Search Results

2nd Women in Machine Learning Un-Workshop The 2nd WiML virtual Un-Workshop is co-located with virtual ICML on Wednesday July 21st, 2021. 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. The workshop started at the 2006 Grace Hopper Celebration and moved to NeurIPS in 2008. A History of WiML poster was created in 2015 to celebrate the 10th workshop. ​ This is the 2nd WiML Un-Workshop and is co-located with ICML . This event along with ICML are virtual events due to COVID-19. ​ The term “un-workshop” is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Different from the workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations. ​ Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at AAAI conference, 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 Celia Cintas Research Scientist, IBM Research - Nairobi Yingzhen Li Lecturer at Department of Computing. Imperial College London, UK Sarah Hooker Research Scientist at Google Brain Luciana Benotti Associate Professor at the Universidad National de Cordoba (UNC) Argentina Location This un-workshop takes place virtually due to COVID-19. Please check the program book for a complete overview of the program. Rocket.chat info desk and tech support If you have general questions or technical difficulties on the day of the event, drop by the Rocket.chat window on the workshop page on icml.cc . Best Practices for virtual events Virtual conferences can be tricky, and there are a lot of unintuitive ways to make your experience (and the experience of others) a little better. You can read some of our tips here . Information on Talks, Panel and Breakout Sessions We will be hosting the talks, panel as a Zoom webinar. We will also host breakout sessions on Zoom. You can join these sessions by clicking the links on the ICML Un-Workshop webpage . As an attendee, you will not be able to unmute yourself. If you have questions about the content of the talk, please submit the questions using the Zoom Q&A feature. Time permitting, and depending on the volume of questions, the moderator will either ask your question for you or confirm with you to ask the question yourself and unmute you at a suitable time. Note that Q&A will be moderated by us so you will only be able to see some of the questions of the other attendees. If you want to send messages to the moderators during the seminar, please use the Zoom chat feature. ​ If you have not used Zoom before, we highly recommend downloading and installing the Zoom client before the meeting. Additional instructions on how to use Zoom during a webinar can be found here . Information on Poster Session and Mentorship Social The WIML Un-Workshop poster session, mentorship social and The Joint Affinity Groups Poster Session takes place in Gather.Town. You can join these sessions by clicking the links on the ICML Un-Workshop webpage . See Gather.Town guidelines to troubleshoot common access issues. If you face any issues, check these common video/audio issues or Gather.Town FAQ . An Important Note on ICML Registration Please note that the application form does not constitute registration for the WiML Un-Workshop. To attend the un-workshop, you need to register for ICML at https://icml.cc . There is no separate registration for the un-workshop. PROGRAM PANELISTS MENTORS ACCEPTED POSTERS The 2021 WiML Un-Workshop at ICML will be held virtually on Wednesday, July 21th, 2021. WiML will also participate in the ICML Affinity Groups Joint Poster Session with Queer in AI on Monday, July 19th. All participants are required to abide by the WiML Code of Conduct . ​ Please use this link to access the Un-Workshop on ICML. ​ Wednesday, July 21th, 2021 ​ Time (ET/New York ) - Event 09:40 – 09:50: Introduction and Opening Remarks 09:50 – 10:00: WiML D&I Chairs Remarks 10:00 – 10:25: Invited talk – Yingzhen Li 10:25 – 11:30: Breakout sessions #1 11:30 – 12:00: Virtual Coffee Break and Poster Session #1 12:00 – 12:25: Invited Talk – Celia Cintas 12:25 – 13:30: Breakout Sessions #2 13:30 – 14:30: Sponsor Expo: Presentations by Microsoft, QuantumBlack, Apple, and Facebook 14:30 – 15:30: Mentoring Social 15:30 – 18:45: Break + Informal Social 18:45 – 19:25: Invited Talk – Sara Hooker 19:25 – 20:30: Breakout Sessions #3 20:30 – 21:00: Virtual Coffee Break and Poster Session #2 21:00 – 21:25: Invited Talk – Luciana Benotti 21:25 – 22:30: Breakout Sessions #4 22:30 – 23:30: Panel Discussion – Sarah Dean, Sarah Aerni, Sylvia Herbert, Kalesha Bullard, Amy Zhang (moderator) 23:30 – 23:45: Closing Remarks Our sponsor booths are open during the Un-Workshop. Please find information on their schedules and events here . ​ For more details about the breakout sessions (e.g. affiliations), please use this link . ​ You can submit your questions to the panelists through this link . ​ Breakout session #1, 10:25 AM – 11:30 AM ET ​ ID - Session title - Leaders - Facilitators ​ 1.1 Catching Out-of-Context Misinformation with Self-supervised LearningShivangi AnejaMamatha