👋 About Me

Hello! I’m Xutian Chen, a passionate student and researcher in the field of Artificial Intelligence and Machine Learning. I am currently pursuing my Master’s degree in Artificial Intelligence at Beihang University, having graduated with a Bachelor’s degree in Artificial Intelligence from Jinan University.

My research interests lie in the intersection of AI for Bio&Chem, Diffusion Models, LLM Inference Acceleration, and Autonomous Vehicle Path Planning. I am always open to appropriate opportunities in research and tech industries, and I am excited about how GenAI can be applied to revolutionize specific problems in any domain.

🎓 My Advisors: During my undergraduate studies at Jinan University, I was fortunate to be advised by Lecturer Deping Li, whose research focuses on intelligent visual perception, including 3D object pose estimation, point cloud processing, visual foundation models, SLAM, and robotic visual perception. At Beihang University, I am fortunate to be advised by Prof. Huijie Zhao, whose research covers multi/hyperspectral intelligent perception, multi-modal perception and data fusion, and 3D visual perception and data processing, and Associate Prof. Na Li of the School of Artificial Intelligence.

📧 Contact: chenxutian@buaa.edu.cn
🔗 GitHub: github.com/Blossom0913
🔗 Gitee: gitee.com/chenxutian

Remember brick walls let us show our dedication. They are there to separate us from the people who don't really want to achieve their childhood dreams.

📰 News

• 2026.07: Our paper “A Benchmark Dataset for Rat Social and Aggressive Behavior Classification” was published in Scientific Data.
• 2026.03: Started my internship as a Decision & Planning Intern at Meituan Autonomous Vehicle Department, Beijing.
• 2025.09: Started my Master’s degree in Artificial Intelligence at Beihang University, Beijing.
• 2025.07: Graduated from Jinan University with a Bachelor’s degree in Artificial Intelligence.

📖 Education

• 2025.09 - Present: Master of Engineering in Artificial Intelligence, Beihang University, Beijing, China
  • Major Courses: Reinforcement Learning, Deep Learning, Multi-Robot Swarm Intelligence, Algorithm Design and Analysis
• 2021.09 - 2025.07: Bachelor of Engineering in Artificial Intelligence, Jinan University, Zhuhai, China
  • Major Courses: Natural Language Processing, Machine Learning, Principles of Artificial Intelligence, Agile Software Development

💼 Experience

Internship

Decision & Planning Intern | Meituan Autonomous Vehicle Department

2026.03 - present | Beijing, China

• Optimized the interaction decision-making algorithm for intersection driving scenarios: deepened the exit-lane judgment logic, refined the corresponding rule functions, introduced an ego-vehicle steering-completion indicator, and designed a saturated linear-interpolation scheme for dynamic boundary computation, validated through simulation regression for algorithm consistency; also performed performance profiling and code optimization for the intersection reference-line generation module.
• Fixed and rebuilt the intersection data closed-loop pipeline: diagnosed and resolved issues in the daily human-driving data collection pipeline (Jenkins workflow), including upstream data gaps and Protobuf compilation conflicts; backfilled historical missing human-driving intersection data; built a parallel daily collection pipeline for autonomous-driving intersection data following the existing framework, producing full left-turn/right-turn datasets with dedicated data cleaning and filtering logic.
• Investigated and validated end-to-end model training: completed feature export, data re-processing, and training-pipeline integration for the intersection decoder model, adapted it to the upstream encoder, and verified the training scripts, laying the data and pipeline foundation for future iterations of the intersection exit-lane selection model.

Research Experience

Research on AI4S and AI+Chem | Guangdong Institute of Intelligent Science and Technology

Research Intern | 2025.02 - 2025.09 | Zhuhai, China

• Large-Scale Computational Framework: Engineered a parallel computing framework deployed on a 4-GPU (RTX 2080Ti) cluster, successfully processing 43.8 million molecular docking tasks to accelerate drug discovery pipelines. Source code: Dock Repository.
• Algorithm Research & Benchmarking: Designed an experimental framework and benchmark dataset for rat social and aggressive behavior classification, using DeepLabCut for keypoint labeling; conducted comparative experiments on LightGBM, LSTM, CNN, and GMM models to evaluate performance. Project code: Mouse-Behavior-Classifier-Train. This work was published as co-first author in Scientific Data: “A Benchmark Dataset for Rat Social and Aggressive Behavior Classification”.
• System Development: Contributed to the development of a multi-robot path planning system and a lightweight task management platform, resulting in 2 software copyright registrations (Top-3 Author).

