Yawei Li

Affiliation. Email.

yawei_photo.JPG

Institut für Statistik

LMU München

Ludwigstraße 33, 80539

München, Germany

My academic journey started in China, where I obtained a bachelor in Laboratory Medical Science from Southern Medical University in Guangzhou, China. However, my passion for engineering led me to pursue a B.Sc. in Electrical Engineering at TU Darmstadt in Germany. Intrigued by the intersection of technology and medicine, I then completed a M.Sc. in Biomedical Computing at Technical University of Munich (TUM).

Currently, I am a Ph.D. student at the Statistical Learning and Data Science (SLDS) group at LMU Munich, under the supervision of Prof. Dr. Bernd Bischl. I am also a junior member of Munich Center for Machine Learning (MCML). In 2024, I was also a visiting researcher at the Deep Learning Lab of Prof. Dr. Kenji Kawaguchi at the National University of Singapore (NUS).

My research interests lie in trustworthy deep learning, with a particular focus on explainability and uncertainty quantification.

news

Jan 21, 2026 Our paper Steering Large Reasoning Models towards Concise Reasoning via Flow Matching is accepted by TMLR.
Jun 15, 2025 I started my internship as Applied Scientist Intern at Amazon.
Jan 22, 2025 Our paper Calibrating LLMs with Information-Theoretic Evidential Deep Learning is accepted by ICLR 2025.
Nov 01, 2024 Our paper A Dual-Perspective Approach to Evaluating Feature Attribution Methods is accepted by Transactions on Machine Learning Research (TMLR).
Oct 09, 2024 Our paper FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models is accepted by Compression Workshop at NeurIPS 2024.

selected publications

  1. Steering Large Reasoning Models towards Concise Reasoning via Flow Matching
    Yawei Li, Benjamin Bergner, Yinghan Zhao, and 3 more authors
    Transactions on Machine Learning Research (TMLR), 2026
    PDF
  2. Calibrating LLMs with Information-Theoretic Evidential Deep Learning
    Yawei Li, David Rügamer, Bernd Bischl, and 1 more author
    In The Thirteenth International Conference on Learning Representations (ICLR), 2025
    PDF
  3. A Dual-Perspective Approach to Evaluating Feature Attribution Methods
    Yawei Li*, Yang Zhang*, Kenji Kawaguchi, and 3 more authors
    Transactions on Machine Learning Research (TMLR), 2024
    PDF
  4. FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models
    Yang Zhang*, Yawei Li*, Xinpeng Wang, and 5 more authors
    Compression Workshop at NeurIPS 2024, 2024
    PDF
  5. Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization
    Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, and 4 more authors
    In The Twelfth International Conference on Learning Representations (ICLR), 2024
    PDF
  6. AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
    Yang Zhang*, Yawei Li*, Hannah Brown, and 5 more authors
    NeurIPS 2023 Workshop XAI in Action, 2023
    PDF
  7. Deep learning-based classification of dermatological lesions given a limited amount of labelled data
    S Krammer, Y Li, N Jakob, and 6 more authors
    Journal of the European Academy of Dermatology and Venereology, 2022
    PDF
  8. Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
    Yang* Zhang, Ashkan* Khakzar, Yawei Li, and 3 more authors
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF
  9. Explaining covid-19 and thoracic pathology model predictions by identifying informative input features
    Ashkan* Khakzar, Yang* Zhang, Wejdene Mansour, and 5 more authors
    In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
    PDF