Dr. Stefano Bruno

I am a Postdoctoral Research Associate in the Department of Industrial Engineering at UNIST (South Korea). I am part of the InnoCORE programme under the AI-driven Space Solar initiative.

My research interests lie at the interface of Machine Learning, Generative AI and Probability. I design algorithms with provable guarantees to tackle fundamental challenges in deep learning and generative modelling.

Until November 2025, I was a Postdoctoral Research Associate in the School of Mathematics at the University of Edinburgh (UK).

During my time at the University of Edinburgh, I have secured a £500,000 UK–South Korea Collaborative R&D grant, funded by Innovate UK, to support my research in collaboration with Neurolabs. The project has also raised an additional £415,000 in South Korea, funded by the Korea Institute for Advancement of Technology.

From November 2024 to April 2025, I worked as a Senior Researcher at Neurolabs, a UK-based Computer Vision startup. In April 2025, Neurolabs secured a $7.8 million Series A funding round.

Email:

sbruno@unist.ac.kr (UNIST, South Korea)

Office:

301-2, 112 Building, 50, UNIST-gil, Ulsan, 44919 Republic of Korea.

Preprint:

Flatness-Aware Stochastic Gradient Langevin Dynamics.

Stefano Bruno, Youngsik Hwang, Jaehyeon An, Sotirios Sabanis, Dong-Young Lim.

Available on ArXiv. First version: October 2025. Submitted. Code.

Publications:

Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients.

Stefano Bruno, Sotirios Sabanis.

Transactions on Machine Learning Research (TMLR), ISSN 2835-8856.Accepted for publication: September 2025. First version on ArXiv: May 2025.

On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates.

Stefano Bruno, Ying Zhang, Dong-Young Lim, Ömer Deniz Akyildiz, Sotirios Sabanis.

Transactions on Machine Learning Research (TMLR), ISSN 2835-8856. Accepted for publication: February 2025. First version on ArXiv: November 2023.

Optimal regularity in time and space for stochastic porous medium equations.

Stefano Bruno, Benjamin Gess, Hendrik Weber.

Annals of Probability, Vol. 50, No. 6, 2288-2343. November 2022.

Grants:

Innovate UK: circa £500,000. Additional funding (over £415,000) will be provided by the Korea Institute for Advancement of Technology as part of the UK-South Korea collaborative R&D grant.

Project: "AI innovation in the supply chain of consumer packaged-goods for recognising objects in retail execution, supply chain management and smart factories: using novel diffusion-based optimisation algorithms and diffusion-based generative models". A brief description of the project can be found here.

London Mathematical Society: £3,000. Support for organising the Hackathon Workshop on Generative Modeling.
UoE School of Mathematics Knowledge Exchange fund: £1,000. Support for organising the Hackathon Workshop on Generative Modeling.

Hackathon on Generative AI:

I was the lead organiser of the Hackathon Workshop on Generative Modeling during the Isaac Newton Institute-Alan Turing Institute satellite programme on "Diffusions in machine learning: Foundations, generative models and non-convex optimisation". Two industry-inspired challenges were provided by Amazon (Edinburgh).

Bio: I completed my PhD in Mathematics at the University of Bath (UK) in 2022, under the supervision of Prof. Hendrik Weber. I was part of the Probability Laboratory (Prob-L@B) and the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics. The focus of my thesis was on Stochastic Partial Differential Equations and the examiners for the PhD viva were Prof. Franco Flandoli (SNS Pisa) and Prof. Alexander Cox. After my doctorate, I worked briefly as Research Associate in Knowledge Exchange focusing on Deep Learning at the Institute for Mathematical Innovation.

Profiles: LinkedIn, Google Scholar, ResearchGate.