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Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 13th Women in Machine Learning Workshop (WiML 2018) The 13th WiML Workshop is co-located with NeurIPS in Montreal, Quebec on Monday, December 3rd, 2018. 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 13th year, the 2018 workshop is co-located with NIPS in Montreal, Canada. A History of WiML poster was created to celebrate the 10th workshop, also held in 2015 in Montreal, Canada. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at ICML and 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 Stanford Po-Ling Loh UW-Madison Raquel Urtasun Uber / University of Toronto Isabel Kloumann Facebook Megan Maher Apple Cascaded Dataset QA Lanlan Liu University of Michigan Jennifer Drexler MIT Amanda Rios USC Katherine M. Kinnaird Smith College Location This workshop takes place in Palais des Congrès in Montreal. Convention Center Rooms More details about the workshop and poster sessions will be provided shortly. PROGRAM MENTORSHIP ROUNDTABLES SPONSOR TABLES Sunday, December 2 12:00 pm – 2:00 pm Registration desk open 6:00 pm – 10:00 pm WiML Dinner (Optional) (Separate registration required) Monday, December 3 All events are held in Rooms 517AB and 516C, except for the evening poster session, which takes place in Room 210. 8:00 am – 12:00 pm Registration Open 8:00 am - 9:00 am Breakfast 9:00 am – 9 :10 am Opening Remarks – (WiML Organizers) 9:10 am – 9:50 am Invited talk 1 – Isabel Kloumann 9:50 am – 10:10 am Contributed talk 1 – Lanlan Liu 10:10 am – 10:30 am Contributed talk 2 – Megan Maher 10:30 am – 10:50 am Coffee Break 10:50 am – 11:30 am Invited talk 2 – Po-Ling Loh 11:30 am – 11:50 am Contributed talk 3 – Amanda Rios 11:50 am – 1:00 pm Mentorship Circles 11:00 pm – 2:30 pm Lunch + Poster Session 2:30 pm – 3:10 pm Invited talk 3 – Raquel Urtasun 3:10 pm – 3:30 pm Contributed talk 4 – Jennifer Drexler 3:30 pm – 3:50 pm Coffee Break 3:50 pm – 4:30 pm Invited talk 4 – Emma Brunskill 4:30 pm – 4:40 pm Closing remarks 6:00 pm – 7:30 pm Poster Session 2 (co-located with NeurIPS reception) NeurIPS Main Conference (NeurIPS registration required) 5:00 pm NeurIPS Opening Remarks This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-15), Career Advice Roundtables (Tables 17-44), Company Career Tables (Tables 45-61). Monday, December 3rd: 11:50am - 1:00pm Tables subject to change Research topics Table 1: Reinforcement learning – Anima Anandkumar NVIDIA/Caltech Professor (post-tenure) Table 2: Bayesian optimization and causal inference – Eytan Bakshy Facebook Research Scientist/ Engineer Table 3: Balance: between academia and industry, work and life – Emily Fox University of Washington Professor (post-tenure) Table 4: Deep learning – Yarin Gal University of Oxford Professor (post-tenure) Table 5: Bayesian models, graphical models, learning theory and statistical inference – Po-Ling Loh UW-Madison Professor (pre-tenure) Table 6: Systems for ML – Kim Hazelwood Facebook Engineering Manager (former tenured Professor) Table 7: Causal inference and counterfactuals – Sara Magliacane IBM Research Researcher Table 8: Computer Vision – Adriana Romero Facebook AI Research Research Scientist and adjunct professor Table 9: Time series – Negar Ghourchian Aerial Technologies Director of AI Table 10: Robotics – Sanja Fidler University of Toronto, NVIDIA Professor (pre-tenure) Table 11: Healthcare applications – Tess Berthier Imagia Research Scientist/ Engineer Table 12: Fairness – Joaquin Quiñonero Candela Facebook Director of AI Engineering Table 13: Natural Language Processing – Aida Nematzadeh DeepMind Research Scientist/ Engineer Table 14: Social science – Svitlana Volkova Pacific Northwest National Laboratory Research Scientist/ Engineer Table 15: Recommender system, information retrieval – Putra Manggala Shopify Data Scientist/ Engineer Table 16: Data Visualization – Fernanda Viegas Google Research Scientist/ Engineer Career and general advice topics Table 17: Work life balance (industry) I – Dilan Gorur DeepMind Research Scientist/ Engineer Table 18: Work life balance (industry) II – Yinyin Liu Intel AI Head of Data Science, Intel AI Table 19: Work life balance (academia) – Isabel Valera Max Planck Institute for Intelligent Systems Group leader Table 20: Life with kids – Corinna Cortes Google Research Scientist/ Engineer Table 21: Getting a job (industry) I – Been Kim Google brain Research Scientist/ Engineer Table 22: Getting a job (industry) II – Lily Hu Salesforce Research Research Scientist/ Engineer Table 23: Getting a job (academia) – Sinead Williamson UT Austin / Amazon Professor (pre-tenure);Research Scientist/ Engineer Table 24: Doing a Post Doc – Timnit Gebru Google Research Scientist/ Engineer Table 25: Academia vs. Industry I – Claire Vernade Google DeepMind Research Scientist/ Engineer Table 26: Academia vs. Industry II – Raquel Urtasun Uber ATG / University of Toronto Chief Scientist, Associate Professor Table 27: Research in Industry I – Joelle Pineau McGill University / Facebook Professor (post-tenure), Research Scientist/ Engineer Table 28: Research in Industry II – Lisa Amini IBM Research AI Research Scientist/ Engineer Table 29: Keeping up with academia while in industry I Ian Goodfellow Google AI Research Scientist/ Engineer Table 30: Keeping up with academia while in industry II David Vazquez Element AI Research Scientist/ Engineer Table 31: Surviving graduate school I – Chelsea Finn Google, UC Berkeley Postdoc;Professor (pre-tenure);Research Scientist/ Engineer Table 32: Surviving graduate school II – Priya Donti Carnegie Mellon University PhD student Table 33: Seeking funding: fellowships and grants – Sarah Tan Cornell / UCSF PhD student Table 34: Establishing collaborations – Eric Sodomka Facebook Research Scientist/ Engineer;Data Scientist/ Engineer Table 35: Joining startups – Rachel Thomas fast.ai Research Scientist/Engineer;co-founder Table 36: Career advice & Work/life balance – Neil Lawrence Amazon, University of Sheffield Machine Learning Director, Professor Table 37: Founding startups – Sarah Osentoski Free Agent Sole Proprietor Table 38: Scientific communication – Katie Kinnaird Brown University Postdoc Table 39: Networking – Inmar Givoni Uber ATG Sr Engineering Manager Table 40: Building your professional brand – Hanna Wallach Microsoft Professor (post-tenure);Research Scientist Table 41: Long-term career planning – Negar Rostamzadeh Element AI Research Scientist/ Engineer Table 42: Commercializing your research – Nesreen Ahmed Intel Research Senior Research Scientist Table 43: Finding Mentors – Feryal Behbahani Latent Logic Research Scientist/ Engineer Table 44: Junior faculty life – Emma Brunskill Stanford Assistant Professor Industry career tables Table 45: Careers @ DeepMind Doina Precup, Anna Harutyunyan, Daniel Toyama Table 46: Careers @ Facebook Amy Zhang Table 47: Careers @ Google Kristen Hofstetter Table 48: Careers @ IBM Lisa Amini Table 49: Careers @ CapitalOne Hongjun Wang Table 50: Careers @ Adobe Dhanashree Balaram Table 51: Careers @ Amazon Dilek Hakkani-Tur, Hongyi Liu, Cheng Tang Table 52: Careers @ Apple Michelle Chen Table 53: Careers @ Dessa Jodie Zhu Table 54: Careers @ Intel Jennifer Healey, Anna Bethke Table 55: Careers @ Microsoft Wendy Tay Table 56: Careers @ Samsung Daedeepya Yendluri, Ghazaleh Moradiannejad Table 57: Careers @ Unity Marilyn Hommes Table 58: Careers @ Element AI Perouz Taslakian Table 59: Careers @ Oracle Labs John Tristan Table 60: Careers @ Shell Neilkunal Panchal, Jeremy Vila, Mauricio Araya, Rayetta Seals Table 61: Careers @ Wayfair Patricia Stichnoth Recruitment Tables Recruitment