SIAM Student Chapter Seminar: Difference between revisions

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*'''When:''' Mondays at 4 PM
*'''When:''' Mondays at 3:30 PM
*'''Where:''' See list of talks below
*'''Where:''' 9th floor lounge (we will also broadcast the virtual talks on the 9th floor lounge with refreshments)
*'''Organizers:''' [https://sites.google.com/wisc.edu/evan-sorensen Evan Sorensen]
*'''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]  
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== Fall 2021  ==
==Spring 2022==


{| cellpadding="8"
{| cellpadding="8"
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!align="left" | title
!align="left" | title
|-
|-
| Sept 20, 4 PM
| Feb 7, 3:30-4 PM
| Ingraham 214
| Virtual [https://meet.google.com/gfs-yjbq-dmv/ (link)]
| [https://sites.google.com/view/julialindberg/home/ Julia Lindberg] (Electrical and Computer Engineering)
| Keith Rush (Senior Software Engineer at [https://www.google.com/ Google])
|''[[#Sept 20, Julia Lindberg |Polynomial system solving in applications]]''
|''[[#Feb 7, Keith Rush |Industry talk]]''
|-
|-
|-
|-
| Sept 27, 4 PM,
| Zoom (refreshments and conference call in 307)
| Wil Cocke (Developer for [https://www.arcyber.army.mil/ ARCYBER])
| ''[[#Sept 27, Wil Cocke |Job talk-Software Development/Data Science]]''
|
|-
|-
| Feb 14, 3:30-4 PM
| Virtual [https://uwmadison.zoom.us/j/91217562664?pwd=SGZOS3JGaFVGa250NXhDZlkrbWU3dz09/ (link)] Passcode: 400453
| [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 4, '''2:45 PM'''
| B119 Van Vleck
| [https://sites.google.com/wisc.edu/nair-anjali/home/ Anjali Nair] (Math)
| ''[[#Oct 4, Anjali Nair|Reconstruction of Reflection Coefficients Using the Phonon Transport Equation]]''
|
|-
|-
|-
|-
| Oct 18, 4 PM
| Feb 21, 3:30-4 PM
| 6104 Social Sciences
| 9th floor lounge
| [https://jasonltorchinsky.github.io/ Jason Tochinsky] (Math)
| Brandon Boggess [https://www.epic.com/ (Epic)]
| ''[[#Oct 18, Jason Torchinsky|Improving the Vertical Remapping Algorithm in the Department of Energy’s Energy Exascale Earth Systems Model]]''
|''[[#Feb 21, Brandon Boggess |Industry talk]]''
|
|-
|-
|-
|-
| Oct 25, 4 PM,
|-
| Zoom (refreshments and conference call in 9th floor lounge)
| Feb 28, 3:30-4 PM
| [https://www.linkedin.com/in/patricktbardsley/ Patrick Bardsley] (Senior Machine Learning Engineer at [https://www.cirrus.com/ Cirrus Logic])
| 9th floor lounge
| ''[[#Oct 25, Patrick Bardsley|Job Talk-Machine Learning]]''
| [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]]''
|-
|-
|-
|-
| Nov 8, 4 PM,
| Zoom (refreshments and conference call in 9th floor lounge)
| [https://www.linkedin.com/in/libanmohamed496/ Liban Mohamed] (Machine Learning Engineer at [https://www.mitre.org/ MITRE])
| ''[[#Nov 8, Liban Mohamed|Job Talk-Machine Learning]]''
|
|-
|-
| 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]]''
|-
|-
| Nov 15, 4 PM,
| Zoom (refreshments and conference call in 9th floor lounge)
| [https://www.linkedin.com/in/kurt-ehlert-320b8397/ Kurt Ehlert] (Trading Strategy Developer at [https://auros.global/about/ Auros])
| ''[[#Nov 15, Kurt Ehlert|Job Talk-Cryptocurrency Trading]]''
|
|-
|-
|-
|-
| Nov 29, 4 PM
| Mar 21, 3:30-4 PM
| 9th floor lounge
| 9th floor lounge
| [https://people.math.wisc.edu/~boakley/ Bryan Oakley] (Math)
| Aidan Howells (UW-Madison)
| ''[[#Nov 29, Bryan Oakley|Optimal Spatially Dependent Diffusion]]''
|''[[#Mar 21, Aidan Howells| A Gentle Introduction to Chemical Reaction Network Theory]]''
|
|-
|-
|-
|-
|-
| Dec 6, 4 PM
| Apr 4, 3:30-4 PM
| 9th floor lounge
| 9th floor lounge
| [https://sites.google.com/view/hongxuchen/ Hongxu Chen] (Math)
| Eza Enkhtaivan (UW-Madison)
| ''[[#Dec 6, Hongxu Chen|Boltzmann equation with Cercignani-Lampis boundary]]''
|''[[#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 ==


=== Sept 20, Julia Lindberg===
=== Feb 7, Keith Rush ===
Polynomial systems arise naturally in many applications in engineering and the sciences. This talk will outline classes of homotopy continuation algorithms used to solve them. I will then describe ways in which structures such as irreducibility, symmetry and sparsity can be used to improve computational speed. The efficacy of these algorithms will be demonstrated on systems in power systems engineering, statistics and optimization
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.


