A Q&A with new faculty member Vladimir Dvorkin

We are delighted to welcome Vladimir Dvorkin to the ECE community. Dvorkin joined the faculty in January 2024 as an assistant professor in the area of power and energy. He leads the ⏻ptiML group which develops optimization and machine learning algorithms to support power grid operations and electricity markets. 

Before joining Michigan, Dvorkin was a Postdoctoral Fellow at Massachusetts Institute of Technology in the Laboratory for Information and Decision Systems (LIDS) and Energy Initiative. He received master’s degrees in Energy Economics from the Higher School of Economics and in Sustainable Energy from the Technical University of Denmark (DTU), and his Ph.D. in Electrical Engineering from DTU.

Dvorkin is a recipient of several awards, including the prestigious Marie Sklodowska-Curie Actions postdoctoral fellowship and the Best Paper Award from IEEE Transactions on Power Systems. He has served as a reviewer for numerous journals and conferences, and has received Outstanding Reviewer awards from IEEE Transactions on Energy Markets, Policy and Regulation, IEEE Transactions on Power Systems, and IEEE Transactions on Sustainable Energy.

He is currently teaching Computational Power Systems, and last year taught EECS 463: Power System Design and Operation and EECS 559: Optimization Methods for Signal and Image Processing and Machine Learning.

Dvorkin is actively seeking new graduate students to join his group; he also welcomes visiting students.

Here is more information about Vladimir Dvorkin, in his own words.

Tell us about your research

I am an assistant professor of Electrical Engineering and Computer Science at the University of Michigan. I lead the ⏻ptiML group, where we harness the power of optimization and machine learning to address the grand challenges in modern power grids. The ⏻ptiML group develops: 1) prescriptive energy analytics to inform decision-making for grid stakeholders (grid planners, power producers, and large consumers like data centers) under uncertainty; 2) algorithmic solutions for energy data privacy and transparency to protect private data (smart meter readings, operational records) from leaks and unauthorized disclosures, while also making the overall grid more transparent and easier to understand and study; and 3) performance guarantees for optimization and learning algorithms to minimize the impacts of their errors on short- and long-term decisions (electricity prices, coordination and investment decisions). Pursuing these three directions, my group constantly exercises intellectual curiosity, acts at the forefront of algorithmic innovation, and spreads solutions by shaping them into recognizable and relatable forms. 

How does your work impact the world around us 

Power grids—critical infrastructure systems that empower societies to grow and prosper—must be constantly monitored and controlled to ensure an uninterrupted supply at a reasonable cost. Striking the right balance between cost and reliability of supply is becoming increasingly challenging due to demand-supply uncertainty, the emerging growth of disruptive AI consumption, limited observability, and the complexity of underlying physical laws. A failure to strike this balance leads to economic and societal unrest, which we unfortunately often read about in the news. My work contributes with formal methods that can formulate and solve such problems efficiently while offering rigorous performance guarantees, ultimately leading to a secure and economical power supply. We rethink how grid operators coordinate with emerging AI demand and other adjacent infrastructures, how grid data can be safely put in open access, how stochastic producers participate in electricity markets, and how planners address policy uncertainty.

What do you enjoy most about your field?

How quickly theory translates into practice. Power systems adopt optimization and ML innovation at a fascinating pace. 

What’s your favorite thing about teaching? 

Teaching brings me back to fundamental knowledge that I often don’t get to otherwise review. It is very rewarding to meet and spend time with young, bright minds in this process. 

What is your approach to mentoring graduate students? 

I have a mentoring plan which I constantly rethink and improve over time.

Do you have any hobbies or anything else you’d like to share?

I do sports that combine exercise, technique, and strategic thinking, such as sailing and Alpine skiing.