About me

I am a Senior Researcher at Microsoft Research New England, based in Cambridge, MA. I work on generative AI models (diffusion models, normalizing flows, language models) and related topics at the intersection of machine learning, statistics, and AI for science. My works include Adjoint Matching, a reward fine-tuning framework for flow models that has been extended to chemistry and robotics, and Energy-Based Fine-Tuning (EBFT), a language model fine-tuning algorithm that relies on matching feature moments, outperforming SFT in perplexity and downstream performance while matching RLVR in downstream performance.

I received my PhD in Computer Science from NYU, where I was advised by Joan Bruna.
During my PhD, I interned at IBM Research and Microsoft Research, and was a Visiting Researcher at Meta FAIR Labs for two years. I obtained a B.S. in Mathematics and a B.S. in Engineering Physics from the Polytechnic University of Catalonia (UPC).

My email address is cd2754 (at) nyu (dot) edu.

Join our weekly Generative Modeling & Sampling Seminar at MSR NE (in-person attendance is available)! Fill out the form and check upcoming talks in the seminar website. Watch recorded talks on our YouTube channel.

Selected works

• Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models
  Samy Jelassi*, Mujin Kwun*, Rosie Zhao*, Yuanzhi Li, Nicolo Fusi, Yilun Du, Sham M. Kakade, Carles Domingo-Enrich* (*Equal contribution). arXiv preprint, March 2026. Project website and code available.
• Adjoint matching: fine-tuning flow and diffusion generative models with memoryless stochastic optimal control
  Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen. ICLR 2025, Spotlight. Code here: https://github.com/microsoft/soc-fine-tuning-sd