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Weill Cornell Medicine · Cornell University

AI and neuroengineering to improve mental health

We build artificial intelligence and neuroengineering tools to understand psychiatric illness and match each patient to the treatment most likely to help them.

A lab led by Logan Grosenick at Weill Cornell Medicine, Cornell University.

Research

Areas of work.

Biological subtyping & biomarkers

Psychiatric diagnoses group together patients whose brains and treatment responses differ enormously, which is part of why finding an effective therapy is so often trial and error. We develop statistical and machine-learning methods that define data-driven biological subtypes from neuroimaging, multi-omics, and clinical data, and that predict which patients will respond to which treatment, from ketamine and TMS in depression to molecularly grounded subtypes of autism.

Personalized treatment & digital twins

Choosing a treatment means reasoning about a counterfactual: what would happen to this patient under that therapy. We build generative digital twins and counterfactual models (including diffusion world models and reinforcement-learning methods for dynamic treatment regimes) that estimate the treatment most likely to help an individual, and that make those predictions interpretable enough to trust in the clinic.

Machine learning for neural & biomedical data

Brain and biomedical data are noisy, high-dimensional, and irregularly structured. We develop new machine-learning methods built for them: graph neural networks that learn over the structure of brain networks, robust and reproducible estimators of connectivity, and representation-learning tools for comparing neural dynamics across animals and patients. Methods are released as open-source software.

Neuroengineering & neurostimulation

Better models need better measurements. We build and apply neurotechnology to read and perturb brain activity: TMS combined with high-density EEG in patients undergoing accelerated stimulation for treatment-resistant depression, and closed-loop approaches that turn those signals into individualized, adaptive interventions.

See all research →

Our software

All projects on GitHub →

QuantNets

Generalizing CNNs to graphs with learnable neighborhood quantization (NeurIPS 2024).

Python

EpiCare

A reinforcement-learning benchmark for dynamic treatment regimes (NeurIPS 2024).

Jupyter Notebook

ConDA

Contrastive Diffusion Alignment: structured latents for controllable generation (ICML 2026).

Python

Selected publications

Recent work.

Full list →

R. Sandilya, S. Perez, C. Lynch, L. Victoria, B. Zebley, D. M. Buchanan, M. T. Bhati, N. Williams, T. J. Spellman, F. M. Gunning, C. Liston, L. Grosenick (2026).

Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation

International Conference on Machine Learning (ICML)

arXiv ★ Top-3 AI/ML venue

L. Grosenick, C. Liston (2026).

Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging

Biological Psychiatry

M. Ajirak, O. Bein, E. B. Bowen, D. Kanellopoulos, A. Falk, F. M. Gunning, N. Solomonov, L. Grosenick (2025).

Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction

Advances in Neural Information Processing Systems (NeurIPS)

arXiv ★ Top-3 AI/ML venue

I. Kauvar, E. B. Richman, T. X. Liu, C. Li, S. Vesuna, A. Chibukhchyan, L. Yamada, A. Fogarty, E. Solomon, E. Y. Choi, L. Mortazavi, G. Chau Loo Kung, P. Mukunda, C. Raja, D. Gil-Hernández, X. Patron, X. Zhang, J. Brawer, S. Wrobel, Z. Lusk, D. Lyu, A. Mitra, L. Hack, L. Luo, L. Grosenick, P. van Roessel, L. M. Williams, B. D. Heifets, J. M. Henderson, J. A. McNab, C. I. Rodr\'\iguez, V. Buch, P. Nuyujukian, K. Deisseroth (2025).

Conserved brain-wide emergence of emotional response from sensory experience in humans and mice

Science

Paper

I. Osafo Nkansah, N. Gallagher, R. Sandilya, C. Liston, L. Grosenick (2024).

Generalizing CNNs to graphs with learnable neighborhood quantization

Advances in Neural Information Processing Systems (NeurIPS)

Code ★ Top-3 AI/ML venue

M. Hargrave, A. Spaeth, L. Grosenick (2024).

EpiCare: A reinforcement learning benchmark for dynamic treatment regimes

Advances in Neural Information Processing Systems (NeurIPS)

Code ★ Top-3 AI/ML venue

Support

Funded by

News

Latest from the lab.

2026

  • New work on controlling diffusion models for dynamic systems, Contrastive Diffusion Alignment (ConDA), accepted at ICML 2026. Congratulations to Ruchi Sandilya and the team.

2025

  • Logan Grosenick is awarded an NIH ORIP R01 (R01OD039830).
  • Marzieh Ajirak's work on adaptive routing for multimodal clinical prediction is accepted at NeurIPS 2025.
  • Work led by postdoc Isaac Kauvar on the emergence of emotion across humans and mice is published in Science.
  • Congratulations to Dr. Amanda Buch, who starts her own lab at Duke University as Assistant Professor of Psychiatry & Bioengineering.
  • Congratulations to Dr. Isaac Kauvar, now an AI Scientist at Anthropic.
  • Congratulations to Catherine Parkin on her acceptance to the PhD program in neuroscience at Columbia University.

2024

  • QuantNets (generalizing CNNs to graphs) accepted at NeurIPS 2024. Congratulations to Isaac Osafo Nkansah, Neil Gallagher, and Ruchi Sandilya.
  • EpiCare (an RL benchmark for dynamic treatment regimes) accepted at NeurIPS 2024. Congratulations to Mason Hargrave.
  • Marzieh Ajirak's work is accepted at EUSIPCO 2024.
  • Amanda Buch's work on simple and scalable algorithms for cluster-aware precision medicine and subtyping is published at AISTATS 2024.
  • Congratulations to Dr. Amanda Buch on a SFARI Bridge to Independence award, a Burroughs Wellcome Fund / Revson scholarship, and a 2024 Leading Edge Fellowship.
  • Congratulations to Dr. Marzieh Ajirak on the 2024 Perry Scholar Award.
  • Congratulations to Isabella Karabinas on a Best Poster Award at the Machine Learning Summer School (OIST, Okinawa) 2024.

2023

  • Congratulations to Logan Grosenick on a Whitehall Foundation research grant.
  • Logan Grosenick and Dr. Nili Solomonov receive a Cornell Center for Pandemic Prevention and Response (CCPPR) grant.
  • Amanda Buch's work on subtypes of autism spectrum disorder is published in Nature Neuroscience.
  • Congratulations to Dr. Marzieh Ajirak and Dr. Karsten Gimre on T32 postdoctoral fellowships.
  • Congratulations to Dr. Amanda Buch on the 2023 Perry Scholar Award.
  • Congratulations to Ellie Bowen on her acceptance to the University of Michigan Medical School.

2022

  • Logan Grosenick receives the Stephen I. Katz Early-Stage Investigator Award (R01) from NIMH.

Contact

Want to work with us?

Postdocs, PhD students, and collaborators are welcome. See the open positions and how to apply. For other inquiries, contact the lab coordinator, Jordan Serrano-Guedea.

View open positions → jas4060@med.cornell.edu LinkedIn