SUNMONTUEWEDTHUFRISAT
Events for the 1st week of November
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Infectious Disease Forecasting: Methods, Lessons, and Opportunities
Tue, Nov 02, 2021 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ajitesh Srivastava, Research Assistant Professor/Ming Hsieh Department of Computer and Electrical Engineering
Talk Title: Infectious Disease Forecasting: Methods, Lessons, and Opportunities
Abstract: After more than a year, COVID-19 continues to disrupt our lives. Efforts have been underway since the beginning to understand the epidemiological situation and generate short-term forecasts and long-term scenario projections to drive public health decisions. These efforts, called "hubs" are collaborations between government agencies and multiple universities. In this talk, I will discuss the lessons learned from my participation in several such efforts in modeling and projection of COVID-19, and the resulting research opportunities. I will also present my methodology which has evolved over time to now incorporate dynamics of multiple competing variants, vaccination behavior, age-specific contact matrices, and waning immunity. A key feature of the approach is that it avoids overfitting by splitting the model into independent linear regression problems. An additional advantage is that the runtime is low. As an example, learning the model and generating case, death, and hospitalization forecasts for 56 regions of the US, each with around 25 variants and 5 age groups, takes ~20s on a 2-core desktop. This also enables fast scenario projections, where even for each scenario multiple runs are needed to incorporate uncertainty in hyper-parameters and human behavior. This may not be the last pandemic we will face, and therefore the research does not end with COVID-19. In fact, the extensive data-collection, monitoring, and forecasting during this epidemic sets the stage for more impactful research in preparedness for future epidemics.
Biography: Dr. Ajitesh Srivastava is a Research Assistant Professor at the Ming Hsieh Department of Computer and Electrical Engineering. He obtained his PhD in Computer Science at the University of Southern California in 2018. His research interests include network science, modeling, and machine learning applied to epidemics, social good, social networks, and systems. He collaborates with teams around the world, the CDC, and the ECDC for infectious disease forecasting and scenario projections. He is a DARPA Grand Challenge Winner (2014) on predicting the spread of Chikungunya virus.
Host: Dr. Richard M. Leahy, Chair, Ming Hsieh Department of Electrical and Computer Engineering (Systems)
Webcast: https://usc.zoom.us/j/91552972911?pwd=VG5DczVLdk9vQllBK2ZQT2l3dUJuQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/91552972911?pwd=VG5DczVLdk9vQllBK2ZQT2l3dUJuQT09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series
Wed, Nov 03, 2021 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yan Gu, Department of Mechanical Engineering at the University of Massachusetts Lowell
Talk Title: Dynamic Modeling, Hybrid Filtering, and Robust Control of Legged Robot Locomotion on Dynamic Rigid Surfaces
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Legged robots have the potential to assist humans with a wide range of real-world tasks in dynamic, unstructured environments, such as search and rescue on disaster sites, monitoring of natural resources, home assistance, and delivery and courier services. They can navigate natural and built environments prohibitively challenging for wheeled or tracked robots and adapt to human environments without significant modifications of existing facilities (e.g., ladders, stairs, and narrow passageways). While legged locomotion on stationary (even or uneven) surfaces has been extensively studied, legged locomotion on a dynamic rigid surface (DRS), which is a rigid surface that moves in the inertial frame, remains a new robot functionality that has not been tackled. This new functionality will empower legged robots to perform high-risk tasks such as shipboard firefighting and fire suppression as well as disinfection on moving public transportation vehicles to help contain the spread of infectious diseases. Yet, enabling reliable DRS locomotion presents substantial challenges due to the high complexity of the hybrid, time-varying robot dynamics. In this talk, Dr. Yan Gu will present the latest research progress from her group in tackling the challenges associated with the modeling, state estimation, and control of DRS locomotion.
Biography: Dr. Yan Gu received her B.S. degree in Mechanical Engineering from Zhejiang University (China) in June 2011 and her Ph.D. degree in Mechanical Engineering from Purdue University in August 2017. She joined the Department of Mechanical Engineering at the University of Massachusetts Lowell (UML) as an Assistant Professor in September 2017. Her research focuses on nonlinear control and hybrid systems with application to legged robots. Her long-term research goal is to realize provably safe and autonomous legged locomotion in dynamic, complex environments. She has received the NSF CAREER Award, the Frederick N. Andrews Fellowship at Purdue University, and the Chu Kochen Scholarship at Zhejiang University. Her research on legged locomotion has been funded by NSF, ONR, ARL, and Verizon's 5G Lab and reported by various media such as Boston Globe, CNET, Robotics Business Review, and NPR.
Host: Pierluigi Nuzzo and Feifei Qian
Webcast: https://usc.zoom.us/webinar/register/WN_p5OEJlPxQlakO4hqovuGEQLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_p5OEJlPxQlakO4hqovuGEQ
Audiences: Everyone Is Invited
Contact: Talyia White