Marcin Sendera

I am a PhD candidate at Jagiellonian University, advised by Prof. Jacek Tabor, and a recent Research Intern at Mila - Quebec AI Institute (2023-2024), where I worked under the supervision of Prof. Yoshua Bengio.

I have recently submitted my PhD thesis, “Probabilistic deep learning: from efficient sampling to principled generation,” and I am currently awaiting my defense.

My research focuses on Probabilistic Deep Learning, with a specific emphasis on Diffusion Models and advanced sampling methods like GFlowNets. My core mission is to leverage these tools to build AI models that possess stronger Probabilistic Reasoning capabilities while remaining Safe and aligned. I am particularly interested in how structured generation can bridge the gap between rigorous Bayesian inference and modern deep learning.

I am also trying to advance the (amortised) variational inference methods to allow better probabilistic inference (and Bayesian posterior estimation) in complex, large-scale models like LLMs and Diffusion Models.

Current Research Focus:

Reasoning: Improving reasoning capabilities in generative models via structured sampling.

Diffusion: Developing differentiable Top-K methods for discrete data generation and improving continuous diffusion models.

Safety & Unlearning: Embedding safety constraints and machine unlearning directly into the generative process.

Prior to my PhD, I was a Summer Research Student at the University of Cambridge, working on early cancer detection using deep learning.