About me

😄 I am a Ph.D. Candidate and Research/Teaching Assistant in the Elmore Family School of Electrical and Computer Engineering & Agricultural and Biological Engineering at Purdue University. My advisor is Prof. Somali Chaterji. I also collaborate closely with Prof. Saurabh Bagchi and Prof. Yin Li. My research interests include optimizing the performance and energy efficiency of Machine Learning Systems (MLSys) on diverse computing platforms, such as embedded GPUs, server GPUs, and AI Accelerators. Recently, I have been working on several projects targeting performance and energy optimization for Vision-Language Models (VLMs), Large Language Models (LLMs), and Computer Vision (CV) applications on embedded GPUs and AI Accelerators. I am passionate about leveraging my expertise to address real-world problems, particularly through researching, developing, and deploying cutting-edge intelligent systems and softwares. My goal is to optimize latency, accuracy, and energy efficiency for intelligent computer systems, contributing to environmental sustainability.

🎯 Openings

Actively seeking a Fall 2025 full‑time/part-time internship focused on vision & language models, embedded ML systems, or autonomous‑driving perception.

⚡ Research Interests

• Resilient and Adaptive VLM
• Resouce-Efficient VLM/LLM Inference
• Machine Learning Systems (MLSys)

Education

• Ph.D. Candidate Purdue University West Lafayette 2019 ~
  • Major: Electrical and Computer Engineering
• M.S. Tongji University Shanghai 2014 ~ 2017
  • Excellent Graduate of Tongji University in 2017
  • Major: Electronic Science and Technology
  • Minor: Green Economy and Sustainable Development
• B.E. Tongji University Shanghai 2010 ~ 2014
  • Excellent Graduate of Tongji University in 2014
  • Major: Electronic Science and Technology

Work Experience

• Software Engineer Intern - AI ToolChain Sunlune Sping 2025 - Now
  • Developed and validated kernel, runtime, and driver software frameworks for AI accelerators.
  • Integrated kernels and optimized runtime workflows to enable efficient inference of Llama-family LLMs.
  • Performed feature testing, performance tuning, and cross-platform debugging to resolve bottlenecks and improve kernel execution efficiency.
• Generative AI Model Intern Sunlune Santa Clara Summer and Fall 2024
  • Developed AI-enabled design flow for high-performance digital circuit design
  • Designed Reinforcement learning (RL) models for circuit generation
  • Collaborated with IC design engineers to capture design experience with AI models
• Teaching Assistant at Purdue University West Lafayette Spring 2024, 2025
  • ABE591: From Chips to Cloud: Machine Learning in IoT and Computer Systems (Spring 2025)
  • ABE591: Machine Learning for IoT and Computer Systems (Spring 2024)
• Algorithm Engineer at ZTE Corp Shenzhen 2017 ~ 2019
  • Project: 5G New Radio (NR) Communication System
  • Job duties: Undertook wireless communication protocol and algorithm analysis, design, implementation, and verification in both the Physical and MAC layers
• Teaching Assistant at Department of Electronic Science and Technology at Tongji University Shanghai 2014 ~ 2017
  • Semiconductor Physics (Fall 2016, 2015, and 2014)
  • Electromagnetic Fields and Waves (Spring 2016)
  • Electronics and Digital Technology (Spring 2015)

Services

• Artifact Evaluation Committee of MobiSys 2025
• Shadow Program Committee of EuroSys 2024
• Artifact Evaluation Committee of SenSys 2024
• Artifact Evaluation Committee of USENIX OSDI 2022 and ATC 2022

📫 Contact

🌱 Fun fact: I would like to spend one year living in Antarctica if there’s any opportunity. Cannot imagine how cool it is!