CV

Education

• B.Eng. in Software Engineering, Wuhan University, 2025 (expected)

Research Experience

• RNN Analysis on Same-Different Task (Sep. 2024 - Nov. 2024)
  • Advisor: Dr. Xiangbin Teng, Chinese University of Hong Kong
  • Model Training: Trained RNNs on the same-different task under varying noise levels by neurogym, optiomizing the code for readability and extensibility.
  • Model Analysis: Analyzed normalized averages and principal components (PCA) of RNN hidden states, performed linear fitting of activities at different time points to stimuli values, and analyzed the temporal scope.
• Large Model Based Crossmodal Chinese Poetry Creation (Jul. 2024 - Oct. 2024, Wuhan, China)
  • Advisor: Dr. Weiping Zhu, Wuhan University
  • System Development: Led the development of modules supporting cross-modal text and image inputs, enhancing iterative optimization mechanisms.
  • System Evaluation: Evaluated poem quality across different input modalities and optimization on three poem sets.
• Data Analysis on Forward Flow Task (Apr. 2024 - Jun. 2024, Remote)
  • Advisor: Prof. Yunzhe Liu, NKLCNL, Beijing Normal University
  • Data Prepocessing: Pre-processed word data for forward flow tasks, inserting seed words, removing duplicates, and generating embeddings.
  • Correlation Analysis: Analyzed the correlation between participants’ scale scores and statistical indicators, including sequence length, embedding similarity, optimality divergence, semantic distance range, and “forward flow”.

Project Experience

• The Working Memory Capacity of RNN models (Summer 2024)
  • Computational Neuroscience Program, Neuromatch Academy (NMA)
  • Memory Decoding: Built a neural network to decode firing rates into previous inputs, computing correlations to assess the WM capacity of RNN models.
  • Parameter Exploration: Led the exploration of effects of parameter simulating biological factors and interactions on WM capacity.

Publications

Large Model Based Crossmodal Chinese Poetry Creation

L. Yang*, Z. Zhang*, K. Niu, S. Pan, W. Zhu and C. Ma, “Large Model Based Crossmodal Chinese Poetry Creation,” in 2024 IEEE Smart World Congress (SWC), accepted

Skills

• Data Analysis: Machine Learning, Deep Learning, EEG processing, Digital signal processing
• Programming: Python, C/C++
• Tools: Unix Shell, VS Code, Git/GitHub, Zotero
• Language: English(IELTS 7.0), Mandarin(Native)

You can download a PDF copy of my CV here.