SIAM Student Chapter Seminar
- When: Mondays at 3:30 PM
- Where: 9th floor lounge (we will also broadcast the virtual talks on the 9th floor lounge with refreshments)
- Organizers: Evan Sorensen
- Faculty advisers: Jean-Luc Thiffeault, Steve Wright
- To join the SIAM Chapter mailing list: email firstname.lastname@example.org.
|date and time||location||speaker||title|
|Feb 7, 3:30-4 PM||Virtual (link)||Keith Rush (Senior Software Engineer at Google)||Industry talk|
|Feb 14, 3:30-4 PM||Virtual (link) Passcode: 400453||Shawn Mittal (Senior Deliver Data Scientist at Microsoft)||Who, What, Why of Data Science in Industry|
|Feb 21, 3:30-4 PM||9th floor lounge||Brandon Boggess (Epic)||Industry talk|
|Feb 28, 3:30-4 PM||9th floor lounge||Shi Chen (UW-Madison)||Classical limits of direct and inverse wave type problems -- a Wigner transform approach|
|Mar 7, 3:30-4 PM||Virtual (link) Passcode: 400453||Tom Edwards (Software Engineer at Google)||Industry talk|
|Mar 21, 3:30-4 PM||9th floor lounge||Aidan Howells (UW-Madison)||A Gentle Introduction to Chemical Reaction Network Theory|
|Apr 4, 3:30-4 PM||9th floor lounge||Eza Enkhtaivan (UW-Madison)||Reinforcement Learning and Markov Decision Processes|
|Apr 11, 3:30-4 PM||Virtual (link) Passcode: 400453||Micky Steinberg (Data Analyst at Principia Analytics)||Industry talk|
Feb 7, Keith Rush
I'll talk about the kind of work I do today, the way I got here, and any insight I can give for someone hoping to pursue a similar path. I'll also discuss some of the things I've learned, and some of the advantages and disadvantages a mathematician has in the machine learning and computer science world. We'll be sure to have a freewheeling discussion and a good time :).
Feb 14, Shawn Mittal
A short snapshot of what the data science industry looks like followed by some lessons learned on what makes an effective data scientist.
Feb 21,Brandon Boggess
I will be talking about software development and the transition from academic research to enterprise engineering.
Feb 28, Shi Chen
The underlying physics of the same system is different when the system is described at different scales. In classical mechanics, the motion of a particle is governed by the Newton's second law, while in quantum mechanics the status of a particle follows the Schrödinger equation. The classical mechanics and the quantum mechanics are two sides of the same coin, but how can we formally connect the two disparate systems? In this talk, I will introduce the Wigner transform, which is the only known method that seamlessly connects the classical and quantum systems as the Planck constant vanishes. I will keep everything basic and briefly introduce some applications of the Wigner transform to direct and inverse wave type problems.
Mar 7, Tom Edwards
I will talk about comparisons between small and big companies.
Mar 14, Aidan Howells
We'll learn what a chemical reaction network is, with a bunch of real-world examples. There are a number of ways to model these networks as objects of mathematical study, two of which will be discussed. We'll end with a few of the questions mathematicians try to answer about these models, to give you some of the flavor of the field.
Apr 4, Eza Enkhtaivan
In recent years, Reinforcement Learning has found great success in many areas of AI research ranging from research on self-driving cars to achieving superhuman level performance in MOBA games such as Dota 2, Starcraft (Open AI) or Chess and Go (AlphaGo Zero). I will talk about the mathematical framework of Reinforcement Learning and also briefly about its applications in computational neuroscience/psychiatry as well.