About me
😄 I am a Ph.D. Candidate in the Elmore Family School of Electrical and Computer Engineering at Purdue University. My research focuses on optimizing the performance and energy efficiency of Machine Learning Systems (MLSys) across diverse computing platforms, including embedded GPUs, server GPUs, and AI accelerators. I specialize in performance and energy optimization for Vision-Language Models (VLMs), Large Language Models (LLMs), and Computer Vision applications. I am passionate about leveraging my expertise to address real-world challenges through researching, developing, and deploying cutting-edge intelligent systems. My goal is to optimize latency, accuracy, and energy efficiency for intelligent computing systems while contributing to environmental sustainability.
🎯 Openings
🚀 Actively seeking 2026 Summer or Fall full-time opportunities as Machine Learning Engineer or Research Scientist focused on ML systems optimization and efficient inference for vision & language models.
⚡ Research Interests
- Resilient and Adaptive VLM
- Resource-Efficient VLM/LLM Inference
- Machine Learning Systems (MLSys)
Education
- Ph.D. Candidate Purdue University
West Lafayette2019 ~ Now- Major: Electrical and Computer Engineering
- Advisor: Prof. Somali Chaterji, Prof. Saurabh Bagchi
- M.S. Tongji University
Shanghai2014 ~ 2017- Excellent Graduate of Tongji University in 2017
- Major: Electronic Science and Technology
- Minor: Green Economy and Sustainable Development
- Advisor: Prof. Meisong Tong
- B.E. Tongji University
Shanghai2010 ~ 2014- Excellent Graduate of Tongji University in 2014
- Major: Electronic Science and Technology
Work Experience
- Machine Learning Engineer Intern EmbodyX
Fall 2025- Contributed to the development of foundation models for robotic systems
- Applied Machine Learning Systems optimization to accelerate LLM and VLM training and inference
- Explored model compression and related techniques to enhance efficiency and scalability for practical deployment of large models in real-world environments
- Software Engineer Intern - AI ToolChain Sunlune
Spring and Summer 2025- 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
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
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
Shenzhen2017 ~ 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
Shanghai2014 ~ 2017- Semiconductor Physics (Fall 2016, 2015, and 2014)
- Electromagnetic Fields and Waves (Spring 2016)
- Electronics and Digital Technology (Spring 2015)
Services
- Shadow Program Committee of SIGMETRICS 2026
- 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