Thota, Vishwali Mhasawade ​ 1.2 School mapping using computer vision technologySafa SulimanMaryam Daniali ​ 1.3 Data Integration and Predictive Modeling for Precision Medicine in OncologyMehreen Ali Esther Oduntan ​ 1.4 Unsupervised Learning in Computer VisionAyca Takmaz, Clara Fernandez Labrador Naina Dhingra ​ 1.5 Machine Learning for Privacy: An Information Theoretic PerspectiveEcenaz Erdemir, Fatemehsadat Mireshghallah Cemre Cadir ​ 1.6 Fundamentals of Contrastive Learning in VisionSamrudhdhi Rangrej, Ibtihel Amara, Zahra Vaseqi Farzaneh Askari ​ 1.7 Exploring probabilistic sparse inferencing through the triangulation of neuroscience, computing and philosophyGagana B, Stuti Gupta Akash Smaran ​ 1.8 Neural Machine Translation for Low-Resource LanguagesEn-Shiun Annie Lee, Surangika Ranathunga, Rishemjit Kaur, Marjana Prifti SkenduliNiti M KC, Jivat Neet Kaur Breakout session #2, 12:25 PM – 1:30 PM ET ​ ID - Session title - Leaders - Facilitators ​ 2.1 Geometry and Machine LearningMelanie WeberAnkita Shukla ​ 2.2 Leveraging Open-Source Tools for Natural Language ProcessingJennifer Glenskii RanaAneri Rana, Niti M KC ​ 2.3 Challenges and Opportunities in ML for Health Care: How to address interpretability in clinical decision making?Annika Marie Schoene, En-Shiun Annie Lee, Peiyuan Zhou Malinda Vania ​ 2.4 Leading the Way for the Next Generation of Black Women in STEMLouvere Walker-Hannon, Dr. Tracee Gilbert Mozhgan Saeidi ​ 2.5 Un-bookclub Algorithms of OppressionRajasi Desai, Esther Oduntan, Anoush Najarian Sindhuja Parimalarangan ​ 2.6 Research within community: how to cultivate a nurturing environment for your researchRosanne LiuMehreen Ali ​ 2.7 Explainable machine learning: do we have the right tools?Michal Moshkovitz, Chhavi Yadav Shreya Ghosh ​ 2.8 Decision-Making in Social Settings: Addressing Strategic Feedback EffectsMeena Jagadeesan, Celestine Mendler-Dünner Frances Ding Breakout session #3, 7:25 PM – 8:30 PM ET ​ ID - Session title - Leaders - Facilitators ​ 3.1 Does your model know what it doesn’t know? Uncertainty estimation and out-of-distribution (OOD) detection in deep learningJie Ren, Polina Kirichenko, Sharon Yixuan Li, Sergul Aydore, Haleh Akrami Liyan Chen ​ 3.2 ML Applications in Big CodeSonia Kim, Mozhgan Saeidi Shima Shahfar ​ 3.3 Connecting Novel Perspectives on GNNs: A Cross-Domain OverviewIlke Demir, Nesreen Ahmed, Vasuki Narasimha Swamy, Subarna Tripathi Ancy Tom ​ 3.4 Bridging the gap between academia and industryChip Huyen, Sharon Zhou Sasha Luccioni ​ 3.5 Variational Inference for Neural NetworksSahar Karimi, Audrey Flower Gargi Balasubramaniam ​ 3.6 Responsible AI in production: Technical and Ethical considerationsParul Pandey, Himani Agrawal Wanda Wang Breakout session #4, 9:25 PM – 10:30 PM ET ​ ID - Session title - Leaders - Facilitators ​ 4.1 AI and Creativity: Approaches to Generative ArtAneta NeumannAncy Tom ​ 4.2 Attrition of women and minoritized individuals in AIJeff Brown, Christine Custis, Madu Srikumar, Himani AgrawalJeff Brown, Christine Custis, Madu Srikumar ​ 4.3 Safely navigating scalability-reliability trade-offs in ML methodsRuqi Zhang, A. Feder CooperMonica Munnangi Sponsor Expo Presentations, 1:30 PM – 2:30 PM ET ​ Time (ET/New York ) - Sponsor - Speaker - Title ​ 13:30 – 13:45 Microsoft Jennifer Neville Improving Productivity with Graph ML over Content-Interaction Networks ​ 13:45 – 14:00 Quantum Black Viktoriia Oliinyk Algorithmic Fairness: Machine Learning with a Human Face 14:00 – 14:15 Apple Lizi Ottens Machine Learning at Apple ​ 14:15 – 14:30 Facebook Ning Zhang Future of AI-Powered Shopping Mentorship Social, 2:30 PM – 3:30 PM ET ​ ID - Mentor - Topic ​ 1 Dina Obeid (Harvard) A non-linear career path in Machine Learning ​ 2 Shakir Mohamed (DeepMind) Socio-Technical AI Research ​ 3 Been Kim (Google Brain) Industry Research and Managing Up ​ 4 Anna Goldenberg (U Toronto) Two body problem in academia, Raising a family, Grant strategies, Looking for a job to deploying ML in a hospital setting ​ 5 Lalana Kagal (MIT) Maintaining work-life balance 6 Angelique Taylor (Cornell University) Transitioning from PhD to Assistant Professor ​ ​ ​ Invited talk: Celia Cintas Towards fairness & robustness in machine learning for dermatology ​ Abstract: Recent years have seen an overwhelming body of work on fairness and robustness in Machine Learning (ML) models. This is not unexpected, as it is an increasingly important concern as ML models are used to support decision-making in high-stakes applications such as mortgage lending, hiring, and diagnosis in healthcare. Currently, most ML models assume ideal conditions and rely on the assumption that test/clinical data comes from the same distribution of the training samples. However, this assumption is not satisfied in most real-world applications; in a clinical setting, we can find different hardware devices, diverse patient populations, or samples from unknown medical conditions. On the other hand, we need to assess potential disparities