CV

Contact

Chenglong Li
Linköping, Sweden
asuralmax0915@outlook.com · +46 700 253 183 · LinkedIn · GitHub

EDUCATION

Department of Electrical Engineering (ISY), Linköping University
PhD Student in Electrical Engineering with specialization in Automatic Control ｜ Incoming, Linköping, Sweden
Research Focus:: Machine Learning, with emphasis on generative models and instabilities arising from cycles of retraining on mixtures of human- and machine-generated data.

Department of Information Technology, Uppsala University
MSc in Data Science (Machine Learning & Statistics) | August 2023 – Present, Uppsala, Sweden
GPA: 3.6 / 4.0
Key courses: Statistical Machine Learning, Data Engineering I, Applied Linear Algebra for Data Science, Reinforcement Learning, Computer-Intensive Statistics and Applications

School of Computer Science & Technology, Shandong University of Finance and Economics
BSc in Computer Science | September 2019 – June 2023, Jinan, China
GPA: 3.46 / 4.0
Key courses: Data Structure, Computer Organization and Architecture, Algorithms Analysis and Design, Computer Networks, Artificial Intelligence, Machine Learning, Natural Language Processing

School of Finance, Shandong University of Finance and Economics
BEc in Finance (Minor) | September 2020 – June 2023, Jinan, China
GPA: 3.56 / 4.0
Key courses: Micro-Economics, Macro-Economics, Finance, Econometrics, Financial Market, Financial Derivatives, Investment Banking, Financial Engineering, Financial Risk Management

PROJECTS & RESEARCH

Mitigating Representation Bias in Extremely Imbalanced Data via BM-GAN
Master Thesis – Uppsala University | January 2025 – June 2025

• Generated targeted synthetic samples with a conditional BM-GAN, strengthening minority-class representation.
  
• Benchmarked 10 oversampling/generative methods (e.g., SMOTE, cGAN) on 3 datasets, evaluating NB/k-NN/SVM/CNN classifiers.
  
• Built a validation framework to quantify classification robustness and synthetic-data utility.
  

Studying Local and Global Loss Landscapes in Federated Learning
Course Project – Uppsala University | September 2024 – January 2025

• Implemented FedNet/ResNet pipelines with FedAvg & FedAdam across varying client counts and IID/non-IID splits.
  
• Applied path-norm analysis showing FedAvg’s lower variance and greater stability under heterogeneous data.
  
• Discovered five-client non-IID training on CIFAR-10 improved generalization over two-client baselines.
  

Prediction of Financial Trends via Time-Series Models
Bachelor Thesis – Shandong University of Finance and Economics | December 2022 – June 2023

• Proposed an attention-enhanced LSTM cutting forecasting error by 12–18 % versus vanilla LSTM.
  
• Ran 50 + experiments (MAE/MSE/RMSE), achieving 0.055–0.077 error after normalization and window tuning.
  
• Analysed volatility regimes, finding 30 %+ accuracy drops during shocks that guided model refinements.
  

EXPERIENCE

President – ACM Student Society, Shandong University of Finance and Economics
September 2020 – June 2021, Jinan, China

• Recruited members and organised 10 + workshops/contests on data-structures and algorithms.
  
• Maintained the university Online Judge platform, delivering updates and debugging support.
  
• Ran weekly ACM-ICPC training in dynamic programming, graph theory, and related topics.
  

HONORS & SCHOLARSHIPS

• Excellent Bachelor’s Thesis, SDUFE, Oct 2023
  
• Excellent Graduate, SDUFE, Mar 2023
  
• First Prize Scholarship, SDUFE, Oct 2022
  
• Second Prize Scholarship, SDUFE, Oct 2021
  
• Second Prize, National Undergraduate Mathematical Modeling Competition of China, Oct 2021
  
• Second Prize Scholarship, SDUFE, Nov 2020
  
• First Prize, SDUFE Business Plan Competition, Nov 2020
  
• Second Prize, Lanqiao Information Technology Professionals Competition, Oct 2020
  

TECHNOLOGIES

Languages: English (fluent), Chinese Mandarin (native)
Software: Microsoft Office, MySQL
Programming: Python, C++, MATLAB