Upcoming Events

Dr. Mohamed Elgendi 
Mining for Miracles Fellow
Department of Obstetrics and Gynecology
Department of Electrical and Computer Engineering
University of British Columbia
Vancouver, BC
Canada

Challenges and Perspectives in Digital Health

Friday, June 28, 2019, 2:00 pm to 3:30 pm
ASB 10908 (SFU’s Big Data Hub), Simon Fraser University, Burnaby, BC, Canada

Light refreshments will be served.
The event is open to the public.
We would greatly appreciate if you would please register so that we may more accurately estimate the room size and refreshments.
Maps: SFUSFU Burnaby Campus

Abstract

Objectives:

1) Learn about the challenges in App development.

2) Discuss key points that usually are missed in digital health.

3) Explore legal considerations and next steps.

Who should attend?

This workshop is ideal for staff, scientists, students, fellows, or managers in a wide range of disciplines with an interest in learning the challenges in digital health. No equations or mathematical algorithm will be discussed. This is a high-level conceptual talk.

Biography

Dr. Mohamed Elgendi is currently a senior Postdoctoral Fellow at UBC’s Department of Obstetrics and Gynecology, an Adjunct Professor at UBC’s Departement of Electrical and Computer Engineering, a senior member at IEEE, and a senior fellow at Howard Brain Sciences Foundation. In addition to his 10+ years of experience in the field of data analysis, he received training on Big Data Analysis and Leadership in Education from MIT. Dr. Elgendi’s experience in the areas of digital health, data analysis & visualization includes his work in Global Health with the PRE-EMPT Initiative (funded by the Bill and Melinda Gates Foundation), the Institute for Media Innovation at Nanyang Technological University (Singapore), and Alberta’s Stollery Children’s Hospital (Canada). Dr. Elgendi specializes in bridging the areas of engineering, computer science, psychology, and medicine for knowledge translation.

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This event has been postponed to Summer 2019.

Prof. Francesco Sorrentino
Department of Mechanical Engineering
University of New Mexico

Title: Optimal Control of Networks: Energy Scaling and Open Challenges
Monday, December 10, 2018, 2:30 pm to 3:50 pm
Engineering/Computer Science, ECS 130, University of Victoria, British Columbia, Canada

Light refreshments will be served.
The event is open to the public.
We would greatly appreciate if you would please register so that we may more accurately estimate the room size and refreshments.
Map: Engineering/Computer Science

Abstract
Recent years have witnessed increased interest from the scientific community regarding the control of complex dynamical networks. Some common types of networks examined throughout the literature are power grids, communication networks, gene regulatory networks, neuronal systems, food webs, and social systems. Optimal control studies strategies to control a system that minimizes a cost function, for example, the energy that is required by the control action.

We show that by controlling the states of a subset of the nodes of a network, rather than the state of every node, the required energy to control a portion of the network can be reduced substantially. The energy requirements exponentially decay with the number of target nodes, suggesting that large networks can be controlled by a relatively small number of inputs, as long as the target set is appropriately sized.

An important observation is that the minimum energy solution of the control problem for a linear system produces a control trajectory that is nonlocal. However, when the network dynamics is linearized, the linearization is only valid in a local region of the state space and hence the question arises whether optimal control can be used. We provide a solution to this problem by determining the region of state space where the trajectory does remain local and so minimum energy control can still be applied to linearized approximations of nonlinear systems. We apply our results to develop an algorithm that determines a piecewise open-loop control signal for nonlinear systems. Applications include controlling power grid dynamics and the regulatory dynamics of the intracellular circadian clock.

This work is in collaboration with Isaac Klickstein and Afroza Shirin (UNM).

[1] I. Klickstein, A. Shirin, F. Sorrentino, “Energy Scaling of Targeted Optimal Control of Complex Networks”, Nature Communications, 8, 15145 (2017)
[2] I. Klickstein, A. Shirin, F. Sorrentino, “Locally Optimal Control of Complex Networks”, Phys. Rev. Lett., 119, 268301 (2017)

Biography
Francesco Sorrentino received a Ph.D. in Control Engineering from the University of Naples Federico II (Italy). He was first a postdoc and then a visiting assistant professor in the Nonlinear Dynamics and Chaos Group at the University of Maryland at College Park. In 2008 he became an assistant professor at the University of Napoli Parthenope. In 2012 he joined the Department of Mechanical Engineering at the University of New Mexico. His research primarily focuses on cutting-edge topics in Nonlinear Dynamics and Chaos Theory. His work includes studies on dynamics and control of complex networks, adaptive sensor networks, adaptation in complex systems, and identification of nonlinear systems. Other subjects of interest are the dynamics of large networks of coupled neurons and evolutionary game theory. His research is funded by the National Science Foundation, the Office of Naval Research, and the Defense Threat Reduction Agency.