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Conferences, Lectures, & Seminars
Events for February
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Astani Civil and Environmental Engineering Seminar
Mon, Feb 03, 2020 @ 02:00 PM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ruggiero Lovreglio, Massey University, New Zealand
Talk Title: Virtual and Augmented Reality Application for Human Behavior in Disasters
Abstract: Abstract: Understanding how people behave during disasters is fundamental to enhance the safety of building from the design stage to the maintenance stage. To date, new technologies can be used to facilitate the investigation of human behaviour and training. This presentation will illustrate the Virtual Reality (VR) and Augmented Reality (AR) applications carried out by Dr Lovreglio et al. to investigate the evacuation behaviour in building fires and earthquakes and for safety training. The presentation will highlight the advantages and limitations of these technologies as well as possible future implementations of VR and AR.
Biography: Biography: Dr Ruggiero Lovreglio (known as Rino) is Senior Lecturer at Massey University (New Zealand) where he teaches Digital Construction and Research Methods. He got his PhD in 2016 from the Scuola Interpolitecnica (Politecnico di Bari, Milano e Torino) on Human Behaviour in Fire. To date, he has been investigating human behaviour in several disasters such as building fires, earthquakes and wildfires. His research uses new technologies such as Virtual and Augmented Reality to investigate behaviours and training people. He has published more than 50 papers, 30 of them are journal articles. He is an Associate Editor for Safety Science (IF: 3.6), a member of the Editorial Board of Fire Technology (IF: 1.4) and co-author of the 6th edition of the SFPE Handbook. More info regarding Dr Lovreglio previous working experience and achievements is available at http://www.lovreglio.info
Dr Ruggiero Lovreglio
PhD, MEng, BEng
Associate Editor, Safety Science
Editorial Member, Fire Technology
Host: Dr. Burcin Becerik-Gerber
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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Geostationary Littoral Imaging and Monitoring Radiometer
Tue, Feb 04, 2020 @ 11:00 AM - 12:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jeff Puschell, Principal Engineering Fellow and Chief Scientist, Space Systems at Raytheon Space and Airborne Systems in El Segundo, California
Talk Title: Geostationary Littoral Imaging and Monitoring Radiometer
Host: Mahta Moghadda, PhD
More Information: GLIMR SEMINAR 2-4-20 DR. JEFF PUSCHELL Final 1-24-20.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Luz Antunez-Castillo, MBA, EdD
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2020 Eberhardt Rechtin Lecture
Tue, Feb 04, 2020 @ 03:30 PM - 05:00 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: David W. Bates, M.D., M.Sc., Harvard University
Talk Title: Collaboration Between Engineering and Healthcare: Results From the BWH Patient-Safety Learning Laboratory Studies
More Information: 2020 Rechtin Lecture Flyer_1.6.20.pdf
Location: USC Hotel, Center Ballroom
Audiences: Everyone Is Invited
Contact: Grace Owh
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NL Seminar-Why journalism is broken and how data can help fix it
Wed, Feb 05, 2020 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Gabriel Kahn , USC Annenberg
Talk Title: Why journalism is broken and how data can help fix it
Series: Natural Language Seminar
Abstract: Pizza gate, Russian trolls, deep fakes. We live in an information swamp and it sucks. At its core, the crisis in journalism is about a shifting economic model that has made it difficult for legitimate news organizations to survive. The consequences are dire. But harnessing data in the right ways can provide vital information to communities and can help news organizations do more with less. The future of a healthy news environment requires collaboration between news, data and computer science. Gabriel Kahn outlines the current problems and some potential solutions.
Biography: Gabriel Kahn has worked as a newspaper correspondent and editor for three decades, including 10 years at The Wall Street Journal, where he served as Los Angeles bureau chief, deputy Hong Kong bureau chief and deputy Southern Europe bureau chief, based in Rome. He has reported from more than a dozen countries on three continents. He joined USC Annenberg in the fall of 2010, where he jointly runs the Media, Economics and Entrepreneurship program. The goal of M 2e is to bolster students understanding of economics and encourage innovation and experimentation with new ideas in communication and journalism. In addition to his teaching and reporting work, Kahn studies the economic models of the news industry and consults with startups and established news companies on strategy. In 2018, he launched Crosstown, which has pioneered a new approach to local news through data.
Host: Emily Sheng
More Info: https://nlg.isi.edu/nl-seminar
Webcast: https://bluejeans.com/s/FVVU4/Location: Information Science Institute (ISI) - CR 689
WebCast Link: https://bluejeans.com/s/FVVU4/
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: https://nlg.isi.edu/nl-seminar
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Feb 05, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Christopher Ré, Department of Computer Science at Stanford University
Talk Title: If You Want to be Rich, Get a lot of Money: Theory and Systems for Weak Supervision
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: If you want to build a high-quality machine learning product, build a large, high-quality training set. At first glance, this seems as useful as the statement "if you want to be rich, get a lot of money." However, a key idea driving our work is that new theoretical and systems concepts including weak supervision, automatic data augmentation policies, and more, can enable engineers to build training sets more quickly and cost effectively. Along with state-of-the-art results on benchmarks, these concepts have allowed our group and collaborators to build a range of state-of-the-art applications including patient-care monitoring on electronic health records, automatic triage systems for radiologists, and enabling cardiologists to spot rare abnormalities in video MRI-along with widely used products from Apple and Google. This talk describes the theoretical and systems challenges that such applications create.
On the machine-learning theory side, a key problem is estimating the quality and correlation of various sources of training data-”but without ground truth labels. This problem connects to classical questions about estimating the covariance of latent variable models. We describe our new techniques that solve this case and can even improve fully supervised methods for estimating the structure of graphical models.
On the machine-learning systems side, this theory opens up new ways to build machine-learning systems. Here, we describe our recent work on systems that help engineers build and maintain machine learning products-without writing low-level code in frameworks like TensorFlow. These systems draw on recent ideas in machine learning, e.g., zero-code deep learning systems, and twists on classical data management ideas, e.g., schemas to separate the model, the supervision, and down-stream serving code.
Much of this work is open source and available at http://snorkel.org or my website.
Biography: Christopher (Chris) Ré is an associate professor in the Department of Computer Science at Stanford University who is affiliated with the Statistical Machine Learning Group and Stanford AI Lab. His recent work is to understand how software and hardware systems will change as a result of machine learning along with a continuing, petulant drive to work on math problems. Research from his group has been incorporated into scientific and humanitarian efforts, such as the fight against human trafficking, along with products from technology and enterprise companies. He cofounded a company, based on his research into machine learning systems, that was acquired by Apple in 2017. More recently, he cofounded SambaNova systems based, in part, on his work on accelerating machine learning. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016. His research contributions have spanned database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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AME Seminar
Wed, Feb 05, 2020 @ 03:30 PM - 04:30 PM
Conferences, Lectures, & Seminars
Speaker: Pavlos P. Vlachos, Purdue
Talk Title: Fluid Mechanics in Clinical Echocardiography
Abstract: In this talk we will probe flows in cardiac disease using in-vivo measurements in clinical settings, and we will discuss how traditional experimental fluids mechanics tools can translate into clinical practice.
Flows in the cardiovascular system manifest intrinsic complexity, which is often associated with diseased states. Imaging modalities such as ultrasound/echocardiography and phase-contrast MRI provide unique opportunities and challenges for flow measurements in patients. Currently, the relationship between clinical flow measurements and clinical diagnostic parameters is qualitative, and often is reliant on heuristics and non-physical assumptions.
In this talk we will discuss how to overcome these limitations by integrating medical imaging with experimental fluid mechanics, in order to, ultimately, improve accuracy, robustness, and clinical diagnostic utility of these tools.
