Search Results

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

All events WiML Un-Workshop @ ICML 2023 Honolulu, Hawai'i July 28, 2023 9:35 am - 10:50 am The 4th WiML Un-Workshop is co-located with ICML on Friday, July 28th, 2023. For more information or to register, please go here. Previous Next

All events 2nd WiML Mentoring Program for PhD Applications: 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

We maintain a directory of women working in machine learning. re you a woman or gender minority in the field of Machine Learning? Add yourself to the Directory by creating an account. DIRECTORY We maintain a directory of women working in machine learning Are you a woman or gender minority in the field of Machine Learning? Add yourself to the Directory by creating an account here . After confirming your email address (check your spam or promotion folder for the confirmation email!), log in and create a “Public Profile”. Create Account The directory is opt-in: you need to confirm your account and create a Public Profile, selecting the option to appear in the directory to be listed. Please note that if you signed up on the previous directory (through Google forms), you will need to create a new profile. For searching the directory, use this link and filter with space-separated keywords. Additional filters allow you to search for senior positions (academia and industry), and/or for countries that are typically under-represented in research (based on the 2017 list of low to middle-high income countries). If you are organizing an event related to machine learning, please use the directory to look for invited speakers, area chairs, conference committee members, etc. For more details on the directory, please see the FAQ . Feedback on the Directory can be provided through this form .

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 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)

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 16th Women in Machine Learning Workshop (WiML 2021) The 16th WiML Workshop is co-located with virtual NeurIPS on Thursday, December 9th and Friday, December 10th, 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. Now in its 16th year, the 2021 workshop is co-located virtually with NeurIPS . 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 Location This workshop will be virtual. WiML Platform This year WiML would be on GatherTown platform. For detailed instruction, please check: https://www.gather.town/ PROGRAM PANELISTS MENTORSHIP ROUNDTABLES SPONSOR EXPO SOCIAL ACCEPTED POSTERS Thursday, December 9, 2021 WiML Workshop 1 (UTC time in 24 hour format) 2:00 – 3:00 Pre-Workshop Informal Social 3:00 – 3:20 Opening Remarks – WiML 2021 organizers 3:20 – 3:30 WiML D&I Chairs Remarks 3:30 – 4:15 Invited talk – Machine Learning as a Service: The Challenges of Serving diverse client Distributions, Sunita Sarawagi 4:15 – 4:40 Contributed talk #1 – Regret minimization in heavy-tailed bandits, Shubhada Agrawal 4:45 – 5:45 Poster Session #1 5:45 – 6:15 Break 6:15 – 7:00 Invited talk – Learning physics models that generalize, Meire Fortunato Friday, December 10, 2021 WiML Workshop 2 (UTC time in 24 hour format) 2:00 – 3:00 Speed Networking/Social 3:00 – 4:00 Social in Gather Town 4:00 – 5:05 Invited talk – The Unreasonable Effectiveness of Collaborative Research - The Masakhane Story, Jade Abbott, Perez Ogayo, Orevaoghene Ahia 5:05 – 5:30 Contributed talk #2 – Syntax-enhanced Dialogue Summarization, Seolhwa Lee 5:30 – 7:00 Social in Gather Town WiML Workshop 3 (UTC time in 24 hour format) 10:00 – 11:00 Speed Networking/Social 11:00 – 12:45 Mentorship Roundtables I | Sponsor Expo 12:45 – 13:45 Poster Session #2 | Sponsor Expo 13:45 – 13:50 Break 13:50 – 14:35 Invited talk – Seeing the unseen: Inferring unobserved information from limited sensory data, Adriana Romero-Soriano 14:35 – 15:00 Contributed talk #3 – Causal Meta-learning by Making Informative Interventions about the Functional Form Chentian Jiang WiML Workshop 4 (UTC time in 24 hour format) 19:00 – 19:45 Invited talk – Trustworthy Machine Learning via Logic Inference, Bo Li 19:45 – 20:10 Contributed talk #4 – A Graph Perspective on Neural Network Dynamics Fatemeh Vahedian 20:10 – 20:15 Break 20:15 – 21:55 Mentorship Roundtables II | Sponsor Expo 21:55 – 22:40 Panel Discussion: Career and Life 22:40 – 23:00 Closing Remarks All participants are required to abide by the WiML code of conduct . Joint Affinity Groups Poster Session This poster session will be held jointly with other affinity workshops including Black in AI , LatinX in AI , Queer in AI , and Indigenous in AI . Poster IDs: J.001—J.190 Time: Tuesday Dec 7, 5:00 - 7:00 UTC Location: Joint Affinity Groups Poster Session Gather.Town WiML Poster Session #1 Poster IDs: W001—W040 Time: Thursday Dec 9, 4:45 - 5:45 UTC Location: WiML Gather.Town Poster Rooms 1 & 2 A listing of posters presented in this session can be found here . WiML Poster Session #2 Poster IDs: W041—W099 Time: Friday Dec 10, 12:45 - 13:45 UTC Location: WiML Gather.Town Poster Rooms 3, 4, & 5 A listing of posters presented in this session can be found here . WiML Poster Session #1: Poster Room 1 (W001 - W019) Identifying Hijacked Reviews Monika M Daryani*; James Caverlee Polaris: accurate spot detection for biological images with deep learning and weak supervision Emily C Laubscher*; Nitzan Razin; Will Graf; David Van Valen Feedforward Omnimatte Sharon Zhang*; Jonathan Huang; Vivek Rathod Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies Anaelia Ovalle* Identifying ATT&CK Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability Christine Patterson*; Jeffrey Fairbanks; Andres Orbe; Edoardo Serra; Marion Scheepers Self-Supervised Visual Representation Learning for Time-series Clustering Gaurangi Anand*; Richi Nayak A Data-driven Approach to Infer Latent Dynamics of COVID-19 Transmission Model Sujin Ahn*; Minhae Kwon Soil Moisture Estimation with cycleGANs for Time-series Gap Filing Natalia Efremova*; Mohamed-el-amine Seddik; Esra Erten Automated deep lineage tree analysis using a Bayesian single cell tracking approach Kristina Ulicna*; Giulia Vallardi; Guillaume Charras; Alan R Lowe Evaluating the Impact of Embedding Representations on Deception Detection Ellyn Ayton*; Maria Glenski SPP-EEGNET: An Input-Agnostic Self-supervised EEG Representation