My general research interests are centered around optimization, control, and learning in complex, cyber-physical and network systems. I seek to develop foundational theories, methods, and algorithms that enable the deployment of efficient, safe, and autonomous decision-making architectures to address engineering and societal challenges. A distinctive aspect of my research lies in its robust connections between foundational theory and practical applications: our research pipeline initiates with a rigorous synthesis and analysis of decision-making architectures and extends to experiments and field demonstrations, aiming to maximize the impact and the technology transfer.

My research combines tools from optimization, control theory, dynamics, and supervised learning. My current theoretical endeavors focus on:

  • Data-driven optimization and control of physical and dynamical systems;
  • Online optimization and learning;
  • Online optimization with system and human in-the-loop.

Theory and algorithms are primarily motivated by applications in: 

  • Power and energy systems;
  • Autonomous and robotic systems;
  • Electrified transportation;
  • Autonomous RF systems.

 

Current projects 

Online Optimization-Based Feedback Controllers for Dynamical Systems in Stochastic Environments with Partially Known Performance Metrics and Safety Constraints

  • Funded by the Air Force Office of Scientific Research, Dynamical Systems and Control Theory Program 
  • Principal Investigator: Jorge Cortes (University of California San Diego); co-PI: Emiliano Dall'Anese
  • Period of performance: 2023 - 2026.

 

Hybrid System Models and Algorithms for Resilient Transmission Systems

  • Funded by the U.S. Department of Energy, Advanced Grid Modeling Program 
  • Principal Investigator: Guido Cavraro (NREL); co-PIs: Jorge Poveda (University of California San Diego), Emiliano Dall'Anese
  • Period of performance: 2023 - 2025.

 

Closed-loop Optimization and Control of Physical Networks Subject to Dynamic Costs, Constraints, and Disturbances

  • Funded by the National Science Foundation, CMMI DCDS program.  
  • Principal Investigator. Co-PI: Jorge Cortes (University of California San Diego) 
  • Period of performance: January 2021 - December 2023.

 

NSF CAREER: Synthesis of Feedback-based Online Algorithms for Power Grids

  • Funded by the National Science Foundation, Energy, Power, Control, and Networks (EPCN) program.  
  • Principal Investigator
  • Period of performance: February 2020 - January 2025.

 

NSF ERC: Advancing Sustainability through Powered Infrastructure for Roadway Electrification 

  • NSF Engineering Research Center. Lead: Utah State University; team members: University of Colorado Boulder, Purdue University, University of Texas El Paso
  • Principal Investigator for University of Colorado Boulder: Qin Lv. Co-PIs: Dragan Maksimovic, Emiliano Dall'Anese, Bri-Mathias Hodge, Jana Milford, Jacquelyn Sullivan. 
  • Period of performance: February 2020 - January 2025.
 

NSF AMPS: Online and Model-free Optimization of Power and Energy Systems

  • Funded by the National Science Foundation, Division of Mathematical Sciences (DMS), Algorithms for Modern Power Systems (AMPS) program.  
  • Principal Investigator: Stephen Becker (University of Colorado Boulder). Co-PI: Emiliano Dall'Anese (University of Colorado Boulder)
  • Period of performance: August 2019 - December 2023.

 

Past projects

Multi-objective Deep Reinforcement Learning for Grid-Interactive Energy-Efficient Buildings.

  • Funded by the U.S. Department of Energy (DOE), Buildings Technology Office 
  • Principal Investigator: Andrey Bernstein (NREL). Co-PIs: Emiliano Dall'Anese, Gregor Henze (University of Colorado Boulder)
  • Period of performance: July 2019 - June 2022.​

 

Control-theoretic design of data-driven policies for containing transmission of infectious diseases 

  • Funded by the AB Nexus seed grant.  
  • Principal Investigator. Co-PIs: Andrea Buchwald (University of Colorado Anschutz), Jorge I. Poveda (University of Colorado Boulder) 
  • Period of performance: December 2020 - December 2021.

 

Synthesis of Real-time Optimization Algorithms for Autonomous Urban Mobility

  • Funded by the National Renewable Energy Laboratory
  • Principal Investigator
  • Period of performance: April 2020 - September 2020.​
 

Design and Analysis of Online Algorithms for Next-generation Energy Systems

  • Funded by the National Renewable Energy Laboratory
  • Principal Investigator
  • Period of performance: September 2018 - December 2019.

 

Research Support for Autonomous Energy Systems Program

  • Funded by the National Renewable Energy Laboratory
  • Principal Investigator
  • Period of performance: September 2018 - August 2020.

 

Learning to Control Safety-Critical Systems: Providing Formal Correctness Guarantees for Learning-based Control of Safety-critical Systems.

  • Funded by the Research & Innovation Office of the University of Colorado Boulder.
  • Principal Investigator: Ashutosh Trivedi (University of Colorado Boulder). Co-PIs: Emiliano Dall'Anese, Fabio Somenzi (University of Colorado Boulder)
  • Period of performance: August 2019 - July 2020.

 

Real-time optimization and control of next-generation distribution infrastructure

  • U.S. Department of Energy (DOE), Advance Research Project Agency - Energy (ARPA-e), Network Optimized Distributed Energy Systems (NODES) program. 
  • Principal Investigator. Co-PIs: Steven Low (Caltech), Na Li (Harvard University), Sairaj Dhople (University of Minnesota), and Christopher Clarke (Southern California Edison). 
  • Period of performance: July 2016 - July 2019.