Yasmin Kassim is a Schmidt AI in Science Fellow and postdoctoral researcher at UC San Diego specializing in biomedical image processing, computer vision, and deep learning. She previously worked at the NIH and Akoya Biosciences and earned a PhD in Computer Engineering from the University of Missouri–Columbia, receiving the 2017 Outstanding PhD Student Award (EECS). Across academia and industry, she builds rigorous AI tools and foundation models that convert images into quantitative insight and scalable measurements, reflected in awards and a strong publication record. Looking ahead with Schmidt, she will channel this momentum into next-generation AI models and tools for organelle analysis and dynamics. Dr. Kassim's STEM Mentor is Uri Manor, and her AI Co-Mentor is Marc Niethammer.

Uri Manor received his Ph.D. with Dr. Bechara Kachar in the Cellular, Molecular, Developmental Biology and Biophysics program at Johns Hopkins University-NIH Graduate Partnership Program and conducted his postdoctoral work in the Lippincott-Schwartz lab at the NIH. He then joined the Salk Institute for Biological Studies as Director of the Waitt Advanced Biophotonics Core in 2016, where he was the recipient of the Chan-Zuckerberg Imaging Scientist Award. He then joined the UC San Diego faculty in 2023 as an Assistant Professor and Director of the Goeddel Family Technology Sandbox.

Xiaoyu Zhao completed her PhD in cancer genomics at Stony Brook University. As a postdoctoral scholar in the Department of Medicine at UC San Diego, she is deeply interested in understanding the complex biological systems and human diseases by integrating genome editing technologies and artificial intelligence (AI) models. Her current focus is on developing deep learning models aimed at improving variant interpretation in the context of drug responses and disease progression. Dr. Zhao's STEM Mentor is Trey Ideker and her AI Co-Mentor is Vineet Bafna.

Eleonora Rachtman earned her PhD in Bioinformatics and Systems Biology from UC San Diego and is now a postdoctoral scholar in Electrical and Computer Engineering. She develops algorithms to analyze big genomic datasets and infer evolutionary relationships between species. Her postdoctoral work focuses on incorporating deep learning into phylogenomic analysis, specifically by developing machine learning algorithms to add new species to large phylogenies without reconstructing them from scratch. This approach enhances existing binning and species identification techniques and aims to address challenges in bacterial and viral metagenomics, improving pathogen detection, understanding microbial diversity, and unraveling evolutionary dynamics of various species. Dr. Rachtman's STEM and AI Mentors are Siavash Mirarab & Davey Smith.

Konstantinos D. Polyzos is a Postdoctoral Fellow at the Electrical and Computer Engineering department (ECE) at University of California San Diego having recently obtained his Ph.D at the ECE department at the University of Minnesota. His research focuses on learning, inferring and optimizing with just a few data. Specifically, he has been developing and leveraging active-, transfer-, and self-supervised learning and Bayesian optimization methods to learn and/or optimize when only a few input-output data are available due to privacy concerns or high sampling costs, with application to healthcare, 5G networks and robotics. Dr. Polyzos' STEM and AI Mentor is Tara Javidi.

James Zou is an associate professor of Biomedical Data Science, CS and EE at Stanford University. He works on developing cutting-edge AI for biomedical applications. His group developed many widely used innovations including EchoNet AI (FDA cleared for assessing cardiac function), Gradio (used by over a million developers), and SyntheMol (NY Times 2024 Good Tech). He has received the Overton Prize, Sloan Fellowship, NSF CAREER Award, two Chan-Zuckerberg Investigator Awards, a Top Ten Clinical Achievement Award, best paper awards at ICML and other AI conferences, and faculty awards from Google, Amazon, Adobe and Apple.

As a senior manager (AI/ML) of computational imaging group at Biohub, I develop and apply cutting-edge technologies that combine optics, inverse algorithms, and machine learning to reveal the dynamics of cells with increasing precision, resolution, and throughput. I have over 15 years of experience in computational imaging, spanning from signal processing to deep learning. My team's mission is to enable and facilitate collaborative research that addresses fundamental and translational questions in biology and biomedicine. I also teach and mentor the next generation of scientists and engineers through special topics courses at the Marine Biological Laboratory and open source projects on GitHub.

Rose Yu is an associate professor at UC San Diego department of Computer Science and Engineering and Amazon Scholar. She is a primary faculty with the AI Group.