Model for Inter-Dataset Transfer Learning Xiaomin Li*; Vangelis Metsis Across the Pond and Back: Evaluation of News Deception Detection Approaches Across Natural and Synthetic Regional Dialects Robin Cosbey*; Maria Glenski Graph Convolutional Networks for Multi-modality Movie Scene Segmentation Yaoxin Li*; Alexander Wong; Mohammad Javad Shafiee Data Efficient Domain Adaptation using FiLM Sinjini Mitra*; Ankita Shukla; Rushil Anirudh; Jayaraman Thiagarajan; Pavan Turaga Deep Generative Models for Task-Based fMRI Analysis Daniela F de Albuquerque*; Jack Goffinet; Rachael Wright; John M Pearson Active Noise Cancellation for Spatial Computing Li Chen*; Purvi Goel; David Yang; Xiang Gao; Ilknur Kaynar Kabul Self-Supervision for Scene Graph Embeddings Brigit Schroeder*; Adam M Smith; Subarna Tripathi A Vision-Based Gait Analysis Framework for Predicting Multiple Sclerosis Rachneet Kaur*; Manuel Hernandez; Richard Sowers TaxonBags: Clustering and Vote for Precise Metagenomic Taxonomic Classification Induja Chandrakumar* WiML Poster Session #1: Poster Room 2 (W026 - W047) Gaussian Process Bandits with Aggregated Feedback Mengyan Zhang*; Russell Tsuchida; Cheng Soon Ong Privacy-Preserving Federated Multi-Task Linear Regression: A One-shot Linear Mixing Approach Inspired by Graph Regularization Harlin Lee* Comparative Analysis of Machine Learning Techniques for Breast Cancer Detection Jesutofunmi O Afolayan* Drought and Nitrogen Induced Stress Identification for Maize Crop using Deep Learning deployed on Unmanned Aerial Vehicles (Drones) Tejasri Nampally*; Ujwal Sai; Siddha Ganju; Ajay Kumar; Rajalakshmi Pachamuthu; Balaji Naik Banothu Scene statistics and noise determine the relative arrangement of receptive field mosaics Na Young Jun* Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks Yujun Yan*; Milad Hashemi; Kevin Swersky; Yaoqing Yang; Danai Koutra Learning Aerodynamics and Instrument behavior to Fly in Dangerous Conditions Cynthia Koopman*; David Zammit Mangion; Alexiei Dingli Reading the Road: Leveraging Meta-Learning to Learn Other Driver Behavior Anat Kleiman*; Ryan P Adams Commit-Checker: A human-centric approach for adopting bug inducing commit detection using machine learning models Naz Zarreen Oishie*; Banani Roy Using Embeddings to Estimate Peer Influence on Social Networks Irina Cristali*; Victor Veitch Mitigating Overlap Violations in Causal Inference with Text Data Lin Gui*; Victor Veitch Automatic Curricula via Expert Demonstrations Siyu Dai*; Andreas Hofmann; Brian Williams Leveraging Resource Allocation and Approximation for Faster Hyperparameter Exploration Shu Liu* The Many Hats We Wear as Machine Learning Practitioners for Marine Mammal Conservation Louisa van Zeeland*; Gracie Ermi Exploiting Hyperdimensional Computing and Probabilistic Inference for Reasoning Across Levels of Abstraction in Dynamic Biosignal-Based Applications Laura I Galindez Olascoaga*; Alisha Menon; Jan M. Rabaey Augment Your Deterministic Model with Monte Carlo Dropout to Combat Noisy Labels Li Chen*; Karen Chen; Purvi Goel; Ilknur Kaynar Kabul Occlusion-Aware Crowd Navigation Using People as Sensors Ye-Ji Mun*; Masha Itkina; Katherine Driggs-Campbell Physics-assisted Machine Learning Abhilasha Katariya*; Jin Ye; Dipal Gupta; Rohit Malshe; Chinmoy Mohapatra; Gautam Natarajan; Liron Yedidsion An Interpretable Approach to Hateful Meme Detection Tanvi M Deshpande*; Nitya Mani Solving the super rural and super dense delivery with asset-light programs Jin Ye*; Dipal Gupta; Abhilasha Katariya; Rohit Malshe; Natarajan Gautam; Liron Yedidsion; Chinmoy Mohapatra Model-Free Learning for Continuous Timing as an Action Helen Zhou*; David Childers; Zachary Lipton Accelerating Symmetric Rank 1 Quasi-Newton Method with Nesterov's Gradient Indrapriyadarsini Sendilkkumaar*; Shahrzad Mahboubi; Hiroshi Ninomiya; Takeshi Kamio; Hideki Asai WiML Poster Session #2: Poster Room 3 (W050 - W071) Generating Thermal Human Faces for Physiological Assessment using Thermal Sensor Auxiliary Labels Catherine Ordun*; Sanjay Purushotham; Edward Raff Machine Learning API in NASA’s WorldView Satellite Image Search System Kai E Priester*; Daniela Fragoso Syntax-enhanced Dialogue Summarization using Syntax-aware information Seolhwa Lee*; Kisu Yang; Chanjun Park; João Sedoc; Heuiseok Lim COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification Audrey Chung*; Mahmoud Famouri; Andrew Hryniowski; Alexander Wong Predictive classification of clinical ball catching trials with recurrent neural networks Jana Lang* Effectiveness of Transformers on Session-Based Recommendation Sara Rabhi*; Ronay Ak; Gabriel S P Moreira; Jeong Min Lee; Even Oldridge The impact of weather information on machine-learning probabilistic electricity demand predictions Yifu Ding*; Hannah Bloomfield; Malcolm McCulloch Social Representation of Political Inclination of Users Anjali Jha* Combining semantic search and twin product classification for recognition of purchasable items in voice shopping Dieu Thu Le; Verena Weber*; Melanie Bradford Sequential Decision Making with Limited Resources Hallee E Wong*; Maggie Makar; Aniruddh Raghu; John Guttag As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance Fiorella Wever*; Laura Symul; Victor Garcia; T. Anderson Keller Machine learning powered quantitative histologic assessment of disease severity in ulcerative colitis Kathleen Sucipto*; Archit Khosla; Fedaa Najdawi; Michael Drage; Maryam Pouryahya; Stephanie Hennek; Victoria Mountain; Murray Resnick; Amaro Taylor-Weiner; Deepta Rajan; Ilan Wapinski; Andy Beck Improved robustness to disfluencies in RNN-Transducer based Speech Recognition Tina Raissi*; Valentin Raissi; Manuel Giollo; Guglielmo Camporese ``We Don't Talk Anymore?": An analysis of cross-cutting political discussion on Reddit Dulshani Withana Thanthri Gamage* A Graph Perspective on Neural Network Dynamics Fatemeh Vahedian*; Ruiyu Li; Puja Trivedi; Di Jin; Danai Koutra How we browse: Measurement and analysis of digital behavior Yuliia Lut*; Michael Wang; Elissa M. Redmiles; Rachel Cummings Topological characterizations of neuronal fibers and its implications in comparing brain connectomes S.