Art Meets AI Algorithms | Columbia News

What was your career path to becoming an industrial engineer and a professor?

I started as a Wall Street quantitative analyst instead of going for a postdoc. Years later, I became a hedge fund manager, and utilized pattern recognition and statistical signal processing techniques for spotting mispricing and statistical arbitrage. I was always interested in teaching; I started tutoring at age 13, and taught through graduate school, in some cases as an extracurricular activity, simply because I enjoyed it.

From the beginning of my career, I liked to work with data, and I enjoy data mining. My PhD advisor always said, “let data do the talk, models are data-driven.” Throughout my industry years, prior to joining Columbia as a full-time professor, I worked on real applications of mathematical equations, and conducted research with academics in many disciplines. I love to bring industry and academia closer and narrow the gap.

Advice for anyone pursuing a career in engineering?

Always ask questions. Decision-making is data-driven, so work with data, and mine the data. If you are not a programmer, become one!

 What is the best part of teaching at Columbia?

Being challenged by smart students, being asked questions I have never thought of, and watching the excitement in students’ faces any time they see and learn something new. I love to teach by visualization as much as I can, and I take that very seriously.