My research journey spans both academia and industry, unified by a central interest in low-dimensional principles of intelligence - a philosophical overview is available in my writeup for the CPAL conference, for those interested: https://cpal.cc/vision/

 

At the university level, we currently revisit classical sparse and low-rank optimization through the lens of modern AI, developing theory-driven algorithms that accelerate training and inference in large-scale models. We also investigate how algebraic and logical structures emerge during learning, uncovering the interplay between neural and symbolic computation across streamlined architectures, reasoning pipelines, and agentic systems. Our latest research efforts are organized into four thematic thrusts, outlined below.

 

Historically, my work has included deep learning theory, learning-augmented optimization, sparse coding & inverse problems, and visual restoration & understanding — areas in which I remain (somehow) engaged.

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In parallel, my industry experiences have opened several new, distinct research directions inspired by practical deployments and large-scale systems: geometry deep learning and graphs (with Amazon),  video generation (with Picsart), and foundation model training for trading (with XTX Markets)