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Events for March 24, 2015
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CS Colloquium: Justin Solomon (Stanford University) - Transportation Techniques for Geometric Data Processing
Tue, Mar 24, 2015 @ 09:45 AM - 10:50 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Justin Solomon, Stanford University
Talk Title: Transportation Techniques for Geometric Data Processing
Series: CS Colloquium
Abstract: Modeling and understanding low- and high-dimensional data is a recurring theme in graphics, optimization, learning, and vision. Abstracting away application domains reveals common threads using geometric constructs like distances, similarities, and curvatures. This shared structure suggests the possibility of developing geometric data processing as a discipline in itself.
To this end, I will introduce optimal transportation (OT) as a versatile component of the geometric data processing toolkit. Originally proposed for minimizing the cost of shipping products from producers to consumers, OT links probability and geometry using distributions to encode geometric features and developing metric machinery to quantify their relationships.
To transition OT from theory to practice, I will show how to solve previously intractable OT problems efficiently on discretized domains and demonstrate a wide range of applications enabled by this new machinery. I will illustrate the advantages and challenges of OT for geometric data processing by outlining my recent work in geometry processing, computer graphics, and machine learning. In each case, I will consider optimization aspects of the OT problem for relevant geometric domains---including triangulated surfaces, graphs, and subsets of Euclidean space---and then show how the resulting machinery can be used to approach outstanding problems in surface correspondence, modeling, and semi-supervised learning.
This lecture will be streamed HERE.
Biography: Justin Solomon is a PhD candidate and teaching fellow in the Geometric Computing Group at Stanford University studying problems in shape analysis, machine learning, and graphics from a geometric perspective. His work is supported by the Hertz Foundation Fellowship, the NSF Graduate Research Fellowship, and the NDSEG Fellowship. Justin holds bachelors degrees in mathematics and computer science and an MS in computer science from Stanford. He has served as the lecturer for courses in graphics, differential geometry, and numerical methods; his forthcoming textbook entitled Numerical Algorithms focuses on applications of numerical methods across modern computer science. Before his graduate studies, Justin was a member of Pixar's Tools Research group. He is a pianist, cellist, and amateur musicologist with award-winning research on early recordings of the Elgar Cello Concerto.
Host: Computer Science Department
More Info: https://bluejeans.com/301312091/browser
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: https://bluejeans.com/301312091/browser
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Electrical Engineering Seminar
Tue, Mar 24, 2015 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Swagath Venkataramani, Purdue University
Talk Title: Addressing the Efficiency Gap with Approximate Computing
Abstract: The âefficiency gapâ created by diminishing benefits from semiconductor technology scaling on the one hand, and projected growth in computing and data demand on the other, has created an urgent need to identify new sources of computing efficiency across the computing stack. Fortunately, the workloads that drive the demand for computing efficiency also present new opportunities. In data centers and the cloud, the demand for computing is driven by the need to organize, search through, analyze, and draw inferences from, exploding amounts of digital data. In mobile and embedded devices, the need to more naturally and intelligently interact with the physical world, and process richer media drive much of the computing demand. A common pattern that emerges from both ends of the spectrum is that these applications are largely not about calculating a precise numerical answer; instead, âcorrectnessâ is defined as producing results that are good enough, or of sufficient quality, to produce an acceptable user experience. As a result, these workloads are endowed with a high degree of intrinsic resilience to their underlying computations being executed in an approximate or inexact manner. Approximate computing broadly refers to exploiting the forgiving nature (or intrinsic resilience) of applications to design more efficient (faster, lower power) computing platforms. In this talk, I will describe how current workload trends are driving interest in approximate computing, and describe a vision for approximate computing at all layers of the computing stack. To realize this vision, I will outline a holistic approach that includes automatic frameworks to synthesize approximate circuit blocks, a model for programmable approximate processors that explicitly codifies the notion of quality into the HW/SW interface, and finally software techniques to systematically identify resilient computations within an application and to apply approximate computing to achieve a favorable quality-efficiency tradeoff. I will conclude with an overview of the other research directions that I am exploring to address the efficiency gap viz. computing with spintronics, and heterogeneous many-core accelerators for emerging workloads.
Biography: Swagath Venkataramani is a 5-year PhD student in the School of Electrical and Computer Engineering, Purdue University. His research interests include, Approximate Computing, Computing with Spintronic Devices, Heterogeneous Parallel Architectures, and Computational Imaging. His dissertation research was awarded the Intel PhD fellowship in computing leadership and Purdue Bilsland Dissertation fellowship. It has also been featured in MIT Technology Review, Slashdot, Physics Today, and NSF News from the Field. Swagath graduated with a Bachelors degree in Electrical and Electronics Engineering from College of Engineering, Guindy, Anna University, India as the university gold medalist. He has worked with the Exa-scale Computing Group at Intel as part of the US DOEâs FastForward Program, and with the Sensing and Energy Research Group at Microsoft Research.
Host: Prof. Alice C. Parker
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Epstein Institute / ISE 651 Seminar Series
Tue, Mar 24, 2015 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Jimeng Sun, Associate Professor, School of Computational Science and Engineering, College of Computing, Georgia Institute of Technology
Talk Title: Computational Phenotyping on Electronic Health Records using Tensor Factorization
Abstract: As the adoption of electronic health records (EHRs) has grown, EHRs are now composed of a diverse array of data, including structured information (e.g., diagnoses, medications, and lab results), and unstructured clinical progress notes. The interactions among different data sources within an EHR are challenging to model, hampering our ability to leverage traditional analytic frameworks.