Specifically, we will discuss how fluid mechanics can be used in the analysis of echocardiographic imaging for heart failure. We will show an improved approach for clinical implementation of EchoPIV (echocardiographic Particle Image Velocimetry) and a new method for the velocity reconstruction of Color-Doppler flow imaging. Finally, we will present a use-case in the analysis of fetal and neonatal echocardiograms of babies born with single ventricle (hypoplastic left heart syndrome). If time permits, some additional examples of application to 4D flow MRI will be presented.
Host: AME Department
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
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Theory Lunch
Thu, Feb 06, 2020 @ 12:15 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Kobbi Nissim, Georgetown University
Talk Title: Legal Theorems of Privacy
Abstract: There are significant gaps between legal and technical thinking around data privacy. Technical standards such as k-anonymity and differential privacy are described using mathematical language and strive for mathematical rigor whereas legal standards are not rigorous from a mathematical point of view and often resort to concepts such as de-identification and anonymization which they only partially define. As a result, arguments about the adequacy of technical privacy measures for satisfying legal privacy often lack rigor, and their conclusions are uncertain. The uncertainty is exacerbated by a litany of successful privacy attacks on privacy measures thought to meet legal expectations but then shown to fall short of doing so.
We ask whether it is possible to introduce mathematical rigor into such analyses so as to make formal claims and prove "legal theorems" that technical privacy measures meet legal expectations. For that, we explore some of the gaps between these two very different approaches, and present initial strategies towards bridging these gaps. In particular, we focus on the concept of singling out from the EU's General Data Protection Regulation (GDPR). To capture this concept, we define a new type of privacy attack, predicate singling out, where an adversary finds a predicate matching exactly one row in a database with probability significantly better then a statistical baseline. We then argue that any data release mechanism that purports to "render anonymous" data under the GDPR should prevent predicate singling out. Hence, the concept has legal consequences as it can be used as a yardstick for arguing whether data release mechanisms meet the GDPR standard of data anonymization.
Biography: Professor Kobbi Nissim is a McDevitt Chair at the department of Computer Science, Georgetown University and affiliated with Georgetown Law. Nissim's work is focused on the mathematical formulation and understanding of privacy. His work from 2003 and 2004 with Dinur and Dwork initiated rigorous foundational research of privacy and in 2006 he introduced differential privacy with Dwork, McSherry and Smith. Nissim was awarded the Caspar Bowden Privacy for research in Privacy Enhancing Technology in 2019, the Gödel Prize in 2017, IACR TCC Test of Time Awards in 2016 and in 2018, and the ACM PODS Alberto O. Mendelzon Test-of-Time Award in 2013.
Host: Shaddin Dughmi
Location: 213
Audiences: Everyone Is Invited
Contact: Cherie Carter
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CS Distinguished Lecture: Karon MacLean (University of British Columbia) - Making Haptics and its Design Accessible
Thu, Feb 06, 2020 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Karon MacLean, University of British Columbia, Canada
Talk Title: Making Haptics and its Design Accessible
Series: Computer Science Distinguished Lecture Series
Abstract: Today's advances in tactile sensing and wearable, IOT and context-aware computing are spurring new ideas about how to configure touch-centered interactions in terms of roles and utility, which in turn expose new technical and social design questions. But while haptic actuation, sensing and control are improving, the difficulties of incorporating them into a real-world design process poses a major obstacle to adoption in everyday technology.
In this talk I'll overview highlights chosen from of an ongoing effort to understand how to support haptic designers and end-users. These include online experimental design tools, DIY open sourced hardware and accessible means of creating, for example, expressive physical robot motions and evolve physically sensed expressive tactile languages, and major community-based studies of design practice.
To accelerate design practice, we put our systems, designs and datasets online. A central and evolving piece of our larger openhaptics effort is Haptipedia, an expert-sourced, community-based browsable visualization of historical haptic inventions as a resource to future designers.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Karon MacLean is Professor in Computer Science at UBC, with degrees in Biology and Mechanical Engineering (BSc, Stanford; M.Sc. / Ph.D, MIT) and time spent as a professional robotics engineer (Center for Engineering Design, University of Utah) and haptics / interaction researcher (Interval Research, Palo Alto). At UBC since 2000, MacLean's research specializes in haptic (touch) interaction: cognitive, sensory and affective design for people interacting with the computation we touch, emote and move with and learn from, from robots to handheld devices and the situated environment. MacLean leads UBC's Designing for People interdisciplinary research cluster and CREATE graduate training program (25 researchers spanning 11 departments and 5 faculties - dfp.ubc.ca), is Special Advisor, Innovation and Knowledge Mobilization to UBC's Faculty of Science, and will co-chair ACM UIST (User Interface Software and Technology) in 2020.
Host: Heather Culbertson
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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NL Seminar-MACHINE LEARNING THROUGH THE INFORMATION BOTTLENECK
Fri, Feb 07, 2020 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Artemy Kolchinsky, Santa Fe Institute
Talk Title: MACHINE LEARNING THROUGH THE INFORMATION BOTTLENECK
Series: Natural Language Seminar
Abstract: he information bottleneck IB has been proposed as a principled way to compress a random variable, while only preserving that information which is relevant for predicting another random variable. In recent times, the IB has been proposed and challenged as a theoretical framework for understanding why and how deep learning architectures achieve good performance. I will cover: 1. an introduction to the ideas behind IB, 2. methods for implementing information-theoretic compression in neural networks + some possible applications of such methods, 3. the current status of the IB theory of deep learning, 4. recently discovered caveats that arise for IB in machine learning scenarios.
Biography: Artemy Kolchinsky is a postdoctoral fellow at the Santa Fe Institute (Santa Fe, NM). His work lies at the intersection of information theory, statistical physics, and machine learning. He is interested in using tools from statistical physics to derive fundamental bounds on the ability of real-world agents whether protocells, organisms, or computers to acquire and exploit information in adaptive ways.
Host: Emily Sheng
More Info: https://nlg.isi.edu/nl-seminar
Webcast: https://bluejeans.com/298422226Location: Information Science Institute (ISI) - CR #1016
WebCast Link: https://bluejeans.com/298422226
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: https://nlg.isi.edu/nl-seminar
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Fri, Feb 07, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Nikunj Mehta, Falkonry
Talk Title: Discovering and Explaining Patterns in Industrial Multivariate Time Series Data
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Complex assets and process units exhibit many different behaviors during the course of industrial operations. Identifying and removing sources of inefficiency in these operations is essential for advancing manufacturing and process operations. In this talk, we explain how classification as opposed to anomaly detection and forecasting is the essential machine learning problem for Industry 4.0. We explain the main challenges for these machine learning problems to motivate research directions. We then describe a signal processing pipeline and user interface for democratizing such machine learning and real-time processing.
Biography: Dr. Nikunj founded Falkonry after realizing that very valuable operational data produced in industrial infrastructure goes mostly unutilized in the energy, manufacturing and transportation sectors. Falkonry has enabled companies to scale predictive operations. Falkonry has significantly improved their uptime, yield and quality. Prior to Falkonry, Dr. Mehta led software architecture and customer success for C3 IoT. Earlier, he led innovation teams at Oracle focused on database technology and led the creation of the IndexedDB standard for databases embedded inside all modern browsers. He has contributed to standards at both W3C and IETF, and is a member of the ACM.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Medical Imaging Seminar
Mon, Feb 10, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Andrei Irimia, Gerontology, Biomedical Engineering, & Neuroscience at USC
Talk Title: Multimodal Imaging, Machine Learning and Electrophysiology for Connectome Mapping in Traumatic Brain Injury and Alzheimer's Disease
Series: Medical Imaging Seminar Series
Abstract: Mapping brain circuitry and its changes after traumatic brain injury (TBI) benefits substantially from the integration of multimodal neuroimaging techniques to quantify and monitor brain pathology, plasticity and degeneration. We have integrated fMRI and network theory with EEG and other approaches to perform supervised learning of connectome data and to study functional trajectories after mild TBI (mTBI). Our results show that geriatric mTBI is associated with fronto-hippocampo-limbic alterations in the brain's default mode network (DMN), and that many of these alterations are statistically indistinguishable from those observed in AD. By leveraging machine learning, we have shown that AD-like degradation of functional circuits can be predicted by acute cognitive deficits after geriatric mTBI. In addition to establishing a statistical association between brain injury, cognition and AD-like DMN degradation, these findings advance the important goal of acutely forecasting mTBI patients' chronic alterations of brain connectivity along AD-like functional trajectories.
