Teaching

I have been a teaching assistant at the University of Munich in various modules, both at 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

• 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.
• Statistical Methods in the Language Processing (Bachelor). Teaching Assistant (with Dr. Helmut Schmid).
  CIS, LMU Munich, WS 2023-24.
• Advanced Module of Computational Linguistics: Natural Language Processing and Deep Learning (Master). Teaching Assistant (with Prof. Barbara Plank and Dr. Leon Weber).
  CIS, LMU Munich, WS 2023-24 & WS 2022-23.
• Basic Module of Computational Linguistics: Finite State Technologies (Master). Teaching Assistant (with Prof. Klaus Schulz).
  CIS, LMU Munich, WS 2021-22.

Thesis Supervision

• 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. (with Bosch)
  Master Thesis, 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. (Co-supervised with Sheng Liang)
  Master Thesis, 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.