Levenstein lab

PEOPLE// WHO WE ARE

Dan Levenstein

Dan is an Assistant Professor in Yale’s Department of Neuroscience, and a Wu Tsai investigator at the WTI Center for Neurocomputation and Machine Intelligence. He received his B.S. in Biochemistry with a minor Physics from Northeastern University, his M.S. in Biophysics from Cornell University, and his PhD in Neuroscience from New York University under the mentorship of György Buzsáki and John Rinzel. He then did postdoctoral research at the interface of neuroscience and artificial intelligence at McGill University and Mila, the Quebec AI Institute, with Adrien Peyrache and Blake Richards. Dan’s scientific interests lie at the interface of biology, dynamics, and computation, where he enjoys collaborating with experimental colleagues. When not in the lab, he enjoys hiking, board games, weird music, and a good book.

Aidan Schneider

WTI Postdoctoral Fellow

Aidan is a Wu Tsai Institute Postdoctoral Fellow collaborating closely with the Levenstein lab, among others. He is interested in how transient, localized “sleep-like” states emerge during wakefulness and influence neural computation. As a postdoc, he uses multi-scale neural recordings, theoretical frameworks from statistical mechanics and information theory, and machine-learning models to explore how local sleep dynamics regulate cortical activity across space and time. He earned a B.S. in Molecular Biosciences and Biotechnology from Arizona State University and a Ph.D. in Computational and Systems Biology from Washington University in St. Louis. During his Ph.D., he worked with Keith Hengen to study how genetic identity, anatomical location, and arousal state shape neural activity, leveraging complex microelectrode recordings in freely behaving animals and novel machine-learning models. Key to his ongoing research is the discovery of a non-oscillatory, spatiotemporally localized embedding of arousal state in the brain, reflected in brief, local non-oscillatory sleep events—or “flickers”—that transiently disrupt ongoing behavior and network coordination. Outside the lab, he enjoys tabletop role-playing games, walks in East Rock Park, and doting on his cats, Smokey and Bird.

Andrea Cumpelik

Postdoc

Andrea is a postdoctoral researcher working jointly with Dan Levenstein and Tristan Geiller. She enjoys figuring out how circuits of neurons encode information, with a primary focus on spatial learning and decision making. She received her bachelor’s degree in neuroscience at New York University, where she worked with Luke Sjulson in Gyuri Buzsáki’s lab. During her undergraduate research, she conditioned mice to prefer a cocaine-paired location to investigate how hippocampal input to the ventral striatum mediates drug-associated spatial memories. For her PhD, she joined Jozsef Csicsvari’s lab at the Institute of Science and Technology Austria, shifting her focus to the brains of sober rodents. There, she designed a novel associative learning task and found a subset of neurons in the hippocampus and prefrontal cortex that flicker between distinct spatial representations. As a postdoc, she works at the interface of data analysis and computational modeling to examine information representation in the hippocampus and beyond. Her current project focuses on evaluating how a virtual environment is represented by mouse CA3 and a predictive RNN. She also enjoys listening to drum and bass, climbing mountains, and reading techno[dys/u]topian science fiction.

Subhadra Mokashe

Postdoc

Subhadra is a postdoctoral researcher in the Levenstein lab. She received her integrated BS and MS from IISER-Pune, India, and her PhD in Neuroscience from Brandeis University. Her research focuses on the temporal dynamics of learning and memory. As an undergraduate, she studied the effects of stochastic ion channel dynamics on network activity in the lab of Suhita Nadkarni. Following her BS and MS, she worked as a research assistant with Nicolas Brunel, investigating synaptic learning rules that give rise to neural trajectories observed in the cortex. For her PhD, she worked with Paul Miller to explore the role of memory replay in temporal credit assignment. In her postdoc, she plans to investigate continual learning in the brain using computational and theoretical approaches. Outside of the lab, Subhadra enjoys tending to houseplants, maintaining planted fish tanks, playing the violin and sculpting with clay.

Meghan Cum

PhD Student

Meghan is a PhD student working under Dr. Daniel Levenstein and Dr. Tristan Geiller. She is interested in how the brain encodes memories, creating internal world models and enabling flexible and adaptive learning of complex behaviors using neural networks and mouse models. Meghan completed her B.S. in Brain and Cognitive Sciences at MIT where she worked with Dr. Kay Tye where she studied the neural circuitry underlying emotional and motivational states. She then worked as lab manager for Dr. Nancy Padilla Coreano at the University of Florida where she studied the neural circuitry underlying social behaviors in mice, focusing on dominance and social memory. Outside of the lab, Meghan skates for the Connecticut Roller Derby Time, trains Muay Thai and is an avid NYT gamer and crossword-er.

Huihong Li

Masters

Student

I am a master’s student in Computational Biology at Yale University. My current research interests focus on the spontaneous activity and self-organization of neural networks. Before coming to Yale, I earned my Bachelor’s degree in Biotechnology from the Technion – Israel Institute of Technology. My previous research experience includes bioinformatics analysis, building biophysical neuron models, and conducting large-scale neural network simulations. At Yale, I joined the lab to explore how spontaneous neural activity during sleep contributes to task performance through homeostatic plasticity.

Viggy Vanchinathan

Postgraduate Associate

Viggy Vanchinathan is a recent graduate from Johns Hopkins University, where he majored in Biomedical Engineering and Applied Mathematics. There, he conducted research in labs across both departments, most notably leading a design team in a project to characterize functional impairment in Parkinson's patients using machine learning and movement data. He’s also worked for biotech startups and is quite interested in entrepreneurship. He joined the lab as a Postgraduate Associate and computational lab assistant in October 2025. Outside of the lab, he enjoys lifting, cooking, and writing food reviews on Google Maps.

Ishir Rao

Undergraduate Student

Ishir Rao is an undergraduate at Yale University pursuing a degree in Applied Mathematics and Biophysics. His research interests lie at the intersection of deep learning and biological systems, with previous research focusing on virtual cell modeling, computational drug discovery, and reinforcement learning for pandemic mitigation. Ishir joined the Levenstein Lab in January 2026, and is currently designing algorithms to replicate the adaptive plasticity and learning capacity of the human brain in artificial agents. Outside of the lab, he enjoys performing viola, bouldering, and playing basketball in his residential college.