Meng Wang

Meng Wang is a Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where she was promoted to Full Professor in June 2025. She received her B.S. and M.S. degrees (both with honors) in Electrical Engineering from Tsinghua University, China, in 2005 and 2007, respectively, and her Ph.D. in Electrical and Computer Engineering from Cornell University in 2012. After a postdoctoral position at Duke University, she joined RPI in December 2012 as an Assistant Professor, was promoted to Associate Professor with Tenure in 2019, and then to Full Professor in 2025.

Her research spans machine learning and artificial intelligence, high-dimensional data analytics, power system monitoring, signal processing, and optimization methods. She has made fundamental contributions in sparse signal recovery and monitoring and control of smart grid using high frequency data from phase measurement unit (PMU). More recently, she has collaborated with IBM to produce theoretical guarantees of modern AI architectures such as graph neural networks and transformers used in large language models (LLMs).

Wang's recent publications (2023-2025) reveal a strong focus on theoretical foundations of deep learning, particularly transformer architectures and graph neural networks. Her work bridges theoretical guarantees with practical applications in power systems, demonstrating how fundamental insights in machine learning can solve real-world energy challenges. She has increasingly focused on the intersection of AI and energy systems, developing methods for building-level load forecasting, energy disaggregation, and smart grid monitoring with behind-the-meter solar integration.

• AFOSR Young Investigator Program (YIP) Award (2019)
• Army Research Office (ARO) YIP Award (2017)
• James M. Tien '66 Early Career Award and Grant for Faculty (2022)
• School of Engineering Research Excellence Award (2018)
• IEEE Signal Processing Society Best Reviewer Award (2018)

Professor Wang has mentored numerous Ph.D. students who have gone on to successful careers in academia and industry, including HongKang Li (now postdoc at University of Pennsylvania), Yi Ming (postdoc at University of Michigan), and Shuai Zhang (Assistant Professor at New Jersey Institute of Technology). Her research has been supported by multiple grants from the National Science Foundation, Air Force Office of Scientific Research, Army Research Office, and industry partners including IBM.

She is actively involved with research centers including the Center for Future Energy Systems (CFES) and the Center for Materials, Devices, and Integrated Systems (CMDIS), where her group develops cutting-edge methods for power system monitoring and control. Her recent work has increasingly focused on the theoretical foundations of large language models and their applications to energy systems, positioning her at the forefront of AI for critical infrastructure.