Plenary Speakers

Michel Mandjes (Mathematical Institute, Leiden University)

Michel Mandjes received M.Sc. degrees in mathematics and econometrics, and a Ph.D. degree in operations research from the Free University of Amsterdam (The Netherlands), in 1993 and 1996, respectively. After having been a Member of Technical Staff with KPN Research (The Netherlands) and Bell Laboratories (Murray Hill, NJ, USA), a full professor in stochastic operations research with the University of Twente (The Netherlands), and a department head at CWI (Amsterdam, The Netherlands), he currently has full professor positions in probability and operations research at the universities of Leiden and Amsterdam.

Michel is also affiliated as an advisor with EURANDOM, Eindhoven (The Netherlands). He was a visiting professor with Stanford University, New York University, and Columbia University. Since 2015 he is affiliated with the University of Amsterdam’s Institute for Advanced Study (IAS). He is main applicant and project leader of the research programme NETWORKS, in the framework of the prestigious Dutch Gravitation call.

His main research interests include stochastic processes, queueing processes, efficient simulation techniques, and the probabilistic analysis of networks. He has an interest in a broad range of applications, primarily in transportation and communication networks, but in addition in sociology, healthcare, actuarial science, and biology.

He is the author of three books: the single-authored book « Large Deviations for Gaussian Queues: Modelling Communication Networks », and the coauthored books « Queues and Levy Fluctuation Theory » and « The Cramér-Lundberg model and its variants ». He has published over 350 papers in journals and conferences proceedings.

He was the Program Chair of several leading conferences, such as “Performance”, « INFORMS Applied Probability » and « Stochastic Networks ». He is the Editor-in-Chief of « Queueing Systems » and serves on the editorial board of multiple other journals, such as « Stochastic Systems » and « Journal of Applied Probability”.

Title: Dynamic random graphs: analysis and inference.

Abstract:

The bulk of the random graph literature concerns models that are of an inherently static nature, in that features of the random graph at a single point in time are considered. There are strong practical motivations, however, to consider random graphs that are stochastically evolving, so as to model networks’ inherent dynamics.

In this talk I’ll discuss a set of dynamic random graph mechanisms and their probabilistic properties. Key results cover functional diffusion limits for subgraph counts (describing the behaviour around the mean) and a sample-path large-deviation principle (describing the rare-event behaviour, thus extending the seminal result for the static case developed by Chatterjee and Varadhan).

The last part of my talk will be about estimation of the model parameters from partial information. We for instance demonstrate how the model’s underlying parameters can be estimated from just snapshots of the number of edges. We also consider settings in which particles move around on a dynamically evolving random graph, and in which the graph dynamics are inferred from the movements of the particles (i.e., not observing the graph process).

Nadia Oudjane (EDF, France)

Nadia Oudjane is currently a senior research engineer at EDF R&D (Palaiseau, France) expert in optimization and stochastic dynamical systems. Graduated from SupAero (Toulouse, France), she holds a PhD in Applied Mathematics and an HDR in sciences. She has been working for more than 20 years on various topics involving numerical methods for energy management in stochastic environments. Her current research interests include stochastic control and decentralized optimization for distributed generation and consumption flexibilities in power systems.

Title : Optimizing over probability measures to manage distributed flexibilities in power systems

Abstract :
With the massive integration of renewable energies (photovoltaic (PV) and wind power) into the power grid, new uncertainties are impacting system balance. At the same time, advances in « smart » technologies and batteries offer the possibility of controlling the consumption of a large number of electrical appliances (electric vehicle recharging, heat pumps, etc.) which can contribute to system balance and thus compensate for the uncertainties induced by the integration of new renewable energies. In this framework, amajor technical challenge is therefore to optimize the management of this large number of heterogeneous assets distributed across the network. This constitutes a large scale optimization problem under uncertainties, which can benefit from a mean-field approximation approach. This leads us to consider a new class of optimization problems where the decision variables are probability measures.

Michaël Jordan (INRIA Paris, and University of California, Berkeley)

Michael I. Jordan is a researcher at INRIA and Professor Emeritus at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, biological and social sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Foreign Member of the Royal Society. He was the inaugural winner of the World Laureates Association (WLA) Prize in 2022. He was a Plenary Lecturer at the International Congress of Mathematicians in 2018. He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, the David E. Rumelhart Prize, and the ACM/AAAI Allen Newell Award. In 2016, Prof. Jordan was named the « most influential computer scientist » worldwide in an article in Science, based on rankings from the Semantic Scholar search engine.

Title: Contracts, Uncertainty, and Incentives in Decentralized Machine Learning

Abstract:
Contract theory is the study of incentives when parties transact in the presence of
private information. We augment classical contract theory to incorporate a role for
learning from data, where the overall goal of the adaptive mechanism is to obtain desired
statistical behavior. We consider applications of this framework to problems in federated
learning, the delegation of data collection, and recommendation systems. We also consider

systems in which data is a valued good, where principals and where agents are arranged in markets

consisting of multiple layers.

Sergio Grammatico (Delft University of Technology, The Netherlands).

Sergio Grammatico is an Associate Professor at the Delft Center for Systems and Control, TU Delft, The Netherlands. He received the Bachelor’s degree in Computer Engineering, the Master’s degree in Automatic Control, and the Ph.D. degree in Automatic Control, all from the University of Pisa, Italy, in 2008, 2009, and 2013 respectively. In 2013–2015, he was a postdoc researcher in the Automatic Control Laboratory, ETH Zurich, Switzerland. In 2015–2018, he was an Assistant Professor in the Department of Electrical Engineering, Control Systems, TU Eindhoven. He was a recipient of the Best Paper Award at the 2016 ISDG Int. Conf. on Network Games, Control and Optimization, of the 2021 Roberto Tempo Best CDC Paper Award, and co-author for the 2022 IEEE CSS Italy Young Author Best Journal Paper Award. He is currently an Associate Editor of the IEEE Trans. on Automatic Control and of IFAC Automatica. His research interests include dynamic game theory, multi-agent systems and extremum seeking control.

Title: Equilibrium seeking in complex systems

Equilibrium seeking in multi-agent games is the study of decision-making dynamics in terms of stability and robustness. Among other application domains, equilibrium seeking arises in energy markets, power systems, autonomous vehicles. In this talk, we will first focus on complex system features such as private, incomplete and sparse information, stochasticity of the objective functions and existence of multiple equilibria. Next, we will present a receding-horizon framework for open-loop and closed-loop equilibrium seeking in dynamic games, where we discuss key connections between infinite-horizon and finite-horizon equilibrium solutions.