Difference between revisions of "SIAM Student Chapter Seminar"

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__NOTOC__
 
__NOTOC__
  
*'''When:''' Most Friday at 11:30am
+
*'''When:''' Mondays at 3:30 PM
*'''Where:''' 901 Van Vleck Hall
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*'''Where:''' 9th floor lounge (we will also broadcast the virtual talks on the 9th floor lounge with refreshments)
*'''Organizers:''' [http://www.math.wisc.edu/~xshen/ Xiao Shen]
+
*'''Organizers:''' [https://sites.google.com/wisc.edu/evan-sorensen Evan Sorensen]
 
*'''Faculty advisers:''' [http://www.math.wisc.edu/~jeanluc/ Jean-Luc Thiffeault], [http://pages.cs.wisc.edu/~swright/ Steve Wright]  
 
*'''Faculty advisers:''' [http://www.math.wisc.edu/~jeanluc/ Jean-Luc Thiffeault], [http://pages.cs.wisc.edu/~swright/ Steve Wright]  
*'''To join the SIAM Chapter mailing list:''' email [join-siam-chapter@lists.wisc.edu].
+
*'''To join the SIAM Chapter mailing list:''' email [mailto:siam-chapter+join@g-groups.wisc.edu siam-chapter+join@g-groups.wisc.edu].
  
 
<br>
 
<br>
  
== Fall 2019  ==
+
==Spring 2022==
  
 
{| cellpadding="8"
 
{| cellpadding="8"
!align="left" | date
+
!align="left" | date and time
 +
!align="left" | location
 
!align="left" | speaker
 
!align="left" | speaker
 
!align="left" | title
 
!align="left" | title
 
|-
 
|-
|Sept. 27, Oct. 4  
+
| Feb 7, 3:30-4 PM
|[http://www.math.wisc.edu/~xshen/ Xiao Shen] (Math)
+
| Virtual [https://meet.google.com/gfs-yjbq-dmv/ (link)]
|''[[#Sep 27, Oct 4: Xiao Shen (Math)|The corner growth model]]''
+
| Keith Rush (Senior Software Engineer at [https://www.google.com/ Google])
 +
|''[[#Feb 7, Keith Rush |Industry talk]]''
 
|-
 
|-
|Oct. 11
 
|''no seminar''
 
|
 
 
|-
 
|-
 
|-
 
|-
|Oct. 18
+
| Feb 14, 3:30-4 PM
|[https://scholar.google.com/citations?user=7cVl9IkAAAAJ&hl=en Bhumesh Kumar] (EE)
+
| Virtual [https://uwmadison.zoom.us/j/91217562664?pwd=SGZOS3JGaFVGa250NXhDZlkrbWU3dz09/ (link)] Passcode: 400453
|''[[#Oct 18: Bhumesh Kumar (EE)|Non-stationary Stochastic Approximation]]''
+
| [https://www.linkedin.com/in/shawnmittal/ Shawn Mittal] (Senior Deliver Data Scientist at [https://www.microsoft.com/en-us/?ql=5/ Microsoft])  
|
+
|''[[#Feb 14, Shawn Mittal |Who, What, Why of Data Science in Industry]]''
 
|-
 
|-
 
|-
 
|-
|Oct. 25
 
|Max Bacharach (Math)
 
|''[[#Oct 25:|Coalescent with Recombination]]''
 
 
|-
 
|-
 +
| Feb 21, 3:30-4 PM
 +
| 9th floor lounge
 +
| Brandon Boggess [https://www.epic.com/ (Epic)]
 +
|''[[#Feb 21, Brandon Boggess |Industry talk]]''
 
|-
 
|-
|Nov. 1
 
|''no seminar''
 
