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 4th Women in Machine Learning Un-Workshop, ICML 2023 4th Women in Machine Learning Un-Workshop, ICML 2023 The 4th WiML Un-Workshop is co-located with ICML on Friday, July 28th, 2023. Speakers Logistics Program Call for Participation Committee FAQ 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 Un-Workshop is the flagship event in un-conference style 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 4th year, the 2023 un-workshop is co-located with IC ML . Besides this annual un-workshop, Women in Machine Learning also organizes annual workshop at NeurIPS, 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. All participants are required to abide by the WiML Code of Conduct . I'm a paragraph. Click here to add your own text and edit me. It's easy. Invited Speakers Rihab Gorsane Jennifer Doudna Joelle Pineau Location This workshop will be in-person only, co-located with ICML at the Hawaii Convention Centre , Honolulu. 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. PROGRAM PANELISTS BREAKOUT SESSIONS COFFEE MEET & MINGLE SOCIAL The program follows the following color scheme: talks , breakout sessions , program breaks , sponsor round table , and panel discussion . The schedule is in local time zone (HST) . The program book is available at Program Book 2023 . 09:15 - 09.30 [Introduction & Opening Remarks - Priyadarshini Kumari (Sony AI) and Giulia Luise (Microsoft) - Hall 316C ] 09:30 - 10.00 [Invited Talk - Joelle Pineau (Meta AI and McGill University, Canada)] A culture of open and reproducible research in the era of large AI generative models - Hall 316C ] We have seen in the last year an incredible pace of progress in large AI models, with increasing abilities to generate high-quality images, videos, text, sound, and more. The best of these models display signs of creativity, reasoning, generalization, and plasticity beyond what we could imagine just a few years ago. Yet many challenges and open questions remain, both on the technological aspects and the societal impact of these models. Further progress, especially in mitigating the social risks of these models, is hampered by a lack of transparency and reproducibility. In this talk, Joelle will describe ongoing efforts to increase best practices towards the responsible training and deployment of AI research systems, drawing on her experience with the ML reproducibility program and the recent release of several state-of-the-art large models. 10.00 - 10.30 [Coffee Break and Networking] 10:30 - 11.00 [Invited Talk - Jennifer Doudna (UC Berkeley, USA)] Science and Snorkeling: My Journey with CRISPR - Hall - 316C ] In this talk, Jennifer will discuss her professional and personal journey working on CRISPR technology, from its genesis to its applications today, and focus on ethical challenges that mirror challenges with AI/ML. 11:00 - 12:00 [ Breakout session #2 (Three parallel sessions)] 1. 1) Leveraging Large Scale Models for Identifying and Fixing Deep Neural Networks Biases . [Hall 316C] Leader: Polina Kirichenko, Co-leads: Reyhane Askari Hemmat, Megan Richards. Facilitators: Vitória Barin Pacela , Mohammad Pezeshki 1. 2) The Role of Mentorship and Building Long-term Professional Relationships. [Hall 326A] Leader: Arushi Jain. Co-leads: Sangnie Bhardwaj Facilitators: Motahareh Sohrabi , Padideh Nouri 1. 3) Robustness in Machine Learning. [Hall 326B] Leader: Yao Qin. Co-lead: Qi Lei Facilitators: Christina Baek 12:00 - 13:30 [ Lunch and Sponsor Round Table Hall 316C ] Round Table A: Apple -- Finding Mentors and Being a Mentor Rishika Agarwal ( Engineer) Ivy Zhang (Engineer) Round Table B: D. E. Shaw Research -- Machine Learning at D. E. Shaw Research Jocelyn Sunseri (Machine Learning Research Engineer) Round Table C: Google DeepMind -- Keeping Up With the Pace of Change in Industry Kate Baumli (Research Engineer) Kavya Kopparupu (Research Engineer) Round Table D: Google Research -- Life and Work at Google Alicia Parrish (Research Scientist, Responsible AI) Round Table E: Microsoft -- Exploring Pathways: Career Opportunities, Growth, and Work-Life Balance at Microsoft Research Lili Wu (Data and Applied Scientist, Microsoft Research) Cyril Zhang (Senior Researcher, Microsoft Research) Round Table F: Two Sigma -- Your Next Big ML Move: Innovation in Finance Brittany Clarke (Diversity Recruiting Program Manager) Alyssa Lees (Engineering Manager, News Engineering: a NLP Technology Team) 13:30 - 14:00 [Invited Talk - Rihab Gorsane (Instadeep, Tunisia)] My journey at an African AI startup - Hall 316C ] In the talk, Rihab will share her personal journey as a mid-career woman coming from Africa in the field of Artificial Intelligence (AI) and highlight the remarkable experiences she has gained working at an African AI startup. With a focus on both technical accomplishments and driving forces that have propelled her forward, I aim to inspire the audience while providing valuable insights into her professional growth - particularly to women who aspire to build their careers in AI. 14:00 - 15:00 [ Breakout session #3 (Three parallel sessions)] 2. 