All events WiML Virtual Social @ KDD 2021 Virtual August 15, 2021 2:15 pm - 3:00 pm WiML is hosting a virtual social at KDD 2021 on Sunday, August 15, 2.15pm – 3pm Eastern Time. The event is part of the Women in KDD Workshop and consists in a mentoring and networking session conducted on the Icebreaker/Gatheround platform. Joining Information How to join: Everyone registered for KDD is encouraged to attend! Event limited to 200 participants. The Icebreaker/Gatheround link will be shared in Zoom at the end of the panel (see Women in KDD Workshop and https://virtual.2021.kdd.org/women_in_ai.html for Zoom link). Icebreaker/Gatheround will ask you to give it permission to access your camera and microphone. Google Chrome browser recommended. Questions? Email workshop@wimlworkshop.org . By joining the event, you agree to abide by the WiML Code of Conduct . SPONSORS -Platinum- 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

< Back Sidhika Balachandar PhD Student at UC Berkeley WiML Mentorship Program 2nd WiML Mentorship: PhD Applications, 2022–2023, 3rd WiML Mentorship: PhD Applications, 2023–2024, 4th WiML Mentorship: Ph.D. Applications and Job Seekers 2024–2025 "Hello! My name is Sidhika. I am a fourth-year Computer Science PhD student at UC Berkeley. I work on problems at the intersection of machine learning, fairness, and healthcare. I am advised by Emma Pierson and Nikhil Garg. I am fortunate to be supported by the NSF GRFP. I am interested in creating models in settings where outcome data is missing or biased (e.g., ICLR 2024). In particular, my research has focused on the following questions: What are the challenges of using outcome data collected from human decision makers or crowdsourced platforms? What identification approaches can we use to create models in these settings? Can we leverage external, domain specific information or data?" What was your favorite part about serving as a WiML Mentor? "As a WiML mentor, I have been able to meet and learn from some amazing peers! My mentees have shaped my research vision, given me book recs, and have been a pleasure to get to know!" Previous Next

All events 3rd WiML Mentorship Program for PhD Applications: Panel on Recommendation Letters Virtual November 9, 2023 5:00 pm - 6 pm This event, part of the WiML’s 2023-2024 Mentorship Program on the theme of PhD applications, takes place 5-6pm UTC in Zoom. Mentors and mentees of the 2023-2024 Mentorship Program are invited to attend. The panel topic is on “How to ask for a recommendation letter in Master’s & Ph.D applications in ML”. Panelists: Yingzhen Li (Imperial College London), Martha White (University of Alberta), Rose Yu (UC San Diego) Moderator: Erin Grant (UC Berkeley) Previous Next

All events WiML @ Mujeres en Inteligencia Artificial (Women in Artificial Intelligence) El Montonero Restaurant, Alameda Pardo 123, Cercado, Peru and Virtual February 20, 2025 19:00 - 21:30hrs The event will feature a 35-minute panel discussion with three female experts in AI. The panel will explore strategies for empowering women to thrive in the field of Artificial Intelligence and Machine Learning, drawing insights from the speakers' professional journeys. Additionally, the discussion will delve into technical trends and advancements, with each speaker sharing their perspectives on the future of AI within their specialized areas, such as Natural Language Processing (NLP), Computer Vision (CV), and Responsible AI. The session will conclude with a Q&A segment, followed by a dinner and networking opportunity to foster meaningful connections. Organizers: Rosa Paccotacya Yanque, Nils Murrugarra Llerena Additional information can be found by email at nem177@pitt.edu or rypaccotacya@ucsp.edu.pe . Previous Next

< Back WiML Workshop 2012 Lake Tahoe, Nevada The 7th annual Women in Machine Learning workshop was colocated with NIPS 2012 in Lake Tahoe, Nevada in December 2012. Previous Next

