Engineer Alexander Lavin ’12 is one of nine Cornellians to be named to Forbes’ annual ‘30 Under 30’ list, honorees who Forbes describes as “the future leaders of everything.
Lavin was one of 600 candidates selected from a pool of 15,000 applicants. He was honored for his research at Numenta, a company that works in the fields of machine learning and artificial intelligence.
Numenta is currently working to develop applications that incorporate the company’s machine intelligence technology called Hierarchical Temporal Memory, which is modeled after the human brain.
“At Numenta we’re working to reverse-engineer the neocortex to develop machine intelligence,” Lavin said. “My motivation in joining Numenta was twofold — learn from incredibly intelligent and passionate people, and work on challenging projects that advance the field of AI,” he said. “As of late I’ve been focusing on natural language processing, which is arguably the most challenging area of machine learning.”
Lavin said he was taken aback when he discovered that he had been selected as one of Forbes’ 30 under 30 candidates.
“I received an email a few months back that I was nominated and have been chosen as a finalist, along with a few-page questionnaire to send back,” he said. “I never heard anything until the morning they published the list. It was a huge surprise.”
Lavin said his interest in computer technology began when he was a student at Cornell studying mechanical engineering.
“I had been pursuing my academic and professional interests, which along the way had gotten me into things like rocket propulsion research, working on a lunar rover, and research at the intersection of AI and neuroscience,” he said. “I was a mechanical engineer and did research with the flux-pinned spacecraft group in the Space Systems Design Studio.”
Lavin said he advises Cornell students to develop their computing skills regardless of their major and career choice. No matter what industry, Lavin said students should have a basic understanding of computing concepts.
“Be data-literate,” Lavin said. “With the availability of more and more data, the ability to leverage data analysis and machine learning tools is going to be necessary in all industries.”