Mitchell Ostrow

Mitchell Ostrow

PhD candidate, Computational Neuroscience & Machine Learning, MIT
ostrow (at) mit.edu

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I'm a PhD student at MIT working with Ila Fiete. I'm interested in bridging systems neuroscience, cognitive science, and deep learning through the lens of dynamical systems theory. To that end, I design and apply quantitative methods to understand the computations performed by both biological and artificial neural networks. Before MIT, I studied statistics & data science and neuroscience at Yale, and I've worked in medicine (as an EMT), experimental neuroscience, and industry. I'm grateful to have been supported at MIT by the Computationally-Enabled Integrative Neuroscience Fellowship and the Praecis Presidential Fellowship. I'm currently supported by the NSF GRFP.

I also work as a freelance editor, especially for college admission essays and graduate school statements of purpose. Reach out if you're interested.


Selected papers

  1. A metric for comparing complex systems by their dynamics. Ostrow, Eisen, Kozachkov, Redman, Fiete. In review, 2026. [paper] [code]
    dynamical systems, Koopman operators, similarity metrics, neural data analysis
  2. InputDSA: Demixing, then comparing recurrent dynamics and external input. Huang*, Ostrow*, Singh, Kozachkov, Rajan. ICLR, 2026 (top 2%). (*equal contribution) [paper] [code]
    input-driven dynamics, system identification, RNNs
  3. Characterizing control between interacting subsystems with deep Jacobian estimation. Eisen, Ostrow, Chandra, Kozachkov, Miller, Fiete. NeurIPS, 2025 (spotlight). [paper] [code]
    interacting subsystems, control, Jacobian estimation
  4. Delay embedding theory of neural sequence models. Ostrow, Eisen, Fiete. ICML Workshops on Next Generation Sequence Models & Mechanistic Interpretability, 2024. [paper] [code]
    sequence models, transformers, state-space models, delay embeddings
  5. Beyond geometry: Comparing the temporal structure of computation in neural circuits with dynamical similarity analysis. Ostrow, Eisen, Kozachkov, Fiete. NeurIPS, 2023. [paper] [code]
    dynamical similarity, Koopman operators, RNNs, learning rules

Selected talks

  1. A metric for comparing complex systems by their dynamics
    Contributed Talk, 7th International Conference on the Mathematics of Neuroscience and AI, 2026 (top 10%)
    Flatiron Center Junior Theoretical Neuroscientist Workshop, July 2026
  2. Introduction to Koopman operator theory [notes]
    Flatiron Center Junior Theoretical Neuroscientist Workshop, July 2026
  3. InputDSA: Demixing, then comparing recurrent dynamics and external input
    Contributed Talk, COSYNE 2026 (top 2%) [video]
  4. Beyond geometry: Comparing the temporal structure of computation in neural circuits with dynamical similarity analysis
    Contributed Talk, COSYNE, March 2024 (top 2%) [video]
    Contributed Talk, CCN, August 2023 (top 5%)