Her research interests lie primarily in machine learning, especially for large-scale spatiotemporal data. She is particularly excited about AI for scientific discovery. She has won Presidential Early Career Award for Scientists and Engineers (PECASE), DARPA Young Faculty Award, ECASE Award, NSF CAREER Award, Hellman Fellowship, Faculty Awards from Sony, JP Morgan, Meta, Google, Amazon, and Adobe, several Best Paper Awards, Best Dissertation Award at USC. She was named as MIT Technology Review Innovators Under 35 in AI.

David Van Valen is an Assistant Professor in the Division of Biology and Bioengineering at Caltech. Before becoming faculty, he studied mathematics (B.S. 2003) and physics (B.S. 2003) at the Massachusetts Institute of Technology, applied physics (Ph.D. 2011) at Caltech, medicine (M.D. 2013) at UCLA, and bioengineering as a postdoctoral fellow at Stanford University. At Caltech, his research group develops new technologies at the intersection of imaging, genomics, and machine learning to produce quantitative measurements of living systems with single-cell resolution. David is the recipient of several awards, including a Hertz Graduate Fellowship (2005), a Rita Allen Scholar award (2020), A Pew-Stewart Cancer Research Scholar award (2021), a Heritage Medical Research Investigator award (2021), a Moore Inventor Fellowship (2021), and the NIH New Innovator award (2022).

A. Gilad Kusne received his B.S. and Ph.D. degrees from Carnegie Mellon University. He is a Staff Scientist with the National Institute of Standards and Technology (NIST), Gaithersburg, Maryland and a Fellow of the American Physical Society. His research is part of the White House’s Materials Genome Initiative at NIST, a project which aims to integrate experiment, computation, and theory to accelerate research discoveries to market. He leads machine learning teams of cross-disciplinary efforts to build autonomous research platforms, with the goal of advancing solid state, soft, and biological devices. For these systems, machine learning performs experiment design, execution (in the lab and in silico), and analysis. For his work, he has been awarded the NIST Bronze Award twice (top NIST award). He is also a founder and organizer of the annual Machine Learning for Materials Research Bootcamp and Workshop—educating next generation and mid-career researchers in machine learning.

Ulugbek S. Kamilov is the Leon and Elizabeth Janssen Associate Professor of Electrical and Computer Engineering (ECE) at the University of Wisconsin–Madison, where he founded and leads the Computational Imaging Group (CIG). He received his BSc/MSc in Communication Systems in 2011 and his PhD in Electrical Engineering in 2015 from EPFL, Switzerland. Prior to joining UW–Madison, he held academic and research appointments as the Donald L. Snyder Associate Professor at Washington University in St. Louis, Visiting Professor at École Normale Supérieure in Paris, Visiting Faculty Researcher at Google, and Research Scientist at Mitsubishi Electric Research Laboratories (MERL).

Prof. Kamilov is a recipient of the IEEE Signal Processing Society’s 2024 Pierre-Simon Laplace Early Career Technical Achievement Award, the IEEE Signal Processing Society 2017 Best Paper Award, and the NSF CAREER Award. He was named a Scialog Fellow for Advancing Bioimaging in 2021 and was a finalist for the EPFL Doctorate Award in 2016. In recognition of his teaching, he received the Outstanding Teaching Award from WashU’s Department of Electrical & Systems Engineering in 2023. He currently serves on the IEEE Signal Processing Society’s Computational Imaging Technical Committee, and has previously served as a Senior Editorial Board Member of IEEE Signal Processing Magazine, Associate Editor of IEEE Transactions on Computational Imaging.

Tara Javidi is Jacobs Family Scholar and Professor of Electrical and Computer Engineering at UCSD. Besides being an active member of Center for Wireless Communications (CWC), Tara is a founding co-director of the Center for Machine-Intelligence, Computing, and Security (MICS) and a Faculty Fellow of Halıcıoğlu Data Science Institute.

Tara Javidi’s research interests are in theory of active learning, information theory, and stochastic optimization and their applications to wireless communications and communication network design. She is a Fellow of IEEE, a Distinguished Lecturer of both IEEE Information Theory (2017/18) and Communications (2019/20) Societies, and a member of the Board of Governors of the IEEE Information Theory Society (2017/18/19-2020/21/22). She and her PhD students are recipients of the 2021 IEEE Communications Society & Information Theory Society Joint Paper Award. She also received the 2018 and 2019 Qualcomm Faculty Award for her contributions to wireless technology.