tables from our major sponsors will be set up in room 516c for the duration of the workshop. Table A: Careers @ IBM Table B: Careers @ Apple Table C: Careers @ Samsung Table D: Careers @ Google Table E: Careers @ Unity3D Table F: Careers @ Amazon Table G: Careers @ Facebook Table H: Careers @ Adobe Table I: Careers @ Microsoft Table J: Careers @ Deepmind Table K: Careers @ Dessa Table L: Careers @ Intel Call for Participation The 13th WiML Workshop is co-located with NIPS in Montreal, Quebec on Monday, December 3rd, 2018. The workshop is a one-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. Important Dates September 7th, 2018 11:59pm PST – Abstract submission deadline October 15th, 2018 – Notification of abstract acceptance TBA – Travel grant application deadline TBA – Registration Deadline December 3rd, 2018 – Workshop Day Submission Instructions We strongly encourage students, post-docs and researchers who primarily identify as women or nonbinary 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 women — 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 will be automatically added to the reviewer pool and asked to review. Student and post-doc authors who review for WiML will be eligible for travel awards. Submission page: WiML 2018 CMT 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 in NIPS format. Upload the PDF, do not paste in the abstract box. 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 the submitting author, or another author who identifies primarily as a woman or nonbinary. 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 Travel Awards are available for presenting authors only. To qualify, the author must be a student or postdoc, 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. All travel grants are administered as refunds and no funding is allocated before the conference. Area Chairs If you are interested in being an area chair, please email wiml2018@wimlworkshop.org with subject line “Area Chair for WiML 2018”. The role of area chairs is to evaluate the reviews, write a final meta-review and suggest acceptance/reject decisions for each abstract. We expect each area chair to be responsible for 10 short abstracts with each abstract having a maximum word limit of 500 words. Organizers Audrey Durand (McGill University) Aude Hofleitner (Facebook) Nyalleng Moorosi (CSIR) Sarah Poole (Stanford University) Amy Zhang (McGill University / Facebook AI Research) PLATINUM SPONSORS DIAMOND SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTERS BRONZE SPONSORS Committee ORGANIZERS Audrey Durand Mila / McGill University Aude Hofleitner Facebook Nyalleng Moorosi Google AI Sarah Poole Verily Amy Zhang Mila / McGill University / Facebook BOARD OF DIRECTORS Katherine M. Kinnaird (President) Smith College Allison Chaney (Vice President) Princeton University Jennifer Healey (Vice President) Intel Labs Jessica Thompson (Secretary) Université de Montréal Sarah Brown (Treasurer) Brown University Tamara Broderick Massachusetts Institute of Technology Raia Hadsell DeepMind Abigail Jacobs University of California, Berkeley Been Kim Google Brain Katie Niehaus Freenome Sarah Tan Cornell University / UCSF SENIOR ADVISORY COUNCIL Hanna Wallach (WiML Co-Founder) Microsoft Research / UMass Amherst Jenn Wortman Vaughan (WiML Co-Founder) Microsoft Research Emma Brunskill Stanford University Finale Doshi-Velez Harvard University Barbara Engelhardt Princeton University Marzyeh Ghassemi University of Toronto / Vector Institute Inmar Givoni Uber ATG Katherine Heller Duke University Pallika Kanani Oracle Labs Claire Monteleoni University of Colorado Boulder Sarah Osentoski Mayfield Robotics Svitlana Volkova Pacific Northwest National Laboratory Sinead Williamson University of Texas at Austin Alice Zheng Amazon 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