Papers

  1. A metric for comparing complex systems by their dynamics. Ostrow, Eisen, Kozachkov, Redman, Fiete. In review, 2026. [paper] [code]
    dynamical systems, Koopman operators, similarity metrics, neural data analysis
  2. The geometry of ignorance: How LLMs encode and adjust a Bayesian prior. Liu, Arora, Bao, Ostrow, et al. In submission, ICLR, 2027.
    LLMs, Bayesian priors, representations
  3. Traversing the solution space of neural networks with Hessian null-space continuation. Huang, Ostrow, Lu, Redman, Kozachkov. In submission, ICLR, 2027.
    loss landscapes, optimization
  4. InputDSA: Demixing, then comparing recurrent dynamics and external input. Huang*, Ostrow*, Singh, Kozachkov, Rajan. ICLR, 2026 (top 2%). (*equal contribution) [paper] [code]
    input-driven dynamics, system identification, RNNs
  5. Fast dynamical similarity analysis. Behrad, Ostrow, Fahkarian, Fiete, Safavi. In review, Nature Communications, 2025. [code]
    dynamical similarity, scalability
  6. Characterizing control between interacting subsystems with deep Jacobian estimation. Eisen, Ostrow, Chandra, Kozachkov, Miller, Fiete. NeurIPS, 2025 (spotlight). [paper] [code]
    interacting subsystems, control, Jacobian estimation
  7. The McClelland lectures: Neural network models of human cognition. Benjamin*, Beyer*, …, Ostrow*, …, Saxe, McClelland. PMLR, 2025. [paper]
    neural network models of cognition
  8. Computation-through-dynamics benchmark: Simulated datasets and quality metrics for dynamical models of neural activity. Versteeg, McCart, Ostrow, Zoltowski, …, Pandarinath. PLoS Computational Biology, 2025. [paper]
    benchmarks, latent dynamics models, neural data
  9. How the brain creates cognitive maps of related concepts. Ostrow, Fiete. Nature (News & Views), 2024. [paper]
    cognitive maps, hippocampus, commentary
  10. Delay embedding theory of neural sequence models. Ostrow, Eisen, Fiete. ICML Workshops on Next Generation Sequence Models & Mechanistic Interpretability, 2024. [paper] [code]
    sequence models, transformers, state-space models, delay embeddings
  11. How diffusion models learn to factorize and compose. Liang, Liu, Ostrow, Fiete. NeurIPS, 2024. [paper]
    diffusion models, compositionality, generalization
  12. Does maximizing neural regression scores teach us about the brain? Schaeffer, Khona, Chandra, Ostrow, Miranda, Koyejo. NeurIPS Workshops (NeurReps, UniReps), 2024. [paper]
    NeuroAI, model–brain comparison
  13. Beyond geometry: Comparing the temporal structure of computation in neural circuits with dynamical similarity analysis. Ostrow, Eisen, Kozachkov, Fiete. NeurIPS, 2023. [paper] [code]
    dynamical similarity, Koopman operators, RNNs, learning rules
  14. Associative memory under the probabilistic lens: Improved transformers and dynamic memory creation. Schaeffer, Khona, Zahedi, Ostrow, Fiete, Gromov, Koyejo. NeurIPS Workshop on Associative Memory & Hopfield Networks, 2023.
    transformers, Hopfield networks, memory
  15. Representational geometry of social inference and generalization in a competitive game. Ostrow, Yang, Seo. RSS Workshop on Social Intelligence in Humans and Robots, 2022. [paper] [code] [video]
    theory of mind, deep RL, representational geometry
  16. Examining the viability of computational psychiatry: Approaches into the future. Ostrow. Yale Undergraduate Research Journal, 2021. [paper]
    computational psychiatry, review

Talks

  1. A metric for comparing complex systems by their dynamics
    Contributed Talk, 7th International Conference on the Mathematics of Neuroscience and AI, 2026 (top 10%)
    Flatiron Center Junior Theoretical Neuroscientist Workshop, July 2026
    Safavi Lab (TU Dresden), November 2026
  2. Introduction to Koopman operator theory [notes]
    Flatiron Center Junior Theoretical Neuroscientist Workshop, July 2026
  3. InputDSA: Demixing, then comparing recurrent dynamics and external input
    Contributed Talk, COSYNE 2026 (top 2%) [video]
    Safavi Lab (TU Dresden), 2026
  4. Comparing neural population dynamics by identifying optimal linearizing embeddings
    Carney Institute, Brown University, September 2025
    Olveczky Lab (Harvard), August 2025
  5. Building representations from the bottom up
    Santa Fe Institute, September 2024
  6. Internship project presentation (ML methods for EMG decoding)
    Meta Reality Labs, September 2024
  7. Beyond geometry: Comparing the temporal structure of computation in neural circuits with dynamical similarity analysis
    Kriegeskorte Lab (Columbia), August 2024
    Neuromatch Academy, Contributed Guest Tutorial, July 2024 [video]
    Contributed Talk, COSYNE, March 2024 (top 2%) [video]
    Workshop on Data-Driven and Task-Driven Models of Neural Computation, COSYNE, March 2024
    Cognitive Science Lunch Talks, MIT BCS, October 2023
    Contributed Talk, CCN, August 2023 (top 5%)
    SFI Complexity-GAINS Workshop, August 2023
  8. Investigating the interplay of anatomical, biophysical, and functional modularity in task-optimized RNNs
    International Brain Laboratory, February & June 2023
  9. Do deep neural networks have concepts? (with Chen, Zhang, Sung)
    Philosophy of Deep Learning Conference, 2023
  10. How neuroscience and AI drive each other forwards
    Instructor Spotlight, Inspirit AI Summer School, 2022
  11. Representational geometry of social inference and generalization in a competitive game
    Spotlight Talk, RSS Workshop on Social Intelligence in Humans and Robots, June 2022 [video]
    Yale Neuroscience Research in Progress, April 2022
  12. Deep meta-learning in a generalized context produces semantic neural representations
    Yale Neuroscience Undergraduate Research Organization, February 2021
  13. Low-D sensory processing neural activity best explains mouse behavior in a visual discrimination task
    Neuromatch Academy Virtual Conference, July 2020