The goal of this project is to address these challenges by developing a general computational framework for transforming EHR data into meaningful phenotypes with only modest levels of expert guidance. We represent and analyze EHR data as inter-connected high-order relations i.e. tensors (e.g. tuples of patient-medication-diagnosis, patient-lab, and patient-symptoms). The proposed analytic framework generalizes several existing data mining methodologies, including dimensionality reduction, topic modeling and co-clustering, which all arise as limited special cases of analyzing second order tensors. It will also enable flexible refinement of candidates to incorporate feedback from domain experts.
The significance of the resulting phenotypes will have diverse clinical applications, including: a) cohort construction, where case and control patients are identified with respect to specific phenotype combinations; b) genome wide association studies (GWAS), where target phenotypes of patients are tested against DNA sequence variation for significant statistical associations; and c) clinical predictive modeling, where a model is developed to predict target phenotypes or diseases will be demonstrated. The framework is developed with public accessible data from MIMIC-II and CMS and validate in real clinical environments at Northwestern Memorial Hospital and VUMC through several high-impact disease targets (including hypertension, type 2 diabetes, hypothyroidism, atrial fibrillation, rheumatoid arthritis, and multiple sclerosis).
Biography: Jimeng Sun is an Associate Professor of School of Computational Science and Engineering at College of Computing in Georgia Institute of Technology. Prior to joining Georgia Tech, he was a research staff member at IBM TJ Watson Research Center. His research focuses on health analytics using electronic health records and data mining, especially in designing novel tensor analysis and similarity learning methods and developing large-scale predictive modeling systems.
Dr. Sun has worked on various healthcare applications such as computational phenotyping from electronic health records, heart failure onset prediction and hypertension control management. He has collaborated with many healthcare institutions including Vanderbilt university medical center, Children's healthcare of Atlanta, Center for Disease Control and Prevention (CDC), Geisinger Health System and Sutter Health.
He has published over 70 papers, filed over 20 patents (5 granted). He has received ICDM best research paper award in 2008, SDM best research paper award in 2007, and KDD Dissertation runner-up award in 2008. Dr. Sun received his B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, and PhD in Computer Science from Carnegie Mellon University in 2007.
More Information: Seminar-Jimeng Sun.docx
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Georgia Lum
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Viterbi Keynote Lecture
Tue, Mar 24, 2015 @ 04:00 PM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. H. Vincent Poor / Dean, School of Engineering and Applied Science, Princeton University
Talk Title: Fundamental Limits on Information Security and Privacy
Series: Distinguished Lecturer Series
Abstract: As has become quite clear from recent headlines, the ubiquity of technologies such as wireless communications and on-line data repositories has created new challenges in information security and privacy. Information theory provides fundamental limits that can guide the development of methods for addressing these challenges. After a brief historical account of the use of information theory to characterize secrecy, this talk will review two areas to which these ideas have been applied successfully: wireless physical layer security, which examines the ability of the physical properties of the radio channel to provide confidentiality in data transmission; and utility-privacy tradeoffs of data sources, which quantify the balance between the protection of private information contained in such sources and the provision of measurable benefits to legitimate users of them. Several potential applications of these ideas will also be discussed.
Biography: H. Vincent Poor (Ph.D., Princeton 1977) is Dean of the School of Engineering and Applied Science at Princeton University, where he is also the Michael Henry Strater University Professor. From 1977 until he joined the Princeton faculty in 1990, he was a faculty member at the University of Illinois at Urbana-Champaign. He has also held visiting appointments at a number of other universities, including most recently at Stanford and Imperial College. His research interests are primarily in the areas of information theory and signal processing, with applications in wireless networks and related fields. Among his publications in these areas is the recent book Principles of Cognitive Radio (Cambridge University Press, 2013). At Princeton he has developed and taught several courses designed to bring technological subject matter to general audiences, including âThe Wireless Revolutionâ (in which Andrew Viterbi was one of the first guest speakers) and âSix Degrees of Separation: Small World Networks in Science, Technology and Society.â
Dr. Poor is a member of the National Academy of Engineering and the National Academy of Sciences, and is a foreign member of the Royal Society. He is a former President of the IEEE Information Theory Society, and a former Editor-in-Chief of the IEEE Transactions on Information Theory. He currently serves as a director of the Corporation for National Research Initiatives and of the IEEE Foundation, and as a member of the Council of the National Academy of Engineering. Recent recognition of his work includes the 2014 URSI Booker Gold Medal, and honorary doctorates from several universities in Asia and Europe.
Host: Dr. Sandeep K. Gupta
More Info: https://bluejeans.com/770154652
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
Event Link: https://bluejeans.com/770154652
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Busan Graduate Information Session
Tue, Mar 24, 2015 @ 07:00 PM - 09:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Students who have earned or are in the process of earning a Bachelor's degree in engineering, math, or a hard science (such as physics, biology, or chemistry) are welcome to attend to learn more about applying to our graduate programs.
The session will include information on the following topics:
Master's & Ph.D. programs in engineering
How to Apply
Scholarships and funding
Student life at USC and in Los Angeles
There will also be sufficient time for questions. Refreshments will be provided.
Please contact us at viterbi.gradprograms@usc.edu if you have any inquiries about the event.
We look forward to seeing you there.
For more information about the event and to register, please visit the event pageAudiences: Students with an undergraduate backrgound in engineering, math or science
Contact: William Schwerin
