Conferences, Lectures, & Seminars
Events for June
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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. -
Robotics and Autonomous Systems Center (RASC) Seminar
Fri, Jun 20, 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: Prof. Dinesh Jayaraman, University of Pennsylvania
Talk Title: Engineering Better Robot Learners: Exploration and Exploitation
Abstract: Industry is placing big bets on "brute forcing" robotic control, but such approaches ignore the centrality of resource constraints in robotics on power, compute, time, data, etc. Towards building a true engineering discipline of robotics, my research group has been "exploiting and exploring" robot learning: exploiting to push the limits of what can be achieved with today's prevalent principles at various resource constraints, and "exploring" better design principles for efficient and minimalist robots in the future. As examples of “exploit”, we have trained quadruped robots to perform circus tricks on yoga balls and robot arms to perform household tasks in entirely unseen scenes with unseen objects. As examples of “explore”, we are studying the sensory requirements of robot learners: what sensors do they need and when do they need them during training and task execution? In this talk, I will highlight these examples and discuss some lessons we have learned in our research towards better-engineered robot learners.
Biography: Dinesh Jayaraman is an assistant professor at the University of Pennsylvania's CIS department and GRASP lab. He leads the Perception, Action, and Learning (Penn PAL) research group, which works at the intersections of computer vision, robotics, and machine learning. Dinesh received his PhD (2017) from UT Austin, before becoming a postdoctoral scholar at UC Berkeley (2017-19). Dinesh's research has received a Best Paper Award at CORL '22, a Best Paper Runner-Up Award at ICRA '18, a Best Application Paper Award at ACCV '16, the NSF CAREER award '23, an Amazon Research Award '21, and been covered in The Economist, TechCrunch, and several other press outlets. His webpage is at: https://www.seas.upenn.edu/~dineshj/
Host: Prof. Erdem Biyik
Webcast: https://usc.zoom.us/j/97616702619?pwd=aiV4aX7mgVCUO3qUVmJ5DIWipZBy12.1Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/97616702619?pwd=aiV4aX7mgVCUO3qUVmJ5DIWipZBy12.1
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. -
Robotics and Autonomous Systems Center (RASC) Seminar
Thu, Jun 26, 2025 @ 11:00 AM - 12:00 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.