Biography: Andrei Irimia is Assistant Professor of Gerontology, Biomedical Engineering and Neuroscience at USC. He holds a PhD in biophysics from Vanderbilt University and has done postdoctoral research at UCSD and UCLA prior to joining USC. His research leverages structural MRI, fMRI, DTI and EEG to study the relationship between traumatic brain injury and Alzheimer's disease. His laboratory in the Davis School of Gerontology is funded by the NIH and DoD.
Host: Richard Leahy, leahy@sipi.usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White
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ISE 651 - Epstein Seminar
Tue, Feb 11, 2020 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Zhijian (ZJ) Pei, Professor, Texas A&M University
Talk Title: Ceramic Binder Jetting Additive Manufacturing: Three Methods to Increase Density
Host: Prof. Yong Chen
More Information: February 11, 2020.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Vanderley M. John, Ph.D.
Wed, Feb 12, 2020 @ 02:00 AM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Vanderley M. John, Ph.D., Professor of Building Materials Construction Engineering, Polytechnic School, University of Sao Paulo
Talk Title: Overview of Research on low-carbon cement and industrial ecology at Poli USP
Abstract: Overview of Research on low-carbon cement and industrial ecology at Poli USP
Vanderley M. John
Professor of Building Materials of Polytechnic School, University of Sao Paulo.
The environmental crisis, the global demand for more and better-built environment, adaptation to population aging and the digital industrial revolution are setting an accelerating pace of innovation. The construction sector will be forced to innovate.
To make possible the reduction of environmental impacts we need metrics suited to use in today's decision-making made by non-experts. LCA is too expensive and complex for that. From a construction point of view, it is incomplete. Its efficacy is reduced by generalized use of secondary data, which also defeats the capacity to identify the best supplier and drive poor performers out of the market. Producing meaningful benchmarks for each LCA indicator is unpractical. Results of ongoing research focused on developing simplified LCA-based metrics, focused on construction grand environmental challenges will be presented. The indicators are cheap and easy to measure, making possible to build benchmark using primary data. They are simple to understand and interpret and suited to be applied at multiple scales of built environment. Examples will be given on wood and cement-based materials, including industry-wide benchmarks and new resource use efficiency metrics.
Cement-based materials are the most largely used artificial materials -“ some 30 billion metric ton each year - making the bulk of the stock of built environment. Currently it uses 1/3 of the flow of materials and emits 8% of anthropogenic CO2, shares that are growing. Cement industry is considering carbon capture and storage technology, an environmentally risky and costly technology. Example of a new low-cost technology that combines packing for minimum water demand, dispersion and replacing binders, by large fractions of fillers will be given. It allows reducing +60% of the total binder +50% the CO2 footprint and 40% of water consumption, in comparison with our global benchmark. The technology has been tested at industrial conditions. The technology is scalable, and a UN Environment estimates a mitigation potential of 900 MtCO2 /year by 2050.
Finally, considering the urgency of technological change, it is crucial to partner with industry to accelerate innovation and increase success rates. The development of the Sustainable Construction Innovation Center (CICS USP), an innovation hub entirely funded by private money will be present. It includes the construction of the CICS living lab, a building designed to demonstrate new construction technologies in actual use conditions accelerated innovation and to foster the investigation of user well being and user-building interactions.
Host: Dr. Lucio Soibelman
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Salina Palacios
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Feb 12, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mykel Kochenderfer, Department of Aeronautics and Astronautics at Stanford University
Talk Title: Automated Decision Making for Safety Critical Applications
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Building robust decision making systems is challenging, especially for safety critical systems such as unmanned aircraft and driverless cars. Decisions must be made based on imperfect information about the environment and with uncertainty about how the environment will evolve. In addition, these systems must carefully balance safety with other considerations, such as operational efficiency. Typically, the space of edge cases is vast, placing a large burden on human designers to anticipate problem scenarios and develop ways to resolve them. This talk discusses major challenges associated with ensuring computational tractability and establishing trust that our systems will behave correctly when deployed in the real world. We will outline some methodologies for addressing these challenges.
Biography: Mykel Kochenderfer is a professor of aeronautics and astronautics at Stanford University. He is the director of the Stanford Intelligent Systems Laboratory (SISL), conducting research on advanced algorithms and analytical methods for the design of robust decision making systems. In addition, he is the director of the SAIL-Toyota Center for AI Research at Stanford and a co-director of the Center for AI Safety. He received a Ph.D. in informatics from the University of Edinburgh and B.S. and M.S. degrees in computer science from Stanford University. Prof. Kochenderfer is an author of the textbooks "Decision Making under Uncertainty: Theory and Application" and "Algorithms for Optimization", both from MIT Press.
Host: Paul Bogdan, pbogdan@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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ECE Seminar: Internet Architectural Evolution
Wed, Feb 12, 2020 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Barath Raghavan, Dept of CS, USC
Talk Title: Internet Architectural Evolution
Abstract: The core architectural features of today's Internet were codified three decades ago. They have served us well over these years, both in practice and as something to inveigh against in research. To remedy numerous weaknesses, some have developed clean-slate designs that reimagine the Internet anew, while others have sought and achieved incremental change. What all agree upon is that architectural evolution is hard.
I will describe a line of research, a decade in the making, to enable architectural change in the Internet. This research has three key aims: pluralism, deployability, and meta-deployability. Since we cannot know what the future holds, we designed an architectural "framework" that enables pluralism -“ the seamless co-existence of many different Internet architectures. Since the high cost of deployment has inhibited experimentation and innovation, we ensured the deployability of new architectures through this framework. And since deployment of the framework itself is a barrier to enabling such architectural evolution, we designed for meta-deployability -“ for the framework itself to be incrementally deployable in today's Internet.
Biography: Barath Raghavan joined USC as an assistant professor of computer science in 2018. Previously he led the engineering team at Nefeli Networks, was a senior staff researcher at ICSI, was CTO of a social-impact nonprofit, developed networked systems at Google, and taught complexity theory at Williams College. His work spans an equally diverse range of areas including Internet architecture, network function virtualization, digital agriculture, network security and privacy, rural Internet access, network troubleshooting and testing, and computing for urban resilience. He received his PhD from UC San Diego in 2009 and his BS from UC Berkeley in 2002. He has received a number of paper awards including from ACM SIGCOMM, ACM DEV, ACM CHI, and the IRTF.
Host: Prof. Richard M. Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Theory Lunch
Thu, Feb 13, 2020 @ 12:15 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Salil Vadhan, Harvard University
Talk Title: Derandomization Beyond Connectivity: High-Precision Estimation of Random Walks and Laplacian Solvers in Small Space
Abstract: I will describe a series of works that attacks the derandomization of space-bounded computation (e.g. seeking to prove RL=L) using a combination of ideas from the literature on time-efficient Laplacian solvers (Spielman and Teng, STOC '04; Peng and Spielman, STOC '14; Cheng et al. '15; Cohen et al. FOCS '16, STOC '17, FOCS '18) with ones used to show that Undirected S-T Connectivity is in deterministic logspace (Reingold, STOC '05 and JACM '08; Rozenman and Vadhan, RANDOM '05).
