Reza is an Assistant Professor in the Department of Neurology and the Department of Bioengineering and Therapeutic Sciences at UCSF. He is a core faculty member at the UCSF Neuroscape Center, a Weill Neurohub Investigator, and the Director of Data Analytics and Visualization at the UCSF Weill Institute for Neuroscience.
Before joining UCSF, Reza was a scientist at the Allen Institute for Brain Science in Seattle. He completed his PhD and MSc in Electrical Engineering and Computer Sciences at UC Berkeley in 2018, where he worked with Bin Yu and Jack Gallant to develop interpretable machine learning tools with applications in computational neuroscience. Reza received his MSc in Biomedical Engineering from Sharif University of Technology in 2013 and BSc in Electrical Engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2010. He is the recipient of the New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research (PBBR) in 2021, and the Eli Jury Award from UC Berkeley, Department of Electrical Engineering and Computer Sciences in 2018. He received the May J. Koshland Fund in Memory of H.A. Jastro Award from UC Berkeley Graduate Division in 2016, the Excellence Award in Biomedical Engineering from Sharif University of Technology in 2013, and the Excellence Award in Electrical Engineering from Tehran Polytechnic in 2010.
Roozbeh is a postdoctoral scholar at the Abbasi Lab working at the intersection of computational neuroscience and machine learning. His project is focused on understanding the relationships between neural structure and neural activities in the primary visual cortex. Before joining UCSF, he was a postdoc at the University of Pennsylvania under the supervision of Konrad Kording where he studied the theory of deep learning and how the morphologies of neurons contribute to the computation in the brain. Roozbeh received his Ph.D. in Mathematics from Sharif University in 2017 where he worked on the emergent patterns in neural networks.
Zhinoos is a Postdoctoral scholar at the Abbasi Lab working on brain mapping project.
Before joining UCSF, she was a Research fellow at Department of Medicine in
the University of Melbourne, Melbourne Australia. She has received her PhD from
Data analytic labs, School of Computer Science at Royal Melbourne Institute of
Technology (RMIT), Australia. Prior to joining RMIT, Zhinoos was a researcher at the data analytic group at IBM, Melbourne, Australia; a research scientist at University of Aveiro,
Portugal; and a research scientist at the Georgia Institute of Technology, Atlanta,
USA. She contributed to many machine learning projects and co-supervised master and PhD projects in the multidisciplinary field of seizure detection/prediction and machine learning. Her research areas include data science, machine learning, signal processing, and computational neuroscience
PhD Student, Bioengineering
Gavin is a PhD student in the UC Berkeley-UCSF joint Bioengineering program. Before coming to the program, Gavin was an undergraduate researcher at McGill University, where he generated MRI-based simulations of transcranial Direct Current Stimulation. Gavin’s background in Neuroscience and Medical Imaging prompts him to explore projects that apply machine learning methods for predicting neurological disorder prognosis. Currently, Gavin’s focus in the lab is to build models that can combine longitudinal MRI data and genomics data to improve Multiple sclerosis prognosis.
PhD Candidate, Neuroscience
Maria is a PhD candidate in the UCSF Neuroscience program working with time-series data from neural implants, iEEG, and wearable sensors advised jointly by Reza and Phil Starr. Currently, she is developing neuro-electrophysiological predictive algorithms for continuous symptom measures in patients with Parkinson’s Disease that are physiologically interpretable. She is also developing closed-loop deep brain stimulation algorithms using direct EEG signal feedback that update patient stimulation parameters to alleviate symptoms and adverse stimulation-related effects. Prior to starting her PhD, she worked in the Neuroradiology Department at UCSF under the supervision of Dr. Leo Sugrue. During this time, she designed a neuroimaging framework that allows for real-time quantification of language hemisphere localization for surgical patients to guide resection procedures and developed lab-wide statistical tools for biological and behavioral data. In her spare time, she enjoys running long distances, climbing outdoors, and reading comprehensive documentation.
Rob is a research assistant at the Abbasi Lab working on computational approaches to advance the science of longevity and age-related disease. His current focus is applying deep learning to uncover novel patterns in gene expression datasets for development and aging. Before joining UCSF, he spent over a decade in the tech industry. Rob was co-founder and CEO of Jhana, an edtech company, which was successfully acquired in 2017. He then was Executive VP of Product Management and Innovations at FranklinCovey, a leading public edtech company. He will be completing his M.S. in Bioinformatics from Johns Hopkins University in 2022 and recently completed the Advanced Biosciences Program at UC Berkeley. Previously, he received his M.S. in Management Science & Engineering and B.A. in Economics with Honors from Stanford University.
Meera is an undergraduate student at UC Berkeley studying Chemical Biology and Computer Science. At the Abbasi Lab, she is working on using machine learning methods to associate multi-omics and GWAS data to allow the interpretation of MS variants. Previously she has worked as a research apprentice at the Plant Gene Expression Center on a project utilizing CRISPR to study the role of miRNA’s on plant immunity.
Zeyu is an undergraduate student majoring in Computer Science and Math at UC Berkeley. At Abbasi Lab, he is working on designing a method to estimate the stability of patterns extracted using deep autoencoders. His project involves leveraging these stability-driven analyses to identify low-dimensional representations in spatial gene expression datasets.
Chirag is an undergraduate at UC Berkeley majoring in Computer Science and Physics. Chirag is currently working on a collaboration project with the UCSF Weill Institute for Neurosciences and Google Health to analyze time-series motion data of Parkinson’s patients from video data. This project seeks to develop few-shot machine-learned models that can predict symptom severity and extract meaningful clinical features for identifying the incidence of motion-related neurological disorders.
Gaurav is an undergraduate Electrical Engineering and Computer Science major at UC Berkeley. Gaurav is currently working on designing an interpretable machine learning framework for multi-variable time-series classification. In the future, Gaurav will apply his framework to multi-modal bio-sensing datasets, as well as continue to develop interpretable machine learning methods. Prior to joining the lab, he worked on research in applying reinforcement learning for network scheduling.
Austin is an undergraduate student studying Electrical Engineering and Computer Science at UC Berkeley. At Abbasi Lab, he is interested in developing tools to interpret and train deep learning models to understand neurological responses to image stimuli. His project involves assessing biophysical models of the brain through state-of-the-art machine learning models.
Neelroop Parikshak, Neurology Resident
Rohan Divate, Undergraduate researcher
Arbaaz Muslim, Undergraduate researcher
Kevin Chen, Undergraduate researcher
Ahyeon Hwang, Research Assistant
Franklin Heng, Research Assistant
Anmol Parande, Undergraduate researcher
We have several openings for post-docs, graduate and undergraduate students and software developers. Contact Reza directly at Reza.AbbasiAsl@ucsf.edu to learn more!