Rajesh Ranganath
Associate Professor of Computer Science and Data Science
Develops probabilistic and causal inference methods, with applications to machine learning in healthcare and medicine.
Rajesh Ranganath's lab at NYU works on probabilistic & causal inference and efficient training & inference. 30 papers since 2023, 19 at top venues, mostly ICML and ICLR. 8 PhD students. 2 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 63%
- PhD students
- 8
- MS students
- 0
- alumni listed
- 2
Papers by yearfrom DBLP, 2023–2026
- 3 papers2026 · T1 3 · collecting
- 8 papers2025 · T1 4
- 11 papers2024 · T1 6
- 8 papers2023 · T1 6
T1 venuesOtherVenues: ICML, ICLR, NeurIPS, AISTATS, AAAI
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference4 papers
- Efficient training & inference3 papers
- inference methods0 papers
- develops probabilistic0 papers
- causal inference0 papers
Paper list
From DBLP.
20263 papers
20258 papers
- ICML (T1)A General Framework for Inference-time Scaling and Steering of Diffusion Models (opens in a new tab)
- J. Am. Medical Informatics Assoc.AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation (opens in a new tab)
- Findings of EMNLPLearning Is Not A Race: Improving Retrieval in Language Models via Equal Learning (opens in a new tab)
- Trans. Mach. Learn. Res.On the Challenges and Opportunities in Generative AI (opens in a new tab)
- ICML (T1)Preference learning made easy: Everything should be understood through win rate (opens in a new tab)
- NeurIPS (T1)Test Time Scaling for Neural Processes (opens in a new tab)
- ICLR (T1)Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do (opens in a new tab)
- PatternsWhen accurate prediction models yield harmful self-fulfilling prophecies (opens in a new tab)
202411 papers
- ICML (T1)Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction (opens in a new tab)
- NeurIPS (T1)Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities (opens in a new tab)
- NeurIPS (T1)Explanations that reveal all through the definition of encoding (opens in a new tab)
- BMC Medical Informatics Decis. Mak.From algorithms to action: improving patient care requires causality (opens in a new tab)
- Trans. Mach. Learn. Res.Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation (opens in a new tab)
- NeurIPS (T1)Preference Learning Algorithms Do Not Learn Preference Rankings (opens in a new tab)
- npj Digit. MedicineQuantifying impairment and disease severity using AI models trained on healthy subjects (opens in a new tab)
- Mach. Learn. Sci. Technol.Robust anomaly detection for particle physics using multi-background representation learning (opens in a new tab)
- ICML (T1)Stochastic Interpolants with Data-Dependent Couplings (opens in a new tab)
- Trans. Mach. Learn. Res.Towards Minimal Targeted Updates of Language Models with Targeted Negative Training (opens in a new tab)
- ICML (T1)What's the score? Automated Denoising Score Matching for Nonlinear Diffusions (opens in a new tab)
20238 papers
- ICML (T1)An Effective Meaningful Way to Evaluate Survival Models (opens in a new tab)
- Trans. Mach. Learn. Res.Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics (opens in a new tab)
- AISTATS (T1)DIET: Conditional independence testing with marginal dependence measures of residual information (opens in a new tab)
- AISTATS (T1)Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation (opens in a new tab)
- NeurIPS (T1)Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy (opens in a new tab)
- AAAI (T1)Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection (opens in a new tab)
- MLHCWhen More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations (opens in a new tab)
- ICLR (T1)Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions (opens in a new tab)
Where alumni went2 listed
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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