I attended a week-long workshop titled “Beyond Convexity: Emerging Challenges in Data Science”, hosted by the Casa Matemática Oaxaca (CMO) in Oaxaca, Mexico.
The workshop consisted of talks, breakout sessions, and many discussions on topics including semidefinite programming, nonlinear/nonconvex optimization, deep learning, and statistics. Much time was spent brainstorming about unsolved problems and discussing emerging topics in data science. The combination of a beautiful and secluded venue, and a small size (roughly 30 attendees) led to many thought-provoking discussions. I returned to Madison with new knowledge, new ideas, and new colleagues. Couldn’t ask for more!
As part of the workshop, I gave a 30-minute talk where I presented recent work by Bin Hu and myself on using dissipativity theory to analyze and interpret the convergence properties of optimization algorithms. A video of my talk is available here and my slides are available here.
I’m grateful for the hard work put in by the organizers: Tamara Kolda (Sandia National Labs), and my colleagues at UW-Madison: Rob Nowak, Becca Willett, and Stephen Wright. Bravo! The photo above is a panorama taken at Monte Albán, one of Oaxaca’s most famous archaeological sites.