Jon Kleen
Principal Investigator
Jon is originally from rural Minnesota. He earned a PhD studying neuroscience from Dartmouth College along with training in engineering and computer science, and his MD from Dartmouth Medical School. At UCSF, he completed a residency in neurology, clinical fellowship in epilepsy, and postdoctoral training in human neurophysiology research. He is both a physician and a scientist in the Department of Neurology, Division of Epilepsy, and UCSF Weill Institute for Neurosciences.
Edwina Tran
Edwina is a UCSF medical student who was born and raised in San Francisco. She double majored in Molecular and Cellular Biology—Infectious Diseases and Public Health at UC Berkeley. Previously, she worked on building a bioinformatics pipeline to modify mNGS and host transcriptome profiling protocols for the detection of TB meningitis in resource-poor settings. Her current research interests include understanding the neurophysiology of memory processing using intracranial ECoG data from patients with medication-resistant epilepsy and other emerging neurosurgical interventions, including neurostimulation, that may rescue cognitive function.
Kevin Chang
Kevin was born and raised in San Francisco, received his B.A. in Neuroscience and Psychology from Vanderbilt University, and is now back as a medical student at UCSF. Prior to medical school, Kevin studied mechanisms of network hypersynchronization in mouse models of Alzheimer’s disease, particularly trying to understand the role of inhibitory interneurons and oscillatory brain rhythms. Now he is interested in leveraging information visualization approaches to better understand seizure spread and gain novel insight for clinical and surgical decision making.
David Caldwell
David is a neurosurgery resident at UCSF who received his BSE and MSE in biomedical engineering at the University of Michigan, and subsequently his MD and PhD in bioengineering at the University of Washington. His PhD work was focused on engineering direct electrical stimulation of human sensorimotor cortex, working with patients that were implanted with electrodes in their brains for clinical care of epilepsy and movement disorders such as Parkinson’s and Essential Tremor. He is broadly interested in neural engineering and intracranial electrophysiology.
Ebenezer Chinedu-Eneh
Ebenezer grew up in Minnesota, earned his BS in Biology while minoring in German at the University of Minnesota, and is currently a medical student at UCSF. He is broadly interested in leveraging technology to improve healthcare, and previously worked in a consumer health start-up towards that aim. His current interests involve utilizing AI to aid in visualization of neural networks using intracranial ECoG data.
Natalia Sucher Munizaga
Natalia grew up in New York City, earned her BA in Ancient Greek and Latin at Swarthmore College, and now is back in school for her post-baccalaureate at UC Berkeley and City College of San Francisco studying computational neuroscience. Her focus is conducting epilepsy research, specifically writing algorithms in MATLAB and Python to integrate semiology and electrophysiology in order to localize and track the propagation of seizure activity in the brain.
Raphaël Christin
Raphaël grew up in Québec, Canada. He earned his bachelor's degree in Cognitive Science (Neuroscience specialization) and Computer Science from McGill University, where he obtained research experience in consciousness using signal processing, as well as neurobiological research in mood and anxiety disorders. He is very interested in applying computational methods in research to help patients and push forward our understanding of neurological diseases. As a Junior Specialist in the lab, he works on building data pipelines and analyzing software for epilepsy and cognition research using Python and Matlab.
Da Zhang
Da is a postdoctoral scholar at UCSF who received her Ph.D. in electrical and computer engineering at the University of Miami under the mentorship of Professor Mansur R. Kabuka. During her Ph.D., she studied deep representation learning for biological networks and uncovered hidden patterns in sequential data. Her current research uses machine-learning and deep-learning-based methods to understand the semantic basis of neural signals.