In particular, we obtain deterministic, nearly logarithmic-space algorithms for (a) estimating random walk probabilities to within polynomially small error and (b) approximately solving linear systems given by graph Laplacians, with both results holding for Eulerian directed graphs and hence also undirected graphs. Previously both of these problems were known to be solvable for general directed graphs by randomized algorithms in logarithmic space (Aleliunas et al. FOCS '79; Doron, Le Gall, and Ta-Shma RANDOM '17), and hence by deterministic algorithms using space O(log^{3/2} N) (Saks and Zhou, FOCS '95 and JCSS '99).
Joint works with Murtagh, Reingold, and Sidford (FOCS '17 and RANDOM '19) and Ahmadinejad, Kelner, Murtagh, Peebles, and Sidford (arXiv:1912.04524)
Biography: Salil Vadhan is Vicky Joseph Professor of Computer Science and Applied Mathematics at Harvard University. After completing his undergraduate degree in Mathematics and Computer Science at Harvard in 1995, he obtained his PhD in Applied Mathematics from Massachusetts Institute of Technology in 1999, where his advisor was Shafi Goldwasser. His research centers around the interface between computational complexity theory and cryptography. He focuses on the topics of pseudorandomness and zero-knowledge proofs. His work on zig-zag product, with Omer Reingold and Avi Wigderson, was awarded the 2009 Gödel Prize.
Host: Shaddin Dughmi
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Cherie Carter
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Sonny Astani Civil and Environmental Engineering Seminar
Thu, Feb 13, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ameet Pinto, Northeastern University
Talk Title: How do we manage the drinking water microbiome?
Abstract: See attached abstract.
Host: Dr. Adam Smith
More Information: A. Pinto Abstract _2-13-2020.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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CS Colloquium: Scott Niekum (UT Austin) - Scaling Probabilistically Safe Learning to Robotics
Fri, Feb 14, 2020 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Scott Niekum, The University of Texas at Austin
Talk Title: Scaling Probabilistically Safe Learning to Robotics
Series: Computer Science Colloquium
Abstract: Before learning robots can be deployed in the real world, it is critical that probabilistic guarantees can be made about the safety and performance of such systems. In recent years, safe reinforcement learning algorithms have enjoyed success in application areas with high-quality models and plentiful data, but robotics remains a challenging domain for scaling up such approaches. Furthermore, very little work has been done on the even more difficult problem of safe imitation learning, in which the demonstrator's reward function is not known. This talk focuses on new developments in three key areas for scaling safe learning to robotics: (1) a theory of safe imitation learning; (2) scalable reward inference in the absence of models; (3) efficient off-policy policy evaluation. The proposed algorithms offer a blend of safety and practicality, making a significant step towards safe robot learning with modest amounts of real-world data.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Scott Niekum is an Assistant Professor and the director of the Personal Autonomous Robotics Lab (PeARL) in the Department of Computer Science at UT Austin. He is also a core faculty member in the interdepartmental robotics group at UT. Prior to joining UT Austin, Scott was a postdoctoral research fellow at the Carnegie Mellon Robotics Institute and received his Ph.D. from the Department of Computer Science at the University of Massachusetts Amherst. His research interests include imitation learning, reinforcement learning, and robotic manipulation. Scott is a recipient of the 2018 NSF CAREER Award and 2019 AFOSR Young Investigator Award.
Host: Stefanos Nikolaidis
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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CS Colloquium: Cheng Tan (Courant Institute / New York University) - Auditing Outsourced Services
Tue, Feb 18, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Cheng Tan, Courant Institute / New York University
Talk Title: Auditing Outsourced Services
Series: CS Colloquium
Abstract: How can users of a cloud service verify that the service truly performs as promised? This question is vital today because clouds are complicated black boxes, running in different administrative domains from users. Their correctness can be undermined by internal corruptions---misconfigurations, operational mistakes, insider attacks, unexpected failures, or adversarial control at any layer of the execution stack.
This talk will present verifiable infrastructure, a framework that lets users audit outsourced applications and services. I will introduce two systems: Orochi and Cobra, which verify the execution of, respectively, untrusted servers and black-box databases. Orochi and Cobra introduce various techniques, including deduplicated re-execution, consistent ordering verification, GPU accelerated pruning, and others. Beyond these two systems, I will also discuss verifiable infrastructure more generally.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Cheng Tan is a computer science Ph.D. candidate in the Courant Institute at New York University. His interests are in operating systems, networked systems, and security. His work on the Efficient Server Audit Problem was awarded best paper at SOSP 2017. His work on data center network troubleshooting at Microsoft Research has been deployed globally in more than 30 data centers in Microsoft Azure.
Host: Barath Raghavan
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ISE 651 - Epstein Seminar
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Tue, Feb 18, 2020 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Dennis Kon-Jin Lin, University Distinguished Professor, The Pennsylvania State University, University Park
Talk Title: Order-of-addition Experiments: Design and Analysis
Host: Dr. Qiang Huang
More Information: February 18, 2020.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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Grand Challenges Lecture Series
Tue, Feb 18, 2020 @ 05:30 PM - 06:30 PM
Viterbi School of Engineering Student Affairs
Conferences, Lectures, & Seminars
Speaker: Dr. Carol Peden,
Talk Title: Why Healthcare Needs Engineers
Abstract: Grand Challenges Lecture Series - Health - Dr. Carol Peden
Host: Grand Challenge Scholars Program
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: GCSP Participants
Contact: Viterbi Undergraduate Programs
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Joint Math-FLDS/ CPS Seminar
Wed, Feb 19, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Reinhard Heckel, Department of Electrical and Computer Engineering Technical, University of Munich
Talk Title: Image Recovery and Recognition via Exploiting the Structural Bias of Neural Networks
Abstract: Deep neural networks are highly successful tools for image classification, recovery, and restoration. This success is often attributed to large amounts of training data. However, recent findings challenge this view and instead suggest that a major contributing factor to this success is that the architecture imposes strong prior assumptions-”so strong that it enables image recovery without any training data. In this talk we discuss two instances of this phenomena: First, we show that fitting a convolutional network to a corrupted and/or under-sampled measurement of an image provably removes noise and corruptions from that image, without ever having trained the network. Second, we show that it is possible to learn from a dataset with both true and false examples, obtained without explicit human annotations, by exploiting the phenomena that neural networks fit true examples faster than false ones.
Host: Mahdi Soltanolkotabi, soltanol@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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AME Seminar
Wed, Feb 19, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Alberto Aliseda, University of Washington
Talk Title: Fluid Mechanics of Intracranial Aneurysms: Fundamental Aspects and Application to Clinical Decision-Making
Abstract: The fluid mechanics inside intracranial aneurysms dominate the efficacy of endovascular treatment methods, modulating the mechanical stresses and residence times inside the sac and at the aneurysmal neck. Embolic coils and flow-diverting stents, the two dominant types of endovascular devices for treatment, are designed to slow down flow inside the aneurysmal volume and reduce stresses on the aneurysmal sac, creating an environment that enables successful thrombosis in the aneurysm, which eliminates the risk of rupture.
In-vitro experiments characterize the hemodynamics inside intracranial aneurysms, prior to treatment and post-treatment with flow-diverting stents. We use stereo (2D-3C) and 3D (3D-3C) particle image velocimetry (PIV) to explore the parameter space of aneurysms in a large cohort of patients followed along several years. The flow measurements are interpreted as a combination of two canonical flows: flow in a curved pipe and cavity flow. As such, the parent-vessel Reynolds and Dean numbers are the relevant non-dimensional parameters. Unsteadiness in the cardiac cycle introduces the Womersley number as a third component of flow inertia. Despite inertia dominating the parent-vessel flow, flow-diverting stents significantly reduce the velocity inside the aneurysmal sac, leading to viscous-dominated flow. A critical Dean number is identified that separates two opposite flow behaviors that could help predict treatment success.