in outcomes that can be translated and deepen in our ML solutions. In this presentation, we will discuss how to evaluate skin-tone representation in ML solutions for dermatology and how we can enhance the existing models’ robustness by detecting out-out-distribution test samples (e.g., new clinical protocols or unknown disease types) over off-the-shelf ML models. Invited talk: Yingzhen Li Evaluating approximate inference for BNNs ​ Abstract:Bayesian Neural Network is one of the major approaches for obtaining uncertainty estimates for deep learning models. Key to the success is the selection of the approximate inference algorithms used to compute the approximate posterior, with mean-field variational inference (MFVI) and MC-dropout being the most popular variants. But is the good downstream uncertainty estimation performance of BNNs attributed to good approximate inference? In this talk I will discuss some of our recent results towards answer this question. I will also discuss briefly the computational reasons of the preference of MFVI/MC-dropout and describe our latest work to make BNNs more memory efficient. Invited talk: Sara Hooker Characterizing the Generalization Trade-offs Incurred By Compression ​ Abstract: To-date, a discussion around the relative merits of different compression methods has centered on the trade-off between level of compression and top-line metrics such as top-1 and top-5 accuracy. Along this dimension, compression techniques such as pruning and quantization are remarkably successful. It is possible to prune or heavily quantize with negligible decreases to test-set accuracy. However, top-line metrics obscure critical differences in generalization between compressed and non-compressed networks. In this talk, we will go beyond test-set accuracy and discuss some of my recent work measuring the trade-offs between compression, robustness and algorithmic bias. Characterizing these trade-offs provide insight into how capacity is used in deep neural networks — the majority of parameters are used to represent a small fraction of the training set. Formal auditing tools like Compression Identified Exemplars (CIE) also catalyze progress in training models that mitigate some of the trade-offs incurred by compression. Invited talk: Luciana Benotti Errors are a crucial part of dialogue ​ Abstract: Collaborative grounding is a fundamental aspect of human-human dialogue which allows people to negotiate meaning; in this talk, I argue that current deep learning approaches to dialogue systems don’t deal with it adequately. Making errors, and being able to recover from them collaboratively, is a key ingredient in grounding meaning, but current dialogue systems can’t do this. I will illustrate the pitfalls of being unable to ground collaboratively, discuss what can be learned from the language acquisition and dialog systems literature, and reflect on how to move forward. I will urge the community to proceed by addressing a research gap: how clarification mechanisms can be learned from data. Novel research methodologies which highlight the importance of the role of clarification mechanisms are needed for this. I will present an annotation methodology, based on a theoretical analysis of clarification requests, which unifies a number of previous accounts. Dialogue clarification mechanisms are an understudied research problem and a key missing piece in the giant jigsaw puzzle of natural language understanding. I will conclude this talk with an open call for collaborators that share the vision presented. WiML Accepted Posters in Poster Session s (11:30 AM – 12:00 PM ET and 20:30 PM – 21:00 PM ET) and Joint Affinity Poster Session on Gather.Town (Monday 19 Jul 9:00 PM — 11:00 PM ET) ​ Machine Learning Applications in Animal Sciences A mbreen Hamadani* (PhD Scholar, Animal Genetics and Breeding, SKUAST-K), Nazir A Ganai (Professor, Animal Genetics and Breeding, SKUAST-K) ​ Emulating Aerosol Microphysics with Machine Learning Paula Harder* (University of Kaiserslautern) Duncan Watson-Parris (University of Oxford), Domink Strassel (Fraunhofer ITWM), Nicolas Gauger (University of Kaiserslautern), Philip Stier (University of Oxford), Janis Keuper (Offenburg University) ​ Network Experiment Design for estimating Direct Treatment Effects Zahra Fatemi*(University of Illinois at Chicago), Elena Zheleva (Universty of llinois at Chicago) ​ Adversarial Robust Model Compression using In-Train Pruning Manoj Rohit Vemparala (BMW Group), Nael Fasfous (Technical University of Munich), Alexander Frickenstein (BMW Group), Sreetama Sarkar* (BMW Group), Qi Zhao (Karlsruhe Institute of Technology), Sabine Kuhn (BMW Group), Lukas Frickenstein (BMW Group), Anmol Singh (BMW Group), Christian Unger (BMW), Naveen Shankar Nagaraja (BMW Group), Christian Wressnegger (Karlsruhe Institute of Technology), WALTER STECHELE (Technical University of Munich) ​ Iterative symbolic regression for learning transport equations Mehrad Ansari*, Heta A. Gandhi*, David