Stefano Ermon
CEO of Inception, developer of Mercury diffusion LLMs; Associate Professor of Computer Science at Stanford
- CEO, Inception2
- Associate Professor, Department of Computer Science (Artificial Intelligence Lab), Stanford University1
Stefano Ermon is the CEO of Inception, an AI research and product company building diffusion-based large language models, and an Associate Professor in the Department of Computer Science at Stanford University, affiliated with the Artificial Intelligence Lab. Ermon's research is in machine learning and generative AI, and Ermon is a senior author of the Mercury technical report, which describes Inception's diffusion language models.2314
Research
Ermon describes developing principled machine learning methods motivated by concrete real-world applications. With Yang Song, Ermon co-authored early work on score-based generative models, later extended into a framework based on stochastic differential equations.156
Ermon is also a co-author of Direct Preference Optimization, which fine-tunes language models on preference data without a separately trained reward model. Later work applied diffusion to discrete data such as text, and Inception's about page lists papers on masked diffusion and block diffusion among the technologies its team has developed.783
Inception
As CEO, Ermon writes that Inception was started to build fast, efficient AI models and that it launched Mercury, its first diffusion-based LLM, in February 2025. Mercury models generate blocks of text in parallel and refine them over a small number of denoising steps rather than producing one token at a time.23
Teaching
At Stanford, Ermon has taught Probabilistic Graphical Models (CS 228), Deep Generative Models (CS 236) and Data for Sustainable Development (CS 325B).1
Work
Mercury (external site: arxiv.org)Proprietary product
Senior author of the 2025 Mercury report, which presents commercial-scale language models based on diffusion that are trained to predict multiple tokens in parallel, starting with the Mercury Coder models for coding.4
Score-based generative modeling (external site: arxiv.org)Published research
Co-author with Yang Song of the 2019 paper on generative modeling by estimating gradients of the data distribution, and of the 2020 paper framing score-based generative modeling through stochastic differential equations.56
Discrete diffusion by estimating data ratios (external site: arxiv.org)Published research
Co-author with Aaron Lou and Chenlin Meng of a 2023 paper on discrete diffusion modeling for language by estimating ratios of the data distribution.8
Career timeline
Jul 12, 2019
Co-authored the paper on generative modeling by estimating gradients of the data distribution.5
Nov 26, 2020
Co-authored the paper on score-based generative modeling through stochastic differential equations.6
May 29, 2023
Co-authored the Direct Preference Optimization paper on aligning language models with preferences.7
Oct 25, 2023
Co-authored the paper on discrete diffusion modeling by estimating ratios of the data distribution.8
Feb 2025
Inception launched Mercury, its first diffusion-based large language model.2
Jun 17, 2025
Was a senior author of the Mercury report on ultra-fast diffusion language models.4
Honors and recognition
- ICML 2024 Best Paper Award · International Conference on Machine Learning, 20241
- ICLR 2022 Outstanding Paper Award · International Conference on Learning Representations, 20221
- ICLR 2021 Outstanding Paper Award · International Conference on Learning Representations, 20211
- Sloan Research Fellowship · Alfred P. Sloan Foundation1
- IJCAI Computers and Thought Award · International Joint Conferences on Artificial Intelligence1
- NSF CAREER Award · National Science Foundation1
- ONR Young Investigator Award · Office of Naval Research1
Related people
More sourced profiles at the same organization, in the same field, or from the same school.
- Aditya GroverAlso at Inception
- Albert GuAlso from Stanford
- Abigail SeeAlso at Stanford University
- Andrew NgAlso at Stanford University
- Beren MillidgeLanguage models
- Abubakar AbidAlso from Stanford
- Azalia MirhoseiniAlso at Stanford University
- Diederik P. KingmaImage, video, and 3D generation
- Andrej KarpathyAlso from Stanford
- Brian HieAlso at Stanford University
- Fabian PedregosaMachine-learning theory
- Anima AnandkumarAlso from Cornell
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