* Shailja; B.S. Manjunath Towards Automated Evaluation of Explanations in Graph Neural Networks Vanya BK*; Balaji Ganesan; Aniket Saxena; Devbrat Sharma; Arvind Agarwal Graph Representation Learning on Trajectory-Encoded Volumetric Heatmaps for Human Motion Generation Michelle Wu*; Zhidong Xiao; Hammadi Nait-charif Opening the Black Box: High-dimensional Safe Policy Search via Sim-to-real Aneri Muni* , Matteo Turchetta, Andreas Krause Transformer-based Self-Supervised Learning for Medical Images Mariia Dobko*; Mariia Kokshaikyna Fixed Neural Network Steganography: Train the images, not the network Varsha Kishore*; Xiangyu Chen; Yan Wang; Boyi Li; Kilian Weinberger WiML Poster Session #2: Poster Room 4 (W078 - W088) Regret Minimization in Heavy-Tailed Bandits Shubhada Agrawal*; Sandeep K Juneja; Wouter M Koolen Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning Siddha Ganju*; Sayak Paul Classification of Shoulder Impingement Syndrome using Transfer Learning model Raquel Marasigan* Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation Hanjie Chen*; Yangfeng Ji A Neural Network Ensemble Approach to System Identification Elisa Negrini*; Giovanna Citti; Luca Capogna Predicting Fake News and Real News Spreaders' Influence Amy Zhang*; Daniel Hammer; Aaron Brookhouse; Francesca Spezzano; Liljana Babinkostova Parkinson’s Disease Detection using Imputed Multimodal Datasets Hetvi Jethwani*; Bhumika Chopra How Much Data Analytics is Enough?: The ROI of Machine Learning Classification and its Application to Requirements Dependency Classification Gouri Deshpande*; Guenther Ruhe; Chad Saunder Propagation on Multi-relational Graphs for Node Regression Eda Bayram* Do You See What I See: Using Augmented Reality and Artificial Intelligence Shruti Karulkar*; Louvere Walker-Hannon; Sarah Mohamed Strategic Clustering Ana-Andreea Stoica*; Christos Papadimitriou WiML Poster Session #2: Poster Room 5 (W106 - W130) Interpretable Machine Learning with Symbolic Regression Aurélie Boisbunon*; Carlo Fanara; Ingrid Grenet; Jonathan Daeden; Alexis Vighi; Marc Schoenauer Clipping Range Methods in Proximal Policy Optimization Mónika Farsang* The Two-sample Problem in High Dimension: A Ranking-based Method Myrto Limnios*; Stephan Clémençon; Nicolas Vayatis Causal meta-learning by making informative interventions about the functional form Chentian Jiang*; Chris Lucas Maintenance planning framework using online and offline deep reinforcement learning Zaharah Bukhsh*; Nils Jansen; Hajo Molegraaf Machine Learning-based Mobility Assessment from Passively Sensed Digital Biomarkers Emese Sükei*; Pablo M Olmos; Antonio Artés Getting Started with Model Cards Maitreyi Chitale*; Anoush Najarian; Helen Chigirinskaya; Sindhuja Parimalarangan; Louvere Walker-Hannon; Rajasi Desai; Kyle Rawding; Brian Liu Interpretable & Hierarchical Topic Models using Hyperbolic Geometry Simra Shahid*; Tanay Anand; Sumit Bhatia; Nikaash Puri; Balaji Krishnamurthy; Nikitha Srikanth Fairness properties do not transfer: do we have viable solutions for real-world applications? Jessica Schrouff*; Natalie GHarris; Sanmi Koyejo; Ibrahim Alabdulmohsin; Eva Schnider; Krista Opsahl-Ong; Alex Brown; Subhrajit Roy; Diana Mincu; Christina Chen; Awa Chen; Yuan Liu; Vivek Natarajan; Katherine Heller; Alexander D'Amour Combining Transfer Learning And Transformer Attention Mechanism to Increase Aqueous Solubility Prediction Performance Magdalena Wiercioch*; Johannes Kirchmair Application of an interpretable graph neural network to predict gene expression in histopathological images Ciyue Shen*; Collin Schlager; Deepta Rajan; Victoria Mountain; Ilan Wapinski; Amaro Taylor-Weiner; Maryam Pouryahya; Robert Egger Self-supervised pragmatic reasoning Jennifer Hu*; Roger Levy; Noga Zaslavsky AI-Driven Predictive Analytics to Inform Nuclear Proliferation Detection in Urban Environments Anastasiya Usenko*; Joonseok Kim; Ellyn Ayton; Svitlana Volkova Measuring the Cause and Effect in Scientific Productivity: A Case Study of the ACL Community Jasmine R Eshun*; Maria Glenski; Svitlana Volkova Scalable Bayesian Network Structure Learning with Splines Charupriya Sharma*; Peter van Beek Accurate Multi-Endpoint Molecular Toxicity Predictions in Humans with Contrastive Explanations Bhanushee Sharma*; Vijil Chenthamarakshan; Amit Dhurandhar; Shiranee Pereira; James Hendler; Jonathan S Dordick; Payel Das Visual Question Answering (VQA) Models for Hypothetical Reasoning Shailaja K Sampat* Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment Hanxiao Chen* Importance of Data Re-Sampling and Dimensionality Reduction in Predicting Students’ Success Eluwumi Folake Buraimoh*; Ritesh Ajoodha; Kershree Padayachee Using computer vision to measure spatial-temporal change of building conditions in neighborhoods with street view imagery Evelyn C Fitzgerald*; Tingyan Deng; Daniel Chen; Lijing Wang; Jackelyn Hwang Efficient evaluation metrics for evaluating the performance of GANs Architecture Ramat Ayobami Salami*; Sakinat O Folorunso Targeted active semi supervised learning for new customers in virtual assistants Dieu Thu Le; Verena Weber*; Melanie Bradford Graph Neural Networks for automated histologic scoring of NASH liver biopsy Maryam Pouryahya*; Jason Wang; Kenneth Leidal; Harsha Pokkalla; Dinkar Juyal; Zahil Shanis; Aryan Pedawia; Quang Huy Le; Victoria Mountain; Sara Hoffman; Murray Resnick; Michael Montalto; Andy Beck; Katy Wack; Ilan Wapinski; Oscar Carrasco-Zevallos; Amaro Taylor-Weiner Application of a Bayesian CAR Prior to Analyzing Ancient Statistical Records of the Inca Empire Anastasiya Travina* Depth without the Magic: Inductive Biases of Natural Gradient Descent Anna Mészáros*; Anna Kerekes; Ferenc Huszar We have three types of mentorship roundtables: Research roundtables, Career and Life roundtables, and Sponsor roundtables. The mentorship session is at two time slots on Fri Dec 10, 11:00 AM - 12:45 PM and 20:15 PM - 21:55 PM (UTC time in 24-hour format) in the Roundtable Rooms in WiML Gather.Town. To allow WiML attendees to gain exposure to a wide range of topics, as well as to allow mentors and mentees to connect with a variety of people, attendees (but not mentors) at the mentorship session will rotate between the discussion tables throughout the event. Specifically, there will be 3 timed opportunities to rotate between the Research, Career and Life, and Sponsorship tables. Each discussion period will last for approximately 15 minutes (during which the participants will be asked to remain at their seats for the ongoing