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All events 4th WiML Mentorship Program for PhD Applications: Panel on Funding and Work-Life Balance Virtual October 23, 2024 9:00 am - 10:00 am This event, part of the WiML’s 2024-2025 Mentorship Program on the theme of PhD applications, takes place 9-10am PT in Zoom. Mentors and mentees of the 2024-2025 Mentorship Program are invited to attend. Panelists: Paul Pu Liang (MIT Media Lab), Serina Chang (UC Berkeley), Tim Dettmers (Allen Institute for Artificial Intelligence, Carnegie Mellon University) Moderator: Judy Shen (Stanford University) We will cover: How to find and reach out to professors you are interested in working with (PhD Admissions) Key tips and advice to apply to and access funding (scholarships, fellowship, salary etc) Overview on programs and resources (classes, health care, travel allowance etc) Q&A session to answer participant questions Previous Next

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All events WiML Workshop 2013 Lake Tahoe, Nevada December 9, 2013 08:00 am — 06:00 pm The 8th annual Women in Machine Learning workshop was colocated with NIPS 2013 in Lake Tahoe, Nevada in December 2013. The workshop website is no longer maintained. The organizers were: Jennifer Healey, Katie Kinnaird, Zornitsa Kozareva, Talieh S. Tabatabaei, Sonia Todorova. If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org Previous Next

All events 4th WiML Mentorship Program for PhD Applications: Panel on CV and Cover Letters Virtual October 8, 2024 8:00 am - 9:00 am This event, part of the WiML’s 2024-2025 Mentorship Program on the theme of PhD applications, takes place 8-9am PT in Zoom. Mentors and mentees of the 2024-2025 Mentorship Program are invited to attend. Panelists: Arpita Singhal (Stanford), Tijana Zrnic (Stanford), Duroux Diane Magali Anna (ELLIS) Moderator: Luisa Cutillo (University of Leeds) We will cover: Key tips and advice for the graduate programs application process Overview of research areas and opportunities in ML at the ELLIS program (Europe) and other US-based institutions Q&A session to answer participant questions Previous Next

< Back Amy Zhang, PhD WiML Director (2020-2022)

All events WiML Social @ ICLR 2022 Virtual April 25, 2022 7:00 pm - 9:00 pm A one hour Virtual Panel session (45 min + 15 min Q&A from the audience) which will take place on April 25th, 19:00-20:00 GMT. The topic of the panel will be the interplay of academia and geographic location. During the Virtual Panel we hope to encourage discussions about geographically specific challenges experienced in academia. To apply for registration funding go to: https://forms.gle/A6zyMdXt21kHQZNSA Moderated by Dr. Caroline Weis Panelists Include: Akiko Eriguchi, Senior Researcher, Microsoft. Akiko is a Senior Researcher at Microsoft.　Her research interests lie in multilingual NLP and deep learning. With her position in the Microsoft Translator team, she has developed MT systems and multilingual NLP applications. Her work has been published in ACL, EMNLP, etc. She has served as a reviewer for ACL, EMNLP, NeurIPS, AAAI. She is also a 2021-2022 co-organizer of the Workshop on Asian Translation. Prior to joining Microsoft, she was a Research Fellow (DC1) at the Japan Society for the Promotion of Science. She obtained her doctoral degree from the University of Tokyo, Japan, where her PhD thesis received the sixth AAMT Nagao Student award from the Asia-Pacific Association for Machine Translation. Nora Hollenstein, Assistant Professor, University of Copenhagen. Nora is an assistant professor in NLP & Cognitive Science at the University of Copenhagen. Before joining the Center for Language Technology at the University of Copenhagen, Nora was a PhD candidate at DS3Lab at ETH Zurich working on cognitively inspired natural language processing. She was also a lecturer at the Institute of Computational Linguistics of the University of Zurich. The focus of her research lies in enhancing NLP applications with cognitive data such as eye-tracking and brain activity recordings. She is especially interested in multi-modal learning, learning from limited data, and the interpretability and cognitive plausibility of machine learning models. Jessica Schrouff, Senior Research Scientist, Google Research. Jessica is a Senior Research Scientist at Google Research working on machine learning for healthcare. Before joining Google in 2019, she was a Marie Curie post-doctoral fellow at University College London (UK) and Stanford University (USA), developing machine learning techniques for neuroscience discovery and clinical predictions. Throughout her career, Jessica’s interests have lied not only in the technical advancement of machine learning methods, but also in critical aspects of their deployment such as their credibility, fairness, robustness or interpretability. Previous Next

