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< Back Luisa Cutillo, PhD WiML Director Visit my Profile

< Back Rachel Thomas, PhD WiML Director (2019-2020) Visit my Profile

All events WiML Workshop 2012 Lake Tahoe, Nevada December 3, 2012 08:00 am — 06:00 pm The 7th annual Women in Machine Learning workshop was colocated with NIPS 2012 in Lake Tahoe, Nevada in December 2012. The workshop website is no longer maintained. The organizers were: Tamara Broderick, Minmin Chen, Pallika Kanani, and Tejaswini Narayanan, with faculty advisor Raquel Urtasun. If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org Previous Next

< Back Archana Vaidheeswaran WiML Director Visit my Profile

< Back Sinead Williamson, PhD WiML Director (2015-2018, 2020-2021) Visit my Profile

< Back Kate Niehaus, PhD WiML Director (2018-2020)

WiML is committed to advancing the careers of women and non-binary people studying and working in machine learning. MAILING LIST Please use our mailing list to post job postings, announcements, calls for participation, etc. DIRECTORY & PROFILES OF WOMEN IN MACHINE LEARNING Please use our directory of women working in machine learning to find invited speakers, area chairs, conference committee members, etc. Also check out our profiles on women in machine learning . OTHER CONFERENCES & WORKSHOPS Grace Hopper Celebration Women in Data Science Conference Rising Stars EECS Workshop CRA-W Grad Cohort Workshop NextProf workshop CODE OF CONDUCT & CONFERENCE GUIDELINES NAACL Conference Anti-Harassment Policy SIGPLAN Conference Code of Conduct Policy CRA-W guidelines for running an inclusive conference LOCAL MEETUPS WiML does not organize local meetups. However, WiMLDS, another organization does! Check them out at their website or Twitter . FUNDING OPPORTUNITIES Google Travel and Conference Grants L'Oreal USA For Women in Science Fellowship GENDER BIAS Avoiding gender bias in reference writing Gender bias calculator CONFERENCE TIPS “Nine things I wish I had known the first time I came to NIPS ” by Jennifer Wortman Vaughan, WiML co-founder AWARD OPPORTUNITIES CRA-W awards ACM Athena Lecturer award OTHER DIVERSITY GROUPS Women in Computer Vision Widening NLP Black in AI LatinX in AI Queer in AI

All events WiML Luncheon @ COLT 2019 Phoenix, Arizona June 26, 2019 12:30 pm — 02:00 pm WiML is hosting a luncheon at COLT 2019 in Phoenix, Arizona. The organizer is Ruth Urner. Date: June 26, 12:30-2pm in room 102A Location: Phoenix, ArizonaVenue: Phoenix Convention Center Registration: Register during COLT registration ( http://learningtheory.org/colt2019/ ). If you registered for COLT but did not register for the WiML lunch, check if you can amend your registration to add WiML lunch registration. If not, email Ruth Urner (ruth AT eecs DOT yorku DOT ca) to attend the lunch. If you are not attending COLT but wish to attend the lunch, also email Ruth. SPONSORS -Platinum- Previous Next

< Back Nevena Lazic, PhD WiML Director (2009-2012)

All events WiML Workshop 2008 Vancouver, Canada December 8, 2008 08:00 am — 05:00 pm The 3rd annual Women in Machine Learning workshop was colocated with NIPS 2008 in Vancouver, Canada in December 2008. The workshop website is no longer maintained. The organizers were: Luiza Antonie, Anna Koop, and Jo-Anne Ting, with faculty advisor Joelle Pineau. The invited speakers were: Fei-Fei Li, Kristin Bennett, Daphne Koller, and Corinna Cortes. 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

