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Events for April 18, 2017

  • PhD Defense - Rongqi Qiu

    Tue, Apr 18, 2017 @ 10:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Candidate: Rongqi Qiu

    Committee: Ulrich Neumann (CS, chair), Panayiotis Georgiou (EE), Aiichiro Nakano (CS)

    Title: Geometric Modeling and Shape Analysis of 3D Point Clouds

    Time: April 18 (Tuesday) 10-12pm

    Room: SAL 322


    Automatic reconstruction of large-scale scenes from 3D point clouds has been a complex problem. It can be decomposed into two sub-problems, namely, primitives and parts. While primitives are regular geometric shapes, parts are relatively irregular and isolated objects.

    In primitive reconstruction, two systems under different scenarios are presented. The first one reconstructs pipe-runs from industrial site point clouds. The key idea is that by adopting statistical analysis over point normals, global similarities are discovered from raw data to guide primitive fitting, thus increasing robustness. The second system extracts pole-like objects from urban point clouds and posed multi-view images. The presented method takes advantage of the complementary information from 3D point clouds and 2D posed images to recover these objects.

    In part reconstruction, a modeling-by-recognition strategy is followed. Instead of directly meshing on a noisy scan, a similar object is retrieved from a pre-defined CAD model library. Then, geometric analysis is applied on the query and template point cloud to accomplish two tasks. The first one is to compute dense correspondences between query and template objects, thus making it possible to transfer real-world color to template models. The method segments both point clouds into parts consistently and then computes part-level correspondences. The dense mapping allows color or other parameter transfers. The second task is to segment an object into functional parts using a small set of pre-segmented template objects as examples. The main idea is to seek partial matches and transfer segmentation labels from examples to the input object. The resulting segmentation is a key step towards shape understanding.

    Location: Henry Salvatori Computer Science Center (SAL) - 322

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

  • USC Stem Cell Seminar: Catriona Jamieson, University of California, San Diego

    Tue, Apr 18, 2017 @ 11:00 AM - 12:00 PM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars

    Speaker: Catriona Jamieson, University of California, San Diego

    Talk Title: TBD

    Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series

    Host: USC Stem Cell

    More Info: http://stemcell.usc.edu/events

    Webcast: http://keckmedia.usc.edu/stem-cell-seminar

    Location: Eli & Edythe Broad CIRM Center for Regenerative Medicine & Stem Cell Resch. (BCC) - First Floor Conference Room

    WebCast Link: http://keckmedia.usc.edu/stem-cell-seminar

    Audiences: Everyone Is Invited

    Contact: Cristy Lytal/USC Stem Cell

    Event Link: http://stemcell.usc.edu/events

  • PhD Defense - Xinran He

    Tue, Apr 18, 2017 @ 01:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    Phd Candidate: Xinran He

    Yan Liu (chair)
    David Kempe
    Kristina Lerman
    Thomas Valente

    Date/Time: April 18th 1-3pm

    Room: PHE 223

    Title Understanding Diffusion Processes: Inference and Theory


    Nowadays online social networks have become a ubiquitous tool for people's social communications. Analyzing these social networks offers great potential to shed light on the human social structure, and create better channels to enable social communications and collaborations. While most social analysis tasks begin with extracting or learning the social network and the associated parameters, it remains a very challenging task due to the amorphous nature of social ties and the noise and incompleteness in the observations. As a result, the inferred social network is likely to be of low accuracy and high level of noise which impacts the performance of analysis and applications depending on the inferred parameters.

    In this thesis, we study the following important questions with a special focus on analyzing diffusion behaviors in social networks to achieve real practicality: (1) How to utilize special properties of social networks to improve the accuracy of the extracted network under noisy and missing data? (2) How to characterize the impact of noise in the inferred network and carry out robust analysis and optimization?

    To address the first challenge towards accurate network inference, we tackle the issue of mitigating the impact of incomplete observations with a focus on learning influence function from incomplete observations. To address the challenge of data scarcity in inferring diffusion networks, we propose a hierarchical graphical model to jointly infer multiple diffusion networks accurately. To utilize the rich content information in cascades, we propose the HawkesTopic model to analyze text-based cascades by combining temporal and content information.

    To address the second challenge towards designing robust Influence Maximization algorithms, we first propose a framework to measure the stability of Influence Maximization with the Perturbation Interval Model to characterize the noise in the inferred diffusion network. We then design an efficient algorithm for Robust Influence Maximization to find influential users robust in multiple diffusion settings.

    Location: Charles Lee Powell Hall (PHE) - 223

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

  • Best Dissertation Symposium

    Tue, Apr 18, 2017 @ 03:00 PM - 04:30 PM

    Viterbi School of Engineering Doctoral Programs

    Receptions & Special Events

    Each year, the Viterbi School of Engineering's academic departments awards PhD candidates who submitted exceptional research dissertations the honor of Best Dissertation. Each PhD student who receives the Best Dissertation award will also be nominated to compete for the William F. Ballhaus, Jr. Prize for Excellence in Graduate Engineering Research. A faculty committee will review the dissertations, then narrow down the nominees to three finalists. The finalists will be invited to present their research at the Best Dissertation Symposium on April 18, 2017. From these nominees, a final winner will be selected.

    RSVP requested via https://gapp.usc.edu/events/best-dissertation-symposium by April 17, 2017.

    More information can be found at https://gapp.usc.edu/students/doctoral-students/awards/best-dissertation-symposium

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    Audiences: Everyone Is Invited

    Contact: Jennifer Gerson

  • Epstein Seminar, ISE 651

    Tue, Apr 18, 2017 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Mark Brown, Professor, Columbia University

    Talk Title: Taylor's Law via Ratios, for Some Distributions with Infinite Mean

    Host: Prof. Sheldon Ross

    More Information: April 18, 2017_Brown.pdf

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Contact: Grace Owh

  • Professional Licensure Presentation: More on the FE & PE Exams

    Tue, Apr 18, 2017 @ 05:00 PM - 06:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions

    Are you interested in learning more about Professional Licensure. Do you have questions about the FE or PE exams? Then attend this session to learn more! Mike Donelson, PE, and Susan Christ, PE, both Senior Registrars for Engineering from the Board for Professional Engineers, Land Surveyors and Geologists will be presenting.

    More Information: ProfLicensurePresBrochure.pdf

    Location: Ronald Tutor Hall of Engineering (RTH) - 211

    Audiences: All Viterbi

    Contact: RTH 218 Viterbi Career Connections