## October 2020

## Hadamard Differential Calculus and Applications

Michel Delfour (Université de Montréal)

Online

ABSTRACT The Hadamard differential was introduced in 1923 by Hadamard and promoted in 1937 by Fréchet who extended it to vector spaces of functions. Infinite dimension is equivalent to the Fréchet differential introduced in 1911, but in function spaces, Hadamard is more general than Fréchet, which is restricted to normed vector spaces. In 1978 Penot gave the appropriate definition of a semi-differential in the sense of Hadamard by using semi-paths with a semi-tangent. The Hadamard semidifferentiable functions are probably the…

Find out more »## SOAP: New Breakthroughs in Scheduling Theory

Mor Harchol-Balter (Carnegie Mellon University)

Online

ABSTRACT Scheduling policies are at the heart of computer systems. The right scheduling policy can dramatically reduce response times, ensure fairness, provide class-based priority, etc., without requiring additional resources. While stochastic response time analysis of different scheduling policies has been the focus of thousands of theoretical papers, results are limited to analyzing a relatively small number of "simple" scheduling policies. In this talk, we introduce the SOAP class of scheduling policies: Schedule Ordered by Age-based Priority. The SOAP policies include…

Find out more »## September 2020

## Distributed Machine Learning over Networks

Francis Bach (INRIA)

Online

ABSTRACT The success of machine learning models is in part due to their capacity to train on large amounts of data. Distributed systems are the common way to process more data than one computer can store, but they can also be used to increase the pace at which models are trained by splitting the work among many computing nodes. In this talk, I will study the corresponding problem of minimizing a sum of functions which are respectively accessible by separate…

Find out more »## November 2019

## LIDS Seminar – Rayadurgam Srikant (University of Illinois at Urbana-Champaign)

Rayadurgam Srikant (University of Illinois at Urbana-Champaign)

32-155

TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.

Find out more »## LIDS Seminar – Sujay Sanghavi (University of Texas at Austin)

Sujay Sanghavi (University of Texas at Austin)

32-155

TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.

Find out more »## October 2019

## The Age of Information in Networks: Moments, Distributions, and Sampling

Roy Yates (Rutgers University)

32-155

We examine a source providing status updates to monitors through a network with state defined by a continuous-time finite Markov chain. Using an age of information (AoI) metric, we characterize timeliness by the vector of ages tracked by the monitors. Based on a stochastic hybrid systems (SHS) approach, we derive first-order linear differential equations for the temporal evolution of both the age moments and a moment generating function (MGF) of the age vector components. We show that the existence of…

Find out more »## LIDS Seminar – George Pappas (University of Pennsylvania)

George Pappas (University of Pennsylvania)

32-155

TBD Bio: ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.

Find out more »## Data-driven Coordination of Distributed Energy Resources

Alejandro Dominguez-Garcia (University of Illinois at Urbana-Champaign)

32-155

The integration of distributed energy resources (DERs), e.g., rooftop photovoltaics installations, electric energy storage devices, and flexible loads, is becoming prevalent. This integration poses numerous operational challenges on the lower-voltage systems to which the DERs are connected, but also creates new opportunities for the provision of grid services. In the first part of the talk, we discuss one such operational challenge—ensuring proper voltage regulation in the distribution network to which DERs are connected. To address this problem, we propose a…

Find out more »## September 2019

## Power of Experimental Design and Active Learning

Aarti Singh (Carnegie Mellon University)

E18-304

Classical supervised machine learning algorithms focus on the setting where the algorithm has access to a fixed labeled dataset obtained prior to any analysis. In most applications, however, we have control over the data collection process such as which image labels to obtain, which drug-gene interactions to record, which network routes to probe, which movies to rate, etc. Furthermore, most applications face budget limitations on the amount of labels that can be collected. Experimental design and active learning are two…

Find out more »## Dynamic Monitoring and Decision Systems (DyMonDS) Framework for Data-Enabled Integration in Complex Electric Energy Systems

Marija Ilic (MIT)

32-155

In this talk, we introduce a unifying Dynamic Monitoring and Decision Systems (DyMonDS) framework that is based on multi-layered modeling for aggregation and minimal coordination of interactions between the layers of complex electric energy systems. Using this approach, distributed control and optimization problems are formulated so that: (1) the low-level decision-makers optimize cost of local interactions while accounting for their heterogeneous technologies, as well as for their social and risk preferences; and, (2) the higher layer aggregators and coordinators optimize…

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