Swartz Postdoctoral Fellows

Yusi Chen

Yusi received her PhD from the University of California, San Diego, mentored by Dr. Terrence Sejnowski, and earlier her undergraduate degree from Tsinghua University. She is interested in understanding computation principles in the brain using tools from machine learning, neural dynamics and information theory. She’s now examining the precision of spike timing and conductance delays across various brain regions, shedding light on the brain’s implementation of predictive and postdictive learning mechanisms. Her previous work also includes efficient causality inference from high-dimensional time traces and pose estimation from kinematic measures. For her most recent publications and projects, please visit her personal webpage at https://yschen13.github.io

Leenoy Meshulam

Leenoy investigates how function and computation emerge from the coordinated activity of large neuronal populations. She is particularly interested in the interface between physics and neuroscience, and in developing theoretical approaches that can uncover not only the principles of brain function but also new physics concepts guiding this complex biological system. Recent work of Leenoy’s includes modeling the collective activity of hippocampal neurons, writing down Ising models (maximum entropy) for population activity, and using renormalization group-like approaches to coarse-graining neural dynamics.

Leenoy received her PhD from Princeton University, advised by William Bialek, David Tank and Carlos Brody. Earlier, she completed her Masters studying physics and biology in the Lautman honors program in Tel Aviv University.

She loves large waterfalls, fresh snow, Debussy and dark chocolate.

leenoy at uw dot edu

Fereshteh Lagzi

Fereshteh has a background in Control theory, Machine learning, and Theoretical Neuroscience. During her PhD at Bernstein Center Freirburg, she investigated nonlinear dynamics of interaction and competition between subnetworks of spiking networks. Later, she showed that residual neural networks (a type of ANNs) also follow similar dynamics of competition. She is currently interested in plasticity and dynamics of learning, and in particular how inhibitory neurons shape these dynamics. Fereshteh aims at finding emerging network properties that can explain functions and computation in the brain. To this end, she uses tools such as control theory and nonlinear dynamics.

lagzi at uw dot edu