WiML Social @ ICLR 2026
Rio de Janeiro
April 24, 2026
12:00 PM – 3:00 PM
Date: April 24, 2026
Time: 12:00 PM – 3:00 PM
Location: Room 203C, Centro de Convenções / Convention Center
The Women in Machine Learning (WiML) Social at ICLR 2026 is an opportunity to connect with members of the WiML community in an informal and welcoming setting. The event will bring together researchers and practitioners from academia and industry to foster meaningful conversations, exchange experiences, and build new connections.
This year’s social will feature a panel discussion on:
There’s no single path: Navigating careers in academia, industry, and beyond.
The panel will explore different career trajectories in machine learning, highlighting transitions across academia, industry, and other paths. Panelists will share their experiences, challenges, and perspectives on building a career in ML, followed by an open discussion with the audience.
Program
12:00 PM – 12:10 PM Opening Remarks
12:10 PM – 12:30 PM Icebreaker Game
12:30 PM – 1:45 PM Networking & Lunch
1:45 PM – 2:45 PM Panel Discussion
2:50 PM – 2:55 PM Closing Remarks
Panelists
Aleksandra Faust
Aleksandra Faust is a Director of Research at Google DeepMind, where she leads Frontier AI Health efforts. Her research focuses on foundation models and world models for complex adaptive systems, treating the AI design pipeline as a learnable, sequential, and self-improving decision-making process. This methodology has driven state-of-the-art improvements across drug discovery, robotics, autonomous driving, and web agents, and led to her founding the field of Automated Reinforcement Learning (AutoRL). Notably, she co-authored the seminal "Levels of AGI" framework and led the Gemini Self-improvement research team, developing the reinforcement learning methods behind the Gemini model family. Previously, Aleksandra served as Chief AI Officer at Genesis Molecular AI and held foundational leadership roles at Google Brain, Google Robotics, and Waymo/X. Earlier in her career, she was a Senior R&D Engineer at Sandia National Laboratories. Faust holds a Ph.D. in Computer Science with distinction from the University of New Mexico and an M.S. from the University of Illinois at Urbana-Champaign. She is an IEEE Fellow and a recipient of the IEEE RAS Early Career Award for Industry and the Tom L. Popejoy Dissertation Award, and was named a Distinguished Alumna of the UNM School of Engineering. Her work has been featured in The New York Times, The Economist, and Forbes, and has received multiple Best Paper Awards at premier robotics, machine learning, and systems architecture venues.
Franziska Boenisch
Franziska Boenisch is a tenure-track faculty at the CISPA Helmholtz Center for Information Security, where she co-leads the SprintML lab. Her research focuses on private and trustworthy machine learning; during her Ph.D. at Freie Universität Berlin and Fraunhofer AISEC she pioneered the notion of individualized privacy in ML. Before joining CISPA, she was a Postdoctoral Fellow at the University of Toronto and the Vector Institute. She received an ERC Starting Grant in 2025 for research on privacy in foundation models and has been recognized with the Fraunhofer ICT Dissertation Award (2023), a GI Junior Fellowship (2024), and a Werner‑von‑Siemens Fellowship (2025).
https://franziska-boenisch.de/
Noa Garcia
Noa Garcia is an Associate Professor at the Institute for Advanced Co-Creation Studies and D3 Center, The University of Osaka (Japan). She earned her Ph.D. in Computer Science from Aston University (UK), specializing in multimodal retrieval and instance-level recognition. She moved to Japan in 2018 as a postdoc, and has been conducting research at The University of Osaka since then. Her research sits at the intersection of computer vision, natural language processing, fairness, and art. Her recent work includes investigating demographic bias in computer vision, analyzing visual datasets, and exploring how generative models can reinforce social stereotypes.
Valentina Pyatkin
Valentina Pyatkin is a postdoctoral researcher at the Allen Institute for AI and the University of Washington. Additionally, she is affiliated with the ETH AI Center, where she mentors students and works on post-training for the Swiss AI Initiative. She obtained her PhD in Computer Science from Bar Ilan University. Her work has been awarded an ACL Outstanding Paper Award and the ACL Best Theme Paper Award, and has been supported by a Schmidt Sciences Postdoctoral Award. During her doctoral studies, she conducted research internships at Google and the Allen Institute for AI, where she received the AI2 Outstanding Intern of the Year Award.
https://valentinapy.github.io/
Moderator
Karen Ullrich
Karen is a research scientist at FAIR NY and she is actively collaborating with
researchers from the Vector Institute and the University of Amsterdam. Her main research focus lies in the intersection of information theory and probabilistic machine learning / deep learning. She completed her PhD under the supervision of Prof. Max Welling. Prior to that, she worked at the Austrian Research Institute for AI, Intelligent Music Processing and Machine Learning Group lead by Prof. Gerhard Widmer. https://karenullrich.info/
WiML Social Organizers
Ivona Najdenkoska
Ivona is a postdoctoral researcher at the University of Amsterdam. Her research focuses on multimodal foundation models and generative AI, with an emphasis on scalable vision–language understanding and generation. She also works on AI-generated image detection, developing robust methods for distinguishing synthetic from real content. She recently completed her PhD at the University of Amsterdam under the supervision of Marcel Worring and Yuki Asano, where her work focused on learning from context with multimodal foundation models. During her PhD, she spent time at Meta working on context-aware image generation. She is also a member of the ELLIS Society, and her research has been published at leading AI conferences, such as ICLR and ECCV.
Paula Feldman
Paula is a postdoctoral researcher working at the intersection of AI and medical imaging at Weill Cornell Medicine. Her work focuses on generative AI and cardiovascular applications, collaborating closely with clinical teams as part of the group led by Mert Sabuncu. She completed her PhD at Universidad Torcuato Di Tella and Universidad Nacional del Sur under the supervision of Emmanuel Iarussi and Claudio Delrieux, where her research focused on deep generative modeling of 3D vascular structures. Her research has been published in leading conferences and journals in the field, including venues such as MICCAI and the journal Medical Image Analysis. More broadly, she is interested in multimodal learning and synthetic data generation.
Mila Soares de Oliveira de Souza
Mila de Oliveira is a research software engineer currently working on AI applications to address public health challenges in Brazil. She completed her MSc in Electrical Engineering (Computer Vision) at the Universidade Federal do Rio de Janeiro, where she developed baseline methodologies for real-time detection of breeding sites of Aedes aegypti (primary vector of dengue, the most pressing public health threat in Brazil) under the supervision of Eduardo Antonio Barros da Silva and Sergio Lima Netto. She has extensive experience in software engineering for AI products in industry, having previously worked at Apple and Microsoft Advanced Technology Labs. Her current interests include GPU programming, machine learning compilers and formal verification of algorithms using proof assistants, as well as broader applications of ML in healthcare and science.
We welcome all ICLR attendees to join us for an afternoon of discussion, networking, and community building.
Additionally, we are collecting CVs to share with our sponsors who are actively recruiting: 👉 https://forms.gle/4FasmbmiLBh5zqyu6
Thanks to our sponsors