All events WiML-CWS Event: Community-Driven Mentoring Event and Panel @ AISTATS 2021 Virtual April 13, 2021 12:30 pm - 2:00 pm WiML is excited to announce a joint event with the Caucus for Women in Statistics at AISTATS 2021. The event has two components: community-driven mentoring , and a panel . The event will be held on the Icebreaker.video platform on Tuesday, April 13, 2021, 12.30pm – 2pm PT. Event Format Agenda (all times approximate) 12:30 – 12:45pm PT – 1:1 mentor-mentee random pairings 12:45 – 1:10pm PT – Small group mentoring on time management tips and conducting research 1:10 – 1:45pm PT – Panel on publishing and reviewing 1:45 – 2pm PT – Small group debrief on panel What is community-driven mentoring? It means anyone can be a mentor on a topic of their expertise! Upon entering the Icebreaker link, you will be asked to indicate if you want to be a mentor or mentee. The Icebreaker platform will distribute mentors among groups as much as possible. There will be a series of mentoring sessions, both 1:1s and in small groups. Read more about the mentoring prompts below. Who can mentor? Mentoring topics will range from general life-work balance to general research questions, thus we encourage a larger number of participants, ranging from mid-PhD to senior levels, to participate as mentors. Mentors can be of any gender. What is the panel on? The panel, moderated by Sinead Williamson (University of Texas at Austin) with panelists Bin Yu (UC Berkeley), Tomi Mori (St. Jude Children’s Research Hospital), Po-Ling Loh (University of Cambridge), Jessica Kohlschmidt (Ohio State University), is on the topic of “Reviewing and Publishing”. The rapid growth of the machine learning and statistics community has made the reviewing process of peer-reviewed conferences more challenging. Besides sharing their experiences, panelists will discuss publishing venues in ML and Statistics, as well as take questions from the audience. Read more about the panelists below. Joining Instructions How to join: You can find the Icebreaker link on the AISTATS portal: https://virtual.aistats.org/virtual/2021/affinityworkshop/2033 (AISTATS registration required to access). Event limited to 200 participants. You’ll be asked to sign in to Google, and give Icebreaker permission to access your camera and microphone. Google Chrome browser recommended. Participant instructions: Whether you will participate as a mentor or mentee, we suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss. During the panel, you can type questions for the panelists in Icebreaker chat, so bring any questions on reviewing and publishing! See below for more information on Icebreaker. Questions? Email workshop@wimlworkshop.org or cws@cwstat.org . Note that this is a separate event from the AISTATS mentoring sessions . By joining the event, you agree to abide by the AISTATS Code of Conduct and WiML Code of Conduct . Icebreaker how-to guide and mentoring prompts Upon joining the platform, you will be given an option to join as either a “Mentee” or a “Mentor”. Select your preferred option, enter your full name, and click on “join event”. For each mentoring session, you can choose if you want to participate or wait for the next one. Panelists and Moderator bios Professor Bin Yu, UC Berkeley Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley. She leads the Yu Group which consists of 15-20 students and postdocs from Statistics and EECS. She was formally trained as a statistician, but her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics, and precision medicine. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice. She is a member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner. She is serving on the editorial board of Proceedings of National Academy of Sciences (PNAS) and the scientific advisory committee of the UK Turing Institute for Data Science and AI. Professor Tomi Mori, St. Jude Children’s Research Hospital Tomi Mori is a Member and Endowed Chair of the Department of Biostatistics at St. Jude Children’s Research Hospital in Memphis TN. She is an elected Fellow of the American Statistical Association and is currently President of the Caucus for Women in Statistics. Her statistical research interests include: designs of early phase clinical trial designs for drug combinations and precision oncology strategies, biomarker discovery and validation, predictive modeling, and risk stratification. 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. Dr. Jessica Kohlschmidt, Ohio State University Comprehensive Cancer Center Jessica Kohlschmidt is a Ph.D. Biostatistician at the Clara D. Bloomfield Center for Leukemia Outcomes Research at The Ohio State University Comprehensive Cancer Center. Her research group looks retrospectively at patient data to try to determine what gene mutations and expression (or combinations) predict which patients will have better survival. Jessica also teaches business analytics for the Fisher College of Business at The Ohio State University. She is a long time officer of the Caucus for Women in Statistics (CWS), serving for 10 years as Secretary and in 2018 became the first Executive Director and currently oversees the operations of CWS. Jessica is currently serving on the committee for the International Year of Women in Statistics and Data Science (IYWSDS) of ISI. She is also actively involved with the American Statistical Association (ASA) and is serving as Treasurer for the ASA Survey Research Methods Section, as well as President of the ASA Columbus Chapter and as Chair of the ASA History of Statistics Interest Group. Professor Sinead Williamson, University of Texas at Austin Sinead Williamson is an Assistant Professor of Statistics at the University of Texas at Austin, in the IROM Department and the Division of Statistics and Scientific Computation. She obtained her Ph.D. from the Computational and Biological Learning group at the University of Cambridge and spent two years as a postdoc in the SAILING laboratory at Carnegie Mellon University. Previous Next

