated for a session, as well as some ideas on how you would encourage discussion and participant interaction during the session. The PDF must not include identifying information, as it will be reviewed blind. In particular, the PDF should not contain information of the leaders or facilitators. Instead, submit their information in the application form . Selection criteria for breakout sessions: The degree to which it is expected that participants will find the topic interesting and valuable. Diversity of leaders and facilitators, including diversity of experience/seniority, affiliation, race, viewpoint and thinking regarding the topic, etc. Plans for encouraging discussion and participant interaction during the session. 2. Facilitators: If you are interested in facilitating a breakout session but have not yet connected with anyone submitting a breakout session proposal, you can indicate your interest in the application form . The role of facilitators is take notes and encourage participant interactions. Organizers will match selected facilitators to breakout sessions. Facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Also note that facilitators can be of any gender. 3. Posters: If you wish to present a poster, submit EITHER a short abstract (max 1500 characters) OR a PDF of the poster (only if you have it already). The poster may describe new, previously, concurrently published, or work-in-progress research. Posters in theory, methods, and applications are welcome. Accepted posters will be presented in a casual, informal setting. This setting is very different from formal poster sessions, e.g. at WiML Workshop at NeurIPS. While the exact presentation format is still being determined, we expect to be able to provide spots for everyone to display a physical poster. There are no oral or spotlight presentations, but you will be invited to submit a 5-10 minute video presentation uploaded to a video streaming service. Note that there are no proceedings. Submission instructions for posters: Submitted materials may contain identifying information, as posters for this un-workshop are not reviewed blind. Your submission should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we acknowledge that space is limited, some experimental results are likely to improve reviewers’ opinions of your poster. The poster presenter be woman or nonbinary; other authors can be of any gender. The poster presenter does not need to be the first author of the work. Only one poster submission per presenter is allowed. If your poster is not prepared yet, you can submit a one-page abstract, examples of accepted abstracts from previous years can be found here , and advice on writing a one-page abstract can be found here . 4. Volunteering: We are seeking volunteers to help with technical setup and virtual technology testing before the event, as well as help during the event, e.g. letting people into Zoom rooms, etc. We may also need emergency reviewers for breakout session proposals. You can indicate if you can help in any way in corresponding section of the application form . Note: We also encourage you to apply for ICML volunteer and funding opportunities, which are separate and independent of WiML funding. Check the ICML website directly for details. 5. Participation instructions: To participate in ANY of the above roles and/or apply for registration fee funding, please fill in the application form by June 17, 2022. Selected breakout session leaders, facilitators, poster presenters, volunteers, and funding recipients will be notified individually by the dates mentioned above. If you only wish to attend, we still recommend you fill in this form to provide your timezone and topic preferences. All participants are required to abide by the WiML Code of Conduct. 6. Registration fee funding: To apply for funding, you should: (i) be a woman or nonbinary; (ii) be a student, postdoc, or have an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical regions); (iii) participate in at least one breakout session as a leader, facilitator, or attendee. Due to limited funding, we may not be able to support everyone eligible; however, we hope to support as many eligible applicants as possible. Accepted breakout session leaders who fulfill the above eligibility criteria and do not have any other sources of funding will be prioritized for WiML funding. Other participants are also encouraged to apply. Priority will be given to individuals from underrepresented regions or groups, first-time attendees of ICML or similar conferences, and individuals who would benefit the most from this funding. Further questions? Check out the FAQs or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS Committee ORGANIZERS Paula Gradu General Chair Vinitra Swamy Program Chair Giulia Clerici Breakout Program and Logistics Co-Chair Mozhgan Saeidi Breakout Program and Logistics Co-Chair Noor Sajid Student Program and Volunteers Chair Yina Lin Networking and Mentorship Chair Shweta Khushu Finance and Sponsorship Chair Deeksha Shama Social Event Chair ADVISORY Danielle Belgrave D&I chair Tatjana Chavdarova WiML Board POC SUPER VOLOUNTEERS Archana Vaidheeswaran Women who code 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 ! 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

MENTORSHIP PROGRAM Give and receive advice, gain knowledge, and make connections WiML is committed to advancing the careers of women and non-binary people studying and working in machine learning. Our annual long-term mentorship program fosters meaningful long-term connections and provides targeted advice to early career researchers. 4th Annual WiML Mentorship Program (2024-2025) The pilot WiML Mentorship Program was launched in 2021-2022, and it has subsequently expanded to provide guidance to many members of the WiML community. Application form and additional information available here. Mentees target audience Women and non-binary individuals who are looking at progressing their career in machine learning or AI. There will be 3 cohorts: Post-graduate degree applicants applying for research-oriented degree programs in machine learning, such as Master’s and doctorates, starting in 2024-2025. Industry job seeker, with priority given to those who are looking for their first industry job during 2024-2025. Academic job seekers, post-Master's/post-Ph.D. job seeker for faculty jobs in machine learning during 2024–2025. Timeline Priority will be given to applications received by the 15th of September. The final deadline is on the 10th of December 2024. Please refer to the program website for more details. We recommend bi-weekly to monthly 1-on-1 mentor-mentee meetings. Please only sign up if you can commit to meeting regularly for 5-6 month duration of the program, so that we can make sure that participants get the most out of it. Your commitment is valuable and appreciated! Data usage and sharing Your submitted information will be shared with WiML Organizers and the WiML Board. You will also be asked for consent to contribute aggregated, anonymized data to WiML reports and publicity.

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All events WiML Luncheon @ ICML 2011 Bellevue, Washington June 30, 2011 12:00 pm — 01:40 pm WiML is hosting a luncheon at ICML 2011 in Bellevue, Washington. All women working on machine learning are invited. Date: Tuesday, June 30, 2011, 12pm-1.40pm Venue: Maple Room,Hyatt Regency Bellevue, Bellevue Event details: http://www.icml-2011.org/forms/Brochure_Final4.pdf 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

All events WiML Dinner @ ICLR 2019 New Orleans, Louisiana May 6, 2019 07:30 pm — 10:30 pm WiML is hosting a dinner at ICLR 2019 in New Orleans, Louisiana 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 organizer is Jaelle Scheuerman. The invited speakers are Karen Livescu, Alice Oh, and Parisa Kordjamshidi. Date: Monday, May 6, 2019, 7.30pm-10.30pm Venue: Mulate’s, 201 Julia St, New Orleans Registration: https://www.eventbrite.com/e/wiml-iclr-dinner-2019-tickets-60212858280 SPONSORS -Platinum- Previous Next

Partners Our generous partners sponsor WiML’s events, activities and programs on an annual basis. We also seek sponsors specifically for WiML Workshop, our flagship annual workshop co-located with NeurIPS. For any inquiries regarding yearlong partners or workshop sponsorship, contact sponsorship@wimlworkshop.org. For any other enquiries, contact info@wimlworkshop.org . Corporate Partners Non-Profit Partners

All events WiML Virtual Gathering @ COLT 2020 Virtual July 8, 2020 11:00 am — 12:00 pm WiML is hosting a virtual gathering at COLT 2020. The organizers are Claire Vernade and Ruth Urner. A panel discussion will be held on topics including career advice and mentoring. The panelists are: Alina Beygelzimer Alexandra Carpentier Kamalika Chaudhuri Sandra Zilles Date: July 8, 11am ET Registration: https://forms.gle/EecL5Nkj4yLGp3Xa8 SPONSORS -Platinum- -Diamond- Previous Next