University of Arizona electrical and computer engineering assistant professor Gregory Ditzler has received a five-year, $500,000 National Science Foundation Faculty Early Career Development Award to support his machine learning research. 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."
Ditzler's work is all about developing mathematical models and algorithms that computers use to recognize patterns and identify relevant features.
For example, researchers might show a computer science vs engineering a series of electronic medical records taken from patients – some with and some without cancer. Over time, the computer learns to recognize which features are indicative of the disease and which aren't relevant.
"Our goal is to develop a mathematical model the computer can use so if we give it a new item it has never seen before, the machine can infer whether that individual has cancer," Ditzler said. "Machine learning is such a hot topic right now because it's integrated into everything we use in our daily lives – from the computers we use to create Word documents to the cell phones we use to make phone calls, take photos and text."
"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."
Ditzler's work is all about developing mathematical models and algorithms that computers use to recognize patterns and identify relevant features.
For example, researchers might show a computer science vs engineering a series of electronic medical records taken from patients – some with and some without cancer. Over time, the computer learns to recognize which features are indicative of the disease and which aren't relevant.
"Our goal is to develop a mathematical model the computer can use so if we give it a new item it has never seen before, the machine can infer whether that individual has cancer," Ditzler said. "Machine learning is such a hot topic right now because it's integrated into everything we use in our daily lives – from the computers we use to create Word documents to the cell phones we use to make phone calls, take photos and text."
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