people

Will Redman is an Assistant Professor in the Electrical and Computer Engineering Department at Johns Hopkins. Prior to JHU, Will was a Senior Research Scientist at Johns Hopkins Applied Physics Lab, working in the Intelligent Systems Center. He got his PhD at UC Santa Barbara in the Interdepartmental Graduate Program of Dynamical Neuroscience and did his undergrad at NYU in Mathematics and Physics. Will enjoys, running and cycling (both of which are complicated by his innately poor sense of direction), and reading.

Edouard de Ponnat is an incoming PhD student in Electrical and Computer Engineering. Prior to JHU, Edouard studied at EPFL, ENS de Lyon, and ETH Zürich, where his academic journey developed around mathematics, neuroscience, machine learning, and dynamical systems. He is particularly interested in how mathematical and computational tools can be used to understand learning, neural dynamics, and intelligent behavior. Outside of research, Edouard used to work as a sound system engineer while pursuing his studies. He also enjoys cycling and listening to music.

Oluwajomiloju Fasehun is a junior at Johns Hopkins University majoring in Neuroscience with a computational focus. She works with Prof. Will Redman analyzing how temporal shifts in neural activity modulate grid cell structure in the medial entorhinal cortex. Her current research at DIG involves computational analysis of grid scores, delta tuning, and population level structure to understand how stable spatial representations emerge from noisy neural activity. More broadly, she is interested in the computational principles that link neural dynamics to movement, coordination, and interaction with the environment. Outside of research, she spends her time playing basketball, building RC planes, and exploring music through several instruments.

Aarav Sinha is a research intern working with Prof. Will Redman at Johns Hopkins University, where he studies how learned neural network dynamics support navigation-related computation. In DIG, his current work includes analyzing recurrent neural networks trained on path-integration tasks to understand when predictive and grid-cell-like representations emerge and how essential they are for performance. He is also involved in embodied emulation projects across academia and industry that connect neural activity to behavior in closed-loop simulated environments. Outside of research, he plays violin in his school orchestra, pursues his interest in entomology, and enjoys drinking matcha with friends.