Shekoofeh Azizi is a staff research scientist and a research lead at Google DeepMind. Her research is focused on developing approaches for the translation of AI solutions into tangible clinical impact, such as designing foundation models for biomedical applications. She has led the moonshot project behind Med-PaLM and Med-PaLM 2, Google's flagship medical large language models, as well as the Med-PaLM M the first demonstration of a generalist biomedical AI. Her research has been covered in various media outlets and recognized by multiple awards including the Governor General’s Canada Academic Gold Medal for contributions in improving diagnostic ultrasound. 

Sandra Avila is an Assistant Professor and Research Scientist in the Institute of Computing at the University of Campinas (UNICAMP), Brazil. She is also a faculty member of the recod.ai (Artificial Intelligence Lab). She completed her Ph.D in Computer Science from 

Sorbonne Université (UPMC Paris 6, France) & Federal University of Minas Gerais (UFMG, Brazil).  Her research interests lie in Machine Learning, Computer Vision, Image Processing, and Pattern Recognition. She is awarded the Top 2% of most-cited researchers in the world according to the Stanford University Elsevier list; the  Human Rights Academic Recognition Award in association with UNICAMP-Vladimir Herzog Institute; and the Google Awards for Inclusion Research.

Boyi Li is a researcher at NVIDIA Research and a postdoctoral scholar at UC Berkeley, advised by Prof. Jitendra Malik and Prof. Trevor Darrell. She received her Ph.D. at Cornell University, advised by Prof. Serge Belongie and Prof. Kilian Q. Weinberger. Her research interest lies in machine learning and multimodal systems. She aims to develop generalizable algorithms and interactive intelligent systems, with an emphasis on large language models, generative models, and robotics. To accomplish this, she works on aligning representations from multimodal data, specifically vision and language, to enhance, redefine, and extend the capabilities of intelligent systems in perceiving, understanding, and interacting with the world in a human-like manner. 

Guoying Zhao holds prestigious positions as an Academy Professor at Academy Finland and the University of Oulu, and as a visiting professor at Aalto and Stanford Universities. She has progressed from a senior researcher to a full professor at CMVS, University of Oulu, earning her Ph.D. from the Chinese Academy of Sciences in 2005. Recognized for her contributions, she was elected to the Finnish Academy of Sciences and Letters and Academia Europaea, and has authored over 310 papers with more than 25,570 citations. Zhao has played significant roles in academic conferences and has received numerous accolades, such as the Fellow of IEEE, IAPR, ELLIS, and AAIA. Her research spans image and video analysis, facial and motion recognition, and multi-modal learning, impacting both academia and industry.

Kate Saenko is a Full Professor of Computer Science at Boston University (currently on leave) and AI Research Scientist at FAIR Labs, Meta. She leads the Computer Vision and Learning Group at BU, is the founder and co-director of the Artificial Intelligence Research (AIR) initiative, and member of the Image and Video Computing research group. Previously she was a consulting professor for the MIT-IBM Watson AI Lab. Kate received a PhD from MIT and did her postdoctoral training at UC Berkeley and Harvard. Her research interests are in the broad area of Artificial Intelligence with a focus on dataset bias, adaptive machine learning, learning for image and language understanding, and deep learning.

Cornelia Fermüller cofounded the Autonomy Cognition and Robotics (ARC)  Lab and co-lead the Perception and Robotics Group at the University of Maryland. She is the principal investigator for an NSF-sponsored Science of Learning Center Network for Neuromorphic Engineering and co-organizes the Neuromorphic Engineering and Cognition Workshop. Her research spans Computer Vision, Robotics, and Human Vision, focusing on biologically-inspired solutions for active vision systems, including work on robot vision for collaborative robots and fast active robots using bio-inspired sensors. Fermüller's work integrates perception, action, and reasoning to advance robot capabilities, notably in interpreting human actions and enhancing motion processing for robots like drones.