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Events for March 21, 2019
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CS Colloquium: Amy Zhang (MIT) - Systems to Improve Online Discussion
Thu, Mar 21, 2019 @ 09:30 AM - 10:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Amy Zhang, MIT
Talk Title: Systems to Improve Online Discussion
Series: CS Colloquium
Abstract: Discussions online are integral to everyday life, affecting how we learn, work, socialize, and participate in public society. Yet the systems that we use to conduct online discourse, whether they be email, chat, or forums, have changed little since their inception many decades ago. As more people participate and more venues for discourse migrate online, new problems have arisen, and old problems have intensified. People are still drowning in information and must now juggle dozens of disparate discussion silos in addition. Finally, an unfortunately significant proportion of this online interaction is unwanted or unpleasant, with clashing norms leading to people bickering or getting harassed into silence. My research in human-computer interaction is on reimagining outdated designs towards designing novel online discussion systems that fix what's broken about online discussion. To solve these problems, I develop tools that empower users and communities to have direct control over their experiences and information. These include: 1) summarization tools to make sense of large discussions, 2) annotation tools to situate conversations in the context of what is being discussed, as well as 3) moderation tools to give users more fine-grained control over content delivery.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Amy X. Zhang is a graduate student at MIT's Computer Science and Artificial Intelligence Laboratory, focusing on human-computer interaction and social computing, and a 2018-19 Fellow at the Harvard Berkman Klein Center. She has interned at Microsoft Research and Google Research, received awards at ACM CHI and CSCW, and featured in stories by ABC News, BBC, CBC, and more. She has an M.Phil. in CS at University of Cambridge on a Gates Fellowship and a B.S. in CS at Rutgers, where she captained the Division I Women's tennis team. Her research is supported by a Google PhD Fellowship and an NSF Graduate Research Fellowship.
Host: Nora Ayanian
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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RASC seminar - How to Make, Sense, and Make Sense of Contact in Robotic Manipulation
Thu, Mar 21, 2019 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
University Calendar
"How to Make, Sense, and Make Sense of Contact in Robotic Manipulation"
Dexterous manipulation is still one of the key open problems for many new robotic applications, owing in great measure to the difficulty of dealing with transient contact. From an analytical standpoint, intermittent frictional contact (the essence of manipulation) is difficult to model, as it gives rise to non-convex problems with no known efficient solvers. Contact is also difficult to sense, particularly with sensors integrated in a mechanical package that must also be compact, highly articulated and appropriately actuated (i.e. a robot hand). Articulation and actuation present their own challenges: a dexterous hand comes with a high-dimensional posture space, difficult to design, actuate, and control. In this talk, I will present our work trying to address these challenges: analytical models of grasp stability (with realistic energy dissipation constraints), design and use of sensors (tactile and proprioceptive) for manipulation, and hand posture subspaces (for design optimization and teleoperation). These are stepping stones towards achieving versatile robotic manipulation, needed by applications as diverse as logistics, manufacturing, disaster response and space robots.
Matei Ciocarlie is an Associate Professor of Mechanical Engineering at Columbia University. His current work focuses on robot motor control, mechanism and sensor design, planning and learning, all aiming to demonstrate complex motor skills such as dexterous manipulation. Matei completed his Ph.D. at Columbia University in New York; before joining the faculty at Columbia, he was a Research Scientist and Group Manager at Willow Garage, Inc., a privately funded Silicon Valley robotics research lab, and then a Senior Research Scientist at Google, Inc. In recognition of his work, Matei has been awarded the Early Career Award by the IEEE Robotics and Automation Society, a Young Investigator Award by the Office of Naval Research, a CAREER Award by the National Science Foundation, and a Sloan Research Fellowship by the Alfred P. Sloan Foundation.