All events WiML @ LIDA Data Science Careers Fair 2025 University of Leeds March 17, 2025 On 17 March 2025, WiML had the pleasure of being part of the LIDA Data Science Careers Fair 2025 at the University of Leeds. The fair brought together students, alumni, and industry representatives from across sectors including food, retail, finance, civil service, consultancy, data, and technology, all eager to explore career opportunities in data science and tech. Representing WiML, Luisa Cutillo engaged with attendees to share our mission, discuss pathways for women and underrepresented genders in machine learning, and highlight the importance of community and support for early-career data professionals.) WiML is proud to stand alongside a diverse group of institutions and companies (e.g., Google, KPMG, NHS England, Morrisons, The Data City, Answer Digital, and others) in supporting and inspiring the next generation of data scientists. A big thanks to the University of Leeds and the Leeds Institute for Data Analytics (LIDA) for organizing this successful event and for inviting WiML to contribute! More info here: https://www.leeds.ac.uk/events-staff/events/event/490/lida-data-science-careers-fair-2025 Previous Next

< Back Barbara Engelhardt, PhD WiML Director (2013-2016) Visit my Profile

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

This page is a list of partners from prior years. Former Partners Our generous partners sponsor WiML’s events, activities and programs on an annual basis. We also seek sponsors specifically for WiML Workshop, our flagship annual workshop co-located with NeurIPS. For any inquiries regarding yearlong partners or workshop sponsorship, contact sponsorship@wimlworkshop.org. For any other enquiries, contact info@wimlworkshop.org . Former Partners 2024-2025 Former Partners 2023-2024 Former Partners 2022-2023 Former Partners 2021-2022 Former Partners 2020-2021 Former Partners 2019-2020

All events WiML Virtual Social @ ICLR 2021 Virtual May 3, 2021 9:00 am - 11:00 am WiML is hosting a virtual social, involving a panel discussion and socializing, at ICLR 2021 on Monday, May 3, 9.00am – 11.00am Eastern Time . The panel will take place in Zoom. After the panel, we will adjourn to the Icebreaker/Gatheround platform for socializing. Event Format Agenda (all times approximate) 9:00 – 9:05am ET – Meet in Zoom. Welcome and introductions 9:05 – 9:50am ET – Panel on “Starting and Navigating Careers Through COVID-19″ 9:50 – 10:00am ET – Wrap-up and adjourn to Icebreaker/Gatheround platform 10:00 – 11:00am ET – Socializing in Icebreaker/Gatheround platform What is the panel on? The panel, moderated by Ehi Nosakhare (Data Science Manager, Microsoft) with panelists: Candace Ross (PhD student in Computer Science, MIT) Christina Papadimitriou (Machine Learning Engineer, JPMorgan Chase) Claire Vernade (Research Scientist, DeepMind) Po-Ling Loh (Lecturer in the Department of Pure Mathematics and Mathematical Statistics, University of Cambridge) Sinead Williamson (Assistant Professor of Statistics, University of Texas at Austin) is on the topic of “Starting and Navigating Careers Through COVID-19”. The panel features ML researchers at various career stages who will talk about their experience networking, job hunting, collaborating and/or starting a new position in a primarily online environment. Read more about the panelists below. Joining Information How to join: Everyone registered for ICLR is encouraged to attend! Event limited to 200 participants. You can find the Zoom link on the ICLR portal: https://iclr.cc/virtual/2021/social/4398 (ICLR registration required to access). The Icebreaker/Gatheround link will be shared in Zoom at the end of the panel. Icebreaker/Gatheround will ask you to give it permission to access your camera and microphone. Google Chrome browser recommended. Participant instructions: During the panel, you can type questions for the panelists, so bring any questions on starting and navigating careers through COVID-19! If you will participate in the post-panel social, we suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss. Additional opportunities: WiML is also offering two more opportunities at ICLR 2021 for women and/or non-binary individuals: Thanks to ICLR’s DEI action fund ( https://iclr.cc/public/DiversityInclusion ) as well as WiML sponsors, WiML is able to fund registrations for eligible individuals to attend ICLR. If you are a student, postdoc, or early-career, underrepresented individual in machine learning, apply here by April 26: https://forms.gle/B2eJ4xWuPBodVeyGA Regardless of whether you are attending ICLR or the WiML social, you can submit your resume to our WiML@ICLR 2021 resume book. The resume book will be shared with WiML sponsors. Submit here by May 1: https://forms.gle/ARs8BcnfgSyraPUDA Questions? Email workshop@wimlworkshop.org . By joining the event, you agree to abide by the WiML Code of Conduct . Panelists and Moderator bios Candace Ross, MIT Candace Ross is an EECS Phd Student in the InfoLab at MIT. She works on language grounding, particularly grounding in vision, and weakly supervised models