Jeffrey Brown

Email: jffbrwn, followed by @mit.edu

Jeff is a PhD candidate in MIT’s EECS department. He graduated from Stanford University with Masters’ degrees in Computer Science and Electrical Engineering after completing his bachelors in Philosophy. His research interests center around developing computational tools for studying perturbations in biological networks. 

Publications

The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions

arXiv | 2023

Gal Haspel (NJIT), Ben Baker (Colby College), Isabel Beets (KU Leuven), Edward S Boyden (MIT), Jeffrey Brown (MIT), George Church (Harvard University), Netta Cohen (University of Leeds), Daniel Colon-Ramos (Yale University), Eva Dyer (Georgia Institute of Technology), Christopher Fang-Yen (Ohio State University), Steven Flavell (MIT), Miriam B Goodman (Stanford University), Anne C Hart (Brown University), Eduardo J Izquierdo (Rose-Hulman Institute of Technology), Konstantinos Kagias (MIT), Shawn Lockery (University of Oregon), Yangning Lu (MIT), Adam Marblestone (Convergent Research), Jordan Matelsky (University of Pennsylvania), Brett Mensh (Optimize Science), Talmo D Pereira (Salk Institute), Hanspeter Pfister (Harvard University), Kanaka Rajan (Harvard Medical School), Horacio G Rotstein (NJIT), Monika Scholz (Max Planck Institute for Neurobiology of Behavior), Joshua W. Shaevitz (Princeton University), Eli Shlizerman (University of Washington), Quilee Simeon (MIT), Michael A Skuhersky (MIT), Vineet Tiruvadi (Hume AI), Vivek Venkatachalam (Northeastern University), Donglai Wei (Boston College), Brock Wester (Johns Hopkins APL), Guangyu Robert Yang (MIT), Eviatar Yemini (UMass), Manuel Zimmer (University of Vienna), Konrad P Kording (University of Pennsylvania) (2023) The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions, arXiv:2308.06578 [q-bio.NC].