conversation). After each 15-minute session, the WiML organizers will announce that it is time for the participants to move to a different roundtable, and all participants will have 10 minutes to explore the different rooms and decide on their discussion topic of interest. All mentors and mentees will be free to use the last 10 minutes of the roundtables session as they wish, either remaining at their discussion tables or moving between tables to meet the other WiML participants. Mentorship Roundtable I: Friday, December 10: 11:00 - 12:45 (UTC time in 24-hour format) Table 1 (Research): Reinforcement learning I (Feryal Behbahani) Table 2 (Research): Reinforcement learning II (Minhae Kwon) Table 3 (Research): Control and online learning (Katja Hofmann) Table 4 (Research): Probabilistic graphical models and Bayesian methods (Isabel Valera) Table 5 (Research): Statistical inference and estimation (Emtiyaz Khan) Table 6 (Research): Learning theory (Arthur Gretton) Table 7 (Research): AutoML (Katharina Eggensperer) Table 8 (Research): Computer Vision (Enzo Ferrante) Table 9 (Research): Robotics (Daniela Pamplona) Table 10 (Research): Fairness, accountability, and ethics in machine learning (Jessica Schrouff) Table 11 (Research): Social science applications (Alice Oh) Table 12 (Career and Life): Navigating the job search (industry) and doing research in the industry (Lavanya Tekumalla) Table 13 (Career and Life): Developing a long-term research plan (Ferenc Huszar) Table 14 (Career and Life): Navigating academia (job search and tenure application process) (Razvan Pascanu) Table 15 (Career and Life): Choosing between academia and industry (Alessandra Tosi) Table 16 (Career and Life): Seeking funding (academia edition): PhD fellowships / professorship grants (Ioana Bica) Table 17 (Career and Life): Establishing collaborations (Yarin Gal) Table 18 (Career and Life): Work-life balance (academia) (Nando de Freitas) Table 19 (Career and Life): Surviving graduate school (Truyen Tran) Table 20 (Career and Life): Work-life balance (industry) (Shane Legg) Table 21 (Career and Life): Scientific communication (Shakir Mohamed) Table 22 (Career and Life): Taking on the leadership roles (academic + industry) (Po-Ling Loh) Table A (Sponsor): Entering AI Research from other STEM fields [Deepmind] (Andy Brock, Michela Paganini) Table B (Sponsor): Careers at G-Research [G-Research] (Clara Dolfen, Jamie Watson, Olivia Bateman) Table C (Sponsor): Machine Learning at Microsoft Research [Microsoft] (Rianne van den Berg) Table D (Sponsor): Careers at NVIDIA [NVIDIA] (Monica Spehar) Mentorship Roundtable II: Friday, December 10: 20:15 - 21:55 PM (UTC time in 24-hour format) Table 1 (Research): Deep learning (Ian Goodfellow) Table 2 (Research): Reinforcement learning I (Sham Kakade) [Canceled] Table 3 (Research): Reinforcement learning II (Amy Zhang) Table 4 (Research): Optimization (Tatjana Chavdarova) Table 5 (Research): Learning theory (Karan Singh) Table 6 (Research): Natural language processing (Layla El Asri) Table 7 (Research): Data-efficient machine learning (Nicolas Le Roux) Table 8 (Research): Interpretability and explainability in machine learning (Jennifer Wortman Vaughan) Table 9 (Research): Causal inference and counterfactuals (Sarah Tan) Table 10 (Research): Robotics (Eugene Vinitsky) Table 11 (Research): Computer vision (Jennifer Hobbs) Table 12 (Research): Music applications (Pablo Samuel Castro) Table 13 (Research): Machine learning for healthcare (Adriana Romero-Soriano) Table 14 (Research): AI 4 Science (Animashree Anandkumar) Table 15 (Career and Life): Navigating the job search (industry) and doing research in industry (Timnit Gebru) Table 16 (Career and Life): Finding mentors throughout your career (Yisong Yue) Table 17 (Career and Life): Navigating academia (job search and tenure application process) (Sinead Williamson) Table 18 (Career and Life): Choosing between academia and industry (Negar Rostamzadeh) Table 19 (Career and Life): Seeking funding: negotiating compensation in industry (Samy Bengio) Table 20 (Career and Life): Establishing collaborations (Anitha Vijayakumar) Table 21 (Career and Life): Surviving graduate school (Katherine Niehaus) Table 22 (Career and Life): Building your professional brand (Chelsea Finn) Table 23 (Career and Life): Work-life balance (industry) (Wonmin Byeon) Table 24 (Career and Life): Life with kids (Sarah Poole) Table 25 (Career and Life): Scientific communication (Been Kim) Table 26 (Career and Life): Non-traditional paths to machine learning (Jennifer Wei) Table 27 (Career and Life): Doing a postdoc I (Hyeji Kim) Table 28 (Career and Life): Networking (Bethany Edmunds) Table 29 (Career and Life): Democratizing ML research: Non-traditional research methods (Jade Abbott) Table E (Sponsor): Apple Internships [Apple] (Lauren Araujo, Lauren Hannah) Table F (Sponsor): Task Oriented Dialog Research @ ASAPP [ASAPP] (Ramya Ramakrishnan, Ryan McDonald, Sravana Reddy) Table G (Sponsor): Careers in AI and ML at Capital One [Capital One] (Helen Lee-Righter, Vannia Gonzalez Macias) Table H (Sponsor): Machine Learning at D. E. Shaw Research [D. E. Shaw Research] (Jocelyn Sunseri) Table I (Sponsor): Present and Future of AI Research at Intel [Intel] (Huma Abidi, Lama Nachman) Table J (Sponsor): AI Careers at Meta (Beliz Gokkaya, Kavya Srinet, Sahar Karimi) Table K (Sponsor): Machine Learning at Microsoft Research [Microsoft] (John Langford, Nicolas Le Roux) Table L (Sponsor): From Academia to Quantitative Finance – Careers at PDT Partners [PDT Partners] (Kurt Miller, Winnie Yang) Table M (Sponsor): Meet & Greet Qualcomm AI Research [Qualcomm] (Dipika Khullar, Sangeetha Siddegowda, Shreya Kadambi) Table N (Sponsor): QuantumBlack Careers Roundtable [QuantumBlack] (Huilin Zeng, Marta Lopez, Xilin Cecilia Shi) Table O (Sponsor): Early Career Advice for Industry [Salesforce] (Shelby Heinecke, Vena Li) Table P (Sponsor): Software Story for Accelerators and Engineer Experience in a Startup World [SambaNova Systems] (Mary Jo Doherty, Weiwei Chen) Table Q (Sponsor): Self-Driven Women: Careers at Waymo [Waymo] (Drago Anguelov, Wei Chai, Chen Wu, Congcong Li, Kevin Peterson) There will also be sponsor booths in the expo room, staffed at the times below. Sponsor talks are playable by participants on-demand in the Sponsor Expo Room at Gather.Town and at the NeurIPS