| 2025 | ICML | Wasserstein Flow Matching: Generative Modeling Over Families of Distributions. | Doron Haviv, Aram-Alexandre Pooladian, Dana Pe'er, Brandon Amos |
| 2024 | ICML | Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformer. | Doron Haviv, Russell Zhang Kunes, Thomas Dougherty, Cassandra Burdziak, Tal Nawy, Anna Gilbert, Dana Pe'er |
| 2023 | AAAI | Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping. | Russell Z. Kunes, Mingzhang Yin, Max Land, Doron Haviv, Dana Pe'er, Simon Tavar |
| 2016 | ICML | Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data. | Sandhya Prabhakaran, Elham Azizi, Ambrose J. Carr, Dana Pe'er |
| 2013 | DAC | Can CAD cure cancer? | Smita Krishnaswamy, Bernd Bodenmiller, Dana Pe'er |
| 2003 | UAI | Learning Module Networks. | Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller, Nir Friedman |
| 2002 | ISMB | Minreg: Inferring an active regulator set. | Dana Pe'er, Aviv Regev, Amos Tanay |
| 2001 | ISMB | Inferring subnetworks from perturbed expression profiles. | Dana Pe'er, Aviv Regev, Gal Elidan, Nir Friedman |
| 2000 | RECOMB | Using Bayesian networks to analyze expression data. | Nir Friedman, Michal Linial, Iftach Nachman, Dana Pe'er |
| 1999 | UAI | Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm. | Nir Friedman, Iftach Nachman, Dana Pe'er |