=== Sept 27, Wil Cocke ===
=== Feb 21,Brandon Boggess ===
I mostly work as a software developer with an emphasis on data science projects dealing with various Command specific projects. The data science life-cycle is fairly consistent across industries: collect, clean, explore, model, interpret, and repeat with a goal of providing insight to the organization. During my talk, I will share some lessons learned for mathematicians interested in transitioning to software development/ data science.  
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.


=== Oct 4, Anjali Nair ===
=== Mar 7, Tom Edwards ===
The phonon transport equation is used to model heat conduction in solid materials. I will talk about how we use it to solve an inverse problem to reconstruct the thermal reflection coefficient at an interface. This takes the framework of a PDE constrained optimization problem, and I will also mention the stochastic methods used to solve it.
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.


=== Oct 18, Jason Torchinsky ===
=== Apr 4, Eza Enkhtaivan ===
A vertical Lagrangian coordinate has been used in global climate models for nearly two decades and has several advantages over other discretizations, including reducing the dimensionality of the physical problem. As the Lagrangian surfaces deform over time, it is necessary to accurately and conservatively remap the vertical Lagrangian coordinate back to a fixed Eulerian coordinate. A popular choice of remapping algorithm is the piecewise parabolic method, a modified version of which is used in the atmospheric component of the Department of Energy's Energy Exascale Earth System Model. However, this version of the remapping algorithm creates unwanted noise at the model top and planetary surface for several standard test cases. We explore four alternative modifications to the algorithm and show that the most accurate of these eliminates this noise.  
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.


=== Oct 25, Patrick Bardsley ===
During the course of a PhD, students typically enter a proverbial `coal mine’ to extract new information about one or more problems, and in the process become a domain expert in a small niche of the technical and scientific world. Upon leaving the academy, unless one lands a job in their niche domain, much of their problem- and domain-specific knowledge is no longer essential. However, mathematics is broad and general, arguably the most general of all scientific disciplines. This fact alone is a mathematician’s greatest asset and ‘leg-up’ when entering the industrial workforce. In this talk, I will discuss some details of my work, both inside and outside of the academy, with the goal of highlighting the skills and concepts that have been the most general and transferable for me. For example, my academic work on hyperbolic inverse problems helped me learn signal processing concepts I now use daily, while my studies on polycrystalline grain growth pushed me to learn thermodynamics, which translated well to the information theory concepts I now utilize. I will also give you some idea of my current day-to-day responsibilities, and close with my thoughts and suggestions on job searches.
=== Nov 8, Liban Mohamed ===
I work as a researcher at MITRE, a company that manages R&D contracts (FFRDCs) with federal agencies. I am nominally a machine learning engineer, but my department supports a diverse array of initiatives with the IRS. In this talk I'll give an overview of the FFRDC space, give a sketch of what I work on and how I spend my time, and share my thoughts about navigating the transition from academia to industry.
=== Nov 15, Kurt Ehlert ===
After graduating from the UW, I ventured into the world of trading. My first job was at Virtu, a high-frequency market-maker, and currently I work at Auros, which is a high-frequency trading firm that focuses on cryptocurrencies. During the talk, I will give an overview of the industry, job market, and interview process from the perspective of a "quant". Then I will describe the day-to-day work and give a high-level description of typical projects.
=== Nov 29, Bryan Oakley ===
The solution to the diffusion equation is known to converge exponentially to its steady state, and the rate is given by the spectral gap of the elliptic operator. Using variational techniques, we will maximize the spectral gap over choices of spatially dependent diffusion functions. Using this characterization, we can obtain bounds on the optimal rate of convergence.
=== Dec 6, Hongxu Chen ===
Boltzmann equation is a fundamental kinetic equation that describes the dynamics of dilute gas. In this talk I will focus on the boundary value problem of the Boltzmann equation and introduce the Cercignani-Lampis boundary, which is a physical boundary that describes the intermediate reflection law between diffuse reflection and specular reflection.
<br>


== Past Semesters ==
== Past Semesters ==
*[[SIAM Student Chapter Seminar/Fall2021|Fall 2021]]
*[[SIAM_Student_Chapter_Seminar/Fall2020|Fall 2020]]
*[[SIAM_Student_Chapter_Seminar/Fall2020|Fall 2020]]
*[[SIAM_Student_Chapter_Seminar/Spring2020|Spring 2020]]
*[[SIAM_Student_Chapter_Seminar/Spring2020|Spring 2020]]

Revision as of 01:14, 10 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