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Events for November 19, 2013
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KIUEL Viterbi Impact Clothing DriveTue, Nov 19, 2013 @ 08:30 AM - 05:00 PM
Viterbi School of Engineering Student Affairs, Viterbi School of Engineering Student Organizations
Student Activity
Do you have clothes that you donââ¬â¢t wear much or donââ¬â¢t need? You could clear out space in your closet and make a #ViterbiImpact at the same time by donating those clothes to the Klein Institute for Undergraduate Engineering Life (KIUEL) clothing drive. Please bring any donations to the donation box in RTH110 before 5pm on the 22nd!
All clothes must be clean and in good condition (not stained or torn).
Collected clothing will be donated to the Salvation Army.Location: Ronald Tutor Hall of Engineering (RTH) - 110
Audiences: Everyone Is Invited
Contact: KIUEL
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Simultaneous microPET/microMRI: Why's, What's and Applications
Tue, Nov 19, 2013 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Russell E. Jacobs, California Institute of Technology (CALTECH)
Talk Title: Simultaneous microPET/microMRI: Why's, What's and Applications
Series: Medical Imaging Seminar Series
Abstract: Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are used extensively in both the research and clinical settings. In this project we combine them in a single instrument that will simultaneously record data in both imaging modalities. his system is dedicated to the study of small animal model systems at the highest spatial and temporal resolutions attainable. We have built a high resolution, relatively high sensitivity multi-slice mPET scanner integrated within a customized 7T/30cm small animal MR system that simultaneously records MR and PET images. In this talk I will briefly introduce the PET and MR imaging methodologies, discuss the rational for combining them in a single instrument, and present some results in oncology, multiple sclerosis, & vascular disease. Briefly, simultaneous mPET/mMRI recordings provide important correlations not available from temporally and spatially separate scans. The melded system provides high resolution anatomical reference systems for mPET studies. The 'in register' mMR images can be used to compute scatter and attenuation in the mPET images and to estimate partial volume errors in the PET scans, thus aiding quantification of the PET signal. This system will open up a number of opportunities not possible with current independent technologies. Among them are:
- Time correlated mPET and MR spectroscopy studies of drug distributions; cardiac, brain and tumor cell metabolism.
- Simultaneous fMRI and mPET neuroreceptor brain mapping studies in small animals.
- Validation of new MRI probes using their PET counterparts.
- Dual PET/MRI labels will allow for "zooming-in" the MRI data collection scheme to those regions of the specimen
containing the label, as well as providing for precise registration of the PET & MR images.
Biography: Russell E. Jacobs has more than 30 years experience in the theory, hardware/software development and application of high resolution preclinical MRI. Extensive history of supervising successful Post Doctoral fellows, graduate students, undergraduates, and technicians. Animal models understudy have included: embryonic development, multiple sclerosis, Alzheimer's Disease, cancer, and substance abuse. He is also heavily involved in several multimodal imaging efforts including contrast agent developments and implementation of a simultaneous dual PET/MRI scanner, and quantitative analysis of MR images using an array of computational warping and statistical parametric analyses.
Host: Professor Justin Haldar
More Info: http://mhi.usc.edu/medical-imaging-seminar-series/
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia Veal
Event Link: http://mhi.usc.edu/medical-imaging-seminar-series/
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PhD Defense - Derya Ozkan
Tue, Nov 19, 2013 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Towards Intelligent Virtual Listeners: Computational Models of Social Nonverbal Behaviors
PhD Candidate: Derya Ozkan
Committee:
Louis-Philippe Morency (Chair)
Gerard Medioni
Jonathan Gratch
Stacy Marsella
Shrikanth Narayanan (outside member)
Human nonverbal communication is a highly interactive process, in which the participants dynamically send and respond to nonverbal signals. These signals play a significant role in determining the nature of a social exchange. Although human can naturally recognize, interpret and produce these nonverbal signals in social context, computers are not equipped with such abilities. Therefore, creating computational models for holding fluid interactions with human participants has become an important topic for many research fields including human-computer interaction, robotics, artificial intelligence, and cognitive sciences. Central to the problem of modeling social behaviors is the challenge of understanding the dynamics involved with listener backchannel feedbacks (i.e. the nods and paraverbals such as ``uh-hu'' and ``mm-hmm'' that listeners produce as someone is speaking).
