Vladimir Dvorkin
EECS Assistant Professor @UMich

EECS 4240
1301 BEAL AVE, MI
Ann Arbor, MI 48109
I am an Assistant Professor of Electrical Engineering and Computer Science at the University of Michigan. I lead the ⏻ptiML group, where we use power engineering, operations research, machine learning, and economics to address the grand challenges in modern power grids. Specifically, the ⏻ptiML group works on:
Grid analytics: We develop analytic models that combine mathematical optimization and machine learning (ML) to support grid operations and electricity markets under uncertainty. We integrate ML into optimization algorithms to enhance decisions, while using optimization to inform and guide ML toward more reliable predictions.
Grid integration of AI: We work on integration of AI as software (models, analytics) and hardware (data centers) in a way that respects the objectives and constraints of power grids and data centers. Our work focuses on feedback loops in grid–AI and AI-grid-AI interaction and how they can be operationalized to ensure the sustainable integration of AI.
Energy data privacy and security: We develop rigorous methods to enable controllable and trustworthy transparency of power systems, where sensitive data can be sourced on demand while guaranteeing agent privacy and system security.
I graduated from the Technical University of Denmark in 2021. During my doctoral research, I was also visiting the School of Industrial and Systems Engineering at Georgia Tech. Before joining the University of Michigan, I was a MSCA postdoctoral fellow at the Laboratory for Information and Decision Systems (LIDS) and the MIT Energy Initiative at the Massachusetts Institute of Technology. For more details, please see my CV.
News
- May 2025: Spoke on Grid-Aware Strategies for AI Integration at the workshop at Boston University (event, slides)
- Organized a pannel on grid privacy and cyber security at the IES Symposium. Thank you A. Scaglione, M. Govindarasu and J. Hong for joining!
- Congratulations to Milad on winning the Best Poster Award at the inaugural IES Symposium at the University of Michigan
- Apr 2025: Michigan welcomes a new course, Computational Power Systems, devoted to modern optimization, control, and analytics for grid operation and electricity markets. Please visit the course page for the syllabus, lecture slides, notes and Julia tutorials!.
- Mar 2025: New preprint Synthesizing grid data with cyber resilience and privacy guarantees lead by Shengyang Wu
- New preprint on optimization over trained neural networks using difference-of-convex algorithms lead by Xinwei Liu
- Feb 2025: A warm welcome to undergraduate students Thomas Payne and Shriramu Ramesh! They will be developing The Current Affairs — an electricity market simulator and game.
- Jan 2025: Regression equilibrium in electricity markets is accepted for publication in the IEEE Transactions on Energy Markets, Policy and Regulation.
- Spoke on the engineering and privacy challenges of grid integration of AI on the NewHydrogen podcast (news, video).
- Dec 2024: Acknowledged as a 2024 Outstanding Reviewer for IEEE Transactions on Energy Markets, Policy, and Regulation (recognition).
- New preprint on Probabilistic dynamic line rating forecasting with line graph convolutional LSTM with Minsoo Kim and Jip Kim.
- Nov 2024: Gave a CISE seminar at Boston University on the grid integration of AI under privacy and engineering constraints (slides).
- Spoke on the Nash regression equilibrium at the University of Michigan's Control Seminars (slides, video).
- Oct 2024: Presented my work on harvesting data centers spatiotemporal flexibility for power systems at the 2024 IEEE CSS Day (slides, paper).
- Organized a session power systems on the road between optimization and learning at the 2024 INFORMS Annual Meeting.
- Sep 2024: Welcomed new PhD students Xinwei Lui, Shengyang Wu and Milad Hoseinpour to the group.
- Aug 2024: Excited to announce a new graduate course, Computational Power Systems, coming Winter 2025 (syllabus).
- AgentCONCUR—regression mechanism for data center coordination with power grids—is published in IEEE Transactions on Power Systems!
- Invited to the National Academies' workshop on Macroeconomic Implications of Decarbonization Policies and Actions in Washington DC.
- Jul 2024: Participated in the panel Decision-centric machine learning for power systems operation: Theory and application at the IEEE Power & Energy Society General Meeting in Seattle, Washington. Thanks to Yuanyuan Shi of UCSD for the invitation! (slides)
- Presented our work on trustworthy deep learning for electricity markets at the Federal Energy Regulatory Commission (recording)
- Presented Regression Equilibrium at the EURO conference in Copenhagen. Thanks to Lesia Mitridati of DTU for the invitation! (slides)
- May 2024: New preprint Regression equilibrium in electricity markets on market forces harmonizing private wind power forecasts
- Our new study on the offshore electricity market design has been accepted for publication in IEEE TEMPR (preprint, paper)
- Mar 2024: Presented our work on formal privacy guarantees in power systems at the Communication and Signal Processing seminar (video, slides)
- Jan 2024: Moved to Ann Arbor and started a new position as an Assistant Professor at the University of Michigan!
- Dec 2023: Our paper on the cost-optimal scheduling of renewables at the scale of the New York ISO has been published in IEEE TEMPR (paper)
- Nov 2023: Presented our work on price-aware predictions and fairness in electricity markets at the LIDS Climate Tea Talk at MIT (slides)
- Oct 2023: Price-aware deep learning is accepted at the NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning
- Presented our synthetic dataset generation algorithms for power systems at the 2023 INFORMS conference in Phoenix (slides)
- Sep 2023: New preprint Agent coordination via contextual regression (AgentCONCUR) for data center flexibility
- Aug 2023: New preprint Price-aware deep learning for electricity markets
- Jul 2023: Our work on aiding gas network optimization with input-convex neural networks is accepted at the CDC conference in Singapore
- Jun 2023: Our paper on synthetic dataset generation is accepted at the IEEE Control Systems Letters with the option to present at the CDC conference
- Presented our work on synthetic dataset generation at the Federal Energy Regulatory Commission (video)
- May 2023: Presented our work on gas network optimization with neural networks at the CCAI Workshop at the 2023 ICLR conference (video)
- Mar 2023: New preprint Differentially private algorithms for synthetic power system datasets
- Feb 2023: Our paper Multi-stage investment decision rules for power systems with performance guarantees is accepted for publication in IEEE TPWRS
- Sep 2022: New preprint Emission-constrained optimization of gas networks: Input-convex neural network approach
- New preprint Privacy-preserving convex optimization: When differential privacy meets stochastic programming
- Jun 2022: Presented our work on stochastic control of natural gas networks with linepack at the XXII PSCC conference in Porto (slides)