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Events for April 23, 2019
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Explore USC - Admitted Student DayTue, Apr 23, 2019
Viterbi School of Engineering Undergraduate Admission
University Calendar
Explore USC is the most comprehensive campus visit program for admitted students. It is a full-day program that allows you to interact with dozens of our current students, tour the campus, learn more about financial aid, gives you opportunities to sit in on classes, and start the morning with the Viterbi School of Engineering.
Your time with us in the Viterbi School will take you through an informative session on our academic programs. We will arrange a meeting with faculty from the major you are interested in as well as engineering facility tours of that same area. For lunch we will have you hanging out with some of our engineering students for a few hours, eating in the dinning facilities, seeing the residence halls, but most importantly experiencing the full USC atmosphere.
Once admitted, students can find the RSVP link in their USC Applicant Portal.Audiences: Admitted Students & Family Members
Contact: Viterbi Admission
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PhD defense - Yaguang Li
Tue, Apr 23, 2019 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Yaguang Li
Tue, April 23rd, 2019
1:00 pm - 3:00 pm
Location: PHE 325
Title:
Spatiotemporal Prediction with Deep Learning on Graphs
PhD Candidate: Yaguang Li
Date, Time, and Location: Tuesday, April 23rd, 2019 at 1pm in PHE 325
Committee: Prof. Cyrus Shahabi, Prof. Yan Liu, and Prof. Antonio Ortega
Abstract:
Spatiotemporal data is ubiquitous in our daily life, ranging from climate science, via transportation, social media, to various dynamical systems. The data is usually collected from a set of correlated objects over time, where objects can be sensors, locations, regions, particles, users, etc. For instance, in the transportation network, road sensors constantly record the traffic data at various correlated locations; in social networks, we observe activity data of correlated users, as indicated by friendships, evolving over time, and in dynamical systems, e.g., physics, climate, we observe the movement of particles interacting with each other. Spatiotemporal prediction aims to model the evolution of a set of correlated objects. It has various applications, ranging from classical subjects such as intelligent transportation system, climate science, social media, physics simulation to emerging fields of sustainability, Internet of Things (IoT) and health-care.
Spatiotemporal prediction is challenging mainly due to the complicated spatial dependencies and temporal dynamics. In this thesis, we study the following important questions in spatiotemporal prediction: (1) How to model complex spatial dependency among objects that are usually non-Euclidean and multimodal? (2) How to model the non-linear and non-stationary temporal dynamics for accurate long-term prediction? (3) How to infer the correlations or interactions among objects when they are not provided nor can be constructed a prior?
To model the complex spatial dependency, we represent the non-Euclidean pair-wise correlations among objects using directed graphs and then propose the novel diffusion graph convolution which captures the spatial dependency with bidirectional random walks on the graph. To model the multimodal correlations among objects, we further propose the multi-graph convolution network. To model the non-linear and non-stationary temporal dynamics, we integrate the novel diffusion graph convolution into the recurrent neural network to jointly model the spatial and temporal dependencies. To capture the long-term temporal dependency, we propose to use the sequence to sequence architecture with scheduled sampling. To utilize the global contextual information in the temporal correlation modeling, we further propose the contextual gated recurrent neural network which augments the recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. To infer correlation among objects, we propose a structure-informed variational graph autoencoder based model, which infers the explicit interactions considering both observed movements and structural prior knowledge, e.g., node degree distribution, edge type distribution, and sparsity. The model represents the structural prior knowledge as differentiable constraints on the interaction graph and optimizes it using gradient-based methods.
We conduct extensive experiments on multiple real-world large-scale datasets for various spatiotemporal prediction tasks, including traffic forecasting, spatiotemporal demand forecasting, travel time estimation, relational inference and simulation. The results show the proposed models consistently achieve clear improvements over state-of-the-art methods. The proposed models and their variants have been deployed in real-world large-scale systems for applications including road traffic speed prediction, Internet traffic forecasting, air quality forecasting, travel time estimation, and spatiotemporal demand forecasting.
Location: Charles Lee Powell Hall (PHE) - 325
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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IoT Solutions with Amazon Web Services
Tue, Apr 23, 2019 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Chris Azer, Amazon Web Services
Talk Title: IoT Solutions with Amazon Web Services
Abstract: There are billions of devices in homes, factories, oil wells, hospitals, cars, and thousands of other places. With the proliferation of devices, you increasingly need solutions to connect them, and collect, store, and analyze device data. AWS IoT provides broad and deep functionality, spanning the edge to the cloud, so you can build IoT solutions for virtually any use case across a wide range of devices. This session will explore customer use cases and dive deep into some of these core IoT services in the cloud and at the edge.
Biography: As an IoT Specialist Solutions Architect for AWS Public Sector, Chris Azer is responsible for supporting federal and state government agencies and partners with their IoT initiatives. With over 15 years of experience, it has been his main goal to help customers extract value from connected devices within the public sector community and industrial automation. Today, Chris helps his customers improve quality of life for populations, business operations, quality of care from service providers, environmental sustainability, and host of other use case scenarios.
Host: Bhaskar Krishnamachari, CCI
More Information: 190423_AWS_Chris Azer Flyer.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Brienne Moore
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Epstein Institute Seminar - ISE 651
Tue, Apr 23, 2019 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Chi Zhou, Assistant Professor, University of Buffalo, The State Univ. of NY
Talk Title: Additive Manufacturing of Multiscale, Multifunctional Structures
Host: Dr. Yong Chen
More Information: April 23, 2019.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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CS Distinguished Lecture: Dr. Dan Boneh (Stanford University) – Cryptography for Blockchains
Tue, Apr 23, 2019 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Dan Boneh, Stanford University
Talk Title: Cryptography for Blockchains
Series: Computer Science Distinguished Lecture Series
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Boneh is a Professor of Computer Science at Stanford University where he heads the applied cryptography group and co-directs the computer security lab. Dr. Boneh's research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, cryptography for blockchains, and cryptanalysis. He is the author of over a 150 publications in the field and is a recipient of the 2014 ACM prize, the 2013 Godel prize, the RSA award in mathematics, and six best paper awards. In 2016 Dr. Boneh was elected to the National Academy of Engineering.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Senior Design Student Presentations
Tue, Apr 23, 2019 @ 04:00 PM - 06:30 PM
Alfred E. Mann Department of Biomedical Engineering
Student Activity
Come along to the Department of Biomedical Engineering's senior design student presentations. Each presentation will last about 20 minutes, with demonstration and Q&A.
Tuesday 04/23 - 4 pm to 6:30 pm:
Team 1: Aid for self-administered mucus clearance and airway function assessment for paraplegic patients. Lin Cao, Preethi Chaudhari, Asma Nawaz, Daniel Yen
Team 2: eye drop dispenser for subjects with deficient fine motor control. Jared Chen, Victor Ong, Swetha Raman, Jenna Wahbeh
Team 3: Improved pill tracker for clinical trials. Jiarui Fu, Manjima Sarkar, Nina Singh, Sabrina Teo
Team 4: Stress management device: Kylie Chinn, Priya Lee, Edward Min, Cheryl Tan
Team 5: Mama-Strut 2.0 - post-partum instrumented brace system: Mana Mehraein, Vincent Mei, Luann Raposo, Afra YaghoubianLocation: Corwin D. Denney Research Center (DRB) - 351
Audiences: Department of Biomedical Engineering faculty and students
Contact: Greta Harrison
