Ph.D. in Computer Science
Email: firstname[dot]lastname[dot]inf[at]aalto[dot]fi
[Google Scholar/ ResearchGate/ Github/ CV]
About me
Hi, welcome! I’m currently a Principal Investigator at the ELLIS Institute Finland and a Tenure-track Assistant Professor in the Department of Computer Science at Aalto University. I am also a member of ELLIS. Before this, I was a postdoctoral fellow at the Data Science Institute (DSI) and Robot Vision & Learning Lab at the University of Toronto, supported by the DSI Postdoc Fellowship and advised by Prof. Florian Shkurti. Additionally, I serve as a Faculty Affiliate Researcher at the Vector Institute. I got my Ph.D. (dissertation with honor) from Laval University in Mar. 2024, where I was supervised by Prof. Mario Marchand. Prior to this, I was a senior algorithm engineer at Baidu and Bytedance. I obtained my master of science in engineering (diplôme d’ingénière) at Institut Polytechnique de Paris - Telecom Paris and my bachelor’s from Chien-Shiung Wu College, Southeast University in China.
Recruiting
- We are recruiting PhD students and postdocs through the ELLIS Institute Finland PhD and Postdoc Call. We also have an Entrepreneurial Postdoc opportunity for researchers interested in turning AI research into a startup. I am a scientific advisor for the theme Continually Learning and Self-Improving AI, covering continual learning, adaptive agents, and AI systems that learn and improve over time. Deadline for Application: September 21, 2026!!!
- If you are interested in joining our group, please fill out the Application Form.
Research Goals
I work on making AI more Trustworthy and Efficient. My research focuses on enhancing the generalization of modern deep learning models in out-of-distribution settings and dynamically changing environments, considering potential fairness and bias issues. I see these as fundamental challenges in building responsible AI and advancing toward AGI.
My PhD research tries to interpret basic human intelligence from the perspective of acquiring and exploiting prior knowledge. I believe real intelligence hinges on efficient knowledge modulation—how to effectively learn, store, retrieve, and compose knowledge—integrating high-level reasoning and verification processes.
This perspective drives my broad interests in meta-learning, continual learning, generative models, algorithmic fairness, Bayesian optimization, and reinforcement learning. I explore these areas from both theoretical and algorithmic standpoints, with a strong desire to apply them to high-impact domains such as scientific discovery.
News
- [08/26] Happy to be the main organizer of the ELLIS Summer School AI4Research 2026, now taking place at Aalto University.
- [05/26] I gave a talk at the University of Manchester. Thanks for the invitation!
- [05/26] One paper accepted to ICML 2026, see you in Seoul.
- [03/26] I gave a remote talk at CSML seminar of Lancaster University. Thanks for the invitation!
- [02/26] Thanks to the Gemini Academic Program Award for supporting our research.
- [11/25] Appointed as Action Editor of TMLR.
- [11/25] I gave a talk at the ELLIS Institute Finland Scientific Seminar.