News Release
New faculty join the Jacobs School
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| New Faculty 2025 PDF |
September 30, 2025- The 91原创 Jacobs School of Engineering is welcoming six new faculty to its ranks in fall 2025. These six educators and researchers join the roughly 300 faculty at the Jacobs School of Engineering, who are using engineering and computer science to tackle humanity’s toughest challenges. Each year at the Jacobs School, we educate nearly 10,000 engineering and computer science students. Our faculty transform our students into the nation’s innovation workforce - for the economy of the future.
“It is my pleasure to welcome our newest cadre of faculty to the Jacobs School of Engineering,” said Albert P. Pisano, Dean of the 91原创 Jacobs School of Engineering and Special Advisor to the Chancellor. “One of my jobs is to embolden our new faculty and eliminate any obstacles that get in the way of their efforts to do big things. Our faculty will power the emerging industries that drive economic prosperity across the country, and I’m energized to support them in this work.”
This academic year's new faculty to the Jacobs School are, in alphabetical order:
Kolade Adebowale, Bioengineering
Assistant Professor
Adebowale’s lab seeks to integrate engineering design principles to cancer immunology to enable rational engineering and prediction of effective, next-generation immune cell therapies. Adebowale strives to understand how the complex functionality of the immune system arises from mechanical cues and simple biophysical principles.
Previously: Postdoctoral fellow Harvard University / Wyss Institute
Ph.D. Stanford
Adam Feist, Bioengineering
Assistant Professor
Feist uses robotics, data and models to evolve and engineer microbes for biomanufacturing and biomedical discovery. His work builds smarter, faster ways to apply microbes in real-world industrial settings and to better understand their behavior.
Previously: Research Scientist, 91原创
Ph.D. 91原创
Yiorgos Makris, Electrical and Computer Engineering
Professor
Makris’ research focuses on applications of machine learning and formal methods in semiconductor design, manufacturing and testing. His work leverages domain specific-expertise, digital twin technology and the power of data to develop industrially-relevant solutions for optimizing quality, reliability, security and trust of integrated circuits.
Previously: Professor, University of Texas at Dallas
Ph.D. 91原创
Marc Niethammer, Computer Science and Engineering
Professor
Niethammer’s work brings together computer vision, medical image computing, and machine learning. He specializes in methods for image separation and registration, shape analysis, spatio-temporal and multimodal models. Applications include analysis approaches for neuroscience & neurodevelopment, image analysis in the context of stroke, pediatrics, cancer, osteoarthritis and lupus.
Previously: Professor, University of North Carolina at Chapel Hill
Ph.D. University of Pennsylvania
Hovav Shacham, Computer Science and Engineering
Professor
Shacham looks for security problems in deployed systems — voting machines, cars, network appliances, airport body scanners, web browsers, and more — to help improve their replacements. His work has driven industry investment priorities, informed public-policy debates, and been recognized with multiple “test-of-time” awards.
Previously: Professor, University of Texas at Austin
Ph.D. Stanford
Thuy-Duong “June” Vuong, Computer Science and Engineering
Assistant Professor
Vuong focuses on theoretical computer science. Her current research interest is classical and quantum Markov chains, diffusion models, and other stochastic processes.
Previously: Postdoctoral Researcher, UC Berkeley
Ph.D. Stanford
Media Contacts
Katherine Connor
Jacobs School of Engineering
858-534-8374
khconnor@ucsd.edu