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Dr. Jun Zheng
Dr. Jun Zheng
Dr. Jun Zheng is a Professor and Chair of the Department of Computer Science and Engineering at the New Mexico Institute of Mining and Technology (New Mexico Tech). He earned his Ph.D. in Computer Engineering from the University of Nevada, Las Vegas, in 2005. Before joining New Mexico Tech, he served as an assistant professor in the Department of Computer Science at Queens College, City University of New York.
Dr. Zheng's research focuses on AI, machine learning, and cybersecurity. He has published more than 130 peer-reviewed journal and conference papers and has been recognized among the world’s top 2% of most-cited scientists by the Stanford–Elsevier global ranking since 2022. To date, he has secured over $5 million in competitive research funding from federal, state, and internal sources. Since 2018, he has served as the PI for the NSF REU Site on Emerging Issues in Cybersecurity.
An active contributor to the research community, Dr. Zheng has served on the editorial boards of several international journals and has reviewed proposals for major funding agencies, including the NSF. Throughout his career, he has taken on numerous leadership roles and contributed as a technical program committee member at more than 140 international conferences and workshops.
Title: Smart Meter Data Analytics and Applications
Abstract: Advanced metering infrastructure (AMI), an essential component of the modern smart grid, collects vast amounts of high-frequency power consumption data from customers through smart meters. Smart meter data analytics leverages data-driven approaches to extract actionable insights from these data, with the goal of enhancing the efficiency, reliability, and sustainability of smart grid operations. In this talk, I will present the fundamentals of smart meter data analytics and discuss the deep learning–based methods developed by my research group for two energy efficiency–oriented applications: building occupancy detection and non-intrusive load monitoring (NILM).
Dr. Peter Zhang
Dr. Peter Zhang
Peter Zhang is an Assistant Professor at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy, with a courtesy appointment in engineering. He earned his PhD in Engineering Systems from MIT and specializes in optimization, robust decision-making, and data-driven analytics for supply chains and transportation.
His research, published in Operations Research, Mathematical Programming, Transportation Research, focuses on resilience, fairness, and predictive modeling in large-scale systems -- challenges central to designing resilient supply chains. He has been recognized with the INFORMS Junior Faculty Paper Competition 1st Place, Koopman Prize, the Wagner Prize, and other awards for advancing both theory and practice-relevant optimization.
Title: Virtual Intelligence, Real Power: Optimizing Data Center Operations for a Smarter Energy Future
Abstract: Data centers increasingly shape global energy demand; in 2024, those in the United States alone consumed roughly twice the annual electricity used by the entire state of Pennsylvania. Yet their spatial and temporal flexibility offers potential value for grid decarbonization. This talk presents an optimization framework that allocates data center workloads across locations and hours to minimize energy cost, carbon intensity, and peak load. Using synthetic cases for NYISO, PJM, and CAISO, results show cost savings and CO2 reduction through coordinated shifting. Synthetic policy experiments highlight how carbon pricing, peak-demand penalties, and curtailment credits could alter these outcomes, revealing trade-offs between efficiency, emissions, and resilience in the future.
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