SUNMONTUEWEDTHUFRISAT
Events for February 23, 2022
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ECE Seminar: Human/System Co-design to Protect Data Privacy
Wed, Feb 23, 2022 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Haojian Jin, PhD Candidate, Human-Computer Interaction Institute, Carnegie Mellon University
Talk Title: Human/System Co-design to Protect Data Privacy
Abstract: Privacy is changing how we build computing systems. Recent regulations, such as General Data Protection Regulation, California Consumer Privacy Act, Children's Online Privacy Protection Act, require developers to offer greater privacy protections. However, developers struggle to turn these high-level privacy principles into low-level code implementation.
The primary cause of this difficulty is that privacy is a multi-stakeholder issue: developers want to achieve more functionality and productivity; users want more control with lower effort; regulators wish to audit systems with limited resources and do not want to stifle innovation; finally, system deployments need to remain proprietary and efficient.
In this talk, I will present two systems to illustrate that these Human/System requirements can jointly inform system design up-front and not be afterthoughts. I will describe (1) applying human/system co-design for data minimization, a foundational privacy principle in modern privacy regulation, and (2) how user and other stakeholder experience is transformed in co-designed systems. I will conclude with plans to create a virtuous cycle ecosystem where building trustworthy systems is rewarded, and developers compete to guarantee greater user protection, not less.
Biography: Haojian Jin is a final-year Ph.D. candidate in the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Jason Hong and Swarun Kumar. His research lies at the intersection of human-computer interaction, privacy, and mobile systems. His work has been recognized with a UbiComp Gaetano Borriello Outstanding Student Award, Research Highlights at Communications of the ACM and GetMobile, and best paper awards at Ubicomp and ACM Computing Reviews. See more at: http://haojianj.in/.
Host: Dr. Bhaskar Krishnamachari, bkrishna@usc.edu
Webcast: https://usc.zoom.us/j/92527250101?pwd=dlQ1YzV1enJTYnRaQmFBbFpnZS9ZQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/92527250101?pwd=dlQ1YzV1enJTYnRaQmFBbFpnZS9ZQT09
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, Feb 23, 2022 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Gaurav Gupta, Amazon Web Services (AWS) AI lab
Talk Title: Operator Learning for Partial Differential Equations
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: The partial differential equations (PDEs) model several real-world setups of Physics, Engineering, biology, Epidemiology. The solution can be formulated as an operator map problem. We show that learning the operator kernels can be efficiently performed by exploiting the fundamental properties. We will discuss a novel multiwavelets-based neural operator approach to achieve a compressed representation and show applications on several benchmarks PDE datasets. Next, we also discuss a class of PDEs called 'Initial Value Problems,' which has applications in predictions and forecasting. We develop a compact non-linear neural operator which maps initial conditions to activities at a later time. The proposed approach yields data efficiency which is necessary to deal with scarce real-world datasets, and as a case study we formulate and solve urgent real-world problems like Epidemic forecasting (e.g., COVID19).
Biography: Gaurav Gupta is currently a researcher (Applied Scientist) at Amazon Web Services (AWS) AI labs. He completed his PhD from USC Viterbi. His research interests span the domain of time-series modeling, learning partial differential equations, information theory for machine learning, fractional dynamical models, complex networks, brain EEG signals modeling. He is working on inter-disciplinary mathematical and applied problems on forecasting, PDEs, and has publications in top venues like Neurips, ICLR, Nature, IEEE Control Society, ACM cyber-physical society.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxwLocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw
Audiences: Everyone Is Invited
Contact: Talyia White