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 12th Women in Machine Learning Workshop (WiML 2017) The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017. 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 12th year, the 2017 workshop is co-located with NIPS in Long Beach, California. 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 Jenn Wortman Vaughan Senior Researcher at Microsoft Research Raia Hadsell DeepMind Tamara Broderick TT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT Hannah Wallach Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst Joelle Pineau Professor of Computer Science at McGill University where she co-directs the Reasoning and Learning Lab Nina Mishra Principal Scientist at Amazon and a visiting scholar at Stanford Location Co-located with NIPS in Long Beach, California. This workshop takes place in Long Beach Convention Center . PROGRAM MENTORSHIP ROUNDTABLES (MONDAY) MENTORSHIP ROUNDTABLES (THURSDAY) POSTERS Sunday 12:00 – 14:00 Registration Desk Opens 19:00 – 22:30 Pre Workshop Dinner (Optional). Separate Registration Required Monday All events are held in Room 104, except for the poster session, which takes place in the Pacific Ballroom 9:00 – 12:00 Registration Desk Opens 11:00 – 11:15 Opening Remarks – Jenn Wortman Vaughan Microsoft Research. Co-Founder of WiML. 11:15 – 11:50 Invited Talk – Tamara Broderick MIT. Bayesian machine learning: Quantifying uncertainty and robustness at scale 11:50 – 12:10 Contributed Talk: Aishwarya Unnikrishnan, Indraprastha Institute of Information Technology Delhi. Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory Game. 12:10 – 12:30 Contributed Talk: Peyton Greenside, Stanford University. Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics. 12:30 – 14:00 Lunch + Poster Session 14:00 – 14:35 Invited Talk – Hanna Wallach Microsoft Research. Machine Learning for Social Science 14:35 – 14:50 Coffee Break 14:50 – 15:50 Research and Career Advice Roundtables 15:55 – 16:15 Contributed Talk: Palak Agarwal, WorldQuant. Fairness Aware Recommendations. 16:15 – 16:35 Contributed Talk: Victoria Krakovna, DeepMind. Reinforcement Learning with a Corrupted Reward Channel. 16:35 – 16:45 Closing Remarks Thursday All events are held in Room 104, except for the poster session, which takes place in the Pacific Ballroom 10:00 – 14:00 Registration Desk Opens 12:00 – 12:45 Lunch 12:45 – 13:05 Opening Remarks – Raia Hadsell , DeepMind 13:05 – 13:40 Invited Talk – Joelle Pineau Head Facebook AI Research (Montreal Lab)/ Mc Gill. Improving health-care: challenges and opportunities for reinforcement learning 13:40 – 13:55 Contributed Talk: Zhenyi Tang, University of Illinois. Harnessing Adversarial Attacks on Deep Reinforcement Learning for Improving Robustness. 13:55 – 14:10 Contributed Talk: Hoda Heidari, ETH Zurich. A General Framework for Evaluating Callout Mechanisms in Repeated Auctions. 14:10 – 14:20 Coffee Break 14:20 – 15:20 Research and Career Advice Roundtable 15:20 – 15:55 Invited Talk – Nina Mishra Amazon. Time-Critical Machine Learning 15:55 – 16:15 Contributed Talk: Sarah Bouchat, Nothwestern University. Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science. 16:15 – 16:35 Contributed Talk: Nesreen K Ahmed, Intel Labs. Representation Learning in Large Attributed Graphs. 16:35 – 16:40 Closing Remarks 16:40 – 18:05 Poster Session (Coffee and Snacks Served) Monday, Dec 4, 12:20 pm to 2:00 pm and Thursday Dec 7, 4:35pm -6:00pm, open to WiML and NIPS attendees 350+ posters covering theory, methodology, and applications of machine learning will be presented across 2 poster sessions. The list of posters and authors can be found in the program book Accepted posters (with abstracts) . Abstracts listed here are for archival purposes and do not constitute proceedings for this workshop. Poster size: Up to 48 inches tall and 60 inches wide. We recommend printing in the stand A0 size (33.11 inches by 46.81 inches). You can orient the poster in portrait or landscape as long as it fits within the specified dimensions. This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-22), Career Advice Roundtables (Tables 23-42), NIPS Paper Discussion (Tables 43-50), Company Career Tables (Tables 51-63). On Monday 4th December, these tables will take place at 2:50pm – 3:50pm Table 1: Reinforcement learning I – Katja Hofmann, Microsoft Research Table 2: Reinforcement learning II – Oriol Vinyals, DeepMind Table 3: Deep learning I – Yoshua