Hosted by: Gaurav Sukhatme
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Ming Hsieh Institute Medical Imaging Seminar Series
Thu, Mar 21, 2019 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Xingfeng Shao, Electrical Computer and Engineering, University of Southern California
Talk Title: Mapping Water Exchange Rate Across the Blood-Brain Barrier
Abstract: The blood-brain barrier maintains the homeostasis within the brain and the dysfunction of blood-brain barrier has been linked to multiple central nervous system diseases and psychiatric disorders. The purpose of this work is to present a novel MR pulse sequence and regularized modeling algorithm to quantify the water exchange rate, kw, across the blood-brain barrier without contrast, and to evaluate its clinical utility in a cohort of elderly subjects at risk of cerebral small vessel disease. Elderly subjects were recruited and underwent two MRIs to evaluate the reproducibility of the proposed technique. Correlation analysis was performed between kw and vascular risk factors, Clinical Dementia Rating scale, neurocognitive assessments, and white matter hyperintensities. kw was significantly higher in subjects with diabetes and hypercholesterolemia. Significant correlations between kw and vascular risk factors, Clinical Dementia Rating scale, executive/memory function, and the Fazekas scale of white matter hyperintensities were also observed. These results suggest that kw may serve as a surrogate imaging marker of cerebral small vessel disease and associated cognitive impairment.
Biography: Xingfeng Shao is a Ph.D. candidate in Dr. Danny JJ Wang's lab in USC Mark and Mary Stevens Neuroimaging and Informatics Institute (INI). He obtained his Bachelor degree in Engineering Physics at Tsinghua University in Beijing, China, and joined USC BME department as a Ph.D. student in 2016. His research focus on MRI pulse sequence development. With background in physics and neurobiology, he has developed several MRI sequences for arterial spin labeling (ASL) and proposed a novel technique to measure water permeability across the blood-brain barrier in-vivo.
Host: Professor Krishna Nayak
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Talyia White
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PhD Defense - Yuan Shi
Thu, Mar 21, 2019 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Time and Location: 3/21 10 am - 11:30 am - PHE 223
PhD Candidate: Yuan Shi
Committee:
Craig Knoblock (Chair)
Yan Liu
T. K. Satish Kumar
Daniel Edmund O'Leary (external member)
Title: Learning to Adapt to Sensor Changes and Failures
Abstract:
Many software systems run on long-lifespan platforms that operate in diverse and dynamic environments. As a result, significant time and effort are spent manually adapting software to operate effectively when hardware, resources and external devices change. If software systems could automatically adapt to these changes, it would significantly reduce the maintenance cost and enable more rapid upgrade. As an important step towards building such long-lived, survivable software systems, we study the problem of how to automatically adapt to changes and failures in sensors.
We address several adaptation scenarios, including adaptation to individual sensor failure, compound sensor failure, individual sensor change, and compound sensor change. We develop two levels of adaptation approaches: sensor-level adaptation that reconstructs original sensor values, and model-level adaptation that directly adapts machine learning models built on sensor data. Sensor-level adaptation is based on preserving sensor relationships after adaptation, while model-level adaptation maps sensor data into a discriminative feature space that is invariant with respect to changes.
Compared to existing work, our adaptation approaches have the following novel capabilities: 1) adaptation to new sensors even when there is no overlapping period between new and old sensors; 2) efficient adaptation by leveraging sensor-specific transformations derived from sensor data; 3) scaling to a large number of sensors; 4) learning robust adaptation functions by leveraging spatial and temporal information of sensors; and 5) estimating the quality of adaptation.
Additionally, we present a constraint-based learning framework that performs joint sensor failure detection and adaptation by leveraging sensor relationships. Our framework learns sensor relationships from historical data and expresses them as a set of constraints. These constraints then provide a joint view for detection and adaptation: detection checks which constraints are violated, and adaptation reconstructs failed sensor values. Our framework is capable of handling multi-sensor failures which are challenging for existing methods.
To validate our approaches, we conduct empirical studies on sensor data from the weather and UUV (Unmanned Underwater Vehicle) domains. The results show that our approaches can automatically detect and adapt to sensor changes and failures with higher accuracy and robustness compared to other alternative approaches.
Location: Charles Lee Powell Hall (PHE) - 223
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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ECE Seminar: Millimeter-Wave Computational Imaging
Thu, Mar 21, 2019 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Andreas Pedross-Engel, Postdoctoral Research Associate/University of Washington, Seattle
Talk Title: Millimeter-Wave Computational Imaging
Abstract: This talk gives an overview on mmWave computational imaging. Millimeter-wave (mmWave) imaging has many applications such as remote sensing, autonomous robotics, non-destructive testing, and security screening. Some recent imaging systems will be presented, including compressed sensing and sparse reconstruction to minimize the amount of mmWave hardware, an enhanced resolution stripmap mode (ERSM) that leverages emerging metasurface antennas to improve image resolution by up to 42%, and an orthogonal coded active illumination (OCAI) approach to mitigate hardware imperfections and improve sensitivity by more than 40 dB. Finally, I will present a partitioned inverse reconstruction algorithm, optimized for GPUs, that yields a speedup of up to 300x.
