Intelligent Convergence Lab (ICL)
Our name reflects what we aim for: intelligent convergence. We bring together multiple sources of intelligence, including data priors, existing models, human expertise, and real-time feedback, to develop learning systems that can continuously adapt, transfer, and converge toward optimal solutions as tasks and environments change.
We work on the fundamentals of machine learning and AI across theory, algorithms, and applications. Our future research will focus on these aspects in next-generation generative AI, including controllable and test-time adaptive generation, reasoning and self-verification, and the theory of compositionality, diversity, and sampling efficiency. We also explore how these ideas can advance AI for Science, integrating foundation models, domain knowledge, human expertise, and experimental feedback to accelerate scientific discovery.
Department of Computer Science, Aalto University · ELLIS Institute Finland
Research Themes
Generative Models
Diffusion and flow models, controllable and test-time adaptive generation, and the theory of generalization and memorization in generative models.
Learning to Adapt
Meta-learning, continual learning and test-time adaptation: systems that keep learning and improving as tasks and data change.
Trustworthy ML
Robustness under distribution shift, algorithmic fairness, privacy, and reliable learning from limited data.
Learning Theory
Information-theoretic and PAC-Bayesian generalization bounds that explain, and guide, transfer and adaptation.
Probabilistic ML
Bayesian optimization, Gaussian processes, neural processes and prior-data fitted networks: principled uncertainty for decisions under limited data.
AI for Science
Foundation-model-guided Bayesian optimization for molecular discovery, and machine learning for healthcare such as digital hearing health.
Join Us
We are recruiting PhD students and postdocs, including through the ELLIS Institute Finland call (deadline 21 September 2026) and the HIIT Postdoctoral Fellow call (deadline 4 October 2026).
News
- Talk at the HealthtechFi Sustainability working group on responsible and trustworthy AI in health technology, “Reliable and Generalizable AI Systems”.
- Qi Chen will serve as an Area Chair for the ICLR 2027 main track.
- We organized the ELLIS Summer School AI4Research 2026 at Aalto University.
- Talk at the Unite! online course on Recent Advances and Research Trends in AI, “Learning with Limited Data”.
- Talk at the Centre for AI Fundamentals, University of Manchester.
- One paper accepted to ICML 2026 as a spotlight. See you in Seoul!