MERL’s Virtual Open House 2021

December 9, 2021

Join us for MERL's virtual open house on December 9. Live sessions will be held from 1:00-5:30pm EST, including an overview of recent activities by our research groups, featured guest speakers and live interaction with our research staff through the Gather platform. Registered attendees will be able to browse our virtual booths at their convenience and connect with our research staff on engagement opportunities, including internship/post-doc openings as well as visiting faculty positions.

Details

Date: Thursday, December 9, 2021
Time: 1:00 - 5:30 PM EST

Live Session Schedule

1:00 - 1:15 EST	Welcome / Opening Remarks [video]
1:15 - 1:45 EST	Keynote One "Computational Imaging: Beyond the limits imposed by lenses" Prof. Ashok Veeraraghavan, Rice University [video]
1:50 - 2:10 EST	Speech & Audio Jonathan Le Roux [video]	Computational Sensing Petros Boufounos [video]
2:10 - 2:30 EST	Computer Vision Alan Sullivan [video]	Connectivity Kieran Parsons [video]
2:30 - 3:15 EST	Open Interaction on Gather Platform
3:15 - 3:45 EST	Keynote Two "Control Meets Learning - On Performance, Safety and User Interaction" Prof. Melanie Zeilinger, ETH Zurich [video]
3:50 - 4:10 EST	Data Analytics Daniel Nikovski [video]	Motors & Devices Jay Thornton [video]
4:10 - 4:30 EST	Controls for Autonomy Stefano Di Cairano [video]	Multi-Physical Systems Chris Laughman [video]
4:30 - 5:30 EST	Open Interaction on Gather Platform

Virtual Exhibit Booths

Attendees are invited to visit our virtual booths at their convenience to learn more about MERL's research activities and internship opportunities. These virtual spaces will provide:

Material that provides a more in-depth view of our latest research results
Links to relevant internship and post-doc opportunities
An opportunity to interact live with researchers on the Gather platform

The event will feature more than a dozen virtual booths in key research areas.

End-to-end Speech and Audio Processing
Multimodal AI
Visual Analysis
Robotic Perception
Machine Learning and Optimization for Robot Control
Power Systems Analytics
Intelligent Secure Communications Networks
Learning and Inference
Signal Modeling and Inverse Problems
Array Processing and Radar Imaging
Autonomous Vehicle Systems
Cyberphysical Systems Control, Learning, Verification
Intelligent and Digital Radio
Dynamical Systems and Control
Advanced Motor Technologies
Multiphysical Systems Modeling, Control, and Learning
Optics and Metasurfaces

Featured Guest Speakers

Prof. Ashok Veeraraghavan, Rice University

Computational Imaging: Beyond the limits imposed by lenses

Abstract: The lens has long been a central element of cameras, since its early use in the mid-nineteenth century by Niepce, Talbot, and Daguerre. The role of the lens, from the Daguerrotype to modern digital cameras, is to refract light to achieve a one-to-one mapping between a point in the scene and a point on the sensor. This effect enables the sensor to compute a particular two-dimensional (2D) integral of the incident 4D light-field. We propose a radical departure from this practice and the many limitations it imposes. In the talk we focus on two inter-related research projects that attempt to go beyond lens-based imaging. First, we discuss our lab's recent efforts to build flat, extremely thin imaging devices by replacing the lens in a conventional camera with an amplitude mask and computational reconstruction algorithms. These lensless cameras, called FlatCams can be less than a millimeter in thickness and enable applications where size, weight, thickness or cost are the driving factors. Second, we discuss high-resolution, long-distance imaging using Fourier Ptychography, where the need for a large aperture aberration corrected lens is replaced by a camera array and associated phase retrieval algorithms resulting again in order of magnitude reductions in size, weight and cost. Finally, I will spend a few minutes discussing how the wholistic computational imaging approach can be used to create ultra-high-resolution wavefront sensors.

Ashok Veeraraghavan is currently a Professor of Electrical and Computer Engineering at Rice University, TX, USA. Before joining Rice University, he spent three wonderful and fun-filled years as a Research Scientist at Mitsubishi Electric Research Labs in Cambridge, MA. He received his Bachelors in Electrical Engineering from the Indian Institute of Technology, Madras in 2002 and M.S and PhD. degrees from the Department of Electrical and Computer Engineering at the University of Maryland, College Park in 2004 and 2008 respectively. His thesis received the Doctoral Dissertation award from the Department of Electrical and Computer Engineering at the University of Maryland. His work has won numerous awards including the Hershel. M. Rich Invention Award in 2016 and 2017, and an NSF CAREER award in 2017. He loves playing, talking, and pretty much anything to do with the slow and boring but enthralling game of cricket.

Prof. Melanie Zeilinger, ETH Zurich

Control Meets Learning - On Performance, Safety and User Interaction

Abstract: With increasing sensing and communication capabilities, physical systems today are becoming one of the largest generators of data, making learning a central component of autonomous control systems. While this paradigm shift offers tremendous opportunities to address new levels of system complexity, variability and user interaction, it also raises fundamental questions of learning in a closed-loop dynamical control system. In this talk, I will present some of our recent results showing how even safety-critical systems can leverage the potential of data. I will first briefly present concepts for using learning for automatic controller design and for a new safety framework that can equip any learning-based controller with safety guarantees. The second part will then discuss how expert and user information can be utilized to optimize system performance, where I will particularly highlight an approach developed together with MERL for personalizing the motion planning in autonomous driving to the individual driving style of a passenger.

Melanie Zeilinger is an Assistant Professor at the Department of Mechanical and Process Engineering at ETH Zurich, Switzerland where she leads the Intelligent Control Systems group. She received the Diploma degree in engineering cybernetics from the University of Stuttgart, Germany, in 2006, and the Ph.D. degree with honors in electrical engineering from ETH Zurich, Switzerland, in 2011. From 2011 to 2012 she was a Postdoctoral Fellow with the Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. She was a Postdoctoral Researcher with the Max Planck Institute for Intelligent Systems, Tübingen, Germany until 2015 and with the Department of Electrical Engineering and Computer Sciences at the University of California at Berkeley, CA, USA, from 2012 to 2014. From 2018 to 2019 she was a professor at the University of Freiburg, Germany. Her awards include the ETH medal for her PhD thesis, a Marie-Curie IO fellowship and an SNF Professorship grant. She is one of the organizers of the new Conference on Learning for Dynamics and Control (L4DC). Her research interests include safe learning-based control with applications to robotics and human-in-the-loop control.

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