Circuits and Computing Faculty Seminar Series
Speaker: Prof. Naveen Verma
Series: Graduate Events
Location: Engineering Quadrangle B205
Date/Time: Wednesday, December 7, 2011, 4:30 p.m. - 5:30 p.m.
The EE faculty in the area of Circuits and Computing will be discussing their current and future research. All students are encouraged to attend and this is a great way to learn about the research being done in our department on circuits and computer engineering. This is especially a great opportunity for first year students who even have a small interest in this field to see if there are any topics that interest them.
Prof. Naveen Verma
Prof. Ruby Lee
Circuit design is about connecting electronic devices into networks to perform high-value functions. In the name of platform design, the circuits world, for the most part, has used the same basic device given to it, and, at the same time, has left the created functions in the hands of algorithm designers. As we have long known, however, there are extremely compelling opportunities available to circuit designers at the cross section of devices and algorithms.
In this talk, I will describe two major projects in my lab. The first looks downwards to emerging devices. It investigates how combing large-area electronics with high-performance silicon-CMOS devices can potentially enable electronic systems with unprecedented sensing and actuation capabilities. The second looks upwards to emerging algorithmic frameworks for advanced inference. Inference implies the ability to analyze and interpret data. We would like our devices to interact with increasingly complex physical systems; in particular, our focus is on physiological systems. Through low-power, chronic sensors, we have the ability to acquire physiologically-indicative signals. We are building platforms that can interpret these for a range of clinical applications, from detecting seizures to monitoring heart disease. In order to do this, we are researching hardware architectures and algorithms that incorporate powerful techniques from the domain of machine learning into very low-power devices.