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Conferences, Lectures, & Seminars
Events for June
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Six Sigma Black BeltTue, Jun 04, 2019 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: Week 1: June 4-7, 2019
Week 2: July 15-19, 2019
Week 3: August 5-9, 2019
9am - 5pm
Learn the advanced problem-solving skills you need to implement the principles, practices, and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements, and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn Six Sigma Black Belt Certificate. This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process, as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis, and simulation. Computer applications are emphasized.
NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Registered Attendees
Posted By: Corporate & Professional Programs
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Six Sigma Black Belt
Wed, Jun 05, 2019 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: Week 1: June 4-7, 2019
Week 2: July 15-19, 2019
Week 3: August 5-9, 2019
9am - 5pm
Learn the advanced problem-solving skills you need to implement the principles, practices, and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements, and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn Six Sigma Black Belt Certificate. This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process, as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis, and simulation. Computer applications are emphasized.
NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Registered Attendees
Posted By: Corporate & Professional Programs
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Six Sigma Black Belt
Thu, Jun 06, 2019 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: Week 1: June 4-7, 2019
Week 2: July 15-19, 2019
Week 3: August 5-9, 2019
9am - 5pm
Learn the advanced problem-solving skills you need to implement the principles, practices, and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements, and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn Six Sigma Black Belt Certificate. This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process, as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis, and simulation. Computer applications are emphasized.
NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Registered Attendees
Posted By: Corporate & Professional Programs
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Six Sigma Black Belt
Fri, Jun 07, 2019 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Abstract: Week 1: June 4-7, 2019
Week 2: July 15-19, 2019
Week 3: August 5-9, 2019
9am - 5pm
Learn the advanced problem-solving skills you need to implement the principles, practices, and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements, and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn Six Sigma Black Belt Certificate. This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process, as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis, and simulation. Computer applications are emphasized.
NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Registered Attendees
Posted By: Corporate & Professional Programs
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Jonathan Habif, Monday, June 10th at 2pm in EEB 248
Mon, Jun 10, 2019 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jonathan Habif, Research Scientist at ISI Waltham
Talk Title: The Laboratory for Quantum-Limited Information: Optical Sensing at the Quantum Limit
Abstract: In this talk I will begin by introducing the Laboratory for Quantum-Limited Information (QLIlab), part of the USC Information Sciences Institute. QLIlab is focused on understanding the fundamental limits for extracting information from physical signals in photon-starved sensing and communications settings and demonstrating the capability to achieve these limits with novel designs for optical receivers. As an example, I will present our theoretical and experimental work on quantum-limited discrimination of photon-starved classical states. In this work the fundamental quantum limit for discriminating between thermal and coherent (laser) light is calculated, along with the error-probability for discrimination that can be achieved by using traditional optical receivers. The Generalized-Kennedy (GK) receiver is shown to have near optimal performance for this discrimination task. The GK receiver has been constructed in the laboratory and shown to out-perform theoretical limits from direct and coherent detection optical receivers. Finally, I will describe how this work will be extended to broader classes of sensing and communications challenges, including passive imaging and weak coherent signals buried in bright noise. A key goal for this talk is to engage with the audience to identify new optical and opto-electronic technologies that can help to realize optical receivers operating at the quantum limit of sensitivity, and ultimately miniaturize the systems so they can be transitioned up the technology readiness level scale.
Biography: Dr. Jonathan L. Habif is an experimental physicist and research lead at the University of Southern California Information Sciences Institute (ISI). His research has focused on photon-starved, classical communication and imaging, quantum-secured optical communications in free-space and fiber, and integrated nano-photonics for both classical and non-classical applications. Prior to joining ISI, Dr. Habif was with BBN technologies where he served as principal investigator for a number of DARPA-sponsored research programs, partnering with university collaborators to demonstrate revolutionary optical technologies impacting traditional communications, sensing and computation systems.
Dr. Habif earned a Ph.D. from the University of Rochester in the field of superconducting quantum computing and continued this course of research as a postdoctoral associate at MIT.
