Difference between revisions of "SIAM Student Chapter Seminar"

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__NOTOC__
 
__NOTOC__
  
 
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*'''When:''' Every other Friday at 1:30 pm
 
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*'''Where:''' B333 Van Vleck Hall
*'''When:''' Every Other Wednesday at 2:15 pm (except as otherwise indicated)
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*'''Organizers:''' [http://www.math.wisc.edu/~xshen/ Xiao Shen]
*'''Where:''' 901 Van Vleck Hall
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*'''Faculty advisers:''' [http://www.math.wisc.edu/~jeanluc/ Jean-Luc Thiffeault], [http://pages.cs.wisc.edu/~swright/ Steve Wright]  
*'''Organizers:''' [http://www.math.wisc.edu/~ke/ Ke Chen]  
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*'''To join the SIAM Chapter mailing list:''' email [join-siam-chapter@lists.wisc.edu].
*'''To join the SIAM Chapter mailing list:''' email [join-siam-chapter@lists.wisc.edu] website.
 
  
 
<br>
 
<br>
  
 
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== Spring 2020 ==
== Fall 2018 ==
 
  
 
{| cellpadding="8"
 
{| cellpadding="8"
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!align="left" | title
 
!align="left" | title
 
|-
 
|-
| Sept. 12
+
|Jan 31
|[http://www.math.wisc.edu/~ke/ Ke Chen] (Math)
+
|[https://lorenzonajt.github.io/ Lorenzo Najt] (Math)
|''[[#Sep 12: Ke Chen (Math)|Inverse Problem in Optical Tomography]]''
+
|''[[#Jan 31, Lorenzo Najt (Math)|Ensemble methods for measuring gerrymandering: Algorithmic problems and inferential challenges]]''
|-
 
| Spet. 26 
 
|[http://www.math.wisc.edu/~kehlert/ Kurt Ehlert] (Math)
 
|''[[#Sept 26: Kurt Ehlert (Math)|  How to bet when gambling]]''
 
 
|-
 
|-
| Oct. 10 
 
|[http://TBD Zachary Hansen] (Atmospheric and Oceanic Sciences)
 
|''[[#Oct 10: Zachary Hansen (Atmospheric and Oceanic Sciences)|  Land-Ocean contrast in lightning  ]]''
 
 
|-
 
|-
| Oct. 24 
+
|Feb 14
|[http://TBD Xuezhou Zhang] (Computer Science)
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|[https://www.math.wisc.edu/~pollyyu/ Polly Yu] (Math)
|''[[#Oct 24: Xuezhou Zhang (Computer Science)| An Optimal Control Approach to Sequential Machine Teaching  ]]''
+
|''[[#Feb 14, Polly Yu (Math)|Algebra, Dynamics, and Chemistry with Delay Differential Equations]]''
 
|-
 
|-
| Nov. 7
 
|[http://TBD Cancelled]
 
|''[[#Nov 7: Cancelled|  ]]''
 
 
|-
 
|-
| Nov. 21  
+
|Feb 21
|[http://TBD Cancelled due to Thanksgiving]
+
|Gage Bonner (Physics)
|''[[#Nov 21: Cancelled| ]]''
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|''[[#Feb 21, Gage Bonner (Physics)|Growth of history-dependent random sequences]]''
 
|-
 
|-
| Nov. 28
 
|[http://TBD Xiaowu Dai] (Statistics)
 
|''[[#Nov 28: Xiaowu Dai (Statistics)| TBD  ]]''
 
 
|-
 
|-
 
|
 
|
 
|}
 
|}
  
 +
== Abstracts ==
  
== Abstract ==
+
=== Jan 31, Lorenzo Najt (Math) ===
 +
'''Ensemble methods for measuring gerrymandering: Algorithmic problems and inferential challenges'''
  
=== Sep 12: Ke Chen (Math) ===
+
We will review some recent work regarding measuring gerrymandering by sampling from the space of maps, including two methods used in a recent amicus brief to the supreme court. This discussion will highlight some of the computational challenges of this approach, including some complexity-theory lower bounds and bottlenecks in Markov chains. We will examine the robustness of these statistical methods through their connection to phase transitions in the self-avoiding walk model, as well as their dependence on artifacts of discretization. This talk is largely based on https://arxiv.org/abs/1908.08881
Inverse Problem in Optical Tomography
 
  
I will briefly talk about my researches on the inverse problems of radiative transfer equations, which is usually used as a model to describe the transport of neutrons or other particles in a certain media. Such inverse problems considers the following question: given the knowledge of multiple data collected at the boundary of the domain of interest, is it possible to reconstruct the optical property of the interior of media? In this talk, I will show you that stability of this problem is deteriorating as the Knudsen number is getter smaller. The talk will be introductory and anyone graduate is welcome to join us.
+
=== Feb 14, Polly Yu (Math) ===
 +
'''Algebra, Dynamics, and Chemistry with Delay Differential Equations'''
  
=== Sept 26: Kurt Ehlert (Math) ===
+
Delay differential equations (DDEs) can exhibit more complicated behavior than their ODE counterparts. What is stable in the ODE setting could exhibit oscillation in DDE. Where do delay equations show up anyway? In this talk, we’ll introduce DDEs, and how (sort-of-)linear algebra gives information about the stability of DDEs.
How to bet when gambling
 
  
When gambling, typically casinos have an edge. But sometimes we can gain an edge by counting cards or other means. And sometimes we have an edge in the biggest casino of all: the financial markets. When we do have an advantage, then we still need to decide how much to bet. Bet too little, and we leave money on the table. Bet too much, and we risk financial ruin. We will discuss the "Kelly criterion", which is a betting strategy that is optimal in many senses.
 