Posters

  1. Traversing the solution space of neural networks with Hessian null-space continuation. Huang, Ostrow, Lu, Redman, Kozachkov. New England Mechanistic Interpretability Conference, 2026 (selected as a talk, 8%).
  2. Fast dynamical similarity analysis. Behrad, Ostrow, Fahkarian, Fiete, Safavi. CCN, 2026.
  3. InputDSA: Demixing, then comparing recurrent dynamics and external input. Huang*, Ostrow*, Singh, Kozachkov, Rajan. COSYNE, 2026 (top 2%, talk selection) & RLDM & Kempner Institute Frontiers in NeuroAI Symposium, 2025. (*equal contribution)
  4. A metric for comparing complex systems by their dynamics. Ostrow, Eisen, Redman, Kozachkov, Fiete. COSYNE & 7th International Conference on the Mathematics of Neuroscience and AI, 2026.
  5. Characterizing control between interacting subsystems with deep Jacobian estimation. Eisen, Ostrow, Chandra, Kozachkov, Miller, Fiete. COSYNE, 2026.
  6. Beyond geometry: Comparing the temporal structure of computation in neural circuits with dynamical similarity analysis. Ostrow, Eisen, Kozachkov, Fiete. CCN, 2023 (top 5%, talk selection) & COSYNE, 2024 (top 2%, talk selection).
  7. Predictive models are not enough for explanation-seeking curiosity: A case study. Sung, Ostrow. Curiosity, Creativity and Complexity Conference, 2023.
  8. Network dimensions alter reversal learning strategies. Naim, Gibson, Papageorgiou, Xie, Ostrow, Graybiel, Yang. COSYNE, 2023.
  9. Neural representations of opponent strategy support the adaptive behavior of recurrent actor-critics in a competitive game. Ostrow, Yang, Seo. COSYNE, 2022.
  10. A deep neural network model adapts flexibly to different opponent strategies in a competitive game. Ostrow, Yang, Seo. Society for Neuroscience, 2021.
  11. Exploring mouse models for tic pathophysiology with relevance to Tourette syndrome. Ostrow, Emmons, Pittenger. Yale Undergraduate Research Symposium, 2019.

Service & mentoring

  1. Reviewer: ICML (2026), NeurIPS (2025, 2026), NeurReps (2022, 2026), CCN (2023), PNAS (2024), Cerebral Cortex (2024)
  2. Student Representative, MIT BCS Faculty Search, 2024
  3. Organizer, Santa Fe Institute Working Group on Compositionality, 2024
  4. Graduate Member, MIT Resources for Easing Friction and Stress (REFS), 2023–present
  5. Mentor, MIT BCS Application Assistance Program, 2022–2023
  6. Teaching assistant:
    1. MIT 9.53: Emergent Computations from Distributed Neural Circuits (Spring 2025)
    2. CCN Mechanistic Interpretability Tutorial (2024)
    3. MIT 9.49: Neural Circuits for Cognition (Fall 2023)
    4. Yale S&DS 312: Linear Models (Fall 2020)

Fellowships & awards

  1. STIRR Initiative Travel Award, 2026
  2. MIT McGovern Institute SPOT Award for Service to the Department, 2025
  3. Irene T. Cheng Fellowship, MIT, 2024
  4. NSF Graduate Research Fellowship (GRFP), 2024
  5. MIT McGovern Institute Science and Technology Award, 2024
  6. Singleton Fellowship, MIT, 2023
  7. NeurIPS Scholar Award, 2023
  8. UCL Analytical Connectionism Travel Grant, 2023
  9. Best Project, SFI Complexity-GAINS Program, 2023
  10. SFI Complexity-GAINS Travel Grant, 2023
  11. Praecis Presidential Fellowship, MIT, 2022
  12. Computationally-Enabled Integrative Neuroscience Fellowship, MIT, 2022
  13. Yale Nominee for the Marshall and Mitchell Scholarships, 2021
  14. Mellon Fellowship, Yale, 2021
  15. Kavli Neuroscience Fellowship, Yale, 2019
  16. Richter Fellowship, Yale, 2019
  17. 2nd Place Poster, Yale Undergraduate Research Symposium, 2019