Ling Zhang
Sustainable Energy Systems Powered By AI
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I am a researcher at Microsoft Research Asia, where I have been working since July 2024. I recently completed my Ph.D. in Electrical and Computer Engineering at the University of Washington, where I was advised by Professor Baosen Zhang.
My research lies at the intersection of AI and optimization, with a focus on sustainable energy systems. These systems pose large-scale, complex optimization challenges that are difficult to solve in real time using traditional methods. While machine learning algorithms offer speed, they often fall short in handling physical constraints and generalizing to new scenarios. My work aims to bridge this gap—leveraging AI to design scalable, reliable, and constraint-aware solutions for coordinating and optimizing renewable energy resources.
Earlier in my research, I explored how to model and forecast residential energy consumption using Generative Neural Networks (GNNs).
Please visit the Research Projects tab to learn more.
Beyond my core research, I’m also interested in data-driven demand-side management, climate-smart energy systems, and the electrification of urban delivery fleets.
Feel free to connect with me at zhangling@microsoft.com or through the Contact tab.
Research Interests
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Applications: Energy consumption behavior modeling, load scenario forecasting, optimal power flow, economic dispatch, security constrained OPF
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Optimization Methodologies: Linear programming, stochastic optimization, convex relaxation and restriction
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ML Methodologies: Tailor-made ML algorithms for engineering applications
Education
University of Washington
Seattle, WA
September 2018 — June 2024
Ph.D. Candidate in Electrical & Computer Engineering
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Zhejiang University
Hangzhou, China
September 2015 — March 2018
M.S. in Information & Communication Engineering
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Ocean University of China
Qingdao, China
September 2011 — June 2015
B.E. in Electronic & Information Engineering
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Industry Experience
Electricity Market Research Intern, Argonne National Laboratory
Lemont, IL • October 2021 — December 2021
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Translated climate data into electric energy systems' operation and planning, supporting climate-aware and resilient decision-making processes within energy systems.
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Identified representative periods of climate and weather extreme events from time-series climate model outputs using machine learning and data analytics techniques.
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Research Assistant, Shenzhen Research Institute of Big Data
Shenzhen, China • October 2017 — July 2018
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Using Natural Language Processing (NLP) techniques to develop a standardized tool for automated labeling and extraction of Chinese medical events from Electronic Medical Records (EMRs) provided by Shenzhen People's Hospital .