Reza is an Assistant Professor in the Department of Neurology and the Department of Bioengineering and Therapeutic Sciences at UCSF. He also serves as 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.
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.
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.
Before joining UCSF, Franklin was an undergraduate researcher at the Video and Image Processing Lab at UC Berkeley where he developed image processing and computer vision algorithms for agricultural, robotic systems. After spending a short time in Samsung’s research team, Franklin joined UCSF Big Data in Radiology Lab, where applied machine learning methods on Brain CTs to predict brain age and find disease correlations; and automate the detection of brain stroke. He also works as a computer vision researcher in The Fahy Lab where he utilizes computer vision to aid the development of effective mucolytics. Franklin is interested in developing machine learning models that will help aid the diagnosis and rehabilitation of patients with neurological diseases.
Before joining the lab, Luke was an undergraduate researcher at the Redwood Center for Theoretical Neuroscience and Gallant Lab at UC Berkeley, where he worked at the intersection of machine learning and vision. Specifically he used synchrony in complex-valued autoencoders to approach the neural binding task in computer vision. He graduated from UC Berkeley majoring in Mathematics and Computer Science in 2019. Luke is interested in using tools such as fMRI imaging to understand how humans build representations of scenery, and he is curious about understanding how computations in the different areas of the human visual cortex take place in terms of modeling tools from mathematics and computer science. He currently plans to pursue graduate school in Vision Science.
Rohan Divate, UC Berkeley
Austin Jang, UC Berkeley
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!