Draft:Kim Stachenfeld
Submission declined on 9 June 2025 by MCE89 (talk).
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|  |
Kim Stachenfeld | |
|---|---|
| Alma mater | Tufts University (B.S.), Princeton University (Ph.D.) |
| Scientific career | |
| Fields | Neuroscience, AI |
| Institutions | Google DeepMind, Columbia University |
| Doctoral advisor | Matthew Botvinick |
Kimberly Lauren Stachenfeld is an American computational neuroscientist and artificial intelligence (AI) researcher. She serves as a Senior Research Scientist at Google DeepMind and holds an affiliate faculty position at the Center for Theoretical Neuroscience at Columbia University.[1] She has made important contributions to the fields of neuroscience and machine learning, on how biological systems learn and represent information, and how these principles can inform AI development.[2]
Education
[edit]Stachenfeld earned dual bachelor's degrees in Mathematics and Chemical & Biological Engineering from Tufts University in 2013.[3] She then pursued a Ph.D. in Quantitative & Computational Neuroscience at Princeton University, completing it in 2018 under the supervision of Dr. Matthew Botvinick.[4] Her doctoral research centered on learning neural representations that support efficient reinforcement learning.[5]
Research and Career
[edit]Stachenfeld's research explores the intersection of neuroscience and AI. In neuroscience, she investigates how animals construct and utilize internal models of their environment to support memory and prediction.[6][7] In AI, she applies these insights to develop deep learning models that emulate cognitive functions.[8] She has contributed to projects involving reinforcement learning, graph neural networks,[9] and learned simulators for physical systems. Her work on predictive representations in the hippocampus has been influential in understanding how the brain anticipates future events.[10]
Notable Works
[edit]- Stachenfeld, Kimberly L.; Botvinick, Matthew M.; Gershman, Samuel J. (2017). "The hippocampus as a predictive map". Nature Neuroscience. 20 (11): 1643–1653. bioRxiv 10.1101/097170. doi:10.1038/nn.4650. PMID 28967910.
References
[edit]- ^ "Kimberly L. Stachenfeld | Center for Theoretical Neuroscience". ctn.zuckermaninstitute.columbia.edu. Retrieved 2025-05-03.
- ^ "Kimberly Stachenfeld | Innovators Under 35". www.innovatorsunder35.com. Retrieved 2025-05-03.
- ^ "NeuroKim". NeuroKim. Retrieved 2025-05-03.
- ^ "Matthew Botvinick | Stanford HAI". hai.stanford.edu.
- ^ "Learning Neural Representations that Support Efficient Reinforcement Learning - ProQuest". www.proquest.com. Retrieved 2025-05-03.
- ^ Cepelewicz, Jordana; Magazine, Quanta (2019-01-18). "A Hexagonal Theory of Memory". The Atlantic. Retrieved 2025-05-03.
- ^ Cepelewicz, Jordana (2019-01-14). "The Brain Maps Out Ideas and Memories Like Spaces". Quanta Magazine. Retrieved 2025-05-03.
- ^ Castro, Pablo Samuel; Tomasev, Nenad; Anand, Ankit; Sharma, Navodita; Mohanta, Rishika; Dev, Aparna; Perlin, Kuba; Jain, Siddhant; Levin, Kyle; Éltető, Noémi; Dabney, Will; Novikov, Alexander; Turner, Glenn C.; Eckstein, Maria K.; Daw, Nathaniel D.; Miller, Kevin J.; Stachenfeld, Kimberly L. (2025). "Discovering Symbolic Cognitive Models from Human and Animal Behavior". bioRxiv 10.1101/2025.02.05.636732.
- ^ Stachenfeld, Kimberly; Godwin, Jonathan; Battaglia, Peter (2020). "Graph Networks with Spectral Message Passing". arXiv:2101.00079 [stat.ML].
- ^ Stachenfeld, Kimberly L.; Botvinick, Matthew M.; Gershman, Samuel J. (2017). "The hippocampus as a predictive map". Nature Neuroscience. 20 (11): 1643–1653. bioRxiv 10.1101/097170. doi:10.1038/nn.4650. PMID 28967910.
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