Our research is at the intersection of Optimization Theory, Signal Processing, and Control. The broad objective is to advance theory, algorithms, and analysis for decision systems and information processing. Focus is on the development of optimization, control, and learning methods for network systems and cyber-physical systems, and the development of algorithms for information processing and machine learning applications. Particular current topics of interest are listed in the following.

Theory:     

  • Synthesis and analysis of online algorithms for time-varying optimization
  • Development of feedback-based online optimization methods, where learning applications are tightly-integrated components of real-time control and optimization systems
  • Optimization methods for dynamical systems
  • Distributed optimization methods
  • Online learning.

Current application domains include: 

  • Smart power and energy systems
  • Transportation networks
  • Machine learning applications
  • Healthcare applications.

 

Research efforts on feedback-based online optimization and learning have the objective of formalizing systematic procedures to develop and analyze online algorithms to seek solutions of convex and nonconvex problems that evolve over time. The temporal variability naturally emerges from data and information streams, as well as performance metrics, constraints, and problem inputs that change over time. The algorithms rely on a hybrid approach, where principled algorithmic steps derived from first-order methods employ measurements from the network and systems to promote adaptability, bypass the need for accurate network models, and learn cost function concurrently with the execution of the algorithm. Research efforts focus on rigorous convergence analysis, and on establishing provable bounds for optimality and stability of the proposed algorithmic frameworks.

In the context of learning and data-processing, the advent of streaming data sources in many engineering and science domains calls for revisiting several facets of workhorse machine learning and statistical learning approaches. The ability to store, process, and leverage information from heterogeneous and possibly high-dimensional streaming data can no longer be taken for granted, especially in settings where communication and computational bottlenecks may lead to processing times that are comparable to (or even larger than) the interval between the arrival of two consecutive data points. The objective is to develop and analyze online algorithms that solve in real time learning, estimation, and inferential optimization problems, in the presence of heterogeneous steams of data, and with possibly corrupted measurements. 

Through applications specifically designed for power systems, the objective of my research is to develop system-theoretic foundations for real-time control and optimization to unify frequency control, voltage control, and economic optimization under a unified framework. The overarching goal is to provide innovative tools that can contribute to support the evolution of power systems towards a massively distributed infrastructure with millions of controllable and interacting nodes, and to enable a transition to operational settings that are markedly different from existing operations, which are catered to a setting where power generation is spatially concentrated at a few, large fossil-fuel-driven power plants, utilization of renewable generation and storage is relatively small, and loads typically operate in an open-loop fashion. 

Recent efforts in the context of transportation systems look at the development of real-time algorithms for congestion control, traffic routing, and platooning. Problems of interest include the interaction between transportation and power system. 

 

4 images in a row. The first has solar panels and wind turbines. Then a complex road system. Then a city with connection icons. The last image is a network graphic.

 

Current projects 

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.

 

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.

 

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 - July 2022.

 

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.​

 

Past projects

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.