I am a postdoctoral researcher at the Massachusetts Institute of Technology, affiliated with MIT Energy Initiative and Laboratory for Information and Decision Systems. Before joining MIT, I pursued a PhD degree in electrical engineering at the Technical University of Denmark in 2017-2020. In 2019, I also visited the School of Industrial and Systems Engineering of Georgia Tech. In March 2021, I defended my thesis titled Stochastic and Private Energy System Optimization (video, slides). Through my PhD studies, I was most interested in decision-making models under uncertainty, in economics of uncertainty, and most recently in algorithmic privacy, all with applications to energy systems. Today, I broaden this portfolio towards certifying decisions under uncertainty (e.g., a priori performance guarantees for multi-stage stochastic programs), providing privacy guarantees for arbitrary convex optimization programs (moving from linear to quadratic and semidefinite private programming), and developing optimization algorithms to assist machine learning tasks. In 2021, I co-organized enOPTIMAL seminar series to promote research exchange during the pandemic.
MSc+PhD in Electrical Engineering, 2021
Technical University of Denmark
MSc in Economics, 2014
Higher School of Economics (Russia)
BSc in Electrical Engineering, 2012
Moscow Power Engineering Institute (Russia)
Dec 2022 Completed MIT Kaufman Teaching Certificate Program
Sep 2022 New preprint on aiding gas network optimization with neural networks is now out!
Jun 2022 Presented our work Multi-Stage Linear Decision Rules for Stochastic Control of Natural Gas Networks with Linepack at the XXII Power System Computational Conference in Porto ( slides )
May 2022 Presented Privacy-Preserving Perturbation of Convex Optimization Programs at the casual seminar series Stats&LIDS Tea Talks at MIT ( slides )
Apr 2022 I won a postdoctoral fellowship co-funded by Marie Skłodowska-Curie Actions and Iberdrola Group to research optimization, learning and privacy for future energy systems at MIT Energy initiative.
Mar 2022 I recieved 2021 Best Paper Award for Differentially Private Optimal Power Flow for Distribution Grids and 2021 Outstanding Reviewer Award from IEEE Transactions on Power Systems .
Mar 2022 We are launching the Spring 2022 edition of the enOPTIMAL: Energy, Optimization and Learning online seminar series (see speakers and seminar logistics here).
Oct 2021 Happy to announce the Fall 2021 enOPTIMAL Seminar Series! Check the new lineup here.
Apr 2021 We are launching a new online seminar series enOPTIMAL: Energy, Optimization and Learning. Many thanks to all speakers who agreed to kick off this seminar series.
Zhao D., Dvorkin V., Delikaraoglou S., Lamadrid A. J., Botterud A.
Uncertainty-informed renewable energy scheduling: A scalable bilevel framework
Dvorkin V., Mallapragada D., Botterud A., Kazempour J. and Pinson P. (2022)
Multi-stage linear decision rules for stochastic control of natural gas networks with linepack
Electric Power Systems Research (XXII PSCC edition) [paper, code]
Dvorkin V., Fioretto F., Van Hentenryck P., Pinson P. and Kazempour J. (2021)
Differentially private optimal power flow for distribution grids
IEEE Transactions on Power Systems [paper, code, presentation]
Dvorkin V., Kazempour J. and Pinson P. (2020)
Chance-constrained equilibrium in electricity markets with asymmetric forecasts
International Conference on Probabilistic Methods Applied to Power Systems [paper]
Radoszynski A.M., Dvorkin V. and Pinson P. (2019)
Accommodating bounded rationality in pricing demand response
IEEE Milan PowerTech [paper]
Stochastic and private energy system optimization
Ph.D. Thesis (2021). Technical University of Denmark [thesis]
Multi-stage strategic investment in CCGTs and wind power units via progressive hedging
M.Sc. Thesis (2017). Technical University of Denmark [thesis]