Nicholas Boffi
Assistant Professor
Develops generative modeling methods and machine learning for scientific applications.
Nicholas Boffi's lab at CMU works on generative & foundation models. 17 papers since 2023, 13 at top venues, mostly ICLR and ICML. 4 PhD students and 3 master's students. 3 alumni. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 76%
- PhD students
- 4
- MS students
- 3
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 3 papers2026 · T1 3 · collecting
- 6 papers2025 · T1 5
- 5 papers2024 · T1 5
- 3 papers2023 · T1 0
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, ECCV, JMLR
Research focuspaper titles that use each topic's words
- Generative & foundation models6 papers
- generative modeling1 paper
- modeling1 paper
- develops generative0 papers
- scientific0 papers
Paper list
From the lab homepage.
20263 papers
- ICLR (T1)Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds (opens in a new tab)
- ICLR (T1)Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models (opens in a new tab)
- ICLR (T1)Much Ado About Noising: Dispelling the Myths of Generative Robotic Control (opens in a new tab)
20256 papers
- NeurIPS (T1)BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation (opens in a new tab)
- TMLR (T1)Flow map matching with stochastic interpolants: A mathematical framework for consistency models (opens in a new tab)
- NeurIPS (T1)How to build a consistency model: Learning flow maps via self-distillation (opens in a new tab)
- Physical Review LettersModel-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking (opens in a new tab)
- ICLR (T1)Shallow diffusion networks provably learn hidden low-dimensional structure (opens in a new tab)
- JMLR (T1)Stochastic Interpolants: A Unifying Framework for Flows and Diffusions (opens in a new tab)
20245 papers
- PNAS (T1)Deep learning probability flows and entropy production rates in active matter (opens in a new tab)
- ICLR (T1)Multimarginal generative modeling with stochastic interpolants (opens in a new tab)
- ICML (T1)Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes (opens in a new tab)
- ECCV (T1)SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers (opens in a new tab)
- ICML (T1)Stochastic interpolants with data-dependent couplings (opens in a new tab)
20233 papers
- L4DCAgile Catching with Whole-Body MPC and Blackbox Policy Learning (opens in a new tab)
- eLifeHow microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution (opens in a new tab)
- Machine Learning Science and TechnologyProbability flow solution of the Fokker-Planck equation (opens in a new tab)
Where alumni went3 listed
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
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