In this thesis, I present a framework for modeling visual backchannels of a listener during a dyadic conversation. I address the four major challenges involved in modeling nonverbal human behaviors, more specifically listener backchannels: (1) high dimensional data, (2) multimodal processing, (3) mutual influence between the participants, and (4) variability in human's behaviors. We address the first challenge by proposing a sparse feature selection method that gives researchers a new tool to analyze human nonverbal communication. To address to second challenge of effective and efficient fusion of multimodal information, we introduce a new model called Latent Mixture of Discriminative Experts (LMDE) that can automatically learn the hidden dynamic between modalities. For the third challenge, we present a context-based prediction framework that models the mutual influence between the participants of a human conversation to improve the final prediction model. Finally, we propose a new approach for modeling wisdom of crowds called wisdom-LMDE, which is able to learn the variations and commonalities among different crowd members.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Student Colloquium
Tue, Nov 19, 2013 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Presenters / Abstracts In Announcement Body, USC
Talk Title: PhD Student Colloquium
Series: CS Colloquium
Abstract: Soravit Changpinyo and Kuan Liu
Title: Similarity Component Analysis
Abstract: Measuring similarity is crucial to many learning tasks. To this end, metric learning has been the dominant paradigm. However, similarity is a richer and broader notion than what metrics entail. For example, similarity can arise from the process of aggregating the decisions of multiple latent components, where each latent component compares data in its own way by focusing on a different subset of features. We propose Similarity Component Analysis (SCA), a probabilistic graphical model that discovers those latent components from data. In SCA, a latent component generates a local similarity value, computed with its own metric, independently of other components. The final similarity measure is then obtained by combining the local similarity values with a (noisy-) OR gate. We derive an EM-based algorithm for fitting the model parameters with similarity-annotated data from pairwise comparisons. We validate the SCA model on synthetic datasets where SCA discovers the ground-truth about the latent components. We also apply SCA to a multiway classification task and a link prediction task. For both tasks, SCA attains significantly better prediction accuracies than competing methods. Moreover, we show how SCA can be instrumental in exploratory analysis of data, where we gain insights about the data by examining patterns hidden in its latent components’ local similarity values.
Boqing Gong
Title: Reshaping Visual Datasets for Domain Adaptation
Abstract: In visual recognition problems, the common data distribution mismatches between training and testing make domain adaptation essential. However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc.) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other to the maximum extent; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric formulation and efficient optimization procedure that can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.
Mrinal Kalakrishnan
Title: Learning Objective Functions for Autonomous Locomotion and Manipulation
Abstract: Efforts on learning from demonstration in robotics have largely been focused on reproducing behavior similar in appearance to the provided demonstrations, loosely classified as Direct Policy Learning. An alternative approach, known as Inverse Reinforcement Learning (IRL), is to learn the objective function that the demonstrations are assumed to be optimal under. With the help of a planner or trajectory optimizer, such an approach allows the system to synthesize novel behavior in situations that were not experienced in the demonstrations. We present new algorithms for IRL that have successfully been applied in two real-world, competitive robotics settings: (1) In the domain of rough terrain quadruped locomotion, we present an algorithm that learns an objective function for foothold selection based on "terrain templates". The learner automatically generates and selects the appropriate features which form the objective function, which reduces the need for feature engineering while attaining a high level of generalization. (2) For the domain of autonomous manipulation, we present a local sampling-based path integral IRL approach to deal with the high dimensional space of trajectories. We apply this method to two problems in robotic manipulation: redundancy resolution in inverse kinematics, and trajectory optimization for grasping and manipulation. Both methods have proven themselves as part of larger integrated systems in competitive settings against other teams, where testing was conducted by an independent test team in situations that were not seen during training.
Host: PhD Committee
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Epstein Institute / ISE 651 Seminar Series
Tue, Nov 19, 2013 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jay Lee, Ohio Eminent Scholar and L.W. Scott Alter Chair Professor,University of Cincinnati & Director NSF Multi-Campus Industry/University Cooperative Research Center on Intelligent Maintenance Systems (IMS) University of Cincinnati, University of Michigan, Miss
Talk Title: "Recent Advances and Trends of Predictive Manufacturing Systems in Big Data Environment"
Series: Epstein Institute Seminar Series
Abstract: In today's competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity. Many manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. As more software and embedded intelligence are integrated in industrial products and systems, predictive technologies can further intertwine intelligent algorithms with electronics and tetherfree intelligence to predict product performance degradation and autonomously manage and optimize product service needs,
The presentation will address the trends of manufacturing transformation in big data environment as well as the readiness of smart predictive informatics tools to manage big data to achieve transparency of machine health, process quality, factory productivity. Advanced prognostics technologies with case studies will be presented. In addition, research advances in designing self-maintenance machinery, cloud-based cyber-physical modeling for next- generation products and machines, prognostics-ready sensors, etc. will be discussed.