Bengio, MILA – Université de Montréal Table 4: Deep learning II – Doina Precup, McGill University / Head DeepMind Montreal Table 5: Bayesian methods I – Meire Fortunato, DeepMind Table 6: Bayesian methods II –Neil Lawrence, Amazon Research Cambridge Table 7: Graphical models – Anima Anandkumar, Amazon Web Services/ Caltech Table 8: Statistical inference, estimation and Optimization – Irina Kukuyeva, Dia&Co Table 9: Neuroscience – Katharina Volz, Founder OccamzRazor Table 10: Robotics I – Bonolo Mathibela, IBM Research Table 11: Black Box vs Open Box ML Approaches – Barbara Engelhardt, Assistant Professor, Princeton Table 12: Natural language processing – George Dahl, Google Brain Table 13: Biological Applications – Luisa Cutillo, University Parthenope of Naples Table 14: Healthcare/Clinical Applications – Marzyeh Ghassemi, MIT/Verily Table 15: Causal Inference and Counterfactuals – Sara Magliacane, IBM Research Table 16: Computer Vision – Amy Zhang, Facebook AI Research Table 17: Fairness, accountability, transparency in ML – Christian Borgs, Microsoft Research Table 18: Social Sciences Application – Timnit Gebru, Microsoft Research Table 19: Music Applications – Vidhya Murali, Spotify USA Inc Table 20: Business Applications – Pallika Kanani, Oracle Labs Table 21: Industrial Applications in AI/ Commercialising your Research – Jennifer Schumacher, 3M Table 22: Technical AGI Safety – Victoria Krakovna, DeepMind Table 23: Creative AI Applications (Art, Music, Design) – Luba Elliott, iambic.a Table 24: Work-Life Balance (Industry) – Hanna Wallach, Microsoft Table 25: Work-Life Balance (Academia) – Joelle Pineau, Head Facebook AI Research Montreal/ Professor McGill University Table 26: Life with Kids / Work-life balance – Caitlin Smallwood Table 27: Getting a Job (Industry) – Beth Zeranski, Microsoft Table 28: Getting a Job (Academic) – Yisong Yue, Caltech/ Tamara Broderick MIT Table 29: Doing a Postdoc – Adriana Romero, Facebook AI Research Table 30: Choosing between Academia and Industry – Daniel Jiang, University of Pittsburgh Table 31: Choosing between Academia and Industry – Samy Bengio, Google Brain Table 32: Doing Research in Industry – Natalia Neverova, Facebook AI Research; Stacey Svetlichnaya, Flickr / Yahoo Research Table 33: Keeping up with academia while in industry – Nevena Lazic, Google Table 34: Surviving Graduate School – Lily Hu, Salesforce Research; Table 35: Establishing Collaborators – Moustapha Cisse, Facebook AI Research Table 36: Scientific Communication – Chris Bishop, Lab Director Microsoft Research Cambridge Table 37: Building your Professional Brand – Katherine Gorman, Talking Machines/Collective Next Table 38: Founding Startups/ Building your Professional Brand – Rachel Thomas, Fast.AI/ University of San Francisco Table 39: Founding Startups – Philippe Beaudoin, Element AI Table 40: Early-Stage Start-ups using Machine Learning/Deep Learning – Lisha Li, Amplify Partners Table 41: Joining Start-ups – Lavanya Tekumalla, Amazon Table 42: Finding Mentors/ Networking – Lisa Amini, Director IBM Research AI Table 43: Long-term Career Planning – Jennifer Chayes, Managing Director, Microsoft Research NE & NYC Table 44: NIPS Paper Discussion: SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement – Maithra Raghu, Google Brain and Cornell University Table 45: NIPS Paper Discussion: Self-supervised Learning of Motion Capture – Katerina Fragkiadaki, Carnegie Mellon University Table 46: NIPS Paper Discussion: Linear regression without correspondence – Daniel Hsu, Columbia University Table 47: NIPS Paper Discussion: A-NICE-MC: Adversarial Training for MCMC – Jiaming Song, Stanford University Table 48: NIPS Paper Discussion: Learning multiple visual domains with residual adapters – Sylvestre-Alvise Rebuffi, University of Oxford Table 49: NIPS Paper Discussion: Efficient Use of Limited-Memory Resources to Accelerate Linear Learning – Celestine Dünner, IBM Research Table 50: NIPS Paper Discussion: Variational Inference via χ Upper Bound Minimization – Adji Bousso Dieng, Columbia University Table 51: NIPS Paper Discussion: Robust Hypothesis Test for Functional Effect with Gaussian Processes – Jeremiah Liu, Harvard University Table 52: NIPS Paper Discussion: Bayesian Dyadic Trees and Histograms for Regression – Stéphanie van der Pas, Leiden University Table 52: Careers@ElementAI Table 53: Careers@Facebook Table 54: Careers@DeepMind Table 55: Careers@Capital One Table 56: Careers@Criteo Table 57: Careers@Microsoft Table 58: Careers@Intel Table 59: Careers@Google Table 60: Careers@Airbnb Table 61: Careers@Apple Table 62: Careers@IBM Table 63: Careers@NVIDIA Table 64: Careers@Pandora This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-25), Career Advice Roundtables (Tables 26-43), NIPS Paper Discussion (Tables 44-57), Company Career Tables (Tables 51-63). On Thursday 7th December, these tables will take place at 2:20pm – 3:20pm Table 1: Reinforcement learning I – Raia Hadsell, DeepMind Table 2: Black Box vs Open Box ML Approaches – Barbara Engelhardt, Assistant Professor, Princeton Table 3: Deep learning I – Anima Anandkumar, Amazon Web Services/ Caltech Table 4: Deep learning II – Amy Zhang, Facebook AI Research Table 5: Bayesian methods I – Chris Bishop, Lab Director Microsoft Research Cambridge Table 6: Bayesian methods II – Zoubin Ghahramani, University of Cambridge Table 7: Graphical models – David Blei, Columbia University Table 8: Generative Models – Ian Goodfellow, Google Brain Table 9: Technical AGI Safety – Shane Legg, Founder DeepMind Table 10: Kernel Methods – Corinna Cortes, Head of Google Research Table 11: Neuroscience – Katharina Volz, Founder OccamzRazor Table 12: Robotics – Table 13: Natural language processing – George Dahl, Google Brain Table 14: Statistical inference and estimation – Timnit Gebru, Microsoft Research Table 15: Biological Applications – Luisa Cutillo, University Parthenope of Naples Table 16: Healthcare/Clinical Applications – Marzyeh Ghassemi, MIT/Verily Table 17: Optimization – Irina Kukuyeva, Dia & Co Table 18: Causal Inference and Counterfactuals – Sara Magliacane, IBM Research Table 19: Computer Vision – Natalia Neverova, Facebook AI Research; Table 20: Fairness, accountability, transparency in ML I – Christian Borgs, Microsoft Research Table 21: Fairness, accountability, transparency in ML II – Nyalleng Moorosi, Council for Scientific and Industrial Research Table 22: Learning Theory – Hoda Heidari, ETHZ Table 23: Social Sciences Application – Lise Getoor, UC Santa Cruz Table 24: Business Applications – Pallika Kanani, Oracle Labs Table 25: Industrial Applications in AI/ Commercialising your Research – Jennifer Schumacher, 3M Table 26: Work-Life Balance (Industry) – Amy Nicholson, Olivia Klose, Microsoft Table 27: Work-Life Balance (Academia) – Neil Lawrence, Amazon Research Cambridge Table 28: Life with Kids / Work-life balance – Caitlin Smallwood, Netflix Table 29: Getting a Job (Industry) – Aleatha Parker-Wood Symantec Table 30: Getting a Job (Academic) – Yisong Yue, Caltech Table 31: Doing a Postdoc – Aida Nematzadeh, UC Berkeley Table 32: Choosing between Academia and Industry – Daniel Hsu, Columbia University Table 33: Choosing between Academia and Industry – Adriana Romero, Facebook AI Research Table 34: Doing Research in Industry – Stacey Svetlichnaya Flickr / Yahoo Research Table 35: Keeping up with academia while in industry – Chew-Yean Yam, Microsoft Table 36: Surviving Graduate School – Shruthi Kubatur, Nikon Research Corporation of America Table 37: Establishing Collaborators/ Long-term Career Planning – Jennifer Chayes, Managing Director, Microsoft Research NE & NYC Table 38: Scientific Communication – Katherine Gorman, Talking Machines/ Collective Next Table 39: Building your Professional Brand/ Founding Startups – Rachel Thomas, Fast.AI/ University of San Francisco Table 40: Founding Startups – Philippe Beaudoin, Element AI Table 41: Early-Stage Start-ups using Machine Learning/Deep Learning – Lisha Li, Amplify Partners Table 42: Joining Start-ups – Lavanya Tekumalla, Amazon Table 43: Networking/ Finding Mentors – Muhammad Jamal Afridi, 3M Table 44: Table 45: NIPS Paper: A-NICE-MC: Adversarial Training for MCMC – Jiaming Song, Stanford University Table 46: NIPS Paper: Learning multiple visual domains with residual adapters – Sylvestre-Alvise Rebuffi, University of Oxford Table 47: NIPS Paper: Variational Inference via χ Upper Bound Minimization – Adji Bousso Dieng, Columbia University Table 48: NIPS Paper: A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control – Fanny Yang, UC Berkley Table 49: NIPS Paper: Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin – Ritambhara Singh, University of Virginia Table 50: NIPS Paper: Style Transfer from Non-parallel Text by Cross-Alignment – Tianxiao Shen, MIT CSAIL Table 51: NIPS Paper: Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model – Devi Parikh, Georgia Tech / Facebook AI Research