< Back Jennifer Healey, PhD WiML Vice President of Events (2017-2019, 2020-2021), Director (2019-2020) Visit my Profile

All events WiML Un-Workshop at ICML 2022 Hybrid-In person Baltimore Convention Center Baltimore MD USA and Virtual July 17, 2022 8:45 am - 10:30 pm Hybrid-In person Baltimore Convention Center Baltimore MD USA and Virtual For more information or to register, please go to: https://sites.google.com/wimlworkshop.org/wiml-unworkshop2022/ Previous Next

All events WiML Workshop 2011 Granada, Spain December 12, 2011 08:00 am — 06:00 pm The 6th annual Women in Machine Learning workshop was colocated with NIPS 2011 in Granada, Spain in December 2011. The workshop website is no longer maintained. The organizers were: Nevena Lazic, Monica Babes-Vroman, Rongjing Xiang, with faculty advisor Hanna Wallach. If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org Previous Next

All events WiML Virtual Un-Workshop @ ICML 2020 Virtual July 13, 2020 8:00 pm - 6:00 pm The 1st Women in Machine Learning virtual Un-Workshop is co-located with virtual ICML on Monday, July 13th, 2020. See the un-workshop website for details. The organizers are Fariba Yousefi, Caroline Weis, Tatjana Chavdarova, Mandana Samiei, Larissa Schiavo. Previous Next

All events 2nd WiML Mentoring Program for PhD Applications: Panel on Essay Writing Virtual October 6, 2022 9:00 am - 10:00 am This event, part of the WiML’s 2022-2023 Mentorship Program on the theme of PhD applications, takes place 9-10am PT in Zoom. Mentors and mentees of the 2022-2023 Mentorship Program are invited to attend. Panelists: Sinead Williamson (University of Texas Austin), Mihaela van der Schaar (University of Cambridge), Sanmi Koyejo (University of Illinois Urbana-Champaign; Stanford University), Awa Dieng (Mila) Moderator: Kristy Choi (Stanford University) Previous Next