SIAM Student Chapter Seminar/Fall2019: Difference between revisions

Revision as of 05:09, 18 September 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.

@@ Line 1: / Line 1: @@
 __NOTOC__
-*'''When:''' Most Friday at 11:30am
+*'''When:''' Every other Friday at 1:30 pm
-*'''Where:''' 901 Van Vleck Hall
+*'''Where:''' B333 Van Vleck Hall
 *'''Organizers:''' [http://www.math.wisc.edu/~xshen/ Xiao Shen]
 *'''Faculty advisers:''' [http://www.math.wisc.edu/~jeanluc/ Jean-Luc Thiffeault], [http://pages.cs.wisc.edu/~swright/ Steve Wright]
@@ Line 9: / Line 9: @@
 <br>
-== Fall 2019  ==
+== Spring 2020  ==
 {| cellpadding="8"
@@ Line 16: / Line 16: @@
 !align="left" | title
 |-
-|Sept. 27, Oct. 4
+|Jan 31
-|[http://www.math.wisc.edu/~xshen/ Xiao Shen] (Math)
+|[https://lorenzonajt.github.io/ Lorenzo Najt] (Math)
-|''[[#Sep 27, Oct 4: Xiao Shen (Math)|The corner growth model]]''
+|''[[#Jan 31, Lorenzo Najt (Math)|Ensemble methods for measuring gerrymandering: Algorithmic problems and inferential challenges]]''
 |-
 |-
-|Oct. 18
+|Feb 14
-|[https://scholar.google.com/citations?user=7cVl9IkAAAAJ&hl=en Bhumesh Kumar] (EE)
+|[https://www.math.wisc.edu/~pollyyu/ Polly Yu] (Math)
-|''[[#Oct 18: Bhumesh Kumar (EE)|Non-stationary Stochastic Approximation]]''
+|''[[#Feb 14, Polly Yu (Math)|Algebra, Dynamics, and Chemistry with Delay Differential Equations]]''
-|
 |-
 |-
-|Oct. 25
+|Feb 21
-|Max (Math)
+|Gage Bonner (Physics)
-|''[[#Oct 25: Max (Math)|Coalescent with Recombination]]''
+|''[[#Feb 21, Gage Bonner (Physics)|Growth of history-dependent random sequences]]''
-|
-|-
-|-
-|Nov. 8
-|Hongfei Chen (Math)
-|''[[#Nov 15: Hongfei Chen (Math)| Brownian swimmers in a channel]]''
-|
-|-
-|-
-|Dec. 10
-|[http://www.maths.manchester.ac.uk/~higham/ Nicholas J. Higham] (University of Manchester)
-|''[[#Dec 10: Nicholas J. Higham  (University of Manchester)|Scientific Writing]]''
 |-
 |-
@@ Line 49: / Line 36: @@
 == Abstracts ==
-=== Sep 27, Oct 4: Xiao Shen (Math) ===
+=== Jan 31, Lorenzo Najt (Math) ===
-'''The corner growth model'''
+'''Ensemble methods for measuring gerrymandering: Algorithmic problems and inferential challenges'''
-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.
-=== Oct 18: Bhumesh Kumar (EE) ===
-'''Non-stationary Stochastic Approximation'''
-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.
-Reference: https://arxiv.org/abs/1802.07759 (To appear in Mathematics of Control, Signals and Systems)
-=== Oct 25: Max (Math) ===
-'''Coalescent with Recombination'''
-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.
-=== Nov 15: Hongfei Chen (Math) ===
-'''Brownian swimmers in a channel'''
-Abstract: Shape matters! I will talk about how their shapes affect their mean reversal time.
-=== Dec 10: Nicholas J. Higham (University of Manchester) ===
-'''Scientific Writing'''
-I will discuss various aspects of scientific writing, including
-• the craft of writing in general,
+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
-• aspects specific to mathematical writing,
+=== Feb 14, Polly Yu (Math) ===
+'''Algebra, Dynamics, and Chemistry with Delay Differential Equations'''
-• English Usage,
+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.
-• workflow, and
-• revising drafts and proofreading.
+=== Feb 21, Gage Bonner (Physics) ===
+''' Growth of history-dependent random sequences'''
-Plenty of examples and links to further information will be given. I will also discuss
+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.
-my experiences in preparing ''Handbook of Writing for the Mathematical Sciences'' (third
-edition, SIAM, 2020).
 <br>