Five CNC students were recently awarded prestigious graduate fellowships. Patrick Zhang, Chase King, Ryan Ressmeyer, and James Hazelden were recipients of the National Science Foundation (NSF) Graduate Research Fellowship, a highly competitive fellowship that supports outstanding US students in technology, engineering, and math graduate programs. CNC graduate student Helena Liu also received a FRQNT fellowship from the Quebec Research Fund to continue work she started through the NSF-supported International Network for Bio-Inspired Computing.

Patrick Zhang, Neuroscience graduate student

Patrick is a second year Neuroscience graduate student co-advised by Dr. Adrienne Fairhall and Dr. Edgar Walker. Patrick is from Seattle and received his undergraduate degree at the University of Washington in computer science. Before rejoining UW as a graduate student, Patrick worked in the tech industry and volunteered with Dr. Azadeh Yazdan-Shahmorad and Dr. Eric Shea-Brown. Patrick is currently working in close collaboration with Dr. Elizabeth Buffalo, hoping to use computational methods to study internal model formation, and how internal models aid in rapid learning.





Chase King, Neural Computation & Engineering minor alumnus

Chase graduated from the University of Washington in 2022 with degrees in Computer Science and Applied & Computational Mathematical Sciences, and a minor in Neural Computation & Engineering. He initially intended a career in software engineering, but partway through his time at UW he discovered the marvelous world of neuroscience through a CNC talk and has been hooked since. He is currently a Data Analyst at the Allen Institute and will be starting his PhD in neuroscience at Columbia University this fall. In his NSF fellowship, he proposed a novel method to model to study coding by diverse cell types in the visual cortex, with the goal of advancing our nebulous understanding of the functional and computational roles played by diverse cell types in mediating cortical activity.


Ryan Ressmeyer, Bioengineering graduate student

Ryan is a graduate student in the UW Bioengineering Department, co-mentored by Dr. Gregory Horwitz and Dr. Jacob Yates from UC Berkeley. A Seattle native, Ryan happily returned to the Pacific Northwest after earning his undergraduate degree in Electrical Engineering at Stanford University. Now, Ryan is developing high-resolution digital eye tracking methods for studying early visual processing in awake non-human primates with unprecedented precision. He plans to use these methods to study the integration of bottom-up and top-down signals in the primate lateral geniculate nucleus, a crucial relay center for visual information.


James Hazelden, Applied Mathematics graduate student

James’s work is focused on developing efficient methods for training spiking neural networks made up of biophysical neuron models (e.g. Hodgkin-Huxley). The ultimate goal of this work is to understand how neuronal properties such as resonance, bursting, rapid firing, etc, can play a role in neural coding. Furthermore, how these properties can be combined together to form simple circuitry and what metrics such as rate/temporal-coding work well to facilitate learning/evaluation on different tasks. Outside of research, James is passionate about graphics and low-level coding in CUDA, C++ and OpenGL and is training to compete at a high level in powerlifting in my free time.


Helena Liu, Applied Mathematics graduate student

Helena is interested in biologically plausible learning rules and bio-inspired network architectures, as well as the application of deep learning theory to analyze the properties of trained networks. In previous work supported by IN-BIC, she leveraged theoretical deep learning tools, such as loss landscape curvature, to explore the generalization properties of networks trained under different learning rules. For Helena’s current work, they are investigating how various forms of bio-inspired initialization influence both the representation and generalization of biological neural networks