Melissa Smith
Associate Professor, Electrical and Computer Engineering

Question 3: Will we be driving cars, or will they be driving us?

Before coming to Clemson in 2006, Melissa Smith spent 12 years as a research associate at the Oak Ridge National Laboratory, a holy grail for research computing run by the Department of Energy and based in East Tennessee. She researched high-energy and nuclear physics instrumentation and began collaborations with the newly formed Future Technologies Group on emerging computing architectures, among other things. Big things.

When she arrived at Clemson, she was searching for a group capable of similar large-scale collaborations that served people in addition to working with people. She has continued to collaborate with some of the top research scientists at Oak Ridge and across the country via high-performance computing, and the results have been prolific.

“I wanted that same kind of opportunity on the academic level,” Smith says. “It was readily apparent to me that this was the same kind of environment Clemson had and that it planned to continue.”

Smith and her group are focused on applying “deep learning” processes, which in computer terms looks like vast amounts of data, to a variety of different problems — so in the case of autonomous vehicles, researching vision and perception capabilities for a car to be able to drive without a driver.

The work is particularly relevant for Smith, whose daughter recently secured her driver’s license.

“When we’re driving a car, it requires vision, hearing, feeling — we’re making controlled decisions,” she says. “Do I brake? Do I accelerate? So in the field of autonomous vehicles, trying in a sense to recreate that dynamic system, it requires people from a lot of different disciplines to come together.”

From a technology standpoint, it requires combining various machine-learning processes. So, machine learning is the process of teaching a computer to carry out a task, rather than programming it to carry out that task step by step. But deep learning amplifies this process to human-level performance, and to do this, computers must be able to handle large amounts of data.

In day-to-day life, deep learning might look like your Alexa virtual assistant understanding what you say and then adapting to your accent, speech patterns and usage to improve its voice recognition technology over time.

For a self-driving car, deep learning looks like developing the vision and perception abilities that actually allow an autonomous vehicle to be autonomous. Fixed cameras use radar (radio waves) and lidar (laser or light) to measure distances and to paint a picture of a vehicle’s surroundings. That video can then be processed in two different ways: one is to identify and classify objects the other is to create a map of the car’s surroundings, something called segmentation.

Every object, at every speed, in every light and weather and traffic scenario, with every road condition must be taken into consideration. Not surprisingly, a giant computer is required to process all that information, which is then used to build the neural network that will ultimately steer a car to drive autonomously.

And then they get to figure out how to downsize it to car scale.

“We think of things in stages,” Smith explains: “Identify problems, come up with a solution and take steps to translate the solution.”

Autonomous driving technology is only one of the active research projects in Melissa Smith’s lab. One of Smith’s chief collaborators is Alex Feltus, a professor of genetics and biochemistry and a member of the Internet 2 board, who has served in various advisory capacities for Clemson Computing and Information Technology (CCIT). Feltus trains the data-intensive computing workforce relevant to industry and academia, and he’s worked with various researchers, including Smith, to attract millions of dollars for research computing at Clemson.

In a collaboration with Washington State University and RENCI, they are designing a distributed cloud system called Scientific Data Analysis at Scale (SciDAS) to enable scientists to get information faster and use much larger datasets. Feltus and Smith secured a $2.95 million NSF grant designed to unite biologists, hydrologists, computer engineers and computer scientists to build SciDAS. Feltus’ lab is engaged with multiple institutions facilitating large-scale genomics data analytics through NSF funded software and workflow engineering projects.

A: Cars are already driving us more than we’re driving them thanks to the kind of autonomous technology the Smith group is working on. Sensors can detect slippery road conditions, and cars can stay in their lanes or even stop if there’s something in their path. This technology will continue to improve and expand.

QUESTION 4 of 6:

Future of Medicine — Man or machine?



Can we feed the world in a healthy sustainable way?

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Can we find better ways to treat cancer?

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Can we save the planet from one of its most damaging pollutants?

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What is the future of banking?

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Heidi Coryell Williams is the director of Writing and Editing Services at Clemson University.

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