General conference information:
The conference takes place in Peter Engel Science center at Saint John's University on April 3rd and 4th of 2020. There is a registration table at the front door which will have a schedule of the talks for both days. There is no fee to register. Please park in Parking Lot P2 or P4.
Abstracts and Schedule (there is a pizza party after the 8:30 pm talk on Friday in PEngel 269, and breakfast in PEngel 269 at 8 am on Saturday morning)
2020 PME Schedule and Abstracts:
Friday, April 3, 8:30pm - Pellegrene Auditorium.
Title: Geometric/Topological problems arising in Artificial Intelligence and Data Science
Abstract: Today, the enhanced ability to observe, collect, and store data in the sciences, in commerce, in medicine, and many other fields as well as the increased numbers of connections (e.g., emergence of smart-phones and social networks as central aspects of our daily life) calls for a change in our understanding of data and information. Increasingly mathematicians of the future will try to understand and extract usable information from massive data arising in applications and within mathematics itself, looking more and more like experimental scientists who collect data to explore conjectures and patterns. But the most exciting part is perhaps the new types of mathematics that will have to be developed. My lecture will focus on how geometry and topology are increasingly playing a role in understanding the shape of data. No priorknowledge of these topics will be assumed.
Saturday, April 4, 10:30am - Pellegrene Auditorium.
Title: Algebraic and Geometric methods in Modern Optimization
Abstract: Optimization is a vibrant growing area of Applied Mathematics. Its many successful applications depend on efficient algorithms and this has pushed the development of theory and software. In recent years there has been a resurgence of interest to use \non-standard" techniques to estimate the complexity of computation and to guide algorithm design. New interactions with fields like algebraic geometry, representation theory, number theory, combinatorial topology, algebraic combinatorics, and convex analysis have contributed non-trivially to the foundations of computational optimization. I will try to give a taste of these new approaches. Most of my time I will focus on the simplest and very useful problem of minimizing a linear function over a region defined by linear inequalities and equation constraints (linear programming!) and describe how \non-traditional" thinking shines new light on the computational methods.