virtual site (NeurIPS registration required to access). Virtual Booths (in the Sponsor Expo Room at Gather.Town ) Apple (Fri Dec 10, 20:15 - 21:55 UTC) Apple at NeurIPS | Career Opportunities | Internship Opportunities | RSVP to Meet Apple | RSVP to Internship Q&A Panel Capital One (Fri Dec 10, 20:15 - 21:55 UTC) Learn more about AI and ML at Capital One | Explore Data Science Roles at Capital One DeepMind (Fri Dec 10, 11:00 - 13:45 UTC) DeepMind at NeurIPS 2021 - Schedule | DeepMind - Careers Info D. E. Shaw Research (Fri Dec 10, 13:00 - 13:45 UTC & 20:15 - 21:55 UTC) D. E. Shaw Research - Brochure G-Research (Fri Dec 10, 12:45 - 13:45 UTC) G-Research: Opportunities | G-Research: Kaggle Competition | G-Research: Spring Insight Week Meta (Fri Dec 10, 20:15 - 21:55 UTC) Meta AI at NeurIPS 2021 | Meta AI Careers Microsoft (Fri Dec 10, 20:15 - 21:55 UTC) Microsoft Research – Emerging Technology, Computer, and Software Research | Microsoft at NeurIPS 2021 - Microsoft Research NVIDIA (Fri Dec 10, 11:00 - 13:45 UTC & 20:15 - 21:55 UTC) NVIDIA @ NeurIPS2021 | NVIDIA Careers | Research at NVIDIA QuantumBlack (Fri Dec 10, 20:15 - 21:55 UTC) SambaNova Systems (Fri Dec 10, 20:15 - 21:55 UTC) ML Accelerators and Performance Sponsor Talks (in the Sponsor Expo Room at Gather.Town and at the NeurIPS virtual site ) Lizi Ottens (Apple) Machine Learning at Apple Cat Posey (Capital One) AI & ML at Capital One Jocelyn Sunseri (D. E. Shaw Research) Machine Learning Initiatives at D. E. Shaw Research Mihaela Rosca, Feryal Behbahani, and Kate Parkyn (DeepMind) Women at DeepMind - Applying for Technical Roles Daniela Massiceti (Microsoft) Advancing real-world few-shot learning with the new ORBIT dataset Anima Anandkumar (NVIDIA) Research at NVIDIA: New Core AI and Machine Learning Lab Garazi Gomez-de-Segura (QuantumBlack) ML for Engineering design Anna Bethke (Salesforce) Actionable Steps to Implement Ethics by Design Qinghua Li (SambaNova) SambaNova Systems: ML Accelerators & Performance Chen Wu (Waymo) Machine Learning for Autonomous Driving at Waymo The following participant-hosted socials will take place before, during, and after the workshop in Gather.Town in the South Garden in WiML Gather.Town. We highly encourage WiML participants to attend, to meet fellow participants in a fun and casual setting! See a description of each social here , and instructions on how to enter the area for each social here ! Pre-workshop socials Dec 9, 2:00 - 3:00 Indrapriyadarsini Sendilkkumaar, Shahrzad Mahboubi – Optimization Techniques Ayushi Sharma, Kiana Alikhademi – Applying to and Navigating PhDs Workshop socials Dec 10, 3:00 - 4:00 Jenna Hong, Devi Bhattarai – Multidisciplinary AI (Neuroscience, Social Science and Ethics) Hope Schroeder, Akshita Ramya Kamsali – Natural Language Processing and Computational Social Science Bing Zhang -- Win and Wine (Fun Social) Dec 10, 5:30 - 7:00 Mehreen Ali, Anoush Najarian – Privacy and Algorithms of Oppression Melissa Fabros -- Murder Mystery (Fun Social) Dec 10, 6:00 - 7:00 Mamatha Thota, Naina Dhingra – Computer Vision Algorithms and Applications Call for Participation WiML Workshop 2021 @ NeurIPS 16th Workshop for Women in Machine Learning Submissions are now closed, but if you would like to participate as a volunteer, poster mentor, or social host, please apply here before November 5, 2021 to be considered! The 16th Workshop for Women in Machine Learning (WiML) will be co-located with NeurIPS in December 2021 and will be held virtually. The workshop is a one-day event with invited speakers, oral and poster presentations. The event brings together members of the academic and industry research community for an opportunity to connect, exchange ideas, and learn from each other. Underrepresented groups and undergraduates interested in pursuing machine learning research are encouraged to participate. There will be virtual mentorship sessions to discuss current research trends and career choices in machine learning. While all presenters will identify as a woman, nonbinary or gender non-conforming, members of all gender identities are invited to attend. All submissions must abide by the WiML Code of Conduct . Submission page: https://cmt3.research.microsoft.com/WiML2021 Registration funding and non-author participation application: here . IMPORTANT DATES September 1, 2021 - Abstract submission opens on CMT October 5, 11:59 pm AoE - Abstract submission deadline October 20, 2021 - Notification of abstract acceptance October 20, 2021 - Application for registration fee funding and volunteering opens November 5, 2021 - Registration funding application deadline November 12, 2021 - Registration funding notification December 9-10, 2021 - WiML Workshop Day SUBMISSION INSTRUCTIONS We strongly encourage students, postdocs, and researchers in all areas of machine learning who identify as a woman, nonbinary or gender non-conforming to submit an abstract (1 page PDF) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. While the presenting author need not be the first author of the work, we request that the presenting author be identifying as a woman, nonbinary or gender non-conforming. Submissions will be reviewed in a double-blind setting. Authors of accepted abstracts will be asked to present their work in a virtual poster session. A few authors will be selected to give spotlight or oral presentations. There are no formal proceedings. Abstracts are non-archival: they may describe completed research or work-in-progress. Please refer to the detailed Submission Instructions . REGISTRATION FEE FUNDING Registration to the NeurIPS virtual conference is required to participate in this year's WiML workshop. Registration fee funding for NeurIPS will be available for eligible WiML participants. To qualify, the participant must be a student, postdoc, or equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented regions or groups), and identify as a woman, nonbinary or gender non-conforming. Priority will be given to poster presenters, workshop volunteers, and first-time attendees of NeurIPS or similar conferences. Funding recipients must participate in the WiML Workshop as either a poster presenter or volunteer as outlined in the application. Funding and volunteering application form: Please check starting October 20, 2021 for the application link, when it will be made ready. The