Multi-agent Path Planning | Jinan University

Research Assistant | 2024.03 - 2024.07 | Zhuhai, China

• Performance Analysis: Designed controlled experiments scaling agents from 10 to 50, revealing that runtime increased 135x (2.3s → 312s) and success rate dropped to 67%. Profiling identified conflict detection (70% of runtime) and deep copy operations as primary bottlenecks.

• Algorithm Optimization (C++/MAPF): Reduced conflict detection complexity from O(n²) to O(n) by implementing incremental checks—comparing only the current agent against others instead of full scans. Reduced memory overhead by replacing deep copies with Copy-on-Write, enabling multiple agents to share conflict tree nodes until a write is required.

• Systems Thinking: Applied knowledge of stack/heap allocation and shallow/deep copy semantics to guide optimization decisions. Derived complexity reduction (N² → N) and presented findings with clear problem-solution-impact narrative—demonstrating interpretability in engineering work.

• Platform Integration: Built messaging architecture between AGV fleet and local server for state synchronization under real-world constraints. Reproduced CL-CBS baseline (paper: CL-MAPF), fixed implementation bugs, and delivered faster planning than Hybrid A* baseline within a 3-month cycle.

Competition Experience

ASC2022 Student Supercomputer Challenge

Team Member | 2021.11 - 2022.06 | Zhuhai, China

• Project goal: Under limited compute (8× Tesla V100 16GB) and power constraints, pre-train a 4.7B-parameter Yuan-1.0 language model and achieve a 55% training speedup (from 45h to 28h).
• Memory optimization: Led application of ZeRO-Offload and ZeRO Stage 1 to offload model states (parameters, gradients, optimizer states) to CPU memory, enabling training of a 4.7B model on 8×16GB GPUs and resolving CUDA OOM issues.
• Parallel & acceleration strategy: Deployed Megatron-DeepSpeed and designed a hybrid parallel scheme with 4-way tensor parallelism + 2-way pipeline parallelism, improving throughput from 4.08 → 4.66 samples/s compared with pure 8-way tensor parallelism.
• Engineering optimizations: Adopted mixed precision training (AMP), built PyTorch with Intel MKL, and used DeepSpeed’s CPU Adam to optimize CPU offload computation and communication.
• Convergence tuning: Tuned learning rate scaling and warmup strategies alongside data pipelines and micro-batching, achieving a final training loss of 5.826 in reproduction runs.
• Deliverables: Responsible for parallel strategy evaluation, memory/performance analysis, and engineering implementation; project notes and partial experiment logs at: ASC Student Supercomputer Challenge Proposal

📝 Publications

• Xutian Chen*, Guangyu Li*, Zihan Zhang*, Mingkun Xu, Zuoren Wang, Qianqian Shi. “A Benchmark Dataset for Rat Social and Aggressive Behavior Classification.” Scientific Data, 2026. (*Co-first authors)

More manuscripts are in preparation. Feel free to reach out for collaboration opportunities!

🎖 Honors and Awards

• 2022.06: 🥈 National Second Prize in ASC2022 (Student Supercomputer Challenge), ranked 22nd among all participants
• 2022.07: Attended the ASC final as visitors at USTC
• 2024: 🥇 Provincial First Prize (preliminary) in Chinese Mathematics Competition (CMC), Guangdong Division
• 2023: 🥈 Provincial Second Prize (preliminary) in Chinese Mathematics Competition (CMC), Guangdong Division

🚀 Projects

Tiny-llm

2026.04 – present

• Focused on Attention implementation and lightweight LLM training/inference pipelines
• Tech stack: vLLM, SGLang, Tool Calling
• Reference: https://github.com/skyzh/tiny-llm

PlayTask - Time Management APP

2023.09 – 2023.12 | Personal Project

• 📱 Designed and built a time management APP from scratch
• 🛠️ Learned Version Control Systems (VCS), Event Response and basic debugging tools in Android Studio
• 🎨 Designed UI/UX with ViewPage2, TabLayout and Fragment independently
• 📝 Source code: Blossom0913/PlayTask

💻 Skills

Programming Languages

• Proficient: Python, C/C++, Java
• Experienced: Rust, Kotlin

Technical Skills

• Development Tools: Git, SSH, Android Studio
• High Performance Computing: CUDA, MPI, OpenMP
• Distributed Training & Optimization: DeepSpeed, Megatron, ZeRO, ZeRO-Offload
• Precision & Acceleration: Mixed Precision (AMP), Intel MKL optimizations
• Machine Learning: TensorFlow, PyTorch, Fine-Tuning
• Systems: Linux, Shell
• Other: Algorithm Design, Multi-Agent Systems, Computer Vision, Model Inference & Deployment