Prof. Dr. Andrey Ustyuzhanin

Prof. Ustyuzhanin’s core expertise lies in developing machine-learning methods that accelerate scientific discovery, which maps directly onto the Autonomous Science and AI Applications directions at Constructor Labs. For over two decades he has built AI systems that formulate and test hypotheses under the demanding constraints of real experimental science — from high-energy physics at CERN (the LHCb and SHiP experiments) to materials design, catalysis, quantum materials, and antibiotics discovery. This work spans the full autonomous-science loop: generative models that propose candidate structures, differentiable and surrogate-based optimization of experiments and detectors, anomaly detection for new-physics searches, and reproducible ML pipelines that make results auditable across large collaborations. These directions are backed by concrete outcomes rather than method work alone: as a member of the LHCb Collaboration he shares in the 2025 Breakthrough Prize in Fundamental Physics, his group’s methods have been adopted in experimental-physics workflows and open-source tooling, and he continues to lead international, cross-disciplinary collaborations — including current work as a Visiting Research Professor at the National University of Singapore (I-FIM) and as coordinator of the Materials Science Working Group in the EU Horizon Europe project SCIANCE (Strategic Coordination of AI-enabled Science in Europe).

His relevance to the EdTech direction is direct and long-standing. As Director of AI Research at Constructor Knowledge, his work targets Personalized Knowledge Transmission and Human–AI Co-Cognition / TeachingAI — systems that turn expert knowledge into adaptive learning artifacts and interfaces where AI agents can explain their reasoning and discoveries to learners. This builds on years of designing and teaching graduate ML curricula, founding and running research-education labs, and mentoring students who now work across academia and industry. He approaches education the same way he approaches science: as a problem of transmitting complex, evolving knowledge efficiently and verifiably to a human on the other side.

What ties these threads together — and what he brings to Constructor Labs specifically — is the conviction that autonomous science and personalized education are two sides of the same challenge: building AI that reasons about a domain well enough to both advance it and teach it. His contributions aim to make that loop concrete, so that the same systems which help discover new physics or materials can also explain those discoveries to the next generation of students and researchers.