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AI-drivenDecision-makingand Optimization Lab

The IDO group works at the intersection of artificial intelligence with power and energy systems, advancing foundational theory and applied methods for modeling, analysis, decision-making, and optimization in complex systems, with a commitment to deploying intelligent algorithms in real-world engineering scenarios.

In artificial intelligence, the group systematically investigates machine learning, deep learning, reinforcement learning, and large-scale models, exploring their applications in intelligent decision-making, optimization decision-making, data-driven modeling, and uncertainty analysis. In power and energy systems, the group focuses on operation optimization, dispatch and control, security assessment, situational awareness, and energy coordination for modern power and energy systems, addressing the challenges brought by high-penetration renewable energy integration.

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Research Areas

  1. 01

    Domain

    Artificial Intelligence

    We study large language models, deep learning, and reinforcement learning for complex decision-making, intelligent optimization, and data-driven modeling in research and engineering settings.

  2. 02

    Domain

    Power and Energy Systems

    We study the operation, optimization, and security analysis of modern power and energy systems, connecting data intelligence with engineering methods for dispatch, sensing, and energy coordination.

  3. 03

    Domain

    Optimization Decision-Making

    We study optimization decision-making, approximation algorithms, and scalable solvers for discrete decisions such as scheduling, assignment, and routing, with a focus on provable guarantees and data-driven heuristics under complex constraints.

Publications

Publication Overview

A database-backed summary of published work, publication types, and venue distribution across the group.

Published Papers

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publications

Publication Mix

Journal ArticlesConference PapersPreprintsOther Outputs
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Top Publication Venues

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Principal Investigator

Qingchun Hou

Qingchun Hou, doctoral supervisor and researcher, received his Ph.D. from the Department of Electrical Engineering, Tsinghua University, under Professor Chongqing Kang (IEEE Fellow, Department Head, National Science Fund for Distinguished Young Scholars) and in collaboration with Professor Ning Zhang (Ministry of Education talent program). As a joint Ph.D. student at the University of Washington, he was advised by Professor Daniel Kirschen (IEEE Fellow, Donald W. and Ruth Mary Close Professor).

His research focuses on AI and decision-making/optimization, large models, the planning, operation, and security of modern power and energy systems with high renewable penetration, and optimization decision-making methods for complex engineering systems.

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Profile

Assistant Professor / Researcher

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Status

Lab Director