Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling. - Nathaniel Diamant; Erik Reinertsen; Steven Song; Aaron D. Aguirre; Collin M. Stultz; Puneet Batra (CoRR, 2021)

Conditional Diffusion with Less Explicit Guidance via Model Predictive Control. - Max W. Shen; Ehsan Hajiramezanali; Gabriele Scalia; Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Andreas Loukas (CoRR, 2022)

A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning. - Austin Atsango; Nathaniel L. Diamant; Ziqing Lu; Tommaso Biancalani; Gabriele Scalia; Kangway V. Chuang (CoRR, 2022)

Cohort design and natural language processing to reduce bias in electronic health records research. - Shaan Khurshid; Christopher Reeder; Lia X. Harrington; Pulkit Singh; Gopal Sarma; Samuel F. Friedman; Paolo Di Achille; Nathaniel Diamant; Jonathan W. Cunningham; Ashby C. Turner; Emily S. Lau; Julian S. Haimovich; et al. (npj Digit. Medicine, 2022)

Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk. - Marcus D. R. Klarqvist; Saaket Agrawal; Nathaniel Diamant; Patrick T. Ellinor; Anthony Philippakis; Kenney Ng; Puneet Batra; Amit V. Khera (npj Digit. Medicine, 2022)

Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling. - Nathaniel Diamant; Erik Reinertsen; Steven Song; Aaron D. Aguirre; Collin M. Stultz; Puneet Batra (PLoS Comput. Biol., 2022)

Improving Graph Generation by Restricting Graph Bandwidth. - Nathaniel Lee Diamant; Alex M. Tseng; Kangway V. Chuang; Tommaso Biancalani; Gabriele Scalia (ICML, 2023)

GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion. - Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Gabriele Scalia (CoRR, 2023)

RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion. - Colin A. Grambow; Hayley Weir; Nathaniel L. Diamant; Alex M. Tseng; Tommaso Biancalani; Gabriele Scalia; Kangway V. Chuang (CoRR, 2023)

Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion. - Alex M. Tseng; Nathaniel Diamant; Tommaso Biancalani; Gabriele Scalia (CoRR, 2023)

Conformalized Deep Splines for Optimal and Efficient Prediction Sets. - Nathaniel Diamant; Ehsan Hajiramezanali; Tommaso Biancalani; Gabriele Scalia (AISTATS, 2024)

Feedback Efficient Online Fine-Tuning of Diffusion Models. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Sergey Levine; Tommaso Biancalani (ICML, 2024)

Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control. - Masatoshi Uehara; Yulai Zhao 002; Kevin Black; Ehsan Hajiramezanali; Gabriele Scalia; Nathaniel Lee Diamant; Alex M. Tseng; Tommaso Biancalani; Sergey Levine (CoRR, 2024)

Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States. - Tianyu Liu 001; Edward De Brouwer; Tony Kuo; Nathaniel Diamant; Alsu Missarova; Hanchen Wang 002; Minsheng Hao; Héctor Corrada Bravo; Gabriele Scalia; Aviv Regev; Graham Heimberg (RECOMB, 2025)

Unsupervised deep learning of electrocardiograms enables scalable human disease profiling. - Samuel F. Friedman; Shaan Khurshid; Rachael A. Venn; Xin Wang; Nathaniel Diamant; Paolo Di Achille; Lu-Chen Weng; Seung Hoan Choi; Christopher Reeder; James P. Pirruccello; Pulkit Singh; Emily S. Lau; et al. (npj Digit. Medicine, 2025)