Osama Raad Rahim Al-Azzawi

Osama Alazzawi received his PhD in Structural Engineering from Huazhong University of Science and Technology. He is a Schmidt AI in Science Postdoctoral Fellow at UC San Diego, where his research focuses on AI-driven structural health monitoring, deep learning, signal processing, and diagnostics for civil infrastructure. He specializes in developing hybrid machine learning models and vibration- and EMI-based techniques for real-time damage detection in complex systems, including bridges and multi-story frame structures. At UCSD, he aims to bridge physics-based modeling with advanced AI to build deployable diagnostic frameworks for resilient, intelligent infrastructure.

Adela DePavia

Adela DePavia is a Schmidt AI in Science Postdoctoral Fellow at UC San Diego, where she researches the intersection of optimization and machine learning. She received her PhD in Computational and Applied Mathematics from the University of Chicago. During her postdoctoral studies, hosted at the Halıcıoğlu Data Science Institute, she applies novel geometric insights from AI problems to (1) design efficient optimization algorithms for large-scale applications, and (2) develop new theory to better understand how optimization algorithms shape modern AI models. Dr. DePavia's STEM mentors are Dmitriy Drusvyatskiy and Mikhail Belkin, and her AI co-mentor is Virginia de Sa. 

Harsha Gouda

Jiachen Li

Jiachen Li obtained his Ph.D. in Materials Science and Engineering at Stanford University, and later served as a postdoctoral scholar in Chemistry at Northwestern University. Jiachen’s research in Chemical Engineering and Data Science at the University of California San Diego focuses on developing knowledge-informed deep learning algorithms for guided electrosynthesis, enabling precise electrocatalytic conversion of small molecules from wastewater and abundant resources into valuable fuels and chemicals for applications in energy, chemical manufacturing, food, and pharmaceuticals.Jiachen’s STEM mentor is David Fenning and his AI co-mentor is Yusu Wang.

Keivan Rahmani

Department: Nanoengineering

Marko Ristić

Marko Ristic focuses on studying neutron star mergers and the neutron-rich material that they eject. He develops pioneering post-merger environment simulations and machine learning emulators to study kilonovae, the radioactively-powered electromagnetic transients following neutron star mergers. Marko aims to help answer questions like "How are the heavy elements, like gold and platinum, made in our Universe?" and "How does matter behave at extreme densities and temperatures?" He is also applying his skills and methodologies to the broader class of radiative transfer problems and leveraging his emulators for optimized simulation placement of computationally expensive models. Marko’s STEM mentor is Floor Broekgaarden and his AI co-mentor is Rose Yu.

Ulises Rosas Puchuri

Ulises Rosas-Puchuri is originally from Peru, and earned his PhD in Ecology and Evolutionary Biology from the University of Oklahoma. His postdoctoral research focuses on extending standard machine learning models to account for the evolutionary history of species. He plans to use these models to better understand the genomes of multiple species within an evolutionary context. Ulises' STEM mentor is Kimberly Cooper, and his AI co-mentor is Siavash Mirarab.

Vikrant Tripathy

Vikrant Tripathy earned his Ph.D. in Chemistry from Indiana University, specializing in physical chemistry and scientific computing. As a postdoctoral fellow, he leverages the computational power of graphics processing units (GPUs) to develop advanced methods in quantum chemistry (QM) and machine-learned many-body potentials, enabling highly accurate simulations of biomolecules. His research aims to develop a novel AI-driven framework that integrates high-quality physics-based computational methods to accelerate the identification of promising drug candidates for therapeutic targets.

Nojan Sheybani

Department: Electrical and Computer Engineering