for language acquisition. Outside of research, she plays lacrosse, participates in efforts for community diversity and inclusion, and enjoys traveling (which surprisingly can be done as a grad student)! Christina Papadimitriou, JP Morgan Christina Papadimitriou (she/her) is a Machine Learning Engineer on the Artificial Intelligence Acceleration team at JPMorgan Chase. Her team accelerates the adoption of AI into the firm’s products and services. Christina is the co-chair of PRIDE, JPMorgan’s LGBTQ+ Business Resource Group in the NY Metro area, and she is the firm’s representative for OPEN Finance. She is also in the NY Leadership Team for Out in Tech, and she serves on the Board of Directors for WiML. Christina holds a Masters in Data Science from UC Berkeley, a Masters in Operations Research from Columbia University, and a Bachelor of Engineering in Chemical Engineering from the University of South Carolina. Claire Vernade, DeepMind Claire Vernade is a Research Scientist at DeepMind in London UK. She received her PhD from Telecom ParisTech in October 2017, under the guidance of Prof. Olivier Cappé. From January 2018-October 2018, she worked part-time as an Applied Scientist at Amazon in Berlin, while doing a postdoc with Alexandra Carpentier at the University of Magdeburg in Germany. She is involved in WiML-T, which connects women in Learning Theory and organizes social and career events at conferences like COLT and ALT. Her research is on sequential decision making. It mostly spans bandit problems, but Claire’s interest also extends to Reinforcement Learning and Learning Theory. While keeping in mind concrete problems — often inspired by interactions with product teams — she focuses on theoretical approaches, aiming for provably optimal algorithms. Professor Po-Ling Loh, University of Cambridge Po-Ling Loh received her Ph.D. in Statistics from UC Berkeley in 2014. From 2014-2016, she was an Assistant Professor of Statistics at the University of Pennsylvania. From 2016-2018, she was an Assistant Professor of Electrical & Computer Engineering at UW-Madison, and from 2019-2020, she was an Associate Professor of Statistics at UW-Madison and a Visiting Associate Professor of Statistics at Columbia University. She began a position as a Lecturer in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge in January 2021. Po-Ling’s current research interests include high-dimensional statistics, robustness, and differential privacy. She is a recipient of an NSF CAREER Award, an ARO Young Investigator Award, the IMS Tweedie and Bernoulli Society New Researcher Awards, and a Hertz Fellowship. She currently serves on the Board of Directors for WiML. Professor Sinead Williamson, University of Texas at Austin Sinead Williamson is an assistant professor of Statistics at the University of Texas at Austin. She works on Bayesian methods for machine learning, with particular interests in Bayesian nonparametrics, scalable sampling methods, and modeling structured data with complex dependency structures. Sinead has recently worked as a research scientist at Amazon and CognitiveScale, and served on the Board of Directors for WiML. Ehi Nosakhare, Microsoft Ehi Nosakhare is a Senior Data and Applied Science Manager at the Microsoft AI development and Acceleration Program (MAIDAP). She leads a team that designs, develops, and implements ML solutions in application projects across Microsoft’s products and services. Prior to joining Microsoft, she earned her PhD in Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). Her thesis work focused on using Latent Variable Modeling to uncover behavioral influences on mental health and well-being. She is deeply passionate about using ML to solve real-world problems and studying the ethical implications of ML/AI. She currently serves on the Board of Directors for WiML. SPONSORS -Platinum- Previous Next

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

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

< Back Been Kim, PhD WiML Vice President of Research & Policy (2019-2020), Director (2016-2018)

< Back Ramya Ramakrishnan, PhD WiML Director (2020-2022)

All events WiML Partner Event: Women of NeurIPS Dinner with QuantumBlack Vancouver, Canada December 10, 2019 06:45 pm — 09:00 pm WiML is excited to announce a co-hosted Women of NeurIPS Dinner with QuantumBlack. Please join for an evening of learning and discussion with thought leaders in data science and machine learning. The event will include networking and dinner with a discussion on ‘Fairness in Machine Learning’ as well as ‘Kedro’, QuantumBlack’s first ever open-source Python library for building robust production-ready data and analytics pipelines. When: December 10 2019, 6:45pm – 9:00pm Where: Water St. Cafe, Vancouver, Canada If you’d like to join, please apply to attend here: https://events.quantumblack.com/womenofneurips Organized by QuantumBlack. Questions? Contact Laura Proudlock at laura.proudlock@quantumblack.com or Amelia Sherren at amelia.sherren@quantumblack.com . QuantumBlack is a WiML Platinum Partner. Previous Next

< Back Jenny Sy WiML Treasurer (2019-2022)