Foster, Andrew D. White; Department of Chemical Engineering, University of Rochester, Rochester, NY 14627 ​ Cost Aware Asynchronous Multi-Agent Active Search Arundhati Banerjee*(School of Computer Science,Carnegie Mellon University), Ramina Ghods (School of Computer Science, Carnegie Mellon University), Jeff Schneider (School of Computer Science, Carnegie Mellon University) ​ Exploration and preference satisfaction trade-off in reward-free learning Noor Sajid (WCHN, U CL), Panagiotis Tigas (OATML, Oxford University), Alexey Zakharov (Huawei, Imperial College), Zafeirios Fountas (Huawei, WCHN, UCL), Karl Friston (WCHN, UCL) ​ HYBRIDNET: A NETWORK THAT LEVERAGES ON CLASSICAL AND NON-CLASSICAL COMPUTER VISION TECHNIQUES FOR FEW SHOT LEARNING ON INFRARED IMAGERY Maliha Arif * (PhD Candidate, Center for Research in Computer Vision – UCF) , Abhijit Mahalanobis ( Associate Professor, Center for Research in Computer Vision – UCF) ​ Reinforcement Learning from Formal Specifications Kishor Jothimurugan (University of Pennsylvania), Suguman Bansal* (University of Pennsylvania), Obsert Bastani (University of Pennsylvania), Rajeev Alur (University of Pennsylvania) ​ Clustering With Financial Fundamentals Jennifer Glenski* (Georgia Institute of Technology), Sara Srivastav (Georgia Institute of Technology), Rachel Riitano (Georgia Institute of Technology), Blake Heimann (Georgia Institute of Technology), Jenil Patel (Georgia Institute of Technology) ​ Application of Knowledge Graph in Industry Samira Korani ​ Contrastive Domain Adaptation Mamatha Thota(University of Lincoln), Georgios Leontidis(University of Aberdeen) ​ Risk Analytics for Renewal of Purchase OrdersRisk Analytics for Renewal of Purchase Orders Shubhi Asthana (IBM Research), Pawan Chowdhary(IBM Research), Taiga Nakamura(IBM Research), Roberta Fadden (IBM) ​ On the (Un-)Avoidability of Adversarial Examples Sadia Chowdhury* (York University), Ruth Urner (Assistant Professor, EECS Department, York University) ​ Extraction of Adverse Drug Reactions from Tweets using Aspect Based Sentiment Analysis Sukannya Purkayastha (TCS Innovation Labs, Kolkata) ​ Interpretation and transparency in acoustic emotion recognition Sneha Das* (Technical University of Denmark), Nicole Nadine Lønfeldt (Child and Adolescent Mental Health Center, Copenhagen University Hospital, Capital Region), Anne Katrine Pagsberg (Child and Adolescent Mental Health Center, Copenhagen University Hospital, Capital Region & Faculty of Health, Department of Clinical Medicine, Copenhagen University), Line H. Clemmensen (Technical University of Denmark) ​ Seasonal forecasts of New Zealand’s local climate conditions with limited GCM inputs using Convolutional Neural Networks Fareeda Begum*(University of Canterbury), Varvara Vetrova (University of Canterbury), Nicolas Fauchereau (NIWA), Eibe Frank (University of Waikato), Tiger Xu(University of Waikato) ​ Assessing the Carbon Intensity of Models Across Different Languages Gauri Gupta [1] (Manipal Institute of Technology), Krithika Ramesh* [1](Manipal Institute of Technology), Mirza Yusuf [1] (Manipal Institute of Technology), Praatibh Surana [1](Manipal Institute of Technology) (Equal contribution for all) ​ A Low-rank Support Tensor Network Kirandeep Kour, Dr. Sergey Dolgov (University of Bath, UK), Prof. Dr. Martin Stoll (TU Chemnitz, Germany), Prof. Dr. Peter Benner (Max Planck Institute and TU Chemnitz, Germany) ​ CricNet : Segment and Classify Cricket Events Sai Siddhartha Maram, Shambhavi Mishra*(Guru Gobind Singh Indraprastha University) ​ Episodically optimized dynamical networks for robust motor control Sruti Mallik(*) (Electrical & Systems Engineering, Washington University in St Louis), ShiNung Ching (Electrical & Systems Engineering, Biomedical Engineering, Washington University in St. Louis) ​ Open Set Detection via Similarity Learning Sepideh Esmaeilpour* (University of Illinois at Chicago), Lei Shu (Amazon AWS AI), Bing Liu(University of Illinois at Chicago) ​ A modified limited memory Nesterov’s accelerated quasi-Newton *S. Indrapriyadarsini (Shizuoka University), Shahrzad Mahboubi (Shonan Institute of Technology), Hiroshi Ninomiya (Shonan Institute of Technology), Takeshi Kamio (Hiroshima University), Hideki Asai (Shizuoka University) ​ Time-series Forecasting of Ionospheric Space Weather using Ensemble Machine Learning Randa Natras* (Technical University of Munich, Germany), Michael Schmidt (Technical University of Munich, Germany) ​ SocialBERT : An Effective Few Shot Learning Framework Applied to a Social TV Setting Debarati Das* (Department of Computer Science, University of Minnesota Twin Cities), Roopana Chenchu (Department of Computer Science, University of Minnesota Twin Cities), Maral Abdollahi (Hubbard School of Journalism, University of Minnesota, Twin Cities), Jisu Huh (Hubbard School of Journalism, University of Minnesota, Twin Cities) and Jaideep Srivastava (Department of Computer Science, University of Minnesota Twin