Abstract: As a student in school, opportunities and expectations seem to be relatively well defined: go to class, take tests, do projects, behave well and success should follow. The transition to a job is less obvious, despite the fact that more opportunities than ever abound for students with strong math, statistics and computer science backgrounds; essentially all industries have embraced the importance of advanced modeling and data driven decisions. Unfortunately, making this transition can be complicated, especially when speaking to a corporate hiring manager that generally only interacts with a specific type of candidate. In this talk, I will discuss my own journey from academia to the startup life and lessons that I learned along the way. I will try to present my experiences in the context of how I currently see ML/AI/data in industry and what I would recommend when preparing for a shift to industry.

About the speaker: Mike studies computational mathematics and statistics in developing methodologies and software for SigOpt, a San Francisco startup. Prior to joining SigOpt, he spent time in the math and computer science division at Argonne National Laboratory and was a visiting assistant professor at the University of Colorado-Denver where he co-wrote a text on kernel-based approximation. Mike holds a PhD and MS in Applied Mathematics from Cornell and a BS in Applied Mathematics from Illinois Institute of Technology. Mike also has been a long-time beneficiary of the great mathematicians in Hong Kong, and specifically the Baptist math department, and is thrilled with the opportunity to speak to students.

Language: English

When: Tuesday, Nov 6, 2018, from 5:30 - 6:30pm

Where: SCT501 (LT1)