I will also discuss a computational investigation of a large population of patients whose aneurysm treatments are followed over time, to determine the mechanism by which endovascular treatment fails to prevent aneurysmal growth. A novel modeling technique that uses high-resolution, synchrotron micro-CT scans to understand the flow inside coiled aneurysm enables homogenization methods for improved porous medium representation of deployed coils or stents, improving the clinical utility of the simulation results.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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AME Seminar
Wed, Feb 19, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Alberto Aliseda, University of Washington
Talk Title: Fluid Mechanics of Intracranial Aneurysms: Fundamental Aspects and Application to Clinical Decision-Making
Abstract: The fluid mechanics inside intracranial aneurysms dominate the efficacy of endovascular treatment methods, modulating the mechanical stresses and residence times inside the sac and at the aneurysmal neck. Embolic coils and flow-diverting stents, the two dominant types of endovascular devices for treatment, are designed to slow down flow inside the aneurysmal volume and reduce stresses on the aneurysmal sac, creating an environment that enables successful thrombosis in the aneurysm, which eliminates the risk of rupture.
In-vitro experiments characterize the hemodynamics inside intracranial aneurysms, prior to treatment and post-treatment with flow-diverting stents. We use stereo (2D-3C) and 3D (3D-3C) particle image velocimetry (PIV) to explore the parameter space of aneurysms in a large cohort of patients followed along several years. The flow measurements are interpreted as a combination of two canonical flows: flow in a curved pipe and cavity flow. As such, the parent-vessel Reynolds and Dean numbers are the relevant non-dimensional parameters. Unsteadiness in the cardiac cycle introduces the Womersley number as a third component of flow inertia. Despite inertia dominating the parent-vessel flow, flow-diverting stents significantly reduce the velocity inside the aneurysmal sac, leading to viscous-dominated flow. A critical Dean number is identified that separates two opposite flow behaviors that could help predict treatment success.
I will also discuss a computational investigation of a large population of patients whose aneurysm treatments are followed over time, to determine the mechanism by which endovascular treatment fails to prevent aneurysmal growth. A novel modeling technique that uses high-resolution, synchrotron micro-CT scans to understand the flow inside coiled aneurysm enables homogenization methods for improved porous medium representation of deployed coils or stents, improving the clinical utility of the simulation results.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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Medical Imaging Seminars - Part 1 of 2
Wed, Feb 19, 2020 @ 04:00 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: E. Brian Welch, Ph.D., M.B.A., Director of Clinical Science, Hyperfine, Guilford, CT
Talk Title: Portable Point-of-Care Bedside MRI
Series: Medical Imaging Seminar Series
Abstract: I will describe the career path that led me from USC (B.S. B.M.E.E. 1998) to graduate school, experiences in industry to academia and back to industry again, and conclude with the most exciting stage of my career so far -“ helping to validate the clinical utility of the world's first portable point-of-care bedside MRI scanner.
Biography: Brian is a biomedical-electrical engineer (B.S. BME-E University of Southern California, 1998) whose Ph.D. training at the Mayo Clinic College of Medicine focused on biomedical imaging. Specifically, he is an expert in methods and software development for magnetic resonance imaging (MRI). His previous and ongoing work concentrates on overcoming the real-world limitations that hinder research and clinical applications of MRI. Strategies to overcome these challenges include hardware and software solutions, alternative data acquisition and reconstruction methods, novel MRI pulse sequences, quantitative imaging methods and associated post-processing tools. Based on more than 20 years of experience in MRI and 6 years of work experience as the on-site Philips Healthcare MR clinical scientist supporting research projects at Vanderbilt University, Dr. Welch acquired deep knowledge of the capabilities of the 3T and 7T human scanners housed at the Vanderbilt University Institute of Imaging Science (VUIIS). Dr. Welch applied that experience and knowledge to his own independent research programs as a Vanderbilt faculty member with contributions in the areas of fat-water MRI, human brown adipose tissue imaging, and continuously moving table MRI. Most recently, Dr. Welch joined the startup company Hyperfine in 2017 as the Director of Clinical Science with the goal of validating the clinical utility of the world's first portable point-of-care bedside MRI scanner.
Host: Prof. Krishna Nayak, knayak@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Medical Imaging Seminars - Part 2 of 2
Wed, Feb 19, 2020 @ 04:30 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Houchun Harry Hu, Ph.D., Clinical Scientist, Hyperfine, Guilford, CT
Talk Title: Fat to Water in Pediatric MRI
Series: Medical Imaging Seminar Series
Abstract: I will share my experience and career path/choices as a MRI physicist working in three large children's hospitals, from Los Angeles (2011-2014), to Phoenix (2014-2017), to Columbus (2017-2019). I will highlight several projects, including my interests in childhood obesity, my work with spiral MRI in the clinical setting, my foray into non-Gadolinium angiography techniques and arterial spin labeling in children, and my interest in non-Cartesian free-breathing techniques. I will conclude with my thoughts on the promises of a portable point of care MRI system in pediatric settings.
Biography: Houchun Harry Hu, Ph.D., Clinical Scientist, Hyperfine, Guilford, CT Talk Title: Fat to Water in Pediatric MRI Series: MHI Distinguished Visitor Seminar Series Abstract: I will share my experience and career path/choices as a MRI physicist working in three large children's hospitals, from Los Angeles (2011-2014), to Phoenix (2014-2017), to Columbus (2017-2019). I will highlight several projects, including my interests in childhood obesity, my work with spiral MRI in the clinical setting, my foray into non-Gadolinium angiography techniques and arterial spin labeling in children, and my interest in non-Cartesian free-breathing techniques. I will conclude with my thoughts on the promises of a portable point of care MRI system in pediatric settings. Biography: Harry has been working in the domain of pediatric MRI over the last 12 years. He obtained his undergraduate degree (B.S., BME/BMEC) from USC in 2001, and went on to earn a PhD (2006) in BME / MRI from the Mayo Clinic in Rochester, Minnesota. From 2006-2011, he spent time in Professor Krishna Nayak's laboratory at USC working primarily on water-fat imaging. From 2011-2014, he transitioned to Children's Hospital Los Angeles to work on NIH-funded projects in brown adipose tissue. From 2014-2017, Harry moved to Arizona to work as a clinical MRI physicist at Phoenix Children's Hospital, collaborating with Dr. James Pipe from the Barrow Neurological Institute on spiral acquisitions in pediatric brain applications. In June 2017, Harry was recruited to Nationwide Children's Hospital in Columbus, Ohio, as the Director of Imaging Research. In late 2019, Harry joined Hyperfine as a Clinical Scientist as a member of Dr. E. Brian Welch's team to focus on the deployment of the company's point-of-care portable MRI systems in pediatric centers. Harry has served as a Deputy Editor for Magnetic Resonance in Medicine (2012-2017) and is currently an Associate Editor for Radiology and the Journal of Magnetic Resonance Imaging.
Host: Prof. Krishna Nayak, knayak@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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Limits of the quantitative approach, or why parallel and distributed system energy management needs to move on
Thu, Feb 20, 2020 @ 10:15 AM - 11:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Kirk W. Cameron, Virginia Tech
Talk Title: Limits of the quantitative approach, or why parallel and distributed system energy management needs to move on
Abstract: In this talk, we begin with the progression of parallel computer system power management over the last two decades. In particular, we focus on the evolution of quantitative design approaches and the emergence of effective parallel and distributed system runtime power management. We observe that the growing complexity of today's machines limits the effectiveness of traditional quantitative approaches. The Compute-Overlap-Stall (COS) model of parallel computation is proposed to accurately capture the effects of emergent orchestrated power management of processor, memory, and thread throttling. The implication of our findings is that as power management techniques pervade, new machine-learning performance evaluation and prediction approaches will be essential to future computer system designs.
