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Saswata Dasgupta
Saswata Dasgupta is a postdoctoral researcher in Chemistry and Biochemistry, having received his PhD in theoretical chemistry at the Ohio State University. He focuses on developing, implementing, and applying an AI-driven computational platform for the discovery and optimization of porous materials capable of capturing atmospheric carbon dioxide and converting it into relevant chemicals.
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Beverly French
Beverly French received her PhD in Marine Biology at UC San Diego and is a postdoctoral fellow at the Scripps Institution of Oceanography. For her postdoctoral work, she uses minimally invasive sampling in combination with large-area imaging to assess the adaptive potentials of corals to a changing ocean. Using machine learning and AI-driven approaches to statistical analysis of genetic sequencing data and the automated segmentation and annotation of coral colonies, Beverly’s work will illuminate the mechanisms of coral environmental resilience in response to climate change. -
Jessica Kendall-Bar
Jessica Kendall-Bar obtained her PhD in ecology and evolutionary biology from UC Santa Cruz and is a postdoctoral researcher at the Center for Marine Biotechnology & Biomedicine at the Scripps Institution of Oceanography. Her approach to her work combines engineering, visualization, and comptation to learn how the brain operates at sea. The focus of her current research is to investigate EEG/ECG patterns during diving to identify and categorize healthy baseline EEG patterns as well as screen for neuropathology, including abnormalities associated with hypoxia and decompression sickness. -
Stephanie Khuu
Stephanie Khuu is a post-doctoral researcher in the Bioengineering department. She completed her PhD in bioengineering from the University of Auckland, and is interested in computational biology and skeletal muscle physiology. Her current project focuses on mechanobiological modeling of skeletal muscle repair and regeneration. Using novel applications of physics-guided deep learning to systems biology, she aims to discover new pathways, molecular interactions, and cell-cell interaction rules that mediate the transition of tissue states in response to exercise. -
Tsz Wai Ko
Tsz Wai Ko obtained his PhD in theoretical chemistry at the Georg-August-Universität Göttingen, and is a postdoctoral researcher in NanoEngineering. His work as a Schmidt AI in Science Postdoc integrates physics, chemistry, and engineering methods to develop a novel machine learning tool that will accelerate the screening of potential candidates for next-generation rechargeable lithium-ion batteries. His ultimate goal is to accelerate the discovery of an energy storage technology that promises to both be safer and achieve better capacity than traditional lithium-ion batteries. -
Neeraj Lal
Neeraj Lal completed his PhD in biochemistry and molecular biology from the University of California, Davis. He is a postdoctoral researcher in the Division of Endocrinology and is working to develop machine learning algorithms to understand the relationship between metabolism, neuronal activity and animal behavior, with the ultimate goal of finding new drug targets for metabolic ailments. -
Phuong Le
Phuong Le received her PhD in nanotechnology and single-molecule imaging from the University of Illinois at Urbana-Champaign. As a postdoctoral scholar at UC San Diego, she is working to apply machine learning and decipher the rules of RNA localization in motor neurons derived from human induced pluripotent stem cells for application in RNA drug design. -
Melissa Quinnan
Melissa Quinnan is a postdoctoral researcher in the Department of Physics, having received her PhD in physics at UC Santa Barbara. She works with the Compact Muon Solenoid experiment at the CERN Large Hadron Collider; her research interests include applying deep learning tools to experimental particle physics searches and measurements and hardware-accelerated fast machine learning for CMS detector triggers. For her postdoctoral fellowship, she plans to develop and deploy a real-time anomaly detection algorithm in the CMS trigger using deep learning in field programmable gate arrays. -
Lauren Severance
Lauren Severance is a postdoctoral fellow in the Department of Bioengineering, where she also obtained her PhD. Her project focuses on developing a novel method for estimating personalized risk of stroke by using AI techniques in conjunction with 4-dimensional image data, with the aim of improving stroke risk stratification and guiding anticoagulation therapy decisions for patients with atrial fibrillation.
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William (Jake) Wright
William (Jake) Wright earned his PhD in neuroscience from the University of Pittsburgh. As a postdoctoral scholar in Neurobiology, Jake is utilizing cutting-edge imaging methods and optical tools to investigate how dynamic activity patterns drive the modification of synaptic connections over the course of learning to reorganize brain function and ultimately determine behavioral adaptation. Specifically, his research will illuminate how the relationship between circuit and synaptic function serves to direct plasticity and reshape brain function over the course of learning.
