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. He is a core member of the UC Berkeley/UCSF Bioengineering and Computational Precision Health graduate programs and the UCSF Bioinformatics graduate program.

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 developed 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 2023 Kunal Patel Catalyst Award, New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research (PBBR) in 2021, and 2022, 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. 

Arefeh is a postdoctoral scholar at the Abbasi Lab working on computational methods for integrating structural and functional data in the mouse primary visual cortex. Her research will include the use of machine learning and deep learning to identify spatially-selective and motion-selective neurons in the V1 area. Arefeh got her PhD from Washington University in St. Louis in physics. Her Ph.D. focused on denoising and signal processing methods for high-density diffuse optical tomography (HD-DOT). After that, she worked as a postdoctoral research associate at the Biophotonics Research Center at Washington University School of Medicine. She is currently developing computational models for fMRI and HD-DOT data to find the neural correlates of the impacts of brain implants such as cochlear implants in speech processing or deep brain stimulators in Parkinson’s disease or essential tremor during resting state.

Gavin is a PhD student in the UC Berkeley-UCSF joint Bioengineering program. His project in the lab is focused on building computational platforms to analyze imaging and genomics data in Multiple Sclerosis. Before joining 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.

Maria is a PhD candidate in the UCSF Neuroscience program working with time-series data from neural implants, iEEG, and wearable sensors jointly advised 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.

Alex is a PhD student in the UCSF BMI program. He is interested in interpretable machine learning and is currently working on developing models for multimodal data integration and pattern identification in molecular imaging data. Previously, Alex worked as a research assistant in the Seeley lab at UCSF and at Vivani Medical. Outside of research, he enjoys cooking and then eating.

Kara is a PhD student in the UCSF Neuroscience program jointly advised by Reza Abbasi-Asl, Doris Wang and Phil Starr. Kara studies the neural representation of sequential movement in Parkinson’s Disease – assessing how the cortico-basal ganglia circuit implements ongoing sequential movements across phases of learning, extracting biomarkers of consolidation during sleep that promote performance improvements, and evaluating the effect of levodopa and deep brain stimulation on these processes.

Russell is a PhD student in the UC Berkeley-UCSF Joint Bioengineering program. He is interested in time-series analysis, dynamics and control, and healthcare decision-making systems. Prior to starting his graduate studies, Russell graduated from UC San Diego where he studied the neural plasticity mechanisms underlying stroke recovery. He also worked as an engineer at Novartis where he designed tissue culture and assay systems for drug discovery applications. In his free time, you may find Russell bricking mid-rangers in pickup, talking too much about economics without knowing much about economics, or drinking obscene amounts of boba.

Rob is a research specialist 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.

Vincent is a research specialist working on interpretable machine learning models to decode emotional content from multi-modal biosensing data. He received his BSc from UC Berkeley with research experience in emotion at the Berkeley Psychophysiology Lab. He has also worked on machine learning interpretability with Google Brain and designed interpretable decoders of hippocampal theta rhythms with the Redwood Center for Theoretical Neuroscience. Outside of research he is learning 3D rendering and animation, hoping to express ideas related to psychoanalysis, philosophy, and religion which may shine light on the relationship between the psyche and matter.

Taking a cognitive neuroscience course set the course for my professional life – Jen fell in love with the brain, its mysteries, and fMRI as a way to investigate them.  After graduating from the University of Pennsylvania studying Psychology and the Biological Basis of Behavior, she learned clinical fMRI at UCLA doing language and motor mapping fMRI with pre-surgical patients, and research into the cognitive and affective neural circuits underlying mood disorders.  Research has afforded her opportunities to follow her intellectual curiosity into publications, engage in scientific debates over tea at conferences around the world, and marvel at the wonders of the human mind.  In medical school at UCSF, she became intrigued with the intersection of psychiatry and other fields like immunology and cardiology and using functional medicine, a holistic approach to illness. Throughout her career, she has been fortunate to work with many amazing, kind clinicians and researchers, and hopes to continue in that tradition of using knowledge and compassion to help others. Current work includes high-resolution fMRI at 3T and 7T and emotion.

Austin is a Master’s 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.

Daniel is an undergraduate student studying Computer Science and Molecular and Cellular Biology at UC Berkeley. At the Abbasi Lab, he is interested in using signal processing and interpretable machine learning to advance our understanding of neurodegenerative diseases. His current project is on computer vision-aided assessment of Parkinson’s disease motor symptoms. Before joining the Abbasi Lab, he was a research assistant at the Cardiac Vision Lab at UCSF, where he worked on numerical simulation and predictive modeling of cardiac dynamics. In his free time, he enjoys cooking and oil painting.

Former core members

Neelroop Parikshak, Neurology Resident (Currently: Physician-Scientist / Associate Director, Neurology, Therapeutic Area Genetics at Regeneron)

Roozbeh Farhoodi, Postdoctoral scholar (Currently: Senior Machine Learning Engineer at Celestial AI)

Former undergraduate researchers

Gaurav Ghosal

Zeyu Yun

Chirag Sharma

Meera Mehta

Anmol Parande

Franklin Heng

Ahyeon Hwang

Kevin Chen

Arbaaz Muslim

Rohan Divate

Join us!

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!