Biography: Professor Kirk W. Cameron directs the stack@cs Center for Computer Systems at Virginia Tech. He pioneered Green HPC (PowerPack, Green500, SPECPower, grano.la) and his software has been downloaded by more than 500,000 people in 160+ countries. His accolades include both NSF and DOE Career Awards, IBM and AMD Faculty Awards, best papers (e.g., HPDC 2017) and the LLNL Science/Technology Excellence Award. His internationally acclaimed SeeMore cluster has been visited by tens of thousands and was named the second best RPi project of all time by MagPi Magazine. In 2017 he was named an ACM Distinguished Scientist and in 2018-2019 he held a Distinguished Visiting Fellowship from the UK Royal Academy of Engineering.
Host: Xuehai Qian, xuehai.qian@usc.edu
More Information: 200220_Kirk Cameron_CENG.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Brienne Moore
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CS Colloquium: Jiapeng Zhang (Harvard) - Sunflowers and Their Applications in Computer Science and Mathematics
Thu, Feb 20, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jiapeng Zhang, Harvard University
Talk Title: Sunflowers and Their Applications in Computer Science and Mathematics
Series: CS Colloquium
Abstract: The sunflower is a simple notion in combinatorics, originally invented and studied by Erdos and Rado in 1960. Surprisingly, it has deep connections to fundamental problems in computer science, such as matrix multiplication, efficient data structures, computational complexity and cryptography. In my talk, I will explain our new results on sunflowers, how ideas emerging from computer science were critical in the proof, and how our new techniques can help shed light on some central problems in computer science and mathematics.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Jiapeng Zhang is a postdoc at Harvard with Prof. Salil Vadhan. He did his PhD at UC San Diego with Prof. Shachar Lovett. His research focuses on boolean function analysis, computational complexity, learning theory and cryptography.
Host: Shaddin Dughmi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE Seminar: Ad-Mageddon: The Next Frontier in Online Privacy
Thu, Feb 20, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Zubair Shafiq, University of Iowa
Talk Title: Ad-Mageddon: The Next Frontier in Online Privacy
Abstract: While online advertising supports the "free" web, it relies on a complex and opaque tracking ecosystem that surveils users across the web. Hundreds of millions of users rely on ad-blocking and anti-tracking tools to counter the negative externalities of online advertising and tracking. Perhaps unsurprisingly, advertisers are increasingly retaliating against the users of such tools -- prompting an arms race.
In this talk, I will first discuss the pain points of the state-of-the-art ad-blocking and anti-tracking tools. I will then describe our recent work on building effective and robust countermeasures against online advertising and tracking using machine learning techniques. I will highlight the unique challenges and opportunities in deploying ad-blocking and anti-tracking tools in web browsers as well as mobile and IoT systems. I will conclude with a discussion of my future research vision for a privacy-respecting web.
Biography: Zubair Shafiq is an assistant professor of computer science at the University of Iowa. Prior to this, he received his Ph.D. from Michigan State University in 2014. His research focuses on building privacy-enhancing tools to counter online tracking and surveillance. More broadly, his work takes a data-driven approach to addressing emerging online privacy and security threats. He is a recipient of the NSF CAREER Award (2018), Andreas Pfitzmann PETS Best Student Paper Award (2018), ACM IMC Best Paper Award (2017), NSF CRII Award (2015), Fitch-Beach Outstanding Graduate Research Award (2013), IEEE ICNP Best Paper Award (2012), and the Dean's Plaque of Excellence for undergraduate research (2007, 2008). More information at https://cs.uiowa.edu/~mshafiq
Host: Professor Konstantinos Psounis, kpsounis@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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MASCLE Machine Learning Seminar: Rose Yu (Northeastern University) - Physics Guided AI for Learning Spatiotemporal Dynamics
Thu, Feb 20, 2020 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rose Yu, Northeastern University
Talk Title: Physics Guided AI for Learning Spatiotemporal Dynamics
Series: Machine Learning Seminar Series hosted by USC Machine Learning Center
Abstract: Applications such as sports, climate science, and aerospace engineering require learning complex dynamics from large-scale spatiotemporal data. Such data is often non-linear, non-Euclidean, high-dimensional, and demonstrates complicated dependencies. Existing machine learning frameworks are still insufficient to learn spatiotemporal dynamics as they often fail to exploit the underlying physics principles. I will demonstrate how to inject physical knowledge in AI to deal with challenges such as non-linear dynamics, non-Euclidean geometry, and multi-resolution structure. I will showcase the application of these methods to problems such as accelerating turbulence simulations, imitating basketball gameplay and combating ground effect in quadcopter landing.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Yu is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University. Previously, she was a postdoctoral researcher at Caltech Computing and Mathematical Sciences. She earned her Ph.D. in Computer Sciences at the University of Southern California. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data, with a particular emphasis on physics-guided AI. Among her awards, she has won Google Faculty Research Award, the NSF CRII award, best dissertation award in USC, best paper award at the NeurIPS time series workshop, and was nominated as one of the 'MIT Rising Stars in EECS'.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
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Astani Civil and Environmental Engineering Seminar
Thu, Feb 20, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Subhayan De , Postdoctoral Associate, Smead Department of Aerospace Engineering Sciences, University of Colorado, Boulder
Talk Title: Design Optimization under Uncertainty using a Stochastic Gradient Approach
Abstract: Design optimization of complex engineering systems requires understanding and modeling the underlying physical phenomena and their interactions. In addition, uncertainties and their influences on both the design objective and the design constraints must be considered to achieve a robust design. Such uncertainties are typically due to intrinsic variabilities in the system or manufacturing processes, as well as the lack of knowledge in precisely describing the governing physics in terms of mathematical/computational models. However, accounting for uncertainty in the optimization process requires, for example, computing the statistical moments of the objective, which may lead to high computational costs. For example, a Monte Carlo approach based on random sampling in such cases requires many forward and adjoint solves, thus requiring significant computational resources. To alleviate this computational burden, in this talk, a stochastic gradient-based approach will be discussed. In this approach, stochastic approximations of the gradients, using only a handful of random samples of the uncertainty, are constructed at every design optimization iteration. Popular variants of stochastic gradient descent algorithms (e.g., AdaGrad and Adam) are used with this approach. In practical engineering settings, often models with different levels of fidelity are employed to describe the problem at hand. Lower-fidelity models (e.g., using coarser grid discretizations) can be simulated cheaply but may lead to inaccurate solutions relative to high-fidelity models (e.g., using fine grid discretizations) that are often expensive to simulate. To reduce the design optimization cost further, these low-fidelity models are incorporated in the optimization process to propose bi-fidelity versions of stochastic gradient descent algorithms with a linear rate of convergence. The stochastic gradient approach for design optimization is illustrated using numerical examples from shape and topology optimizations. These examples show that the use of stochastic gradients along with bi-fidelity approaches can reduce the computational cost of design optimization under uncertainty significantly. In the presence of uncertainty in the microscale properties of the structure, homogenization methods like FE2 require solving boundary value problems to quantify the effect of microscopic heterogeneity at the macroscale for all random samples in a Monte Carlo approach. Instead, the stochastic gradient-based approach is applied to this multiscale optimization problem to reduce the computational effort of design under microstructural uncertainty. The design of a fiber composite beam with uncertain microstructural properties is used to illustrate the proposed stochastic gradient approach. Ongoing work will introduce the application of this approach to 3D structural components with microstructural uncertainty and the limitations applying it to large-scale realistic aerospace structures, such as solid rocket fuel design.
