Gianluca Bianchin

Gianluca Bianchin
Assistant Professor
Department of Mathematical Engineering
ICTEAM institute
University of Louvain


Email: gianluca(dot)bianchin(at)uclouvain(dot)be

Short Bio

I am an Assistant Professor in the ICTEAM Institute at the Université Catholique de Louvain. From 2020 to 2022, I was a Postdoctoral Scholar in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder. I completed the Ph.D. degree in Mechanical Engineering at the University of California Riverside in 2020. Before joining UC Riverside, I received an M.Sc. degree in Controls Engineering and a B.Sc. degree in Information Engineering from the University of Padova in 2014 and 2012, respectively.

Research Interests

My main research interests are in the modeling, control, and analysis of societal-scale network systems, with an application focus on transportation and energy systems.

Topics of current interest:

• Use of optimization algorithms for the control of dynamical systems

• Control of Mobility-on-Demand systems and transportation networks

• Security in cyber-physical systems

Spotlight on Recent Papers

Recent News

• [Mar 23] In our recent paper “Data-Driven Exact Pole Placement for Linear Systems” we show that the closed-loop poles of a linear system can be placed exactly at arbitrary locations without relying on any model description but by using only finite-length trajectories generated by the open-loop system!

• [Sept 22] My interview for the CU Boulder College of Engineering is a great summary of two amazing years spent at CU and a farewell to the beautiful Boulder, CO

• [Aug 22] In our recent paper “k-Dimensional Agreement in Multiagent Systems” we study how a group of distributed agents can compute k independent weighted means of a common vector by running a single distributed algorithm. We hope this algorithm will be utilized in distributed computing problems where agents are interested in evaluating k independent functions at a common point!

• [Jun 22] I presented our paper “Online Projected Gradient Descent for Stochastic Optimization with Decision-Dependent Distributions” at the 2022 American Control Conference in Atlanta, GA

• [Mar 22] Our recent work “Online Optimization of Dynamical Systems With Deep Learning Perception” shows that a combination of neural networks and online optimization techniques can be used to control dynamical systems

• [Mar 22] When can we safely return to normal from the COVID-19 pandemic? At the beginning of 2021, our work on Scientific Reports predicted that a return to normal would not have been safe until the early months of 2022

• [Dec 21] Our paper “On matrix-SIR Arino models with linear birth rate, loss of immunity, disease and vaccination fatalities, and their approximations” characterizes emerging behaviors of epidemics with non-negligible death rates

• [Dec 21] Our paper “Data-Driven Synthesis of Optimization-Based Controllers for Regulation of Unknown Linear Systems” will be presented at the 2021 IEEE Conf. on Decision and Control

• [Sep 21] Our paper “Time-Varying Optimization of LTI Systems via Projected Primal-Dual Gradient Flows” was published in the IEEE TCNS

• [Aug 21] Our paper “Online Stochastic Optimization for Unknown Linear Systems: Data-Driven Synthesis and Controller Analysis” is available on ArXiv

• [Feb 21] Our paper “Online Optimization of LTI Systems Under Persistent Attacks: Stability, Tracking, and Robustness” is available on ArXiv

• [Dec 20] New grant! We received the AB Nexus seed grant to investigate the development of control policies for epidemics

• [Dec 20] I presented our paper “Routing Apps May Cause Oscillatory Congestions in Traffic Networks” at the 2020 IEEE Conf. on Decision and Control

• [Aug 20] Our paper “Online Optimization of Switched LTI Systems Using Continuous-Time and Hybrid Accelerated Gradient Flows” is available on ArXiv

• [March 20] Our paper “Routing Apps May Deteriorate Stability in Traffic Networks: Oscillating Congestions and Robust Information Design” is available on ArXiv