TUESDAY, NOVEMBER 19, 2013
GRACE FORD SALVATORI (GFS) ROOM 101
3:30 - 4:50 PM
Biography: Dr. Jay Lee is Ohio Eminent Scholar and L.W. Scott Alter Chair Professor at the Univ. of Cincinnati and is founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS www.imscenter.net ) which is a multi-campus NSF Industry/University Cooperative Research Center which consists of the Univ. of Cincinnati (lead institution), the Univ. of Michigan, Missouri Univ. of S&T, and Univ. of Texas-Austin. The Center has developed partnerships with over 80 companies from 15 countries since its inception in 2001. The Center has developed a spin-off company Predictronics with support from NSF ICorps Award in 2012. His current research focuses on smart predictive analytics for product design, manufacturing, and
maintenance systems.
Currently, he also serves as advisor to a number of global organizations, including a member of the Manufacturing Executive Leadership Council, member of International S&T Committee of Alstom Transport, France, Scientific Advisory Board of Flanders' MECHATRONICS Technology Centre (FMTC) in Leuven, Belgium, Scientific Advisor Board of SIMTech, Singapore, etc. In addition, he serves as editors and associate editor for a number of journals including IEEE Transaction on Industrial Informatics, Int. Journal on Prognostics & Health Management (IJPHM), etc.,
Previously, he served as Director for Product Development and Manufacturing at United Technologies Research Center (UTRC), E. Hartford, CT as well as Program Directors for a number of programs at NSF during 1991-1998, including the Engineering Research Centers (ERCs) Program, the Industry/University Cooperative Research Centers (I/UCRCs) Program, and the Materials Processing and Manufacturing Program. He also served as advisor to a number of universities including Cambridge Univ., Johns Hopkins Univ. etc.
He is a Fellow of ASME, SME, as well as a founding fellow of International Society of Engineering Asset Management (ISEAM). He is a frequently invited speaker and has delivered over 180 invited keynote speeches at major international conferences and has over 15 patents and 2 trademarks (Watchdog Agentâ⢠and Dominant Innovationââ¢). He received a number of awards including the most recent Prognostics Innovation Award from National Instruments in 2012.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Lee_Jay.doc
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - Room 101
Audiences: Everyone Is Invited
Contact: Georgia Lum
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VARC Workshop - Presentation Skills
Tue, Nov 19, 2013 @ 05:00 PM - 06:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Making presentations are a big part of college - whether in class, in student organizations, or at internships. Come to this workshop for some quick tips on how to be a more effective presenter.
RSVP online now!Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Undergrad
Contact: Viterbi Academic Resource Center
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Graduate Engineering Online Info Session
Tue, Nov 19, 2013 @ 05:00 PM - 06:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
The USC Viterbi School of Engineering is a top-10 ranked graduate engineering program by U.S News and World Report. Join us for an online information session to learn about the exciting opportunities available.
Register NowLocation: Online
Audiences: Everyone Is Invited
Contact: Ray Fujioka/GAPP
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City of Los Angeles, Department of Public Works-Sanitation Information Session
Tue, Nov 19, 2013 @ 05:00 PM - 06:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join representatives of this company as they share general company information and available opportunities.
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Services
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Graduate Engineering Information Session: Stuttgart, Germany
Tue, Nov 19, 2013 @ 07:00 PM - 09:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
You are cordially invited to meet Kelly Goulis, Senior Associate Dean of the Viterbi School of Engineering, at our upcoming information session in Stuttgart, Germany.
Students who have earned or are in the progress 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 information session will include a presentation on: Master's & Ph.D. programs available at USC, how to apply, scholarships, student life, and more. Students will also have the chance to ask questions and receive official brochures and handout information from USC. Light refreshments will be served.
Register to Attend
Location: Le Meridien Stuttgart - Stuttgart, Germany
Audiences: Students with a background in engineering, math or science are welcome to attend.
Contact: Mary Kae