Biography: Bio: Dr. Subhayan De is a postdoctoral associate in the Aerospace Engineering Sciences at the University of Colorado Boulder (CU-Boulder). His research at CU focuses on design optimization under uncertainty and physics-based machine learning. Subhayan received his Ph.D. in Civil Engineering from the University of Southern California in 2018, where he was supported by a Viterbi Ph.D. Fellowship, a Gammel Scholarship and several NSF grants. At USC, he worked on probabilistic model validation, machine learning, uncertainty quantification, and structural control design. Subhayan also holds an MS in Electrical Engineering from USC and an MEng in Structural Engineering from the Indian Institute of Science, Bangalore. He received his B.Eng. in Civil Engineering from Jadavpur University, Kolkata.
Host: Dr. Erik Johnson
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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ASTE Seminar Announcement
Thu, Feb 20, 2020 @ 05:00 PM - 06:00 PM
Astronautical Engineering
Conferences, Lectures, & Seminars
Speaker: Aloissia Russo and David Bernachia, Masters Candidates
Talk Title: LEAPFROG: USC's FLlight Testbed re-thinking Planetary Landers for Next Generation Exploration
Abstract: The SERCS LEAPFROG project will be presented and key technologies being developed to re-architecture how a lunar lander is used on the moons surface.
Host: Astronautical Engineering Department
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 222
Audiences: Everyone Is Invited
Contact: Dell Cuason
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CS Colloquium: Michael Everett (MIT) - Fully Autonomous Robot Navigation in Human Environments
Mon, Feb 24, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Michael Everett, MIT
Talk Title: Fully Autonomous Robot Navigation in Human Environments
Series: CS Colloquium
Abstract: Today's robots are still quite limited in their ability to process information about multiple other objects in order to plan safe and efficient motions through previously unseen environments. Major technical challenges are currently sidestepped by restrictive engineering solutions (e.g., preventing humans from working alongside factory robots, collecting detailed prior maps in every intended operating environment). This talk will present frameworks that enable long-term autonomy for robots embedded among pedestrians and context-guided exploration in new environments. Furthermore, it will discuss future research directions toward safely training and deploying robots in our society.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Michael Everett is a final-year PhD Candidate at MIT working with Prof. Jonathan How. He received the SM degree (2017) and the SB degree (2015) from MIT in Mechanical Engineering. His research addresses fundamental gaps in the connection of machine learning and real mobile robotics, with recent emphasis on developing the theory of safety/robustness of learned modules. His works have won the Best Paper Award on Cognitive Robotics at IROS 2019, the Best Student Paper Award and finalist for the Best Paper Award on Cognitive Robotics at IROS 2017, and finalist for the Best Multi-Robot Systems Paper Award at ICRA 2017. He has been interviewed live on the air by BBC Radio and his robots were featured by Today Show, Reuters, and the Boston Globe.
Host: Nora Ayanian
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Robin Jia (Stanford University) - Building Robust Natural Language Processing Systems
Tue, Feb 25, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Robin Jia, Stanford University
Talk Title: Building Robust Natural Language Processing Systems
Series: CS Colloquium
Abstract: While modern NLP systems have achieved outstanding performance on static benchmarks, they often fail catastrophically when presented with inputs from different sources or inputs that have been adversarial perturbed. This lack of robustness exposes troubling gaps in current models' understanding capabilities, and poses challenges for deployment of NLP systems in high-stakes situations. In this talk, I will demonstrate that building robust NLP systems requires reexamining all aspects of the current model building paradigm. First, I will show that adversarially constructed test data reveals vulnerabilities that are left unexposed by standard evaluation methods. Second, I will demonstrate that active learning, in which data is adaptively collected based on a model's current predictions, can significantly improve the ability of models to generalize robustly, compared to the use of static training datasets. Finally, I will show how to train NLP models to produce certificates of robustness---guarantees that for a given example and combinatorially large class of possible perturbations, no perturbation can cause a misclassification.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Robin Jia is a sixth-year Ph.D. student at Stanford University advised by Percy Liang. His research interests lie broadly in building natural language processing systems that can generalize to unexpected test-time inputs. Robin's work has received an Outstanding Paper Award at EMNLP 2017 and a Best Short Paper Award at ACL 2018. He has been supported by an NSF Graduate Research Fellowship.
Host: Xiang Ren
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE Seminar
Tue, Feb 25, 2020 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Junsong Yuan, PhD, State University of New York
Talk Title: Beyond Deep Recognition: Discovering Visual Patterns in Big Visual Data
Abstract: Thanks to the success of deep learning, many computer vision tasks nowadays are formulated as regression problems. However, often times one has to rely on large amounts of annotated training data to make the high-dimensional regression successful. In this talk, we will discuss a complementary yet overlooked problem beyond deep visual recognition and regression. We will discuss why and how to discover visual patterns in images and videos that are not annotated, e.g., unsupervised and weakly-supervised visual learning and pattern discovery, and explore how to utilize them to better model, search, and interpret big visual data. Applications in visual search, object detection, action recognition, and video analytics will also be discussed.
Biography: Junsong Yuan is an Associate Professor and Director of Visual Computing Lab of CSE Department, State University of New York at Buffalo. Before that he was an Associate Professor at Nanyang Technological University (NTU), Singapore. He received his PhD from Northwestern University and M.Eng. from National University of Singapore. He is currently Associate Editor of IEEE Trans. on Image Processing (T-IP) and Machine Vision and Applications (MVA), and Senior Area Editor of Journal of Visual Communication and Image Representation (JVCI), and served as program co-chair for ICME 2018 and area chair for CVPR/ACM MM/WACV/ACCV/ICIP/ICPR etc. He received Best Paper Award from IEEE Trans. on Multimedia, Nanyang Assistant Professorship from NTU, and Outstanding EECS Ph.D. Thesis award from Northwestern University. He is a Fellow of International Association of Pattern Recognition (IAPR).
Host: Dr. C.-C. Jay Kuo
More Information: Yunsong Yuan Seminar 2.25.20.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Gloria Halfacre
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ISE 651 - Epstein Seminar
Tue, Feb 25, 2020 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Daniel Kuhn, Chair, Risk Analytics and Optimization - The Ecole Polytechnique Fédérale de Lausanne (EPFL)
Talk Title: Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Host: Dr. Phebe Vayanos
More Information: February 25, 2020.pdf
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Grace Owh
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AME Seminar
Wed, Feb 26, 2020 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Garrett Reisman, USC
Talk Title: Human Spaceflight-“Recent Past, Near Future and Educational Activities at Viterbi
Abstract: The presentation will highlight his personal experiences flying on the Space Shuttle and the International Space Station while serving as a NASA Astronaut from 1998 to 2011. After describing these unique experiences he will discuss his transition to SpaceX and the state of the commercial human spaceflight industry. Finally, the human spaceflight graduate coursework which has recently been established in Viterbi ASTE department will be presented.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Location: 159
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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CS Colloquium: Minjoon Seo (University of Washington) - Web-Scale Neural Memory towards Universal Knowledge Interface
Thu, Feb 27, 2020 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Minjoon Seo, University of Washington
Talk Title: Web-scale Neural Memory towards Universal Knowledge Interface
Series: CS Colloquium
Abstract: Modern natural language tasks are increasingly dependent on external world knowledge. My PhD study has particularly focused on three challenges in this literature: handling unstructured knowledge, being scalable, and reasoning over knowledge data. I will mainly discuss my recent and on-going work on a web-scale neural memory that tackles all of the three challenges, and show how it serves as an effective interface for interacting with the world knowledge. I will conclude with an argument that designing a seamless and universal knowledge interface is a crucial research goal that can better address knowledge-dependency problem in machine learning tasks.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Minjoon Seo is a final-year Ph.D. student in the Allen School of Computer Science & Engineering at the University of Washington, advised by Hannaneh Hajishirzi and Ali Farhadi. His research interest has been mostly in the learning model for the extraction of (IE), the access to (QA), and the interplay of (Reasoning) knowledge in various forms of language data. He is supported by Facebook Fellowship and AI2 Key Scientific Challenges Award. He co-organizes the Workshop on Machine Reading for Question Answering (MRQA) and the Workshop on Representation Learning for NLP (RepL4NLP).