Cities) ​ Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification Cristina Garbacea (University of Michigan Ann Arbor), Mengtian Guo (University of North Carolina at Chapel Hill), Samuel Carton (University of Colorado Boulder), Qiaozhu Mei (University of Michigan Ann Arbor) ​ Alignment of Language Agents in V ideogames Gema Parreno ( Mempathy ) ​ Using Weak Supervision to Identify Drug Mentions from Class Imbalanced Twitter Data Ramya Tekumalla* (Georgia State University), Juan M Banda (Georgia State University)) Call for Participation The 2nd WiML Un-Workshop is co-located with ICML on Wednesday, July 21st, 2021. ​ The Women in Machine Learning will be organizing the second “un-workshop” at ICML 2021. This is an event format to encourage more participant interaction, especially with ICML going virtual this year. The un-workshop is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Different from the workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations. ​ The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Students, postdocs and researchers in all areas of Machine Learning who primarily identify as a woman and/or nonbinary are encouraged to submit one-page proposal to lead a breakout session on a certain research topic. ​ While all presenters will identify primarily as a woman and/or nonbinary, all genders are invited to attend. Important dates​ June 14th, 2021 – Application form opens July 4th, 2021 – Deadline (anywhere on Earth) to apply for a breakout session, poster, registration fee funding, facilitating or volunteering July 10th, 2021 – Notification of acceptance of breakout session’s proposals July 10th, 2021 – Notification of acceptance of posters, registration fee funding, facilitators, volunteers July 21st, 2021 – WiML Un-Workshop Day ​ Various ways of participating in WiML un-workshop Lead a breakout session: submit a proposal to lead a breakout session on a certain research topic. Facilitate a breakout session: assist breakout session leaders by taking notes and encouraging participant interactions and taking attendance. Present a poster: present a poster in a casual, informal setting. Volunteer: help with technical setup and in-event needs. Attend: participate in breakout session discussions. ​ Breakout session proposals A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage students, postdocs, and researchers who primarily identify as women and/or nonbinary in all areas of machine learning to submit a proposal to lead a topical breakout session. ​ A complete proposal consists of a 1 page blind PDF (example here ) and the names and bios of leaders submitted separately in the application form. We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators can also be provided if known at submission time. Otherwise, the organizers will match facilitators to breakout sessions. ​ Breakout session leaders must identify primarily as women and/or nonbinary; facilitators can be of any gender. Only one proposal submission per leader is allowed. If there are multiple leaders, only one leader needs to submit the proposal. There are no proceedings. ​ WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below) and do not have any other sources of funding. ​ Breakout session guidelines: Role of leaders: Point-out key characteristics of your topic and make connections with other topics. Describe the key challenges in this research area on a high-level. Describe the key approaches on a high-level to provide intuition. Highlight possible points of discussion/goals to achieve during the session. Use graphics/imagery and materials e.g. slides as needed Encourage inclusive (rather than unilateral) discussions Role of facilitators: take notes and encourage participant interactions. Leaders and facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. While the exact technology is still being determined, we anticipate using video-conferencing software (e.g. Zoom). ​ Submission instructions for breakout sessions: Proposals must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be minimum 11 point font size and page margins must be minimum 0.75 inches (all sides). Your proposal should stand alone, without linking to a longer paper or supplement. You should provide a brief description of the topics you’d like to discuss, any relevant references, a plan for how you’d organize the time (1 hour) allocated for a session, as well as some ideas on how you’d encourage discussion and participant interaction during the session. The PDF must not include identifying information, as it will be reviewed blind. In particular, the PDF should not contain information of the leaders or facilitators. Instead, submit their information in the application form. ​ Selection criteria for breakout sessions: The degree to which it is expected that participants will find the topic interesting and valuable. Diversity of leaders and facilitators, including diversity of experience/seniority, affiliation, race, viewpoint and thinking regarding the topic, etc. Plans