|
 
 
|-
 
|-
 
|-
 
|-
|Nov. 8
+
| Feb 28, 3:30-4 PM
|
+
| 9th floor lounge
|
+
| [https://www.linkedin.com/in/shi-chen-98b7431a0/?originalSubdomain=cn/ Shi Chen] (UW-Madison)
 +
|''[[#Feb 28, Shi Chen| Classical limits of direct and inverse wave type problems -- a Wigner transform approach]]''
 +
|-
 +
|-
 +
|-
 +
| Mar 7, 3:30-4 PM
 +
| Virtual [https://uwmadison.zoom.us/j/91217562664?pwd=SGZOS3JGaFVGa250NXhDZlkrbWU3dz09/ (link)] Passcode: 400453
 +
| Tom Edwards (Software Engineer at [https://www.google.com/ Google])
 +
|''[[#Mar 7, Tom Edwards| Industry talk]]''
 +
|-
 +
|-
 +
|-
 +
| Mar 21, 3:30-4 PM
 +
| 9th floor lounge
 +
| Aidan Howells (UW-Madison)
 +
|''[[#Mar 21, Aidan Howells| A Gentle Introduction to Chemical Reaction Network Theory]]''
 +
|-
 +
|-
 +
|-
 +
| Apr 4, 3:30-4 PM
 +
| 9th floor lounge
 +
| Eza Enkhtaivan (UW-Madison)
 +
|''[[#Apr 4, Eza Enkhtaivan| Reinforcement Learning and Markov Decision Processes]]''
 +
|-
 +
|-
 +
|-
 +
| Apr 11, 3:30-4 PM
 +
| Virtual [https://uwmadison.zoom.us/j/91217562664?pwd=SGZOS3JGaFVGa250NXhDZlkrbWU3dz09/ (link)] Passcode: 400453
 +
| [https://www.linkedin.com/in/micky-soule-steinberg-5361a270/ Micky Steinberg] (Data Analyst at [https://www.principiaanalytics.com/ Principia Analytics])
 +
|''[[#Apr 11, Micky Steinberg| Industry talk]]''
 +
|-
 +
|-
 
|}
 
|}
 +
  
 
== Abstracts ==
 
== Abstracts ==
  
=== Sep 27, Oct 4: Xiao Shen (Math) ===
+
=== Feb 7, Keith Rush ===
'''The corner growth model'''
+
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.
  
Imagine there is an arbitrary amount of donuts attached to the integer points of Z^2. The goal is to pick an optimal up-right path which allows you to eat as much donuts as possible along the way. We will look at some basic combinatorial observations, and how specific probability distribution would help us to study this model.
+
=== 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.
  
=== Oct 18: Bhumesh Kumar (EE) ===
+
=== Mar 7, Tom Edwards ===
'''Non-stationary Stochastic Approximation'''
+
I will talk about comparisons between small and big companies.
  
Abstract: Robbins–Monro pioneered a general framework for stochastic approximation to find roots of a function with just noisy evaluations.With applications in optimization, signal processing and control theory there is resurged interest in time-varying aka non-stationary functions. This works addresses that premise by providing explicit, all time, non-asymptotic tracking error bounds via Alekseev's nonlinear variations of constant formula.  
+
=== 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.
  
Reference: https://arxiv.org/abs/1802.07759 (To appear in Mathematics of Control, Signals and Systems)
+
=== 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.
  
=== Oct 25: Max Bacharach (Math) ===
+
=== Apr 11, Micky Steinberg ===
'''Coalescent with Recombination'''
+
I will talk about a what a typical work day looks like for me, and some advice for getting a similar job coming from academia.
  
I will talk about the continuous time coalescent with mutation and recombination, with a focus on introducing key concepts related to genetic distance and evolutionary relatedness. The talk will be informal and accessible.
 
  
<br>
+
== Past Semesters ==
 +
*[[SIAM Student Chapter Seminar/Fall2021|Fall 2021]]
 +
*[[SIAM_Student_Chapter_Seminar/Fall2020|Fall 2020]]
 +
*[[SIAM_Student_Chapter_Seminar/Spring2020|Spring 2020]]
 +
*[[SIAM_Student_Chapter_Seminar/Fall2019|Fall 2019]]
 +
*[[SIAM_Student_Chapter_Seminar/Fall2018|Fall 2018]]
 +
*[[SIAM_Student_Chapter_Seminar/Spring2017|Spring 2017]]

Latest revision as of 20:14, 9 April 2022



Spring 2022

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


Abstracts

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.

Apr 11, Micky Steinberg

I will talk about a what a typical work day looks like for me, and some advice for getting a similar job coming from academia.


Past Semesters