Host: Xiang Ren
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ECE-EP Seminar - Maiken Mikkelsen, Thursday, February 27th at 2pm in EEB 132
Thu, Feb 27, 2020 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Maiken Mikkelsen, Duke University
Talk Title: Atomic-scale Engineering for On-chip Photonic Devices
Abstract: Nano- and quantum materials with unique optical properties hold the potential for breakthroughs in a wide range of areas from ultrafast optoelectronics and on-chip components for quantum information science to improve bio-sensing. An exciting opportunity to realize such new materials lies in controlling the local electromagnetic environment on the atomic- and molecular-scale (~1-10 nm), which enables extreme local field enhancements and drastically modified local density of states. We use creative nanofabrication techniques at the interface between chemistry and physics to realize this new regime together with ultrafast optical techniques to probe the emerging phenomena. Here, I will provide an overview of our recent research where we sculpt the electromagnetic fields on the atomic scale to realize ultrafast single photon sources, high-speed thermal photodetectors with on-chip spectral filters and metasurface-enhanced biosensors.
Biography: Maiken H. Mikkelsen is the James N. and Elizabeth H. Barton Associate Professor at Duke University in the Department of Electrical & Computer Engineering, and by courtesy, in the Departments of Physics and Mechanical Engineering & Materials Science. She received her B.S. in Physics from the University of Copenhagen in 2004, her Ph.D. in Physics from the University of California, Santa Barbara in 2009 and was a postdoctoral fellow at the University of California, Berkeley before joining Duke University in 2012. Her research explores nanophotonics and new quantum materials to enable transformative breakthroughs for optoelectronics, quantum science, the environment and human health. Her awards include the Maria Goeppert Mayer Award from the American Physical Society, the NSF CAREER award, the Moore Inventor Fellow award from the Gordon and Betty Moore Foundation, Young Investigator Program Awards from the Office of Naval Research, the Army Research Office and the Air Force Office of Scientific Research, the Cottrell Scholar Award from the Research Corporation for Science Advancement, the Early Career Achievement Award from SPIE - the International Society for Optics and Photonics, and is the recipient of an RO1 award from the National Institute of Health.
Host: ECE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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VIP Lecture Series 1
Thu, Feb 27, 2020 @ 03:00 PM - 04:00 PM
Viterbi School of Engineering Student Affairs
Conferences, Lectures, & Seminars
Speaker: DJ Kast, STEM PROGRAMS -JEP
Abstract: VIP Guest Speaker - DJ Kast (STEM PROGRAMS -JEP)
USC Faculty, staff, and community members discuss the societal impacts of engineering. Also, volunteer opportunities will be discussed.
Host: Viterbi Impact Program
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Undergrad
Contact: Viterbi Undergraduate Programs
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Sonny Astani Civil and Environmental Engineering Seminar
Thu, Feb 27, 2020 @ 04:00 PM - 05:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Emily Grubert, Ph.D., Georgia Tech
Talk Title: Conventional Hydroelectricity and the Energy Transition
Abstract: See attached Abstract and Bio.
Host: Dr. Kelly Sanders
More Information: E. Grubert Abstract_ 02-27-2020.pdf
Location: Michelson Center for Convergent Bioscience (MCB) - 102
Audiences: Everyone Is Invited
Contact: Evangeline Reyes
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IISE Western Regional Conference
Fri, Feb 28, 2020
Daniel J. Epstein Department of Industrial and Systems Engineering, USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Various, Various
Talk Title: IISE Western Regional Conference
Abstract: The Institute of Industrial and Systems Engineers at the University of Southern California is hosting the 2020 IISE Western Regional Conference February 28 - March 1, 2020.
Please register here: https://ise.usc.edu/iise-student-conference
This conference is a unique opportunity for students and professionals in the field of Industrial Engineering to network, explore industry trends, and compete for a chance to present their work at the IISE Annual Conference and Expo 2020 in New Orleans, Louisiana.
The conference will be held on the main campus and includes the technical paper competition, keynote speakers, expert panels, plant tours, and other activities.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Info: https://ise.usc.edu/iise-student-conference/
More Information: ConferenceFlyer.pdf
Audiences: Everyone Is Invited
Contact: Greta Harrison
Event Link: https://ise.usc.edu/iise-student-conference/
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Machine Learning for Performance and Power Modeling/Prediction
Fri, Feb 28, 2020 @ 10:30 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Lizy Kurian John, UT Austin
Talk Title: Machine Learning for Performance and Power Modeling/Prediction
Abstract: Estimating the power and thermal characteristics of SoCs is essential for designing its power delivery system, packaging, cooling, and power/thermal management schemes. Power models that estimate the power consumption of each functional unit/hardware component from first principles are slow and tedious to build. Machine learning can be used to create power models that are fast and reasonably accurate. Machine learning can also be used to calibrate analytical models that estimate power. In this talk, I will present some examples of performance and power modeling using machine learning.
Another application for machine learning has been to create max power stressmarks. Manually developing and tuning so called stressmarks is extremely tedious and time-consuming while requiring an intimate understanding of the processor. In our past research, we created a framework that uses machine learning for the automated generation of stressmarks. In this talk, the methodology of the creation of automatic stressmarks will be explained. Experiments on multiple platforms validating the proposed approach will be described.
Yet another application for machine learning is in cross-platform performance and power prediction. If one model is slow to run real-world benchmarks/workloads, is it possible to predict/estimate the performance/power by using runs on another platform? Are there correlations that can be exploited using machine learning to make cross-platform performance and power predictions? A methodology to perform cross-platform performance/power predictions will be presented in this talk.
Biography: Lizy Kurian John is Cullen Trust for Higher Education Endowed Professor in the Electrical and Computer Engineering at the University of Texas at Austin. She received her Ph. D in Computer Engineering from Pennsylvania State University. Her research interests include workload characterization, performance evaluation, memory systems, reconfigurable architectures, and high-performance architectures for emerging workloads. She is a recipient of many awards including The Pennsylvania State University Outstanding Engineering Alumnus 2011, the NSF CAREER award, UT Austin Engineering Foundation Faculty Award, Halliburton, Brown and Root Engineering Foundation Young Faculty Award 2001, University of Texas Alumni Association (Texas Exes) Teaching Award 2004, etc. She has co-authored books on Digital Systems Design using VHDL (Cengage Publishers, 2007, 2017), a book on Digital Systems Design using Verilog (Cengage Publishers, 2014) and has edited 4 books including a book on Computer Performance Evaluation and Benchmarking. In the past, she has served as Associate Editor of IEEE Transactions on Computers, IEEE Transactions on VLSI, IEEE Computer Architecture Letters, ACM Transactions on Architecture and Code Optimization, and IEEE Micro. She is currently the Editor-in-Chief of IEEE Micro. She holds 12 US patents and is an IEEE Fellow (Class of 2009).
Host: Xuehai Qian, xuehai.qian@usc.edu
More Information: 200228_Lizy John_CENG.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Brienne Moore
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IISE Regional ConferenceSat, Feb 29, 2020
Viterbi School of Engineering Student Affairs
Conferences, Lectures, & Seminars
Speaker: ,
Talk Title:
Host:
Audiences: Everyone Is Invited
Contact: Viterbi Undergraduate Programs
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