for encouraging discussion and participant interaction during the session. ​ Facilitators If you are interested in facilitating a breakout session but have not yet connected with anyone submitting a breakout session proposal, you can indicate your interest in the application form. Organizers will match selected facilitators to breakout sessions. Facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. ​ Posters If you wish to present a poster, submit EITHER a short abstract (max 1500 characters) OR a PDF of the poster (only if you have it already). The poster may describe new, previously, concurrently published, or work-in-progress research. Posters in theory, methods, and applications are welcome. ​ The poster presenter must identify primarily as a woman and/or nonbinary; other authors can be of any gender. The poster presenter does not need to be the first author of the work. Only one poster submission per presenter is allowed. ​ Accepted posters will be presented in a casual, informal setting. This setting is very different from formal poster sessions, e.g. at WiML Workshop at NeurIPS. While the exact presentation format is still being determined, it may be as simple as a webpage with poster PDF and pre-recorded video. There are no oral or spotlight presentations. There are no proceedings. ​ Submission instructions for posters: Submitted materials may contain identifying information, as posters for this un-workshop are not reviewed blind. Your submission should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we acknowledge that space is limited, some experimental results are likely to improve reviewers’ opinions of your poster. ​ Registration fee funding The virtual nature of ICML and this un-workshop allows individuals from all over the world to attend. By funding a number of ICML registrations, WiML hopes to further expand the range of participants at this un-workshop. ​ To apply for funding, you should: identify primarily as a woman and/or nonbinary; be a student, postdoc, or have an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical regions). ​ Accepted breakout session leaders who fulfill the above eligibility criteria and do not have any other sources of funding will be prioritized for WiML funding. Other participants are also encouraged to apply. Priority will be given to individuals from underrepresented regions or groups, first-time attendees of ICML or similar conferences, and individuals who would benefit the most from this funding. ​ Funding recipients must participate in at least one breakout session as a leader, facilitator, or attendee. Due to limited funding, we may not be able to support everyone eligible; however, we hope to support as many eligible applicants as possible. ​ We also encourage you to apply for ICML volunteer and funding opportunities, which are separate and independent of WiML funding. Check the ICML website directly for details. ​ Volunteering We are seeking volunteers to help with technical setup and virtual technology testing before the event, as well as help during the event, e.g. letting people into Zoom rooms, etc. We may also need emergency reviewers for breakout session proposals. You can indicate if you can help in any way in the application form here . ​ Participation instructions To participate in ANY of the above roles and/or apply for registration fee funding, please fill in this application form by **July 4, 2021**. Selected breakout session leaders, facilitators, poster presenters, volunteers, and funding recipients will be notified individually by the dates mentioned above. ​ If you only wish to attend, we still recommend you fill in this form to provide your timezone and topic preferences. All participants are required to abide by the WiML Code of Conduct . ​ Important note: This form does not constitute registration for the WiML Un-Workshop. To attend the un-workshop, you need to register for ICML at https://icml.cc . ​ Submission is now open! ​ Organizers Beliz Gokkaya, Facebook Wenshuo Guo, University of California, Berkeley Arushi Majha, University of Cambridge Liyue Shen, Stanford Olivia Choudhury, Amazon Berivan Isik, Stanford Hadia Mohmmed Osman Ahmed Samil, Mila Vaidheeswaran Archana, Continental Automotive ​ Questions? Check out the FAQs or reach us at workshop[at]wimlworkshop[dot]org PLATINUM SPONSORS Committee ORGANIZERS Beliz Gokkaya Software Engineer at Facebook, General Chair Wenshuo Guo PhD Student at University of California, Berkeley, Program Chair Hadia Mohmmed Osman Ahmed Samil Breakout Program and Logistics Co-Chair Berivan Isik PhD Student at Stanford University, Breakout Program and Logistics Co-Chair Olivia Choudhury Researcher at Amazon, Senior Program and Networking Chair Arushi Majha PhD Student at University of Cambridge, Finance and Sponsorship Chair Liyue Shen PhD Student at Stanford University, Funding and Volunteers Chair Vaidheeswaran Archana AI Engineer at Continental Automotive, Virtual Experience Chair Diversity and Inclusion