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Events for June 11, 2025
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Six Sigma Black BeltWed, Jun 11, 2025 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: IISE Faculty, IISE Faculty
Talk Title: Six Sigma Black Belt
Abstract: USC Viterbi School of Engineering's Six Sigma Black Belt program, offered in partnership with the Institute of Industrial and Systems Engineers, enables professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Learn the advanced problem-solving skills you need to implement the principles, practices, and techniques of our Six Sigma Black Belt course in order 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 Certification. 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.
Host: USC Viterbi Corporate and Professional Programs
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
Audiences: Six Sigma Black Belt Students
Contact: VASE Executive Education
Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-black-belt/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Dissertation Defense - Chrysovalatnis Anastasiou
Wed, Jun 11, 2025 @ 12:30 PM - 02:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Recovering Trajectories From Location Data Probablistically
Date and Time: Wednesday, June 11th, 2025 | 12:30p - 2:30p
Location: PHE 106
Committee Members: Cyrus Shahabi (Chair), Jose-Luis Ambite, Marlon Boarnet
Abstract: Understanding urban mobility is crucial for effective city planning, transportation management, and the development of responsive location-based services. However, challenges associated with real-world trajectory data often significantly hamper the derivation of robust insights. These include privacy restrictions limiting access to detailed movement histories, inherent sparsity in collected data points, and uncertainty stemming from sensor inaccuracies. Existing approaches rely on deterministic assumptions (like shortest paths), or necessitate extensive calibration or large, potentially biased training datasets, hindering progress.
This thesis addresses these critical challenges by developing and evaluating a suite of novel data-driven and probabilistic methodologies. We first introduce a purely data-driven technique for time-dependent reachability analysis that leverages raw trajectory data directly, thereby bypassing the complexities of traditional graph-based. To handle data sparsity effectively, we propose time-variant, road network-constrained probabilistic models ("bridgelets"), which realistically represent the inherent uncertainty of movement between sparse location samples. Furthermore, we develop a comprehensive framework (VPE), to reliably estimate vehicle visit probabilities on road segments using observations from uncertain and potentially unreliable roadside sensors.
The practical effectiveness of the proposed methods is rigorously evaluated through extensive experiments using large-scale, real-world datasets from various cities. Quantitative and qualitative results demonstrate that our probabilistic and data-driven approaches significantly improve accuracy and efficiency compared to baseline and traditional techniques. Collectively, the contributions of this thesis provide practical, robust, and innovative tools for researchers, planners, and policymakers to gain deeper, more reliable insights into complex urban mobility dynamics, enabling more informed decision-making even when faced with prevalent data limitations.Location: Charles Lee Powell Hall (PHE) - 106
Audiences: Everyone Is Invited
Contact: Chrysovalantis Anastasiou
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
Suyash P. Awate Seminar - Robust and Data-Scarce Statistical Learning for Improved Neuroimaging, Wednesday, June 11th at 2pm in EEB 132 & Zoom
Wed, Jun 11, 2025 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Suyash P. Awate, Indian Institute of Technology (IIT) Bombay
Talk Title: Robust and Data-Scarce Statistical Learning for Improved Neuroimaging
Series: ECE Seminar
Abstract: Improvements in medical imaging, image-reconstruction, and image-quality-enhancement continue to push towards enabling higher resolution in space and/or time, e.g., in dynamic MRI, and towards lower radiation dose, e.g., in PET and CT. While learning-based approaches hold great potential in pushing the state of the art, they are limited by the unavailability of large (high-quality) datasets for supervised training. This talk describes our recent methods for image reconstruction and quality enhancement that can learn from limited data, model uncertainty estimates associated with their outputs, and exhibit robustness to out-of-distribution data. We design these methods to leverage statistical modeling paradigms using traditional machine learning as well as deep learning.
Biography: Suyash P. Awate is the Asha and Keshav Bhide Chair Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Bombay. His research focuses on quantitative methods and applications in medical image computing, leveraging principles in statistical inference and machine learning. He has around 100 full-length publications in well-known conferences and journals, receiving many best-paper awards/nominations and honors. He was a Program Chair of IEEE ISBI 2022, and serves as an Associate Editor of Medical Image Analysis. More information available at https://www.cse.iitb.ac.in/~suyash/
Host: Richard Leahy
More Info: https://usc.zoom.us/j/91606117125?pwd=zLMkLtb4EjnEvGA1u5O6sxlwnEjaoq.1
More Information: Suyash Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
Event Link: https://usc.zoom.us/j/91606117125?pwd=zLMkLtb4EjnEvGA1u5O6sxlwnEjaoq.1
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
