Faculty Presentations

2:30 PM - 4:30 PM at Price Center Forum (4th Floor)

Hear twenty-minute technical talks by Jacobs School of Engineering faculty.

School of Global Policy & Strategy

2:30 PM

David Victor

David Victor, Professor; Co-director, Laboratory on International Law and Regulation

Achieving Deep Decarbonization of the Global Economy: Engineering and Policy

There is now a widespread technical agreement that stopping global climate change requires essentially zero emissions of carbon dioxide and other warming gases. Much less agreeable has been a political strategy for achieving that goal. This talk will focus on the technology that could likely scale in the real world to achieve zero global emissions. It will also focus on strategies that leading jurisdictions, such as California, could use to accelerate the pace of global decarbonization.

Contextual Robotics

3:00 PM

Laurel D. Riek

Laurel D. Riek, Assoc Professor, Computer Science and Engineering, Faculty-Affiliate, Calit2

Contextual Robotics

Robots are no longer separated from people by cages. They are now entering our daily lives - in the home and on the road, in offices and in hospitals. To operate proximately with people, robots need the ability to dynamically understand and model human activities, understand their context, and select appropriate actions. They also need to work with and learn from people longitudinally, in fluent and contingent ways. My research team explores these topics in depth, and designs algorithms for robots able to achieve these goals. There are many applications of our work, including in neurorehabiltiaton, critical care, healthy aging, and manufacturing. This talk will highlight several recent projects in these areas.


3:30 PM

Darren Lipomi

Darren Lipomi, Professor, NanoEngineering, Jacobs School of Engineering

Virtual Touch: Smart Materials for Human-Machine Interaction

The sense of touch has great power to elicit thoughtful or emotional responses (pleasant or unpleasant), and to convey information. While human culture is replete with artifacts that interface with the senses of sight, hearing, taste, and smell, objects designed to convey information or trigger emotion by interfacing with the sense of touch represent an open area for investigation. My research group is developing soft materials that can simulate different tactile sensations: rough or smooth, hot or cold, soft or hard, or even slimy. We can then use virtual reality and wearable haptic interfaces to transduce these signals to a user. The key innovative element in our work is the development of electroactive polymers and other soft materials that form conformal mechanical interfaces with human skin. This work leverages our experience in stretchable organic semiconductors, wearable sensors, and nanofabrication, and represents an interface between materials engineering and psychophysics. We envision applications in robotic surgery and surgical training, education, and simulated environments for consumer electronics.

Adaptive Computing and Embedded Systems

4:00 PM

Farinaz Koushanfar

Farinaz Koushanfar, Associate professor, Electrical and Computer Engineering, Director, Adaptive Computing and Embedded Systems Lab, Rice University

M^2L: Bringing the Machine into the loop of Machine Learning

Contemporary analytical algorithms are often focused on functionality and accuracy with system performance as an afterthought. As their use/scale grows and the computing platforms become diverse, spanning from servers and desktops to smartphones and Internet of Things (IoT) devices, functionality is not just about algorithmic efficiency and accuracy, but also practicality on real-world computing machines. One-size fits all solutions will not meet the physical needs of emerging analytical application scenarios. In this talk, I will present our research on novel automated computing frameworks that bring hardware into the loop of designing scalable inference algorithms and learning systems, supported by both theoretical and practical results. Proof-of-concept evaluations on diverse datasets, applications, algorithms, and platforms demonstrate orders of magnitude efficiency compared to the best prior art.