Current Phenomenon
Interactive field. Move your cursor through the background. Reload the page to cycle to a different dynamical system.
Learning Dynamics / AI for Science / Generative AI
Antonis Vozikis
PhD researcher in AI for Science, focused on learning dynamics with deep learning, with additional work on generative AI. Supervised by Prof. Efstratios Gavves and Dr. Andrii Zadaianchuk at Universiteit van Amsterdam.
- Learning dynamics
- Generative AI
- Equivariant learning
- Scientific machine learning
- Cellular dynamics
Signature Result
T-cell and cancer-cell interaction dynamics
A time-resolved interaction sequence illustrating the cellular dynamics I study, capturing pursuit, contact, and response as the system evolves.
About
Research focus and background
A core application of my work lies in cellular dynamics, where I study how T cells interact with and attack cancer cells using data driven and physics inspired models.
I hold a Master's in Artificial Intelligence from Vrije Universiteit (VU), in collaboration with Universiteit van Amsterdam (UvA) (Grade 8.5/10). My thesis research focused on Generative AI Architectures for Structured Graphs and received a 9/10.
I am a graduate in Electrical and Computer Engineering with a lifelong passion for Mathematics. From a young age, I found joy in solving mathematical problems, which led me into the field of AI.
When I am not immersed in my research, I enjoy reading, good coffee, and long walks in nature.
Learning Dynamics
My main research focus is learning dynamics with deep learning in the context of AI for Science.
- Supervised by Prof. Efstratios Gavves and Dr. Andrii Zadaianchuk
- Universiteit van Amsterdam
Cellular Dynamics
A core application of my work is understanding how T cells interact with and attack cancer cells.
- Data driven and physics inspired models
- Learning and modeling cellular interaction dynamics
Generative AI
I also work on generative AI, including autoregressive models for structured graphs and the physical behavior of video generative models.
- Autoregressive knowledge graph generation
- Video generative models and physical reasoning
Selected Work
Selected publications
A few selected papers and research outputs.
Probing Newtonian Mechanics in Video Generative Models with Real Physical Systems
ICML 2026 paper probing Newtonian mechanics in video generative models using real physical systems.
Read paper
Autoregressive Models for Small-Scale Knowledge Graph Generation
TMLR paper introducing ARK and SAIL for autoregressive knowledge graph generation with strong semantic validity and controlled graph completion.
Read paper
Selected Publications
Browse the full publication list, including selected papers, preprints, and workshop contributions.
View publicationsPodcast
AI in the Physical World
Conversations with researchers about how AI helps us understand, design, and control the physical world — from robotics and embodied intelligence to physics, biology, and materials.
Can Robots Understand the World?
News
Recent updates
Recent milestones, talks, teaching, and research updates.
Recent news
All newsOct 14, 2026
Jul 15, 2026
May 01, 2026
Apr 01, 2026