Host: ECE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Posted By: Marilyn Poplawski
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Stochastic Regularizer for High Dimensional Small-Sampled Data and Online learning for Time Series Forecasting
Fri, Jun 14, 2019 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sergül Aydöre, Department of Electrical and Computer Engineering of Stevens Institute of Technology
Talk Title: Stochastic Regularizer for High Dimensional Small-Sampled Data and Online learning for Time Series Forecasting
Abstract: In this talk, I will focus on developing efficient Machine Learning algorithms for two situations. One situation is high dimensional, small sampled and noisy data situations as in neuroscience, biology or geology where data collection is expensive. This phenomenon is known as curse of dimensionality that causes overfitting which is often an obstacle for using machine learning techniques. We formulated a structured stochastic regularization that relies on feature grouping. Using a fast clustering algorithm, we define a family of groups of features that capture feature covariations. Inside a stochastic gradient descent loop, we then randomly average these features. Experiments on two real-world datasets demonstrate that our approach produces models that generalize better than those trained with conventional regularizers, and also improves convergence speed, and has a linear computational cost. Another challenging situation that I will talk about is updating a machine learning model with streaming data without iterating through previously seen data. This is also known as "online learning" and one application is forecasting time series. The performance of online learning algorithms is typically evaluated by the regret. We introduce a new local regret for non-convex models in dynamic environments. We present an update rule incurring a cost, according to our proposed regret, which is sublinear in time T. Our update uses time-smoothed gradients. Using a real-world dataset we show that our time-smoothed approach yields several benefits when compared with state-of-the-art competitors: results are more stable against new data; training is more robust to hyperparameter selection; and our approach is more computationally efficient than the alternatives.
Biography: Sergul Aydore has been an Assistant Professor at the Department of Electrical and Computer Engineering of Stevens Insitute of Technology in August 2018. Before joining Stevens, Sergul was a Machine Learning Scientist at Amazon's demand forecasting where she built neural network models to predict the demands of millions of products to enable better in-stock positions. She is also an associate member of Parietal team at Inria, Saclay. She received her PhD degree from the Signal and Image Processing Institute at the University of Southern California in 2014. Her PhD work was on developing robust connectivity measures for neuroimaging data. Prior to Amazon, Sergul was a postdoctoral researcher at Columbia University where she implemented machine learning models for EEG data. She then spent a year as a Data Scientist at JP Morgan. She received her B.S. and M.S. degrees in Electrical and Electronics Engineering from Bogazici University, Istanbul where she developed signal processing techniques to investigate biomedical signals. She was a recipient of the Viterbi School of Engineering Doctoral fellowship and was recognized as a 2014 USC Ming Hsieh Institute Ph.D. Scholar.
Host: Professor Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Posted By: Talyia White
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Medical Imaging Seminar
Mon, Jun 17, 2019 @ 05:15 PM - 06:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Frank Ong , Stanford University
Talk Title: Extreme MRI: Reconstructing Hundred-Gigabyte Volumetric Dynamic MRI from Non-Gated Acquisitions
Series: Medical Imaging Seminar Series
Abstract: In this talk, I will present techniques to reconstruct 3D dynamic MRI of ~100 GBs from continuous non-gated acquisitions. The problem considered is vastly undetermined and demanding of computation and memory. I will introduce a multi scale low rank matrix model to compactly represent dynamic image sequence. This enables compressed storage, which in combination with a stochastic optimization approach, renders the reconstruction of 100s of GBs of images feasible. The proposed method is applied to dynamic contrast enhanced MRI and free breathing lung MRI, with reconstruction resolution of near millimeter spatially, and sub-second temporally. The attached animated gif shows a 3D rendered result from this talk.
(Joint work with Xucheng Zhu, Joseph Cheng, Peder Larson, Shreyas Vasanawala, and Michael Lustig)
Biography: Frank Ong is a post-doctoral researcher at Stanford University, working with Prof. Shreyas Vasanawala and Prof. John Pauly. His research focuses on computational imaging methods in medical imaging, particularly in MRI. He received his PhD degree from UC Berkeley in Fall 2018 with Prof. Michael Lustig.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Posted By: Talyia White
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NL Seminar-Multimodal Communication: A Discourse Approach
Fri, Jun 21, 2019 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Malihe Alikhani, Rutgers University
Talk Title: Multimodal Communication: A Discourse Approach
Series: Natural Language Seminar
Abstract: The integration of textual and visual information is fundamental to the way people communicate. My hypothesis is that despite the differences of the visual and linguistic communication, the two have similar intentional, inferential and contextual properties, which can be modeled with similar representations and algorithms. I present three successful case studies where natural language techniques provide a useful foundation for supporting user engagement with visual communication. Finally, I propose using these findings for designing interactive systems that can communicate with people using a broad range of appropriate modalities.
Biography: Malihe Alikhani is a 4th year Ph.D. student in the department of computer science at Rutgers University, advised by Prof. Matthew Stone. She is pursuing a certificate in cognitive science through the Rutgers Center for Cognitive Science and holds a BA and MA in mathematics. Her research aims at teaching machines to understand and generate multimodal communication. She is the recipient of the fellowship award for excellence in computation and data sciences from Rutgers Discovery Informatics Institute in 2018.
Host: Xusen Yin
More Info: https://nlg.isi.edu/nl-seminar
Webcast: https://bluejeans.com/s/ypWYX/Location: Information Science Institute (ISI) - CR #689
WebCast Link: https://bluejeans.com/s/ypWYX/
Audiences: Everyone Is Invited
Posted By: Peter Zamar
