Daniel Ehrlich
New York, United States
Summary
👤
Senior🎓
Top SchoolDaniel Ehrlich is a computational neuroscientist and machine learning researcher with nine years of experience studying how biological and artificial agents learn, plan, and make decisions. As a postdoctoral researcher at UC Berkeley and previously at Yale School of Medicine, he has designed and executed experiments and models in reinforcement learning, multitask learning, and recurrent neural networks, and led an NSF-funded project to prototype and benchmark Sequential Approximate Bayesian Computation methods. He co-developed PsychRNN, an accessible Python package for training RNNs on cognitive tasks, and routinely reverse-engineers trained networks to reveal representational geometry and causal mechanisms underlying planning and working memory. Based in the San Francisco Bay Area, he brings a blend of rigorous model evaluation, applied tool-building, and interest in AI alignment and memory-control mechanisms that bridges neuroscience and practical ML research.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Arts (B.A.), Quantative Behavioral Economics and Applied Neuroscience, Bachelor of Arts (B.A.), Quantative Behavioral Economics and Applied Neuroscience at New York UniversityDoctor of Philosophy (PhD), Neuroscience, Doctor of Philosophy (PhD), Neuroscience at Yale University
Spanish
Github Skills (7)
recurrent-neural-networks9
tensorflow4
xor3
dfa2
keras1
typescript1
deep-learning1
Programming languages (2)
Github contributions (5)
dbehrlich/dbehrlich.github.io
Oct 2016 - Dec 2016
Contributions:43 pushes, 1 branch in 2 months
murraylab/conditionalDelayedLogicTask
Aug 2022 - Aug 2022
Contributions:25 pushes, 1 branch in 1 day
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