Hananel Hazan
Boston, Massachusetts, United States
Summary
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RockstarHananel Hazan is an interdisciplinary computer scientist and research scientist with 11 years of experience specializing in biologically inspired computing, neurocomputation, and machine learning. He holds a Ph.D. from the University of Haifa and has led experimental and open-source efforts—building a low-cost closed-loop cortical interfacing platform at Technion and spearheading BindsNET, a PyTorch-based spiking neural network framework—while working in top research labs including UMass Amherst and the Allen Discovery Center at Tufts. His work blends hands-on engineering (real-time neural data acquisition and SNN simulation) with theoretical inquiry into computational properties of neuronal and non-neuronal systems. Comfortable across biology, neurobiology, psychology and CS, he focuses on understanding fundamental cognitive functions through cross-disciplinary methods. Based in Boston, he contributes to reproducible open science and tool-building that translates complex neural dynamics into usable ML simulations. An underappreciated strength is his knack for turning experimental neuroscience setups into robust software platforms that accelerate downstream modeling and analysis.
11 years of coding experience
Github Skills (8)
pytorch10
machine-learning10
morph9
accelerated-computing8
neurons8
gpu8
gpgpu8
parallel-computing8
Programming languages (7)
Github contributions (5)
BindsNET/bindsnet
Feb 2018 - Jan 2023
Simulation of spiking neural networks (SNNs) using PyTorch.
Role in this project:
ML Engineer
ML EngineerContributions:6 releases, 7 reviews, 621 commits in 5 years
Contributions summary:Hananel's contributions center around the development and testing of spiking neural networks (SNNs) using PyTorch, as indicated by the code changes. They implemented a benchmark program mimicking the frontpage code of Brian2, integrated timeit for performance analysis, and addressed indexing issues in clamping operations. The user also incorporated the functionality of injecting voltage to the neurons and introduced the capability to normalize network weights. Furthermore, the user worked on various improvements like the use of gym-to-gymnasium and handling dimensions.
pytorchspiking-neural-networksdynamicreinforcement-learningstdp
Hananel-Hazan/rtxi
Jul 2015 - Feb 2018
Contributions:1 PR, 1 push, 1 branch in 2 years 7 months
real-timedata-acquisitionclosedloopacquisition
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