EMO Seminar
Welcome to the EMO (Energy, Markets, and Optimization) Seminar, a virtual seminar series bringing together energy and sustainability researchers from Operations Management, Operations Research, and Electrical Engineering.
Addressing emerging challenges in energy and sustainability requires methodological tools and domain knowledge that span multiple research communities. EMO Seminar provides a cross-community platform where rigorous methodology meets real-world impact, featuring advances in relevant methodologies such as market design, optimization, stochastic modeling, and network analysis, as well as innovative ways that use them to address challenges in energy and sustainability.
The EMO Seminar is organized by Jerry Anunrojwong, Cheng Guo, and Junjie Qin.
Time: 3:00 PM - 4:00 PM ET, biweekly on Wednesdays
Mailing list: Join our Google Group here to receive reminders about upcoming talks.
YouTube channel: Watch past talks here
Upcoming Talk
Enhancing EV and PV Observability via Sparse Decoding
Abstract and Bio
Abstract
Rapid adoption of electric vehicles (EVs) and distributed photovoltaic (PV) systems introduces substantial uncertainty into distribution-grid monitoring and operation. Because these resources are often located behind the meter or observed only through aggregated measurements, system operators may have limited visibility into their individual behaviors. This seminar presents a sparse-decoding framework for improving EV and PV observability by exploiting the inherent structure of their power profiles. PV systems in geographically similar locations share common solar-generation patterns. EV charging curves, meanwhile, exhibit distinct piecewise and event-driven characteristics. By learning these structured semantics, the proposed approach can estimate, separate, and reconstruct otherwise unobserved EV charging and PV generation. The talk will introduce the underlying representation learning and sparse recovery formulations, and discuss identifiability and robustness under limited or noisy measurements. More broadly, this work illustrates how structural priors can turn sparse, noisy measurements into actionable visibility for increasingly decentralized power systems.
Bio
Yize Chen is an assistant professor with ECE Department at the University of Alberta. He got his Ph.D. degree in Electrical and Computer Engineering from University of Washington in 2021; and his undergraduate degree from Chu Kochen College at Zhejiang University in 2016. He was a postdoctoral researcher at Lawrence Berkeley National Lab. He has also held multiple research positions at ISO New England, Microsoft Research, Los Alamos National Laboratory, and Harvard Medical School. Yize’s research focuses on the intersection between control, optimization and machine learning, and he is interested in designing cyber-physical systems, especially power systems with performance guarantees. He is also a recipient of several best paper and prize paper awards at IEEE PES General Meeting (2024, 2022), Power Systems Computation Conference (PSCC) (2020), and ACM e-Energy (2019).
All Talks
Fall 2026
| Date | Speaker | Title |
|---|---|---|
| Kyri Baker (University of Colorado Boulder) | Challenges with Existing Learning-for-OPF Neural Proxies, and how Constrained Learning can Help [Zoom] [Recording] | |
| Fariba Mamaghani (Tulane University) | Contract Choice and Commitment Concessions: Evidence from Individual-Level Electricity Consumption and Payments [Zoom] | |
| Yize Chen (University of Alberta) | Enhancing EV and PV Observability via Sparse Decoding [Zoom] | |
| Selva Nadarajah (University of Illinois at Chicago) | TBA (rescheduled from October 21) [Zoom] | |
| Yury Dvorkin (Johns Hopkins University) | TBA [Zoom] | |
| Devansh Jalota (Georgia Institute of Technology) | TBA [Zoom] |