Victor Geadah

(I pronounce my last name as "Jada")

I'm a PhD Candidate in Applied & Computational Mathematics at Princeton University, working under the supervision of Jonathan Pillow at the Princeton Neuroscience Institute.

My research falls within statistical neuroscience and my interests span dynamical systems, control theory and probabilistic machine learning. Specifically, I am interested in studying the computation that underlies the non-stationary dynamics we observe in animals as they learn and adapt to new environments. I like to approach these questions by developing data-driven methods combined with a more theoretical, normative accounts of the dynamics.

Bio: Before Princeton, I studied applied mathematics at the University of Cambridge (master's student, Part III), and mathematics at the Université de Montréal (BSc), where I worked with Guillaume Lajoie on dynamic coding in recurrent neural networks.

Google Scholar | GitHub | CV

News

May 2025: Excited to announce I'm starting as a research scientist intern at Meta CTRL-Labs (NYC) this summer.
Apr. 2025: I'm honored to have been awarded Princeton's Porter Ogden Jacobus Fellowship for 2025-26!
Feb. 2025: Our ICLR submission on comparing noisy population dynamics has been selected for an oral presentation!

Victor Geadah
Fine Hall 218, Princeton University
Princeton, NJ 08540, U.S.A.
victor.geadah[ at ]princeton.edu

Publications

Journal Articles

Sparse-coding variational autoencoders
Victor Geadah*, Gabriel Barello*, Daniel Greenidge, Adam S. Charles, and Jonathan W. Pillow (2024)
Neural Computation 36(12), 2571-2601. [link | pdf | code]
Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization
Victor Geadah, Stefan Horoi, Giancarlo Kerg, Guy Wolf, and Guillaume Lajoie (2024)
PLOS Computational Biology 20 (12), e1012567. [link | bioRxiv].

*: Equal contribution.

Conference Proceedings

Comparing noisy neural population dynamics using optimal transport distances
Amin Nejatbakhsh, Victor Geadah, Alex H. Williams, and David Lipshutz (2025)
The Thirteenth International Conference on Learning Representations (ICLR). [oral | link]
Inferring system and optimal control parameters of closed-loop systems from partial observations
Victor Geadah, Juncal Arbelaiz, Harrison Ritz, Nathaniel D. Daw, Jonathan D. Cohen, and Jonathan W. Pillow (2024)
The 63rd IEEE Conference on Decision and Control (CDC), 8006-8013. [link | arXiv]
Parsing neural dynamics with infinite recurrent switching linear dynamical systems
Victor Geadah, International Brain Laboratory, and Jonathan W. Pillow (2024)
The Twelfth International Conference on Learning Representations (ICLR). [link | pdf | bibtex]
Low-dimensional dynamics of encoding and learning in recurrent neural networks
Stefan Horoi, Victor Geadah, Guy Wolf, and Guillaume Lajoie (2020)
Canadian Conference on Artificial Intelligence.

Preprints

Modeling neural activity with conditionally linear dynamical systems
Victor Geadah, Amin Nejatbakhsh, David Lipshutz, Jonathan W. Pillow, and Alex H. Williams.
[arXiv | code]
Modeling complex animal behavior with latent state inverse reinforcement learning
Aditi Jha, Victor Geadah, and Jonathan W. Pillow.
[bioRxiv]