1) Key Challenges for Applicable Reinforcement Learning . [Hall 316C] Leader: Fengdi Che. Co-leads: Arushi Jain Facilitators: Yueying Tian 2. 2) Data Diversity and Downstream Impact. [Hall 326B] Leader: Judy Shen. Co-lead: Paula Gradu Facilitators: Kristina Ulicna 2. 3) Deploying Research and Making Real-world Impact [Hall 326A] Leader: Fei Fang. Co-leads: Diyi Yang Facilitators: Bingbin Liu 15.00 - 15.30 [ Coffee Break and Networking] 15:30 - 16:30 [ Panel Discussion: Fostering Women's Leadership in the Realm of Emerging Trends and Technologies - Hall 316C ] Panelists: Joelle Pineau (Meta, McGill University), Pascale Fung (HKUST), Yao Qin (UC Santa Barbara, Google Research), Rihab Gorsane (Instadeep) Moderator: Natasa Tagasovska (Prescient Design, Genentech) The panel session will comprise 45 minutes of moderated discussion and a 15-minute Q&A with the audience. The session aims to bring together two significant themes: advancing women's leadership in AI and the future of machine learning with its emerging trends and technologies. The discussion will focus on empowering women in AI leadership positions to navigate these emerging trends effectively and reshape the landscape of AI. 16:30 - 16:45 [President Remarks: Sarah Tan (Cambia Health, Cornell University) - Hall 316C ] Joelle Pineau Joelle Pineau is the Vice President of AI Research at Meta, supporting labs across North America and Europe. She is also a faculty member at Mila and a Professor and William Dawson Scholar at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains, and on applying these algorithms to complex problems in robotics, health care, games and conversational agents. Learn more about her work at: https://www.cs.mcgill.ca/~jpineau/ Pascale Fung Pascale Fung is a Chair Professor at the Department of Electronic & Computer Engineering at The Hong Kong University of Science & Technology (HKUST), and a visiting professor at the Central Academy of Fine Arts in Beijing. She is an elected Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) for her "significant contributions to the field of conversational AI and to the development of ethical AI principles and algorithms", an elected Fellow of the Association for Computational Linguistics (ACL) for her significant contributions towards statistical NLP, comparable corpora, and building intelligent systems that can understand and empathize with humans. She is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE), an elected Fellow of the International Speech Communication Association and the Director of HKUST Centre for AI Research (CAiRE), an interdisciplinary research centre on top of all four schools at HKUST. Learn more about her work at: https://pascale.home.ece.ust.hk/ Yao Qin Yao Qin is an Assistant Professor at the Department of Electrical and Computer Engineering at UC Santa Barbara, affiliated with the Department of Computer Science. She is also a senior research scientist at Google Research. She obtained her PhD degree at UC San Diego in Computer Science in 2020 and worked at Google Research afterwards. Her research interests primarily focus on robustness in multi-modality models, fairness in generative modeling and AI for healthcare, particularly for diabetes. She has served as Area Chair for ICLR-2023 and ICCV-2023 and co-local Chair for KDD-2023. In addition, she has been recognized as EECS Rising Star at MIT, 2021. Learn more about her at: https://www.ece.ucsb.edu/people/faculty/yao-qin Rihab Gorsane Rihab Gorsane is a Research Engineer and a team lead at InstaDeep. She is currently working on Reinforcement Learning based projects for industrial applications where she is helping to automate the scheduling, routing, and dispatching of trains at a large scale for a national rail operator. Rihab is also involved in research projects within the company focusing on Multi-Agent RL evaluation. She is passionate about AI skills development in Africa, is a Google developer expert in Machine Learning, and has taught DL/RL courses at Tunisian universities. Nataša Tagasovska (moderator) Nataša is a Senior Machine Learning Scientist at Prescient Design, Genentech since January 2022 where she joined the effort of applying ML to accelerate drug design. Her research interests are related to causal learning, generative models and multi-property optimization. Before she was a Senior Data Scientist at the SDSC at EPFL-ETHZ where she worked on translational projects applying ML to domain-specific and social science research efforts. She holds a PhD in Statistics from University of Lausanne and a BS and MSC in Computer Science and Engineering. During her studies she