About Me

Hi, welcome.

I am a final year Ph.D. student at Aalto University supervised by Prof Arno Solin. I specialize in probabilistic machine learning, uncertainty quantification, and few-shot learning. I have obtained an M.Sc. in Machine Learning with Distinction at UCL. My work has led to ten publications including top venues like ICML, WACV, and NeurIPS workshops. I am proficient in Python and PyTorch. I co-organized workshops on Uncertainty Quantification for Computer Vision at top conferences ICCV and ECCV.

View My CV (Academic Version)

View My CV (General Version)

Publications

Conference

Rui Li, Marcus Klasson, Arno Solin, Martin Trapp. “Streamlining Prediction in Bayesian Deep Learning” Under Review

Anton Baumann, Rui Li, Marcus Klasson, Santeri Mentu, Shyamgopal Karthik, Zeynep Akata, Arno Solin, Martin Trapp. “Post-hoc Probabilistic Vision-Language Models. ” Under Review

Rui Li, Martin Trapp, Marcus Klasson, Arno Solin. “Flatness Improves Backbone Generalisation in Few‐shot Classification.” WACV (2025)

Rui Li, ST John, Arno Solin. “Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models”. ICML (2023).

Arno Solin, Rui Li, Andrea Pilzer. “A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching”. FUSION (2022).

Workshops

Rui Li, Marcus Klasson, Arno Solin, Martin Trapp. “Posterior Inferred, Now What? Streamlining Bayesian Deep Learning” NeurIPS Workshop on Bayesian Decision-making and Uncertainty (2024)

Anton Baumann, Marcus Klasson, Rui Li, Arno Solin, Martin Trapp. “Probabilistic Active Few-Shot Learning in Vision-Language Models” NeurIPS Workshop on Bayesian Decision-making and Uncertainty (2024)

Marlon Tobaben, Marcus Klasson, Rui Li, Arno Solin, Antti Honkela. “Differentially Private Continual Learning using Pre-Trained Models” NeurIPS Workshop on Scalable Continual Learning for Lifelong Foundation Models (2024)

Rui Li, ST John, Arno Solin. “Towards Improved Learning in Gaussian Processes: The Best of Two Worlds”. NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems (2022).

Rui Li, Zhengyun You, Yumei Zhang. “Deep Learning for Signal and Background Discrimination in Liquid based Neutrino Experiment”. 18th Workshop on Advanced Computing and Analysis Techniques in Physics Research (2018).

Journals

Chuan Chen, Rui Li, Lin Shu, Zhiyu He, Jining Wang, Chengming Zhang, Huanfei Ma, Kazuyuki Aihara, Luonan Chen. “Predicting future dynamics from short-term time series using an Anticipated Learning Machine”. National Science Review (2020).

Rui Li, Fanghua Ye, Shaoan Xie, Chuan Chen, Zibin Zheng. “Digging into It: Community Detection via Hidden Attributes Analysis”. Neurocomputing (2019).

Contact

Email:
firstname.lastname@aalto.fi