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Dall'Anese, Emiliano

Associate Professor

Positions

Research Areas research areas

Research

research overview

  • The background of Emiliano Dall'Anese is at the intersection of Optimization, Learning, and Control. The broad objective is to uncover mathematical principles for the synthesis of optimization and learning algorithms for decision and information systems to address complex engineering and social challenges. Specific current research themes include: Online optimization and learning, data-enabled optimization of networked and dynamical systems, and stochastic optimization. Current application domains include: smart power and energy systems, transportation networks, machine learning applications, and healthcare applications.

Publications

selected publications

Teaching

courses taught

  • ECEN 3300 - Linear Systems
    Primary Instructor - Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023
    Characterization of linear time-invariant systems in time and frequency domains. Continuous time systems are analyzed using differential equations and Laplace and Fourier transforms. Discrete time systems are analyzed using difference equations, Z-transforms and discrete time Fourier transforms. Sampling and reconstruction of signals using the sampling theorem. Applications of linear systems include communications, signal processing, and control systems.
  • ECEN 5007 - Special Topics
    Primary Instructor - Spring 2018 / Spring 2019
    Examines a special topic in Electrical, Computer and Energy Engineering. May be repeated up to 9 total credit hours.
  • ECEN 5008 - Special Topics
    Primary Instructor - Fall 2018 / Fall 2020
    Examines a special topic in Electrical, Computer and Energy Engineering. May be repeated up to 9 total credits.
  • ECEN 5478 - Online Convex Optimization and Learning
    Primary Instructor - Fall 2021 / Fall 2023
    Covers basics of convex optimization, online learning, time-varying optimization, online first-order methods, learning problems over networks, zeroth-order methods, bandit optimization, projection-free methods, distributed methods for online convex optimization. Application domains considered in the course include Machine Learning, Signal Processing, and Data-driven Control. Specific application examples include the Internet of Things, recommendation systems, power systems, sensor networks, and transportation systems. Previously offered as a special topics course. Recommended prerequisite: ECEN 5448.
  • ECEN 5678 - Coordinated Control of Multi-agent Systems
    Primary Instructor - Fall 2022
    Covers basics of matrix theory and graph theory; distributed averaging and consensus methods on graphs; parallel computation of fixed points; basics of optimization; parallel and distributed optimization methods over graphs; convergence analysis. The techniques and methodologies presented in the course are introduced through application setups including Internet of Things, power and energy systems, sensor networks, transportation systems, and social networks. Previously offered as a special topics course. Recommended prerequisites: ECEN 5448 and courses in convex optimization.

Background

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