Chair Danielle Belgrave, Principal Research Manager at Microsoft Research Supervolunteers We would like to acknowledge and warmly thank our super-volunteers who worked tirelessly to ensure a high quality un-workshop. Belen Saldias, MIT Elre Oldewage, University of Cambridge Mandana Samiei, McGill and Mila Niveditha Kalavakonda, University of Washington Seattle Weiwei Zong, Henry Ford Health System FAQs How do I register for the un-workshop? You need to register to ICML to attend to WiML and then please fill the application form provided. Please refer to call for participation for more details. ​ Is filling the application form enough for register to WiML? No, you need to register to ICML . ​ What is an un-workshop? The un-workshop is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. ​ The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. ​ How is an un-workshop different from WiML workshop at NeurlPS? WiML Workshop at NeurIPS is a one-day event with invited speakers, oral presentations, and posters. This year WiML is bringing a new event format to ICML to encourage more participant interaction, especially with ICML going virtual this year. ​ Different from the workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations. ​ I'm a man. Can I attend WiML? Yes. All genders are welcome to attend! To do so, please register for ICML and fill the application form . Note, however, that all speakers, breakout session leaders and poster presenters will primarily identify as a woman and/or nonbinary, as our goal is to promote them and their work within the machine learning community. ​ Where will the un-workshop take place? This is a virtual event. ​ How much funding is available? Funding is distributed based on geographic location. Support varies from year to year and this year due to COVID-19, it will be a virtual event and ICML registration fee funding is available for participants who fulfill eligibility criteria. ​ Is there a code of conduct? Yes. WiML requires all participants and reviewers to abide by our code of conduct . ​ 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. How can I get more information on un-workshop logistics? Please check out the logistics page! I want to support WiML by providing sponsorship / recruiting at the un-workshop. Who should I talk to? Thank you for your support! Please contact us . ​ How can I join the WiML network? Join our Google Group . ​ When and where do I submit my proposal? You can find more information on call for participation. Submission to the 2021 WiML un-workshop is now closed. ​ How many breakout sessions will be on the day of the un-workshop? There are 4-time slots for 1-hour breakout sessions (marked as Breakout Sessions #1 to #4). Each of these 4-time slots will have several parallel breakout sessions. ​ Why do breakout sessions involve Zoom and Slack? Zoom rooms are mainly for the breakout sessions for the specific one hour period. However, leaders can use Slack a few days before and after to ask participants to read some papers, ask them specific questions and keep the discussions going. Also, participants can ask questions regarding the breakout session’s topic in the Slack channel before the actual session. ​ Can I make breakout rooms in the breakout session as a leader? Yes, leaders can make smaller breakout rooms to engage participants in smaller group discussions. ​ How many attendees will be in each breakout session? We can’t promise the exact number but we are hoping for smaller groups (max 20) to increase interaction between participants. ​ What is the whiteboard in Zoom rooms? Whiteboard is like a digital board and leaders and participants can write on it and explain a specific topic. More instructions are available here. ​ Will we as leaders be given a chance to advertise our proposal topic before the un-workshop? Sure, you can advertise your session’s topic on Twitter for example and tag us on @WiMLworkshop and we can retweet that. Also, attendees will have access to the breakout session topics at least a week before the un-workshop. ​ Can anyone who did not fill the WiML form still join the un-workshop? Anyone who is registered to ICML can join the un-workshop. ​ I am new to the Gather.town platform being used for the live poster session. How can I prepare for it? Check out these guidelines. ​ I have a question that's not answered here. How do I reach you? Contact us . Back To Top

All events WiML Reception @ CoRL 2017 Mountain View, California November 13, 2017 08:00 pm — 11:00 pm WiML is hosting a networking reception at the 2017 Conference on Robot Learning (CoRL). The organizers are Chelsea Finn and Coline Devin. Date: Monday, November 13, 2017, 8pm-11pm Venue: Steins Beer Garden, Mountain View Registration: Register during CoRL registration ( https://sites.google.com/a/robot-learning.org/corl2017/home/corl2017 ) If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org SPONSORS Previous Next

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

