About me
I am a fifth-year Ph.D candidate in Scientific Computing at Uppsala University, supervised by Associate Professor Andreas Hellander. Prior to starting my Ph.D study, I earned MS.c in Computational Science from Uppsala University, MS.c in Chemometrics and BS.c in Chemistry from University of Science and Technology of China.
Research Interests
My research focuses on probabilistic and generative machine learning, with an emphasis on representation learning for foundation models. I am particularly interested in flow-based generative methods and probabilistic methods for multimodal and vision-language systems. Uncertainty quantification is one important application of these methods, but my broader interest is in using probabilistic modeling to understand and improve learned representations. Earlier in my Ph.D., I worked on federated learning, especially data heterogeneity, fairness, and robust optimization, though this is no longer my primary research direction.
News
05/2026: Our paper “Epistemic Uncertainty Quantification for Pre-trained VLMs via Riemannian Flow Matching” got accepted to ICML 2026.
01/2026: I started an internship at Modulai as a machine learning engineer, studying generalization behavior of reinforcement learning with verifiable rewards for large language models post-training.
09/2025: Our paper “Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere” got accepted to NeurIPS 2025.
08/2025: Our team Cat Quartet won 2nd place out of >500 teams at Huawei Wireless Communication Global Hackathon 2025 (1st place in Europe region), building a denoising neural SVD operator to approximate SVD operations in a scalable and robust manner.
11/2024: Our team Hello Kitty secured 2nd place out of 35 teams at Huawei Sweden Hackathon 2024, tackling wireless localisation problems using machine learning methods.
10/2024: I had a poster presentation at the Swedish e-Science Academy about the theoretical analysis for the application of logit adjustment in heterogeneous federated learning.
06/2024: Our paper “Accelerating Fair Federated Learning: Adaptive Federated Adam” got accepted in IEEE Transactions on Machine Learning in Communications and Networking.
04/2024: Our paper “Federated Learning for Predicting Compound Mechanism of Action Based on Image-data from Cell Painting” got accepted in Artificial Intelligence in the Life Sciences.
01/2024: Our paper “Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning” got accepted in IoTDI ‘24.
10/2022: I had a poster presentation at the Swedish e-Science Academy about accelerating fair federated learning in Umeå, Sweden.
06/2022: We released Blades, a simulator for Byzantine-robust federated learning with attacks and defenses.
12/21: Our paper “Proactive autoscaling for edge computing systems with kubernetes” got accepted in UCC ‘21.
09/2021: I started my Ph.D study at TDB, Uppsala University, co-supervised by Andreas Hellander, Prashant Singh, and Salman Toor.