Table 52: Table 53: NIPS Paper: Inferring Generative Model Structure with Static Analysis – Paroma Varma, Stanford University Table 54: NIPS Paper: Concrete Dropout (Topic: Bayesian Deep Learning) – Yarin Gal, University of Oxford Table 55: NIPS Paper: Do Deep Neural Networks Suffer from Crowding? – Anna Volokitin, ETH Zurich Table 56 Table 57: NIPS Paper: Deanonymization in the Bitcoin P2P Network – Giulia Fanti, Carnegie Mellon University Table 58: Careers@ElementAI Table 59: Careers@Facebook Table 60: Careers@DeepMind Table 61: Careers@Capital One Table 62: Careers@Criteo Table 63: Careers@Microsoft Table 64: Careers@Intel Table 65: Careers@Google Table 66: Careers@Airbnb Table 67: Careers@Apple Table 68: Careers@IBM Table 69: Careers@NVIDIA Table 70: Careers@Pandora Call for Participation The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017. The workshop is a two-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 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 identify primarily as female, all genders are invited to attend. This is a technical workshop with exciting technical talks. Important Dates September 12th, 2017 11:59 pm PST – Extended Abstract submission deadline September 8th, 2017 11:59 pm PST – Abstract submission deadline October 16th, 2017 – Notification of abstract acceptance November 1st, 2017 – Travel grant application deadline November 14th, 2017 – Registration Deadline December 4th, 2017 – Workshop Day 1 December 7th, 2017 – Workshop Day 2 Submission Instructions We strongly encourage primarily female-identifying students, post-docs and researchers in all areas of machine learning to submit an abstract describing new, previously, or concurrently published research. We welcome abstract submissions, in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. While the presenting author need not be the first author of the work, we encourage authors to highlight the contribution of female authors — particularly the presenting author — in the abstract. Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minute oral presentations. Submissions will be peer-reviewed in a double-blind setting. Authors are encouraged to sign up to review for WiML, with a sign-up option available upon submission. Student and post-doc authors who review for WiML will be eligible for travel awards. Submission page: https://cmt3.research.microsoft.com/WiML2017 Style guidelines: Abstracts must not include identifying information Abstracts must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be 11 points in size. Do not include any supplementary files with your submission. Content guidelines: Your abstract 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 appreciate that space is limited, some experimental results are likely to improve reviewers’ opinions of your paper. Acceptance criteria: All accepted papers must be presented by a primarily female-identifying author. Abstracts will be reviewed by at least two reviewers plus an area chair, who will use the following criteria: Is this paper appropriate for WiML? I.e. Does it describe original research in Machine Learning or related fields? Does the abstract describe work that is novel and/or an interesting application? Does the abstract adequately convey the material that will be presented? Examples of accepted abstracts from previous years. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract. Travel Scholarships Registration is free. Travel Awards are available for presenting authors only. To qualify, the author must be a student or post-doc, their abstract must be accepted, and they must volunteer to serve as a reviewer for WiML. The amount of the travel award varies by the author’s geographical location and the total amount of funding WiML receives from our sponsors. In the past awards ranging from $300-$900 have been granted. Organizers Negar Rostamzadeh (Element AI) Ehi Nosakhare (MIT) Danielle Belgrave (Imperial College London) Genna Gliner (Princeton University) Maja Rudolph (Columbia University) PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTER Committee ORGANIZERS Genna Gliner PhD student at the University of Princeton Ehimwenma Nosakhare Phd student at MIT EECS Maja Rudolph PhD student at Columbia University Danielle Belgrave Research Fellow at Imperial College of London Negar Rostamzadeh Research Scientist at Element AI 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

< Back Ehi Nosakhare, PhD WiML Director (2021-2024)