interend at Facebook (Meta) AI Research and NATO. During the day of the WiML Un-Workshop @ ICML 2023 there will be three different Breakout Sessions slots! We list the sessions, topics, leaders, and facilitators. Breakout Session #1 (11.00 - 12.00 HST) Leveraging Large Scale Models for Identifying and Fixing Deep Neural Networks Biases [Hall 316C] Leader: Polina Kirichenko Co-leads: Reyhane Askari Hemmat, Megann Richards Facilitators: Vitória Barin Pacela , Mohammad Pezeshki The Role of Mentorship and Building Long-term Professional Relationships [Hall 326A] Leader: Arushi Jain Co-leader: Sangnie Bhardwaj Facilitators: Motahareh Sohrabi, Padideh Nouri Robustness in Machine Learning [Hall 326B] Leader: Yao Qin. Co-leads: Qi Lei Facilitator: Christina Baek Breakout Session #2 (14.00 - 15.00 HST) Key Challenges for Applicable Reinforcement Learning [Hall 316C] Leader: Fengdi Che Co-leader: Arushi Jain Facilitators: Yueying Tian Deploying Research and Making Real-world Impact [Hall 326A] Leader: Fei Fang Co-leads: Diyi Yang Facilitator: Bingbin Liu Data Diversity and Downstream impact [Hall 326B] Leader: Judy Shen Co-leads: Paula Gradu Facilitator: Kristina Ulicna During the workshop program, there are two "program breaks" listed in the agenda : one in the morning (10:00 - 10:30 HST), and one in the afternoon (15:00 - 15:30 HST). These program breaks as an excellent opportunity to facilitate optional community-building activities for workshop attendees. We chose the coffee break activities inspired by the following principles: Optional participation . For both coffee breaks, participation will be encouraged, but is optional. Attendees who wish to simply "take a break" can stay in the room and not participate in the activities. We also have organized activities where participants can organically "walk away" and engage in other conversations at any time. Ease . We also hope to facilitate as low of a barrier to participation as possible, by reducing logistical barriers whenever possible (i.e. holding activities in the same room as the next talk). Inclusivity . We understand that WiML attendees are in significantly different places in their career. We've attempted to design all activities so that all attendees can participate, regardless of their seniority or experience working in ML. We hope the activities can facilitate new connections. Morning Coffee Break: Ask Me About (AMA)... Location: Main Room, 316C TL;DR: Learn from & with your fellow WiML attendees by completing an "Ask Me Anything" name tag at the registration desk! No matter where you are in your career, you never know how your experiences may be helpful to another attendee. Afternoon Coffee Break: Bingo Location: Main Room, 316C TL;DR: Make new friends & connections during our WiML "bingo" icebreaker game! We'll have prizes for the first attendees to finish their cards. Please join us for a reception hosted by the Women in Machine Learning (WiML) organization. The reception will take place before the WiML 2023 Un-Workshop on Thursday, July 27th, from 6 pm - 9 pm HST at Hawaiian Brian's , down the street from the Hawaii Convention Center. Dinner and drink tickets will be provided . Important notes: Registration for this reception is separate from registration for the workshop. To attend the reception, please register here . Due to extremely limited capacity, we ask that you only register if you are committed to attending. Registration is free. Do register early, as we may reach capacity soon. All participants are required to abide by the [WiML Code of Conduct] . 18:25-18:30 (5 minutes) Intro by Arianna Bunnell 18:35-18:50 (15 minutes) Remarks by Frankie Zhu (Assitant Professor at University of Hawaii) 18:50-18:55 (5 minutes) Remarks by Sarah Tan (WiML President, Cambia Health, Cornell University ) Call for Participation WiML 4th Un-Workshop @ ICML 2023 [submissions are now closed ] The Women in Machine Learning will be organizing the fourth un-workshop at ICML 2023. 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 discussions. This is an event format to encourage more participant interaction and we are excited to be able to explore this format fully in-person this year! This year’s goal: the purpose of the un-workshop is to bring together researchers who identify as a woman, non-binary and/or gender non-conforming, fostering an environment for constructive discussions on research and career advancement. This year we particularly encourage mid-career researchers that identify as a woman, non-binary and/or gender non-conforming participate and contribute in the un-workshop! However, everyone, regardless of their career stage or gender, is warmly welcomed to participate and join in the discussions! We'd love for you to submit a one-page proposal to lead one of the breakout sessions. This is just one of the many ways you can contribute to the conversation - check