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 2018 Eli Jury Award from UC Berkeley, Department of Electrical Engineering and Computer Sciences.
Roozbeh is an incoming postdoc who is working on the relationships between neural structure and neural activities in the primary visual cortex. He is concluding his first 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.
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.
Neel is a physician-scientist who is completing clinical training in Neurology at UCSF. He has a background in both biology and mathematics which has him to pursue practical medical questions with rigorous, data-driven approaches. He has joined the lab to combine quantitative approaches with medicine and biology to improve diagnosis and prognosis of disorders and diseases and develop predictive models for disorders and diseases that lead to evidence-based interventions (related to the idea of “precision medicine”). Neel works on basic science questions with the Kriegstein lab.
Gaurav is an undergraduate Electrical Engineering and Computer Science major at UC Berkeley. Prior to joining the lab, he worked on research in applying reinforcement learning for network scheduling. 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 continuing to develop interpretable machine learning methods.
Austin is an undergraduate student studying Electrical Engineering and Computer Science at UC Berkeley. In addition to research with Abbasi Lab, Austin is currently working as a machine learning intern at Intellext, developing an NLP pipeline to process contract data, and doing research with RAISE Lab to use reinforcement learning to optimize a price controller for energy demand response. At Abbasi Lab, he is interested in developing tools to interpret and train deep learning models to understand neurological responses to image stimuli.Friday, May 21st
Before joining the lab, Ahyeon was an undergraduate researcher at the Redwood Center for Theoretical Neuroscience, where she conducted work at the intersection of machine learning and neuroscience. After working briefly at the Computational Neurobiology Laboratory at the Salk Institute, she worked as a research associate at LBNL where she used statistical machine learning methods on medical record data to predict traumatic brain injury outcomes. Ahyeon is interested in developing more interpretable deep learning models to understand the mechanism of neurological disorders and improve clinical diagnoses through precision medicine tools.
Rohan Divate, UC Berkeley
Austin Jang, UC Berkeley
Arbaaz Muslim, UC Berkeley
Kevin Chen, UC Berkeley
Shiladitya Dutta, UC Berkeley
Franklin Heng, Research Assistant
Oluwaseun Adegbite, Rotation PhD student
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!