Teaching

I have been a teaching assistant at the University of Munich in various modules, at both Bachelor and Master level. The topics of these courses include:

  • Foundations of Deep Learning for Natural Language Processing
  • Human-Centric Natural Language Processing
  • Applied Deep Learning
  • Statistical Methods in Natural Language Processing
  • Finite State Technologies in Computational Linguistics

Besides, I also supervise BSc. & MSc. theses and semester projects.

Courses

  • Statistical Methods in the Language Processing (Bachelor). Teaching Assistant (with Dr. Helmut Schmid).
    CIS, LMU Munich, WS 2024-25 & WS 2023-24.
  • Advanced Module of Computational Linguistics: Natural Language Processing and Deep Learning (Master). Teaching Assistant (with Prof. Barbara Plank et al.).
    CIS, LMU Munich, WS 2024-25 & WS 2023-24 & WS 2022-23.
  • Seminar: Human-centric Natural Language Processing (Master). Teaching Assistant (with Prof. Barbara Plank).
    CIS, LMU Munich, SS 2024 & SS 2023.
  • Applied Deep Learning with TensorFlow and PyTorch (Master). Supervisor (with Prof. David Ruegamer et al.).
    Department of Statistics, LMU Munich, SS 2024 & WS 2023-24 & SS 2023.
  • Basic Module of Computational Linguistics: Finite State Technologies (Master). Teaching Assistant (with Prof. Klaus Schulz).
    CIS, LMU Munich, WS 2021-22.

Thesis Supervision

  • Mengmeng Xu (LMU): AI-Assisted Semantic Search on Domain-Specific Unstructured Data.
    Master Thesis (Enterprise thesis at Infineon), WS 2024-25.
  • Bo Shao (TUM): Multilingual Knowledge Incorporation of Large Language Models.
    Master Thesis, SS 2024.
  • Lin Shui (LMU): Enhancing Retrieval Augmented Generation (RAG) for Domain-Specific Content.
    Master Thesis (Enterprise thesis at Bosch), SS 2024.
  • Han Yang (LMU): Enhancing Reasoning and Safety: Integrating Classical Rule-Based AI with Large Language Models.
    Master Thesis, WS 2023-24.
  • Bolei Ma (LMU): Prompt-based finetuning of multilingual models for zero-shot cross-lingual transfer.
    Master Thesis, SS 2023 (Currently PhD candidate with Prof. Frauke Kreuter at Department of Statistics, LMU Munich).
  • Xiaoqian Li (LMU): Extend the cross-lingual retrieval-augmented prompting method to new tasks and settings.
    Master Thesis (Co-supervised with Sheng Liang), SS 2023.

Project Supervision

  • KDD Cup 2024: Meta Comprehensive Retrieval-Augmented Generation (RAG) Benchmark. Course project, SS 2024.
  • SemEval’24 Task 1: Measuring the Semantic Textual Relatedness. Course project, WS 2023-24.
  • SemEval’24 Task 8: Machine-Generated Text Detection. Course project, WS 2023-24.
  • BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. Course project, SS 2023.