Biography: Andreas Pedross-Engel is a postdoctoral Research Associate in the Department of Electrical and Computer Engineering at the University of Washington, Seattle, WA, USA. He is also a co-founder of the millimeter-wave imaging firm ThruWave Inc. He received the Dipl.-Ing. degree and the Ph.D. degree from Graz University of Technology, Graz, Austria, in 2009 and 2014, respectively. His research interests include microwave and millimeter-wave imaging systems, wireless communications, and nonlinear- and mixed- signal processing. In 2018 he received the ASciNA Young Scientist Award from the Austrian Federal Ministry of Education, Science and Research. He is also the recipient of the CoMotion Commercialization Fellows Award from the University of Washington in 2017. Since 2017 he is a Senior Member of the IEEE.
Host: Professor Urbashi Mitra, ubli@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Individual Grammar Tutorials
Thu, Mar 21, 2019 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi graduate and undergraduate students are invited to sign up for individual grammar assistance from professors at the Engineering Writing Program. Sign up for one-on-one individual sessions here: http://bit.ly/grammaratUSC
Questions? Email helenhch@usc.edu
Location: Olin Hall of Engineering (OHE) - 106
Audiences: Graduate and Undergraduate Students
Contact: Helen Choi
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CS Colloquium: Srijan Kumar (Stanford University) - Data Science for Healthy Online Interactions
Thu, Mar 21, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Srijan Kumar, Stanford University
Talk Title: Data Science for Healthy Online Interactions
Series: CS Colloquium
Abstract: The web enables users to interact with one another and shape opinion at an unprecedented speed and scale. However, the prevalence of disinformation and malicious users makes the web unsafe and unreliable, for example, 40% of users have experienced online harassment and platforms have disabled user comments because of trolling. In this talk, I will present data science methods that help us to create a better and safer web ecosystem for everyone. In particular, I will present methods to extract knowledge from the social graph structure and augment with behavior signals to characterize, detect, and mitigate the damage of disinformation and malicious users.
First, I will describe a graph mining collective classification algorithm to identify fake reviews on e-commerce platforms. The method learns trustworthiness scores from the user-to-product review network to identify sophisticated fraudsters. The method is currently being used in production at Flipkart, India's largest e-commerce platform. Next, I will present the first web-scale characterization of multiple account abuse in online discussions and my method of statistical analysis of user interaction graphs to detect them. Finally, I will show how learning embeddings from the social network structure helps to predict online conflicts and to mitigate their damage. These methods power online tools that help administrators in Reddit and Wikipedia.
I will conclude the talk by describing my future research directions that will enable us to proactively predict how malicious behavior will evolve in the future, both on web platforms and face-to-face interactions
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Srijan Kumar (https://stanford.edu/~srijan/) is a postdoctoral scholar in Computer Science at Stanford University. His research investigates data science and machine learning to create healthy online and offline interactions, focusing on developing methods to curb deception, misbehavior, and disinformation. His methods have had a tangible real-world impact and are being used at major tech companies, including Flipkart, Reddit, and Wikipedia. His research has received the ACM SIGKDD Doctoral Dissertation Award runner-up 2018, Larry S. Davis Doctoral Dissertation Award 2018, and WWW Best Paper Award runner-up 2017. His research is interdisciplinary and has been included in the curriculum at several universities, including UIUC, University of Michigan, and Stanford University. His research has been included in documentary (Familiar Shapes) and covered in popular press, including CNN, The Wall Street Journal, Wired, and New York Magazine. He did his Ph.D. in Computer Science from University of Maryland, College Park, and B.Tech. from Indian Institute of Technology, Kharagpur.
Host: Xiang Ren
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Astani Civil and Environmental Engineering Seminar
Thu, Mar 21, 2019 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Issam Najm, Ph.D., PE, Water Quality Treatment Solutions, Inc., Los Angeles
Talk Title: Cyanotoxins in Drinking Water
Abstract: See Attachment
Host: Dr. Amy Childress
More Information: Dr. Najm Issam_ Seminar Announcement.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
