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Events for June 26, 2025
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Six Sigma Green Belt for Process Improvement
Thu, Jun 26, 2025 @ 09:00 AM - 05:00 PM
Executive Education
Conferences, Lectures, & Seminars
Speaker: IISE Faculty, IISE Faculty
Talk Title: Six Sigma Green Belt for Process Improvement
Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course).
Host: USC Viterbi Corporate and Professional Programs
More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/
Audiences: Six Sigma Black Belt Students
Contact: VASE Executive Education
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. -
Robotics and Autonomous Systems Center (RASC) Seminar
Thu, Jun 26, 2025 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering, Thomas Lord Department of Computer Science, USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Prof. Maegan Tucker, Georgia Institute of Technology
Talk Title: Towards Robust and Personalized Exoskeleton Locomotion for People with SCI
Abstract: Despite rapid advances in humanoid robotic locomotion, translating these techniques to bipedal systems involving human models—such as lower-limb exoskeletons—remains an open challenge. These systems hold significant promise for restoring independent mobility to individuals with motor-complete spinal cord injury. However, special care must be given to ensure safety, user comfort, and adaptability to changing and uncertain human models. In this talk, I will explore why standard approaches to humanoid locomotion often fail when applied to human-robot systems, and how my lab is working to address these limitations. Topics that I will discuss include model-based trajectory optimization, reinforcement learning, preference-based learning, and musculoskeletal modeling.
Biography: Maegan Tucker is an Assistant Professor at Georgia Tech, jointly appointed in Electrical and Computer Engineering and Mechanical Engineering (since January 2024). She earned her B.S. in Mechanical Engineering from Georgia Tech (2017) and her M.S. (2019) and Ph.D. (2023) from Caltech. Prof. Tucker's research focuses on the areas of robotic bipedal locomotion, human-robot interaction, and the biomechanics of lower-limb assistive technology. Ultimately, her work aims to develop personalized robotic technology to improve mobility for individuals with locomotor impairments. Prof. Tucker's awards and recognitions include the 2023 Centennial Prize for Best Thesis in Mechanical and Civil Engineering from Caltech, the 2022 Simoudis Discovery Prize from Caltech, and two "Best Paper" awards (Best Conference Paper and Best Paper in Human Robot Interaction) for the IEEE International Conference on Robotics and Automation (ICRA) in 2020.
Host: Prof. Erdem Biyik
Webcast: https://usc.zoom.us/j/98467179605?pwd=XJ1yibzUUhsRFCcCcNFonHKsR4lU99.1Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/98467179605?pwd=XJ1yibzUUhsRFCcCcNFonHKsR4lU99.1
Audiences: Everyone Is Invited
Contact: ERDEM BIYIK
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 - Nan Xu
Thu, Jun 26, 2025 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Building Trustworthy LLMs: Ensuring Inference-Time Coherence and Factuality
Date: Thursday, June 26th, 2025 | 11:00am-1:00pm
Venue: RTH 306 and Zoom https://usc.zoom.us/j/93200441032?pwd=7QqueUvIVf0WXl2LQ7AELW7ix31dNz.1
Committee Members: Xuezhe Ma (Chair), Muhao Chen, Jonathan May, Daniel E. O'Leary, Ram Nevatia
Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text, yet critical issues such as hallucinations and precision errors significantly impact their reliability in high-stakes tasks, warranting further in-depth research. In my thesis, I will first introduce my research aiming to tackle these challenges from the perspective of decoding, which is train-free and driven by models' own understanding of seen and generated texts. Specifically, I focus on 1) reducing undesired repetitions and off-topic generations by analyzing probability distribution of decoding steps for open-ended text generation and 2) mitigating hallucinations by studying models' uncertainty against user prompts for false-premise question answering.
Motivated by the in-context learning (ICL) capabilities of Large Language Models (LLMs), multimodal LLMs incorporating an additional visual modality have demonstrated similar ICL abilities when provided with multiple image-text pairs as demonstrations. As the final part of this thesis, I will investigate whether multimodal LLMs can reliably perform a broad range of tasks without additional fine-tuning, including tasks that were not encountered during pretraining or that may even conflict with the pretraining data.
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Nan Xu
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 - Anand Balakrishnan
Thu, Jun 26, 2025 @ 12:00 PM - 01:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: From Qualitative to Quantitative Objectives for Neurosymbolic Control and Planning
Date and Time: Thursday, June 26, 2025 - 12:00p - 1:30p
Location: EEB 132
Committee Members: Jyotirmoy Deshmukh (chair), Bhaskar Krishnamachari, Mukund Raghothaman, Erdem Biyik
Abstract: Reinforcement Learning (RL) is a popular paradigm by which an autonomous agent learns to perform complex tasks and behaviors through trial and error, facilitated by providing rewards to the agent. Effectively, these reward functions encode the high-level behavior intended by the designer, making the satisfactory performance of the tasks by the RL agent highly dependent on the reward functions. However, this raises concerns about safety and interpretability in the learned control policies. To this end, this dissertation proposes using formal specification paradigms that can express complex behaviors unambiguously, including time-dependent tasks like sequential tasks and patrolling tasks. This dissertation first presents how to extract quantitative rewards from such qualitative specifications without altering them. Through empirical and theoretical analysis, it also demonstrates the various guarantees and trade-offs associated with these techniques. Then, novel representations are derived for the specifications so that their structure can be directly exploited by optimization algorithms and leveraged to perform neurosymbolic control for complex systems.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Anand Balakrishnan
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. -
Robotics and Autonomous Systems Center (RASC) Seminar
Thu, Jun 26, 2025 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering, Thomas Lord Department of Computer Science, USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Dr. Naman Shah, Brown University
Talk Title: Autonomously Learning World-Model Representations For Efficient Robot Planning
Abstract: In recent years, it has been clear that planning is an essential tool for robots to achieve complex goals. However, robots often heavily rely on humans to provide "world models" that enable long-horizon planning. It is not only expensive to create such world models as it requires human experts who understand the domains as well as limitations of the robot, but these human-generated world models are often biased by human intuition and kinematic constraints. In this talk, I will present my research focusing on autonomously learning plannable world models. The talk would involve discussing approaches on task and motion planning, neuro-symbolic abstractions for motion planning, and how we can learn world models for task and motion planning.
Biography: Naman is a Postdoctoral researcher in the Intelligent Robots Lab (IRL) with Prof. George Konidaris. He has completed his PhD from Arizona State University, supervised by Prof. Siddharth Srivastava. His research interest lies in investigating methods for autonomously inventing generalizable and plannable world models for robotics tasks. He has been an intern with Palo Alto Research Center, Amazon Robotics, and Toyota Research Institute. Naman has also achieved several graduate fellowships at ASU and a Best Demo Paper Award at AAMAS 2022.
Host: Prof. Erdem Biyik
Webcast: https://usc.zoom.us/j/93271412501?pwd=uYyZGnx1XgMS0i9JbEJpIx7Nz57Lbk.1More Information: Naman Shah's Visit - 6_26_25.pdf
Location: 248
WebCast Link: https://usc.zoom.us/j/93271412501?pwd=uYyZGnx1XgMS0i9JbEJpIx7Nz57Lbk.1
Audiences: Everyone Is Invited
Contact: ERDEM BIYIK
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.