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

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

< Back Sarah Poole, PhD WiML Director (2020-2022)

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

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

< Back Hanna Wallach, PhD WiML Co-Founder, President (2009-2012), Director (2012-2016) Visit my Profile

All events WiML PhD Applications Mentorship Program: Panel on Fellowship Applications Virtual September 22, 2022 9:00 m -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: Luisa Cutillo (Leeds), Ioana Bica (University of Oxford; Alan Turing Institute), Amy Zhang (University of Washington), Aleksandra Korolova (Princeton University), Eric Wallace (University of California Berkeley) Moderator: Alessandra Tosi (Mind Foundry) Previous Next

All events WiML Social @ ICLR 2024 Vienna, Austria May 8, 2024 12:45PM Date: May 08th, 2024 (Wed.) Time: 12:45 PM- 2:15 PM Place: Hall B, Messe Wien Exhibition Congress Center@Vienna, Austria The topic of the panel will be AI Unplugged: Navigating Everyday Life & Professional Paths. During the panel, we aim to facilitate discussions on various topics, such as mentorship advice and how AI t echnologies are impacting personal and professional domains. Program: 12:45 – 12:50 PM Opening Remarks - Tatjana Chavdarova (WiML board) 12:50 – 1:25 PM Networking & Lunch 12:50 – 1:00 PM Icebreaker games 1:00 – 1:25 PM Networking themed roundtables 1:25 – 2:15 PM Panel discussion: “AI Unplugged: Navigating Everyday Life & Professional Paths” PANELISTS Bahare Fatemi Bahare Fatemi is a Research Scientist at Google Research in Montreal, specializing in graph representation learning and natural language processing. She received her Ph.D. From the University of British Columbia. Her work has been featured in top AI conferences and journals including NeurIPS, ICLR, AAAI, and JMLR. She co-organized the Mining and Learning with Graphs workshop at KDD, Women in Machine Learning (WiML) workshop, and the Montreal AI Symposium. Devi Parikh Devi Parikh, an Associate Professor at Georgia Tech's School of Interactive Computing, previously served as Senior Director of Generative AI at Meta until March 2024. Her diverse career includes roles as Director at Meta's FAIR lab, Assistant Professor at Virginia Tech, and Research Assistant Professor at TTIC. She has held visiting positions at esteemed institutions such as Cornell, UT Austin, Microsoft Research, MIT, Carnegie Mellon, and Facebook AI Research. Her research focuses on generative AI, AI for creativity, multimodal AI, and human-AI collaboration. She boasts numerous awards, including NSF CAREER, Sloan Research Fellowship, and IJCAI Computers and Thought award, among others. Priya Donti Priya Donti is an Assistant Professor and the Silverman (1968) Family Career Development Professor at MIT EECS and LIDS. Her research focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Priya is also the co-founder and Chair of Climate Change AI, a global nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning. Priya received her Ph.D. in Computer Science and Public Policy from Carnegie Mellon University. She hold several awards and fellowships including the MIT Technology Review’s 2021 “35 Innovators Under 35” award and the ACM SIGEnergy Doctoral Dissertation Award. MODERATOR Aya Abdelsalam Ismail Aya Abdelsalam Ismail is a research scientist at Prescient Design in the frontier group. Prior to Prescient, she received her Ph.D. from the University of Maryland where she was advised by Soheil Feizi and Héctor Corrada Bravo. During her Ph.D. her research focused on the interpretability of neural models for sequential data. WiML SOCIAL ORGANIZERS Yutong Zhou Yutong Zhou is a Postdoc Researcher at the Leibniz Centre for Agricultural Landscape Research (ZALF), Germany. Her research interests focus on computer vision and deep learning, particularly in Generative Models, Multi-modal Vision and Language. She is concentrating on Artificial Intelligence × Biodiversity × Smart agriculture, surrounding the goals of ‘Global Sustainability for Better AI’ and ‘AI for Best Global Sustainability’. Lisa Weijler Lisa Weijler is a PreDoc researcher at the Computer Vision Lab (CVL), TU Wien, Austria. Her research interests are computer vision and machine learning for 3D and unstructured data, especially point clouds. Additionally, she is passionate about combining her research with medical, social or political science in interdisciplinary projects. She has applied her research for cancer cell detection in pediatric leukemia patients as well as for methodological advancements like SE(3) equivariant convolutions or OOD 3D scene understanding. Her main research focus currently is open vocabulary 3D scene understanding. WiML BOARD ORGANIZERS Hewitt Tusiime – WiML ICLR liaison Erin Grant – D&I chair Tatjana Chavdarova – VP Events WiML Thanks to our sponsors! Previous Next

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