All events WiML Social @ ICLR 2025 Singapore April 25, 2025 12:30 PM - 2:00 PM Date: April 25, 2025 Time: 12:30 - 2:00p, Location: At the front of Hall 1 Apex Grab lunch, meet fellow researchers, and hear perspectives on navigating academia vs. industry. 🍽️ Lunch provided! Topics: "Papers, patents, or products? Making the right career call across academia & industry" The panel explores key career decisions in today's ML landscape: choosing between research publications and product development, weighing academic freedom against industry resources. The program is: 12:30 pm - 12:35 pm Opening Remarks 12:35 pm - 1:20 pm Networking & Lunch 1:20 pm – 2:00 pm Panel Discussions Panelists Reyhane Askari Rayhane Askari is is a postdoctoral researcher at FAIR (Meta AI), working at the intersection of generative models, synthetic data, and responsible AI. Her research focuses on improving data efficiency through diffusion-based generation with applications in vision-language modeling. She holds a Ph.D. in Computer Science from the University of Montreal (Mila), where she explored theoretical and practical aspects of generative modeling. https://x.com/reyhaneaskari?lang=en Katherine Driscoll Katherine Driscoll serves as Head of AI at Graph Therapeutics, a Vienna-based techbio startup, where she works on optimizing experimental design for drug discovery through AI. Her research combines active learning approaches and foundation models with domain knowledge to enhance target discovery processes. Previously, she completed her Ph.D. in condensed matter physics, focusing on modeling strongly correlated quantum systems. In addition to her professional work, she volunteers with TechBio Transformers, supporting the development of a global community for those interested in the intersection of AI and biology. linkedin.com/in/katherine-driscoll-58482275/ Nouha Dziri Nouha Dziri is an AI research scientist at the Allen Institute for AI (Ai2). Her research investigates a wide variety of problems across NLP and AI including building state-of-the-art language models and understanding their limits and inner workings. She also works on AI safety to ensure the responsible deployment of LLMs while enhancing their reasoning capabilities. Prior to Ai2, she worked at Google DeepMind, Microsoft Research and Mila. She earned her PhD from the University of Alberta and the Alberta Machine Intelligence Institute. Her work has been published in top-tier AI venues including NeurIPS, ICML, ICLR, TACL, ACL, NAACL and EMNLP. She won the best paper award in NAACL 2025. https://x.com/nouhadziri?lang=en https://www.linkedin.com/in/nouha-dziri-3587427b/ Claire Vernade Claire Vernade is a Group Leader at the University of Tübingen, in the Cluster of Excellence Machine Learning for Science (*). She was awarded an Emmy Noether award under the AI Initiative call in 2022 for the project FoLiReL , and an ERC Starting Grant in 2024 for the project ConSequentIAL . Her research is on sequential decision making. It mostly spans bandit problems, and theoretical Reinforcement Learning, but her research interests extend to Learning Theory and principled learning algorithms. Her work " Eigengame: PCA as a Nash Equilibrium " was recognized by an Outstanding Paper Award at ICLR 2021 (with I.Gemp, B.McWilliams and T.Graepel). Her goal is to contribute to the understanding and development of interactive and adaptive learning systems. Between November 2018 and December 2022, she was a Research Scientist at DeepMind in London UK in the Foundations team lead by Prof. Csaba Szepesvari . She did a post-doc in 2018 with Prof. Alexandra Carpentier at the University of Magdeburg in Germany while working part-time as an Applied Scientist at Amazon in Berlin. She received her PhD from Telecom ParisTech in October 2017, under the guidance of Prof. Olivier Cappé. https://x.com/vernadec?lang=en https://www.linkedin.com/in/claire-vernade-82559949/ Erin Grant Erin Grant is a Senior Research Fellow at the Gatsby Computational Neuroscience Unit and the Sainsbury Wellcome Centre at University College London. Erin studies prior knowledge and learning mechanisms in minds, brains, and machines using a combination of behavioral experiments, computational simulations, and analytical techniques, with the goal of grounding higher-level cognitive phenomena in a neural implementation. Erin earned her Ph.D. from the University of California, Berkeley in 2022 with support from Canada’s Natural Sciences and Engineering Research Council . During her Ph.D., Erin spent time at OpenAI , Google Brain , and DeepMind . Erin currently serves on the Women in Machine Learning Board of Directors. https://x.com/ermgrant?lang=en https://www.linkedin.com/in/ermgrant/ WiML Social Organizers Vasiliki Tassopoulou Vasiliki Tassopoulou is a Ph.D. Candidate in Bioengineering at the University of Pennsylvania, conducting research within the Center for AI and Data Science for Integrated Diagnostics Her research focuses on generative modeling of longitudinal neuroimaging data, with applications in neurodegenerative diseases. In parallel with her Ph.D., she completed an M.Sc. in Statistics and Data Science at the Wharton School, concentrating on Bayesian statistics and statistical inference and conformal prediction. She also holds a M.Eng. in Electrical and Computer Engineering from the National Technical University of Athens. https://x.com/vtassop https://www.linkedin.com/in/vasilikitassopoulou/ Melis IIayda Bal Melis Ilayda Bal is a second-year PhD candidate at the Max Planck Institute for Intelligent Systems, in Tübingen, Germany, at the Learning and Dynamical Systems (LDS) research group and a doctoral fellow through the Amazon-MPI Science Hub. She hold an M.Sc . in Operations Research and a B.Sc . in Industrial Engineering, with a minor in Computer Engineering, from Middle East Technical University (METU). Her research focuses on optimization for machine learning, specifically aimed at developing techniques that enhance the robustness and training efficiency of machine learning models. https://x.com/melisilaydabal?lang=en https://www.linkedin.com/in/melis-ilayda-bal-436889123/ Thanks to our sponsors! Previous Next

All events 3rd WiML Mentorship Program for PhD Applications: Panel on Research Statements Virtual October 23, 2023 4:00 pm - 5:00 pm This event, part of the WiML’s 2023-2024 Mentorship Program on the theme of PhD applications, takes place 4-5pm UTC in Zoom. Mentors and mentees of the 2023-2024 Mentorship Program are invited to attend. The panel topic is on “How to write a research statement for Master’s & Ph.D applications in ML”. Panelists: Emma Pierson (Cornell Tech), Sinead Williamson (UT Austin) Moderator: Erin Grant (UC Berkeley) Previous Next

All events 2nd WiML Mentoring Program for PhD Applications: Panel on Next Steps Virtual January 26, 2023 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: Isabel Valera (Saarland University), Maggie Makar (University of Michigan), Po-Ling Loh (University of Cambridge), Andrew Gordon Wilson (New York University) Moderator: Alessandra Tosi (Mind Foundry) Previous Next

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

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

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