2023-2025
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Caitlin Aamodt
Caitlin Aamodt is a postdoctoral scholar in the Department of Pediatrics who uses induced pluripotent stem cell-derived neurons and organoids to characterize the molecular mechanisms dysregulated in autism spectrum disorder. The goal of this work is to use computational tools and machine learning to understand the connections between pharmacogenetics, regulatory mechanisms, and individual phenotypes. Caitlin's doctoral research at UCLA focused on using songbirds to discover novel candidate therapeutics for speech and language deficits. Caitlin's current work includes a continuation of this project in human cell models as well as studying the effects of prenatal SSRI exposure on histone serotonylation in ASD. -
Avik Biswas
Avik Biswas obtained his PhD in Physics at the Center for Biophysics and Computational Biology, Temple University, Philadelphia. As a postdoctoral fellow, Avik is using physics-based machine learning and AI models combined with cryogenic electron microscopy (cryo-EM) to uncover the temporal ordering pathways and mechanisms through which drug resistance evolves in HIV and other viruses, based on viral protein sequence data collected from drug-experienced patients. Avik ultimately hopes to develop the methodology to preemptively predict the emergence of drug-resistant mutations against new and emerging drugs in the clinic. -
Carla Calvo-Tusell
Carla Calvó-Tusell received a PhD in Chemistry from the University of Girona. As a postdoctoral scholar at UC San Diego, Carla works on computational modeling of biochemical processes, with special focus on studying respiratory viruses in aerosols through the lens of a computational microscope. The objective is to use all-atom molecular dynamics simulations and artificial intelligence to understand the activity of new virus variants based on their dynamical behavior in the aerosol environment. Carla's interests also include working toward a more inclusive environment in science. -
Marcelo Caparotta
Marcelo Caparotta is a postdoctoral scholar at the Skaggs School of Pharmacy & Pharmaceutical Sciences. Previously, he served as a postdoctoral associate at the University of Florida's Department of Chemistry and Quantum Theory Project, where he developed enhanced sampling techniques for molecular dynamics simulations. Hailing from Argentina, he attained his PhD in Science and Technology in 2022 from Universidad Nacional de Cuyo. Marcelo's research now revolves around cutting-edge drug discovery utilizing generative AI. His new work focuses on advancing AI's role in ligand discovery by creating data-driven, physics-based methods for predicting affinities and crafting targeted ligands with desired pharmaceutical properties. -
Alexander Gillert
Alexander Gillert received his bachelor’s and master’s degrees in Computer Science at the Technical University Munich and completed his PhD in Computer Vision at the Fraunhofer Institute for Computer Graphics Research (IGD). At present, he is developing and applying AI-based methods for the analysis of ecological image data. His research areas include estimating turnover and lifespans of plant roots with minirhizotrons for insights into global carbon cycle dynamics. He is also measuring wood anatomical parameters in microscopic shrub cross sections, which are often the only source of environmental records for climate reconstruction in harsh and remote regions, such as the Arctic. -
Yu Liang
Yu Liang is a postdoctoral fellow at the Scripps Institution of Oceanography. He completed his PhD in the Department of Earth and Planetary Sciences at Yale. His work has focused on several key elements of the tropical air-sea system, including El Niño-Southern Oscillation, Madden-Julian Oscillation and tropical cyclones. By using AI-based methods, he aims to improve the subseasonal prediction skill of extreme events, including heatwaves and extreme precipitation. He also works on better understanding ENSO dynamics and improving the ENSO prediction using machine learning. -
Joseph Walker
Joseph Walker received his PhD in Applied Ocean Science at the Scripps Institution of Oceanography (SIO). For his postdoctoral work at SIO, he is working to develop machine learning algorithms for signal detection and classification in underwater passive acoustic recordings, primarily for real-time processing for autonomous platforms. -
Yao Yu
Yao Yu received her PhD in Earth Sciences at the Scripps Institution of Oceanography. Her research focuses on using satellite altimetry data to observe and understand small-scale ocean dynamics. Her postdoctoral research focuses on the Surface Water and Ocean Topography (SWOT) satellite mission, with the aim of using machine learning algorithms to improve marine gravity field and produce high-resolution time-variable mean sea surface maps. -
Keming Zhang
Keming Zhang is a postdoctoral researcher in the Department of Astronomy and Astrophysics, having recently received his PhD in Astrophysics from the University of California, Berkeley. His research focuses on gravitational microlensing for the detection of extrasolar planets, with an emphasis on the use of machine learning and simulation-based inference techniques to advance event modeling and theoretical studies in this field. -
Xiaoyu Zhao
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.