out the other options below! While the presentations will be led by woman, non-binary and/or gender non-conforming individuals, all genders are invited to attend! IMPORTANT DATES June 3rd, 2023 -- Application Form opens! June 19th, 2023 June 24th, 2023 -- Deadline ( Anywhere on Earth ) to apply for a breakout session, registration fee funding, or volunteering June 24th, 2023 June 30th 2023 -- Notification of acceptance for all of the above (midnight Anywhere on Earth ) July 28th, 2023 -- WiML Un-Workshop Day Participate in the WiML Unworkshop Lead or engage in a breakout session : submit a proposal to lead a breakout session on a certain topic, either research oriented or about career development. Volunteer : seize this opportunity to contribute to the success of this WiML event! Help is needed with the technical setup and to fulfill the diverse needs that pop up during the event! Attend : participate in breakout session discussions, attend talks and/or panel discussions, come around for a chat with coffee! 1. Breakout session proposals: A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders , with contributions from named participants, and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage women, nonbinary and/or gender non-conforming individuals in all areas of machine learning to submit a proposal to lead or be a named participant in a topical breakout session. Compared to breakout sessions in previous years, we are making the following exciting changes for this year! First, we are expanding beyond technical and research topics. This year, we also encourage proposals related to growth , career development, and other non-technical topics that would be of interest to women, non-binary and/or gender non-confirming individuals in ML (particularly those who are mid-career). Second, we are introducing a new way of participating in breakout sessions: named participants . If you have an interesting idea or project that you think can spark productive discussion, or there is a topic that really interest you and you would be up for discussing it, we encourage you to submit a summary/position paper/poster. This can include both technical and non-technical topics. If there is a good match between your submission and the breakout session proposals, you will be matched with a breakout session leader and asked to contribute to the breakout session as a named participant. The exact nature of your contribution will be determined by your assigned session leader. You may apply to be a breakout session leader and/or apply to be a named participant. Guidance for applying to be a breakout session leader : your one-page proposal PDF should include a description of your proposed topic, why it is important/relevant, potential discussion questions, and how you would incorporate named participants (as described above). Guidance for applying to be a named participant : identify a topic, idea, or project that would be a good starting point for a discussion. This can be anything ranging from a summary of the topic and why you think it is relevant for WiML community, an unpolished idea, or a completed research project. Focus on explaining how your idea/project is relevant to a broader audience and what questions it sparks. Submissions must be one-page PDFs. Try to explain in simple language with minimal technical jargon. More information for leaders: A complete proposal consists of a 1 page PDF, along with the names and bios of leaders and facilitators submitted separately in the application form . Proposals need not be anonymized. We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators should also be provided. WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below). 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 identify as a woman, non-binary and/or gender non-conforming Point-out key characteristics of your topic and make connections with other topics Describe the key challenges and approaches in this research area or career topic on a high-level 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. 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. More information for named participants: Guidelines for and roles of named participants: Breakout session named participants must be women, non-binary and/or gender non-conforming Point out key characteristics of your topic and make connections with other topics. Describe how your work or knowledge contributes to this area. 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 Submission instructions: 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, and specifics around how you could contribute to the conversation. 2. Volunteering: We are seeking volunteers to help with technical setup and help during the event. You can indicate if you can help in any way in the 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. 3. 