application deadline is November 5, 2021. VOLUNTEERING We are seeking volunteers to help with technical setup and virtual technology testing before and during the event, e.g., letting people into Zoom rooms, poster mentors etc. You can indicate if you can help in any way in the application form. OTHER SUBSIDIES We will also consider internet and equipment subsidies for the purpose of attending the workshop. Equipment may include headphones, microphones, funding to cover internet access, and anything else that might facilitate participation in the workshop. Please see the funding and volunteering application form for details. Questions? Check out the FAQs (https://wimlworkshop.org/faq/ ) or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS Committee ORGANIZERS ADVISORY SUPER VOLOUNTEERS Mennatullah Siam University of Alberta Tianlin Xu London School of Economics Weiwei Zong Henry Ford Health System & University of Michigan Gloria Namanya Makerere University Sharvaree Vadgama University of Amsterdam Archana Iyer Sloan Kettering Institute Sofia Bourhim ENSIAS-Mohammed V University Silvia Pagliarini University of California, Los Angeles Liyue Shen Stanford University Maikey Khorani Salahaddin University / College of Engineering Disha Shur Purdue University Naiti Bhatt New York University Patricia Robinson Stanford University Sandareka Wickramanayake National University of Singapore Priya Bannur University of Southern California Varsha Kishore Cornell University Ria Vinod Brown University, IBM Research Niharika Vadlamudi International Institute of Information Technology, Hyderabad Bing Zhang IBM Research Mei Chen University of Waterloo Kajal Puri University of Bonn, Germany AREA CHAIRS Deepti Ghadiyaram Facebook Research Adriana Romero Facebook AI Research Amita Misra IBM Anastasiya Belyaeva MIT Angelica Aviles-Rivero University of Cambridge Ankita Shukla ASU Anna Klimovskaia Susmelj Swiss Data Science Center Anna Kruspe Technische Universität München Besmira Nushi Microsoft Research Buket Yüksel Koç University Celestine Mendler-Dünner UC Berkeley Dalin Guo UC San Diego; Twitter Inc. Erin Grant UC Berkeley Gintare Karolina Dziugaite ServiceNow Ilke Demir Intel Corporation Isabela Albuquerque Institut National de la Recherche Scientifique Kalesha Bullard Facebook AI Research Kuan-Ting Chen National Taiwan University Maria Glenski Pacific Northwest National Laboratory Mayoore Jaiswal University of Washington Mengjiao Wang Amazon Visual Search Nastraran Baradaran Citrix Systems Natalia Efremova Queen Mary University London Nesime Tatbul Intel Labs and MIT Nezihe Merve Gürel ETH Zürich Niha Beig Case Western Reserve University Nora Hollenstein University of Copenhagen Obioma Pelka University of Applied Sciences and Arts Dortmund Pallika Kanani Oracle Labs Peixian Liang University of Notre Dame Pooja Sharma BIT Sindri Rachel Cummings Georgia Tech Samira Daruki Expedia Research Sandhya Prabhakaran Moffitt Cancer Center Sandya Mannarswamy Intel India Sara Magliacane IBM Research Sergul Aydore Amazon Web Services Shinjini Ghosh MIT Shuai Zhang ETH Zürich Sima Behpour Samsung Research America Sinead Williamson UT Austin Spandana Gella Amazon AI Subarna Tripathi Intel Labs Surangika Ranathunga University of Moratuwa Swetasudha Panda Oracle Labs Tania Lorido-Botran Independent Researcher Xenia Miscouridou Imperial College London Xi Rao ETH Zürich Xiao Zhang T-Mobile Xun Tang Yelp Yao Qin University of California, San Diego FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

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

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 3rd Women in Machine Learning Un-Workshop, ICML 2022 The 3rd WiML Un-Workshop is co-located with ICML on Monday, July 18th, 2022. Speakers Logistics Breakout Sessions 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, primarily intended to foster active participant engagement in the program. 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 3th year, the 2022 un-workshop is co-located with ICML . Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch or social at the AISTATS or 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 Location This workshop will be hybrid, co-located with ICML at the Baltimore Convention Center , Baltimore, Maryland USA. Type of registration required to attend Any type of in-person registration (tutorial / workshop / conference / all) grants you in-person access to the un-workshop. Also, an in-person registration includes access to the virtual one. Breakout Sessions Breakout Sessions During the day of the WiML Un-Workshop @ ICML 2022 there will be three different Breakout Sessions. We list the sessions, topics, and leaders. BreakoutGhoshehBreakout Session #1 (9.10AM - 10.10AM) IN-PERSON Breakout Sessions Machine learning real-time applications in health. Leader: Dania Humaidan, Co-leader: Cansu Sen. VIRTUAL Breakout Sessions Deep Generative Models for Electronic Health Records. Leader: Ghadeer Ghosheh, Co-leader: Tingting Zhu. Affective Computing: A Computational Perspective. Leader: Shreya Ghosh, Co-lead: Garima Sharma. Introducing geometry awareness in deep networks. Leader: Ankita Shukla. Breakout Session #2 (11.05AM - 12.05AM) IN-PERSON Breakout Sessions Challenges and opportunities in certified auditing of ML models. Leader: Chhavi Yadav. Robustness of Deep Learning Models to Distribution Shift. Leader: Polina Kirichenko, Co-leads: Shiori Sagawa, Sanae Lofti. VIRTUAL Breakout Sessions Knowledge Distillation through the lense of the capacity gap problem. Leader: Ibtihel Amara, Co-lead: Samrudhdhi Rangrej, Zahra Vaseqi. Improving AI Education. Leader: Mary Smart, Co-lead: Stefania Druga. Statistical Inference & Applications to Machine Learning. Leader: Lilian Wong, Co-lead: Po-ling Loh. Breakout Session #3 (15.25 - 16.25) IN-PERSON Breakout Sessions Robustness of Machine Learning. Leader: Yao Qin Towards efficient and robust deep learning training. Leader: Wenhan Xia. VIRTUAL Breakout Sessions Machine Learning for Physical Sciences. Leader: Taoli Cheng. Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement. Leader: Haoyu Du, Co-lead: Peiyuan Zhou, Annie Lee, Rainah Khan. Detection of Unseen Classes of different Domains using Computer Vision. Leader: Asra Aslam. PROGRAM PANELISTS IN-PERSON MENTORS VIRTUAL MENTORS POSTERS The program follows the following color scheme: talks , breakout sessions , poster sessions , mentoring sessions , program break , sponsor talks , panel discussion . All invited talk titles, and invited speaker/mentor/panelist names are *clickable*. The majority of the program will be streamed and occur synchronously in-person and virtually, except if marked as in-person/virtual only. You can find the zoom links and livestream on the WiML workshop page of the ICML website . 08:30 Introduction & Opening Remarks , Vinitra Swamy all-day Virtual Sponsor Booths , [DeepMind, D.E. Shaw Research, Home Depot, Microsoft Research] all-day In-Person Sponsor Booths , [DeepMind, Google, QuantumBlack] 08:45 Desiderata for Representation Learning: A Causal Perspective , Yixin Wang [Invited Talk] Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions capture. However, these desiderata are often intuitively defined and challenging to quantify or enforce. In this talk, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions, enabling metrics and algorithms that target efficient, non-spurious, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning. Joint work with Michael Jordan . 09:10 Breakout session [in-person only] Machine learning real-time applications in health (Leaders: Dania Humaidan, Cansu Sen) [hybrid] Introducing geometry awareness in deep networks (Leader: Ankita Shukla) [hybrid] Affective Computing: A Computational Perspective (Leaders: Shreya Ghosh, Garima Sharma) [hybrid] Deep Generative Models for Electronic Health Records (Leaders: Ghadeer Ghosheh) 10:10 Poster Session 10:40 Emma Brunskill [Invited Talk] 11:05 Breakout session [in-person only] Challenges and opportunities in certified auditing of ML models (Leader: Chhavi Yadav) [in-person only] Robustness of Deep Learning Models to Distribution Shift (Leaders: Polina Kirichenko, Shiori Sagawa) [hybrid] Knowledge Distillation through the Lens of the Capacity Gap Problem (Leaders: Ibtihel Amara, Samrudhdhi Rangrej, Zahra Vaseqi) [hybrid] Improving AI Education (Leaders: Mary Smart, Stefania Druga) [hybrid] Statistical Inference & Applications to Machine Learning (Leaders: Lilian Wong, Po-ling Loh) 12:05 Mentoring Roundtables [in-person only] /// Mentoring Panel [virtual only] Table 1: Choosing between academia and industry Amy Zhang & Lauren Gardiner Mentors: Jigyasa Grover , Ciara Pike-Burke, Nika Haghtalab, Po-Ling Loh, Hermina Petric Maretic Table 2: Finding mentors and taking on mentorship roles throughout your career / Celestine Mendler-Dünner & Cyril Zhang Moderator: Sinead Williamson Table 3: Establishing and maintaining collaborations Surbhi Goel & Max Simchowitz Table 4: Work-life Balance Ioana Bica & Kishore Kumar 13:05 Lunch Break, joint with NewInML [in-person only] /// Virtual Sponsor Booths [virtual only] 14:40 Harnessing the power of Hybrid Intelligence, Maria Olivia Lihn [QuantumBlack Sponsor Talk] 14:55 Building embodied agents that can learn from their environments and humans, Kavya Srinet [Meta Platforms Sponsor Talk] 15:10 Machine Learning at Apple, Tatiana Likhomanenko [Apple Sponsor Talk] 15:25 Breakout session [in-person only] Robustness of Machine Learning (Leader: Yao Qin) [in-person only] Distributionally robust Reinforcement Learning (Leaders: Laixi Shi, Mengdi Xu) [hybrid] Machine Learning for Physical Sciences (Leader: Taoli Cheng) [hybrid] Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement (Leaders: Haoyu Du, Peiyuan Zhou, Annie Lee, Rainah Khan) [hybrid] Detection of Unseen Classes of different Domains using Computer Vision (Leader: Asra Aslam) 16:30 Poster Session, joint with LXAI 17:00 Social dynamics in prediction, Celestine Mendler-Dünner [Invited Talk] Abstract: Algorithmic predictions inform consequential decisions, incentivize strategic actions, and motivate precautionary measures. As such, predictions used in societal systems not only describe the world they aim to predict, but they have the power to change it; a prevalent phenomenon often neglected in theories and practices of machine learning. In this talk, I will introduce a risk minimization framework, called performative prediction, that conceptualizes this phenomenon by allowing the predictive model to influence the distribution over future data. This problem formulation elucidates different algorithmic solution concepts, optimization challenges, and offers a new perspective on prediction. In particular, I will discuss how performative prediction allows us to articulate the difference between learning from a population and steering a population through predictions, facilitating an emerging discourse on the topic of power of predictive systems in digital economies. 17:25 Best Practices for Research: Increasing Efficiency and Research Impact, and Navigating Hybrid Collaborations [Panel] Panelists: Amy Zhang , Surbhi Goel , Agni Kumar Moderator: Ioana Bica 18:25 Closing Remarks, Tatjana Chavdarova Note: Please navigate the 'Program' menu in the slidebar at the top to find more details about speakers, panelist and mentors. Self-Similarity Priors: Neural Collages as Differentiable Fractal Representation s Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon [poster] Interpretable Adversarial Attacks using Frank Wolfe Tooba Imtiaz1, Morgan Kohler, Jared Miller, Octavia Camps, Mario Sznaier, Jennifer Dy [poster] Robust task-specific adaption of drug-target interaction models Emma Svensson, Pieter-jan Hoedt, Sepp Hochroiter, Gunter Klambauer [poster] Multi-modal Contrastive Learning with CLOOB Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter [poster] Mimicking Iterative Learning with Modern Hopfield Networks for Tabular Data Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter [poster] A Recurrent Neural Network Model of Travel Direction in Humans Lilian Cheng, Elizabeth R. Chrastil, Jeffrey Krichmar [poster] Automated Deep Lineage Tree Analysis Using Deep Learning with a Bayesian Single Cell Tracking Approach Kristina Ulicna, Giulia Vallardi, Guillaume Charras, Alan R. Lowe [poster] Prostate Cancer Malignancy Detection and Localization From MpMRI Using Auto-Deep Learning: One Step Closer to Clinical Utilization W. ZONG, E. CARVER, S. ZHU , E. SCHAFF, D. CHAPMAN, J. LEE, I. CHETTY, N. WEN [poster] Explaining Structure Activity Relationships Using Locally Faithful Surrogate Models Heta A. Gandhi, Andrew D. White [poster] Affects of Remote