  
=== Oct 10: Zachary Hansen (Atmospheric and Oceanic Sciences) ===
+
=== Feb 21, Gage Bonner (Physics) ===
Land-Ocean contrast in lightning
+
''' Growth of history-dependent random sequences'''
  
Land surfaces have orders of magnitude more lightning flashes than ocean surfaces. One explanation for this difference is that land surfaces may generate greater convective available potential energy (CAPE), which fuels stronger thunderstorms. Using a high resolution cloud-resolving atmospheric model, we test whether an island can produce stronger thunderstorms just by having a land-like surface. We find that the island alters the distribution of rainfall but does not produce stronger storms. An equilibrium state known as boundary layer quasi-equilibrium follows, and is explored in more detail.
+
Unlike discrete Markov chains, history-dependent random sequences are sequences of random variables whose "next" term depends on all others seen previously. For this reason, they can be difficult to analyze. I will discuss some simple and fun cases where the long-term behavior of the sequence can be computed explicitly in expectation.
  
=== Oct 24: Xuezhou Zhang (Computer Science) ===
 
An Optimal Control Approach to Sequential Machine Teaching
 
  
Given a sequential learning algorithm and a target model, sequential machine teaching aims to find the shortest training sequence to drive the learning algorithm to the target model. We present the first principled way to find such shortest training sequences. Our key insight is to formulate sequential machine teaching as a time-optimal control problem. This allows us to solve sequential teaching by leveraging key theoretical and computational tools developed over the past 60 years in the optimal control community. Specifically, we study the Pontryagin Maximum Principle, which yields a necessary condition for opti- mality of a training sequence. We present analytic, structural, and numerical implica- tions of this approach on a case study with a least-squares loss function and gradient de- scent learner. We compute optimal train- ing sequences for this problem, and although the sequences seem circuitous, we find that they can vastly outperform the best available heuristics for generating training sequences.
+
<br>
  
=== Nov 7: Cancelled ===
+
== Past Semesters ==
 
+
*[[SIAM_Student_Chapter_Seminar/Fall2019|Fall 2019]]
=== Nov 21: Cancelled ===
+
*[[SIAM_Student_Chapter_Seminar/Fall2018|Fall 2018]]
 
+
*[[SIAM_Student_Chapter_Seminar/Spring2017|Spring 2017]]
=== Nov 28: Xiaowu Dai (Statistics) ===
 
TBD
 
 
 
TBD
 
 
 
 
 
<br>
 

Latest revision as of 12:47, 20 February 2020


  • When: Every other Friday at 1:30 pm
  • Where: B333 Van Vleck Hall
  • Organizers: Xiao Shen
  • Faculty advisers: Jean-Luc Thiffeault, Steve Wright
  • To join the SIAM Chapter mailing list: email [join-siam-chapter@lists.wisc.edu].


Spring 2020

date speaker title
Jan 31 Lorenzo Najt (Math) Ensemble methods for measuring gerrymandering: Algorithmic problems and inferential challenges
Feb 14 Polly Yu (Math) Algebra, Dynamics, and Chemistry with Delay Differential Equations
Feb 21 Gage Bonner (Physics) Growth of history-dependent random sequences

Abstracts

Jan 31, Lorenzo Najt (Math)

Ensemble methods for measuring gerrymandering: Algorithmic problems and inferential challenges

We will review some recent work regarding measuring gerrymandering by sampling from the space of maps, including two methods used in a recent amicus brief to the supreme court. This discussion will highlight some of the computational challenges of this approach, including some complexity-theory lower bounds and bottlenecks in Markov chains. We will examine the robustness of these statistical methods through their connection to phase transitions in the self-avoiding walk model, as well as their dependence on artifacts of discretization. This talk is largely based on https://arxiv.org/abs/1908.08881

Feb 14, Polly Yu (Math)

Algebra, Dynamics, and Chemistry with Delay Differential Equations

Delay differential equations (DDEs) can exhibit more complicated behavior than their ODE counterparts. What is stable in the ODE setting could exhibit oscillation in DDE. Where do delay equations show up anyway? In this talk, we’ll introduce DDEs, and how (sort-of-)linear algebra gives information about the stability of DDEs.


Feb 21, Gage Bonner (Physics)

Growth of history-dependent random sequences

Unlike discrete Markov chains, history-dependent random sequences are sequences of random variables whose "next" term depends on all others seen previously. For this reason, they can be difficult to analyze. I will discuss some simple and fun cases where the long-term behavior of the sequence can be computed explicitly in expectation.



Past Semesters