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Manish Singh

Manish Singh

Assistant Professor
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I am an Assistant Professor in the Department of Electrical and Computer Engineering. I obtained my PhD in Electrical Engineering in June 2021 from Virginia Tech. I completed my undergraduate degree from Indian Institute of Technology, Varanasi in 2013 and worked in power industry during 2013-16 before joining Virginia Tech in 2016.

I indulge in exploring the rich suite of old and new problems in power systems, and grid-facing aspects of power electronics-based generation. In doing so, I abide by the signature rigor and formalism of the optimization and control communities while innovatively capitalizing the ongoing developments in machine learning. My research spans power system modeling, operation and control, network flow, optimization, linear control, and data-based methods. Beyond power systems, my research extends to natural gas, and water networks enabling a holistic approach for the wider energy system landscape.

Department

Electrical & Computer Engineering

Contact

p: (608) 262-0287

  • PhD 2021, Virginia Tech
  • Graduate Certificate 2020, Virginia Tech
  • MS 2018, Virginia Tech
  • BS 2013, Indian Institute of Technology (BHU)

  • Modeling, optimization, and control for power and energy systems
  • data-based methods

  • 2022 ABB, Shortlisted among the top five finalists globally for ABB Research Award (Nominated)
  • 2022 Virginia Tech, Second prize, Bill and LaRue Blackwell Graduate Research Award for best dissertation
  • 2019 Dept. of Electrical and Computer Engineering, Virginia Tech, Prasad Scholarship
  • 2019 INFORMS, Winner, ‘Frame that Problem’, INFORMS Annual Meeting
  • 2007 Government of India, Kishore Vaigyanik Protsahan Yojana (KVPY) fellowship
  • 2006 Atomic Energy Education Society, India, Gold medal for Junior Science Olympiad and honorary mentioned in Junior Mathematics Olympiad

  • Polyzos, K. D., Traganitis, P. A., Singh, M., & Giannakis, G. B. (2024). Ensembles of Informative Representations for Self-Supervised Learning. In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1–6).
  • Jung, H., Helman, S., Singh, M., & Dhople, S. (2024). Inferences from Numerical Model Reduction & Aggregation of Power-system Dynamics Featuring Primary & Secondary Control. In 2024 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1–5).
  • Jung, H. T., Venkatramanan, D., Singh, M., & Dhople, S. (2023). Per-Unit Dynamic Models for Grid-Following Photovoltaic Inverters. In 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC) (pp. 1–6).
  • Singh, M., Polyzos, K. D., Traganitis, P. A., Dhople, S. V., & Giannakis, G. B. (2023). Physics-Informed Transfer Learning for Voltage Stability Margin Prediction. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1–5).
  • Yuan, Z., Cavraro, G., Singh, M., & Cort'es, Jorge, (2023). Learning provably stable local Volt/Var controllers for efficient network operation. IEEE Transactions on Power Systems, 39(1), 2066--2079.
  • Singh, M., Taheri, S., Kekatos, V., Schneider, K. P., & Liu, C. (2022). Joint grid topology reconfiguration and design of Watt-VAR curves for DERs. In 2022 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1–5).
  • Cavraro, G., Yuan, Z., Singh, M., & Cort'es, Jorge, (2022). Learning local volt/var controllers towards efficient network operation with stability guarantees. In 2022 IEEE 61st Conference on Decision and Control (CDC) (pp. 5056–5061).
  • Singh, M., Venkatramanan, D., & Dhople, S. (2022). Towards Optimal Primary-and Secondary-control Design for Networks with Generators and Inverters. In 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 1–6).
  • Jalali, M., Singh, M., Kekatos, V., Giannakis, G. B., & Liu, C. (2022). Fast inverter control by learning the OPF mapping using sensitivity-informed Gaussian processes. IEEE Transactions on Smart Grid, 14(3), 2432--2445.
  • Venkatramanan, D., Henriquez-Auba, R., Rachi, M., Bui, J., Singh, M., Ramasubramanian, D., Hoke, A., Kroposki, B., & Dhople, S. (2022). Grid-forming inverter technology specifications: a review of research reports & roadmaps. Universal Interoperability for Grid-Forming Inverters (UNIFI) Consortium, Tech. Rep. UNIFI-2022-1-1.

  • E C E 330 - Signals and Systems (Spring 2025)
  • E C E 890 - Pre-Dissertator's Research (Spring 2025)
  • E C E 427 - Electric Power Systems (Fall 2024)
  • E C E 699 - Advanced Independent Study (Fall 2024)
  • E C E 890 - Pre-Dissertator's Research (Fall 2024)
  • E C E 330 - Signals and Systems (Spring 2024)
  • E C E 699 - Advanced Independent Study (Spring 2024)