Soumith Chintala is a Scientist-Engineer focused on AI and Robotics, leading influential AI work such as PyTorch, DCGAN and Torch-7; work which is used by several top institutions including NASA, Meta, Google, Tesla, Microsoft, Disney, Genentech, and numerous other Fortune-500 companies and in the curriculum of top-ranked universities such as Stanford, Harvard, Oxford and MIT. He currently leads PyTorch and other AI projects at Meta, is a Visiting Professor at New York University, and maintains advisory roles at various institutions.

Lucía Magis-Weinberg, MD, PhD, is an Assistant Professor in Psychology at the University of Washington. She leads the interACTlab (International Adolescent Connection and Technology Laboratory), focusing on studying how social media impacts adolescent development and mental health. Her lab also designs and evaluates school interventions to promote healthy digital habits. Her work has mostly focused on underserved adolescents in global settings, particularly in Latin America, and is currently funded by the National Institute of Mental Health. Dr. Magis-Weinberg received her MD from the National Autonomous University of México, her MSc in Cognitive Neuroscience and PhD in Experimental Psychology (Developmental) from University College London. Before joining the University of Washington, she conducted research at the Institute of Human Development at University of California, Berkeley, as a postdoctoral scholar. As a longstanding member of the Society for Research in Adolescence’s International Committee, Dr. Magis-Weinberg advocates for inclusive, global research on adolescent development. Committed to science communication, outreach and impact, she is Executive Editor of Neuromexico.org a leading science communication platform for Latin America. Dr. Magis-Weinberg is a member of the American Psychological Association Expert Advisory Panel on Social Media Use in Adolescence.

Prof Vukosi Marivate is an Associate Professor of Computer Science and holds the ABSA UP Chair of Data Science at the University of Pretoria. He specialises in developing Machine Learning (ML) and Artificial Intelligence (AI) methods to extract insights from data, with a particular focus on the intersection of ML/AI and Natural Language Processing (NLP). His research is dedicated to improving the methods, tools and availability of data for local or low-resource languages. As the leader of the Data Science for Social Impact research group in the Computer Science department, Vukosi is interested in using data science to solve social challenges. He has worked on projects related to science, energy, public safety, and utilities, among others. Prof Marivate is a co-founder of Lelapa AI, an African startup focused on AI for Africans by Africans. Vukosi is co-founder and advisor to Masakhane Research Foundation, which aims to develop NLP technologies for African languages. Vukosi is also a co-founder of the Deep Learning Indaba, the leading grassroots Machine Learning and Artificial Intelligence conference on the African continent that aims to empower and support African researchers and practitioners in the field.

Javier Duarte is an Associate Professor of Physics at UC San Diego and a member of the CMS experiment at the CERN Large Hadron Collider. He leads a research group developing new artificial intelligence (AI) techniques for high-energy particle collisions to better measure the properties and interactions of elementary particles, like the Higgs boson, and search for new physics. Before joining UC San Diego, he was a Lederman postdoctoral fellow at Fermilab and received his Ph.D. in Physics at Caltech and his B.S. in Physics and Mathematics at MIT. Prof. Duarte has received the APS Henry Primakoff Award for Early-Career Particle Physics, Sloan Research Fellowship, RCSA Cottrell Scholar Award, DOE Early Career Award, and is a co-PI of the NSF HDR Institute for Accelerated AI Algorithms for Data-Driven Discovery (A3D3).

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.