😄 Hello, I am Pengcheng Wang. 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.
My ultimate goal is to optimize latency, accuracy, and energy efficiency for intelligent computing systems while contributing to environmental sustainability.
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.
My ultimate goal is to optimize latency, accuracy, and energy efficiency for intelligent computing systems while contributing to environmental sustainability.
🎯 Actively seeking 2026 Summer or Fall full-time opportunities as a Machine Learning Engineer or Research Scientist, focused on ML systems optimization and efficient inference for vision & language models.
⚡ Research Interests
- Resilient and Adaptive Vision-Language Model (VLM)
- Resource-Efficient VLM/LLM Inference
- Machine Learning Systems (MLSys)
🎓 Education
- Ph.D. Candidate, Purdue University
West Lafayette | 2019 ~ Now - M.S., Tongji University
Shanghai | 2014 ~ 2017 (Excellent Graduate) - B.E., Tongji University
Shanghai | 2010 ~ 2014 (Excellent Graduate)
🏢 Work Experience
- Machine Learning Engineer Intern at EmbodyX Fall 2025
- Contributed to the development of foundation models for robotic systems
- Applied Machine Learning Systems optimization to accelerate VLM inference
- Explored model compression and related techniques to enhance efficiency and scalability
- Software Engineer Intern - AI ToolChain at Sunlune Spring & 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
- Generative AI Model Intern at Sunlune Summer & 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
- Algorithm Engineer at ZTE Corp 2017 ~ 2019
- Undertook wireless communication protocol and algorithm analysis, design, implementation in Physical and MAC layers
💻 Services
- Program Committee, KDD 2026 AI4Sciences Track
- Reviewer, Journal of Systems Architecture
- Shadow Program Committee of SIGMETRICS 2026
- Artifact Evaluation Committee, EuroSys 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