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 19, 2023 . Selected breakout session leaders, breakout session participants, 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 topic preferences. All participants are required to abide by the WiML Code of Conduct. 4. Registration fee funding: To apply for funding, you should identify as a woman, non-binary and/or gender non-conforming and commit to participating in at least one breakout session as a leader, named participant, 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 or named participants who do not have other sources of registration fee funding will be prioritized for WiML funding. Other participants are also encouraged to apply. In your application, please indicate any funding sources you may have and how WiML's support is needed. Please note that WiML is able to fund registration fees only (not travel and accommodation) for selected participants. Further questions? Check out the FAQs ( https://wimlworkshop.org/faq/ ) or reach us at workshop@wimlworkshop.org 5. A sneak peak of other activities that the workshop will host: We will give more details closer to the event but the workshop will include a sponsor roundtable, where you will have the opportunity to interact and network with our sponsors. Furthermore, we will facilitate networking, mentoring, and impromptu discussions during the event . Stay tuned! PLATINUM SPONSORS PLATINUM SPONSORS PLATINUM SPONSORS Committee ORGANIZERS Giulia Luise General Chair Priyadarshini Kumari Senior Program Chair Stephanie Milani Breakout Program and Logistics Co-Chair Tiffany Ding Finance and Sponsorship Chair WiML RECEPTION ORGANIZER Arianna Bunnell Social Chair ADVISORY Danielle Belgrave D&I chair Bahare Fatemi D&I chair Mandana Samiei WiML Board POC SUPER VOLUNTEERS Mojgan Saeidi Nari Johnson FAQs How do I participate to the un-workshop? Start with filling the application form , especially if you are interested in presenting! The workshop will take place on July 28th 2023, co-located with ICML at the Hawaii Convention Centre in Honolulu. We will give more details nearer to the event. Does registering for the WiML un-workshop also mean I'm registered for ICML? Unfortunately not. You would still need to register separately for ICML – their registration process can be found here. You should only register to ICML if you are interested in attending ICML activities beyond WiML un-workshop. What does un-workshop mean? 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. Please check our Call for Participation for more details! How much travel funding is available? We will be able to sponsor the ICML registration fee for selected participants. Please fill the application form to apply for funding! 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 a man. Can I attend WiML un-workshop? 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? We will update the format of this year's Sponsor Roundtable closer to the event! Is WiML an archival venue? No, WiML is a non-archival venue. Moreover, the un-workshop format does not include paper submissions. Check the Call for Participation to learn how to contribute to the un-workshop! Is there a Code of Conduct? Yes, you can find it here . 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!

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 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

< Back Jessica Montgomery WiML Vice President of Research & Policy (2020-2021), Director (2019-2020, 2021-2022) Visit my Profile

We maintain a mailing list for the Women in Machine Learning network. Are you a women working in machine learning? Join our mailing list. Post directly in our mailing list. Mailing List We maintain a mailing list for the Women in Machine Learning network. Are you a women working in machine learning? Join our mailing list . Have a job posting, announcement, call for participation, etc? Post directly in our mailing list. The mailing list is intended for any female student, postdoc, academic researcher, industrial researcher, and anyone else who wants to post content relevant for this community. You do not have to be a woman to join the mailing list. Please post job postings, announcements, calls for participation, etc. directly to the mailing list. You can also use the mailing list to look for roommates at conferences, discuss machine learning topics, etc. Join/Post to Mailing List

< Back Savannah Thais, PhD WiML Director (2019-2021)

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

< Back Kristy Choi, PhD WiML Director (2022-2024)

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 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

< Back Catherine Wah, PhD WiML Director (2022-2024) Visit my Profile

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

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< Back Kimberly Ferguson-Walter, PhD WiML Director Visit my Profile