Ditzler Receives CAREER Award for Machine Learning Research

ECE assistant professor Gregory Ditzler has received a five-year, $500,000 National Science Foundation Faculty Early Career Development Award. The award will support his machine learning research to make sure technologies like autonomous vehicles and facial recognition stay secure. The CAREER award is the NSF’s most prestigious award in support of exceptional early-career faculty.

“This is about establishing my career moving forward – not just about five years, but how I see things progressing over the next 10 years,” Ditzler said. “I can use this opportunity to shape my entire career.”

With his CAREER award, Ditzler is studying two areas of "adversarial" machine learning and applying his methods to cybersecurity. For one, he is examining why feature selection – the process by which a machine decides what elements of information are important to focus on – can fail in the presence of adversaries. An adversary introduces false data into a learning environment to trick a model into misidentifying features.

Autonomous vehicles, for example, use machine learning to learn how to recognize objects around them and react appropriately – like slowing down when approaching another car or stopping at a stop sign. However, researchers have demonstrated that something as simple as placing a sticky note on a stop sign can trick an autonomous vehicle into seeing a speed limit sign instead. Ditzler is investigating what causes this confusion and how it can be prevented.

His project also addresses the problem of machine learning in nonstationary environments. While researchers can develop algorithms that recognize security threats, new forms of threats come up all the time. So, it’s critical that these systems be able to learn continuously.

“If you took data from 10 years ago to make a model for investing in the stock market and apply it to today’s economy, it wouldn’t work,” Ditzler explained. “Many algorithms are static. You train them and deploy them, but realistically they have to be able to change over time.”