Learning on Academic Performance of High School Students Garima Giri, Robert M. Scott, Snigdha Chaturvedi [poster] Fourier-Based Strategies to Explore Ethnic Feature Generation during Visible-to-Thermal Facial Translation (Work-in-progress) Catherine Ordun, Edward Raff, Sanjay Purushotham [poster] Cross-modal contrastive learning of microscopy image and structure-based representations of molecules Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer [poster] CNN-based Emotion Recognition from Multimodal Peripheral Physiological Signals Sowmya Vijayakumar, Ronan Flynn, Peter Corcoran, Niall Murray [poster] Cancer Health Disparity with BERTopic and PyCaret Evaluation Mary Adewunmi, Saksham Kumar Sharma, Nistha Sharma, N Sudha Sharmaa, Bayangmbe Mounmo [poster] Bayesian Optimisation for Active Monitoring of Air Pollution Sigrid Passano Hellan, Christopher G. Lucas and Nigel H. Goddard [poster] Detecting Seen/Unseen Objects with Reducing Response Time for Multimedia Event Processing Asra Aslam [poster] Automated Adaptive Design in Real Time Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth [poster] [Talk] Early Identification of Tuta absoluta in Tomato Plants Using Deep Learning Lilian Mkonyi, Denis Rubanga, Baraka Maiseli, Dina Machuve [poster] Fast and Accurate Method for the Segmentation of Diabetic Foot UlcerImages Rehema Mwawado,Mussa Dida,Baraka Maiseli [poster] Deep Kernel Learning with Personalized Multi-task Gaussian Processes for Longitudinal Prediction in Alzheimer’s Disease Vasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos [poster] Learning to Solve PDE-constrained Inverse Problems with Graph Networks Qingqing Zhao, David Lindell, Gordon Wetzstein [poster] [Talk] Not All Poisons are Created Equal: Robust Training against Data Poisoning Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman [poster] Call for Participation WiML 3rd Un-Workshop @ ICML 2022 [submissions are now closed] The Women in Machine Learning will be organizing the third un-workshop at ICML 2022. 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 traditional workshop format, the un-workshop’s main focus is topical breakout sessions with short invited talks and casual, informal poster presentations. This is an event format to encourage more participant interaction and we are excited to be able to explore this format in-person for the first time! 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 are woman or non-binary are encouraged to submit a one-page proposal to lead a breakout session on a certain research topic. There are many ways to participate, see below! IMPORTANT DATES May 27th, 2022 -- Application Form opens June 17th 19th, 2022 -- Deadline (Anywhere on Earth ) to apply for a breakout session, poster, registration fee funding, facilitating or volunteering June 20th, 2022 -- Notification of acceptance for all of the above (midnight Anywhere on Earth ) July 18th, 2022 -- 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. 1. 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 are women 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. 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. 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. Guidelines for and roles of leaders: Breakout session leaders must be women or nonbinary 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 Leaders should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. 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 would organize the time (1 hour) allocated for a session, as well as some ideas on how you would 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. 2. 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 . The role of facilitators is take notes and encourage participant interactions. 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. Also note that facilitators can be of any gender. 3. 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. 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, we expect to be able to provide spots for everyone to display a physical poster. There are no oral or spotlight presentations, but you will be invited to submit a 5-10 minute video presentation uploaded to a video streaming service. Note that 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. The poster presenter be woman 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. If your poster is not prepared yet, you can submit a one-page abstract, examples of accepted abstracts from previous years can be found here , and advice on writing a one-page abstract can be found here . 4. 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 corresponding section of the application form . Note: 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. 5. Participation instructions: To participate in ANY of the above roles and/or apply for registration fee funding, please fill in the application form by June 17, 2022. 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. 6. Registration fee funding: To apply for funding, you should: (i) be a woman or nonbinary; (ii) be a student, postdoc, or have an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical regions); (iii) 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. 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. Further questions? Check out the FAQs or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS Committee ORGANIZERS ADVISORY SUPER VOLOUNTEERS Archana Vaidheeswaran Women who code 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 ! What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

All events WiML Luncheon @ COLT 2018 Stockholm, Sweden July 9, 2018 12:00 pm — 02:00 pm WiML is hosting a luncheon at COLT 2018 in Stockholm, Sweden. The organizer is Kamalika Chaudhuri. Date: Monday, July 9, 2018, 12pm-2pm Venue: KTH Campus, Stockholm Registration: Register during COLT registration ( www.learningtheory.org/colt2018/ ). If you registered for COLT but did not register for the WiML lunch, check if you can amend your registration to add WiML lunch registration. If not, email Kamalika Chaudhuri (kamalika AT cs DOT ucsd DOT edu) to attend the lunch. If you are not attending COLT but wish to attend the lunch, also email Kamalika. If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org SPONSORS -Platinum- -Diamomd- Previous Next

< Back Sasha Luccioni, PhD WiML Director (2022-2025)