About
Shengsheng Lin
Shengsheng Lin (林升升) is a Ph.D. student in Computer Science at South China University of Technology and is expected to graduate in June 2027. He is advised by Prof. Weiwei Lin, whose research focuses on cloud computing and big data.
His research spans time series forecasting and post-training for large language models. In time series, he studies efficient forecasting architectures, periodicity-aware modeling, and multivariate dependency learning. His representative works include SparseTSF, CycleNet, SegRNN, and TQNet. More recently, he has been working on post-training for coding models, with interests in training data construction, verifiable reward design, multi-expert model integration (MOPD), training, and evaluation.
Research interests
- LLM post-training for code generation and software engineering
- Training data, verifiable rewards, and evaluation for coding models
- Multi-expert learning and model integration
- Efficient architectures for time series forecasting
- Periodicity-aware and multivariate temporal modeling
Experience
Industry and Engineering Practice
Tencent · WXG
Jun. 2026 - Present
LLM Coding Post-Training Intern
Conducting research and post-training for WeChat Mini Program coding models, aiming to turn a user's one-sentence request into a complete, functional Mini Program. Improving model capabilities through multi-expert model integration (MOPD), covering training data processing, verifiable reward design, model training, and evaluation.
Didi · MPT
Dec. 2025 - May 2026
Elite Program Algorithm Intern, Supply-Demand Scheduling Strategy Team
Optimized supply-demand forecasting models based on SparseTSF to address the high complexity of temporal dependency modeling in the original production system. Improved core business forecasting accuracy by 7%, reduced parameter count by 90%, and accelerated training by 5x, substantially speeding up model iteration and deployment.
Tencent · TEG
Jul. 2021 - Sep. 2021
Backend Development Intern, R&D Management Department
Developed backend services in Go using Gin, Docker, Nginx, Redis, Kafka, and XORM. Built and maintained scheduled-task interfaces and developed CLI tools with Cobra for unified task management.
Selected Papers
Representative Publications
SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters
Introduces cross-period sparse forecasting to reduce model parameters to as few as 1,000 while strengthening long-range dependency modeling, robustness, and generalization; now deployed in Didi ride-hailing demand-supply forecasting.
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
Explicitly models periodic patterns through a periodic-residual decomposition design, improving periodic utilization, residual modeling, and overall interpretability.
Temporal Query Network for Efficient Multivariate Time Series Forecasting
Introduces temporal queries to capture global periodic patterns, enabling more robust multivariate dependency learning and stronger multivariate forecasting performance.
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
Recasts RNN-based long-term forecasting through segment-wise iterations and parallel multi-step forecasting, enabling a single-layer GRU to achieve strong accuracy while reducing training time and memory usage by more than 78%.
Education
Academic Training
South China University of Technology
Sep. 2022 - Present
Ph.D. Student in Computer Science
Conducting research on deep learning and artificial intelligence, with a particular focus on time series forecasting, efficient modeling, and periodic pattern analysis.
South China University of Technology
Sep. 2018 - Jun. 2022
B.E. Student in Computer Science
Built a systematic foundation in computer science, including computer systems, computer networks, software engineering, and core algorithmic training.
Awards
Honors
- National Scholarship (Ph.D. Student) | 博士生国家奖学金
- President Scholarship, South China University of Technology | 华南理工大学校长奖学金
- National Scholarship (Undergraduate) | 本科生国家奖学金
- Gold Award, China International College Students' Innovation Competition | 中国国际大学生创新大赛金奖