People
Meet the BRAINY crew
We are a diverse group of scientists and engineers with a passion for the science of how the brain works and a deep commitment to open and inclusive science.
Interested in what we do? Write to us.
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Kanaka Rajan, PhD
Associate Professor
Kanaka Rajan, PhD, is a computational neuroscientist, Associate Professor of at Harvard Medical School, and a founding faculty member of the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Her research seeks to understand how important cognitive functions — such as learning, remembering, and deciding — emerge from the cooperative activity of multi-scale neural processes. Using data from neuroscience experiments, she applies computational frameworks derived from machine learning and statistical physics to uncover integrative theories about the brain that bridge neurobiology and artificial intelligence.
Christian D. Márton, PhD
Postdoctoral Fellow
Christian is interested in uncovering how the brain keeps the simulation going, in particular how neural networks store and update information in the brain. He works at the interface of computational neuroscience and machine learning. Previously, he was a visiting researcher at the NIH building computational models to study information processing in the brain. Christian holds a PhD and Masters in Bioengineering from Imperial College London, and a BA in Neuroscience from Princeton.
Manuel Beiran, PhD
Postdoctoral Fellow, Rajan lab & Columbia Theory Center
Manuel is interested in the links between neuronal architectures, dynamics and computations. As a postdoctoral researcher in joint appointment with the Rajan lab and Columbia’s Theory Center, he develops tools to connect connectivity recordings to network dynamics. He was trained as a physicist, before studying Computation Neuroscience at the Bernstein Center in Berlin. For his PhD, supervised by Dr. Srdjan Ostojic at the ENS in Paris, he contributed to the theory of neural networks with low-rank connectivity, and studied how recurrent networks solve timing tasks. You can find Manuel, when not in the lab, in one of New York’s movie theaters.
Riley Simmons-Edler, PhD
Postdoctoral Fellow
Riley works on neuro-inspired reinforcement learning (RL) with an interest in bridging the generalization and transfer learning performance gap between computational neural network models and animals. In particular, he investigates modular RL methods which learn to decompose task curricula into reusable subtask-specific modules. Before joining the Rajan lab he was a PhD student at Princeton University working with Sebastian Seung on sampling and exploration methods for reinforcement learning, and a research intern at Samsung AI Center NYC working on robotic RL. In a previous life he worked on computational protein modeling as an undergrad at NYU. When not working he enjoys building computers and cooking, usually not at the same time.
Camille Spencer-Salmon
Graduate Student (MD/PhD, Sinai)
Camille is an MD/PhD candidate at Mount Sinai. She graduated from Brown University (c/o 2014) with a BS in Neuroscience. She spent her undergrad years sorting spikes from human participants with tetraplegia for BrainGate and the three years before medical school puzzling over EEGs in ICU patients with traumatic brain injury at Massachusetts General Hospital. Her interests include neural dynamics in learning and memory and how the brain encodes time. When she’s not debugging Matlab code or learning how to be a doctor, she enjoys people watching in Central Park, advocating for students of color in STEM, arguing, and trying to read more fiction than non-fiction.
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Muhammad Furqan Afzal
Graduate Student (PhD, Sinai)
Furqan is interested in using mathematical and engineering techniques to identify pathological biomarkers for and mechanisms of neurological and psychiatric disorders. He has an undergraduate degree in Electrical Engineering and a Master's in Electrical Engineering/Computational Neuroscience— building computational models of volitional motor control. He has also worked at Cincinnati Children's Hospital on automation of visual reinforcement audiometry, and at Stanford University where he developed algorithms for adaptive deep brain stimulation in Parkinson's disease. Currently, he is a graduate student jointly advised by Drs. Rajan and Helen Mayberg. Beyond science, he enjoys reading and playing racquetball and squash.
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Yosif Zaki
Graduate Student (PhD, Sinai); co-advised by Kanaka Rajan
I completed my B.S. in Behavioral Neuroscience and Computer Science. During that time, I worked in Drs. Sandeep Robert Datta’s, Leon Reijmers’, and Steve Ramirez’s labs, where I developed a technical foundation in experimental neuroscience and became interested in understanding the dynamic nature of memory. Currently, I'm investigating how neural representations of memories change across time and experience, with Drs. Kanaka Rajan and Denise Cai. Outside of lab, I enjoy writing stories with my camera (joezaki.org).
Siyan Zhou
Graduate Student (PhD, Harvard University); co-advised by Kanaka Rajan
Siyan received her B.S. in Biological Sciences with a minor in Computer Application from Tsinghua University in China, where she studied Alzheimer's disease in Yi Zhong's lab. Siyan is currently a graduate student in Neuroscience at Harvard, in the lab of Chris Harvey and co-advised by Kanaka. She is interested in combining RNN modeling and experiments to study the neural basis of navigational decision-making tasks. Beyond science Siyan loves painting, nature and orchestra.
Alumni
Daphne Cornelisse - Associate Researcher - PhD student
Daniel Kepple, Ph.D. - Postdoctoral Fellow - Engineer, Meta AI
Matt Perich, Ph.D. - Postdoctoral Fellow - Faculty, University of Montreal and MILA-Quebec AI Institute