SIAM Student Chapter Seminar: Difference between revisions

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*'''When:''' Fridays at 1 PM unless noted otherwise
*'''When:''' Fridays at 1 PM unless noted otherwise
*'''Where:''' 9th floor lounge (we will also broadcast the virtual talks on the 9th floor lounge with refreshments)
*'''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], Jordan Radke, Peiyi Chen, and Yahui Qu
*'''Organizers:''' Yahui Qu, Peiyi Chen, Shi Chen and Zaidan Wu
*'''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 [mailto:siam-chapter+join@g-groups.wisc.edu siam-chapter+join@g-groups.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].
*'''Zoom link:''' https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09
*'''Zoom link:''' https://uwmadison.zoom.us/j/97976615799?pwd=U2xFSERIcnR6M1Y1czRmTjQ1bTFJQT09
*'''Passcode: 641156'''
*'''Passcode: 281031'''


<br>
== Spring 2024 ==
 
==Spring 2023++


{| class="wikitable"
{| class="wikitable"
!Date (1 PM unless otherwise noted)
|+
!Date
!Location
!Location
!Speaker
!Speaker
!Title
!Title
|-
|-
|1/30
|2/2
|[] and 911 Van Vleck                         
|VV911
|[]                       
|Thomas Chandler (UW-Madison)
|
|Fluid–body interactions in anisotropic fluids
|-
|-
|2/6
|3/8
|[] and 911 Van Vleck                         
|Ingraham 214
|[]                       
|Danyun He (Harvard)
|
|Energy-positive soaring using transient turbulent fluctuations
|-
|-
|2/13
|3/15
|[] and 911 Van Vleck                         
|VV911&Zoom
|[]                       
|Xiaoyu Dong (UMich)
|
|Approximately Hadamard matrices and Riesz bases in frames
|-
|-
|2/20
|3/22
|[] and 911 Van Vleck                         
|VV911&Zoom
|[]                       
|Mengjin Dong (UPenn)
|
|Advancing Alzheimer's Disease Research: Insights and Innovations in MRI-Based Progression Tracking
|-
|-
|2/27
|4/5
|[] and 911 Van Vleck                         
|VV911
|[]                       
|Sixu Li (UW-Madison)
|
|TBD
|-
|-
|
|4/12
|[] and 911 Van Vleck                         
|VV911&Zoom
|[]                       
|Anjali Nair (UChicago)
|
|TBD
 
|}
 
 
==Fall 2022==
{| class="wikitable"
!Date (1 PM unless otherwise noted)
!Location
!Speaker
!Title
|-
|-
|9/23
|4/19
|[https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09 Virtual] and 911 Van Vleck                         
|VV911
|[http://www-personal.umich.edu/~tganders/ Thomas Anderson] (University of Michigan)                        
|Jingyi Li (UW-Madison)
|A few words on potential theory in modern applied math
|TBD
|-
|-
|9/30 ('''11 AM''')
|5/3
|[https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09 Virtual] and 911 Van Vleck
|[https://jeffhammond.github.io/ Jeff Hammond] (Principal Engineer at [https://www.nvidia.com/en-us/ NVIDIA])
|Industry talk
|-
|10/7
|[https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09 Virtual] and 911 Van Vleck
|[https://walterbabyrudin.github.io/ Jie Wang] (Georgia Institute of Technology)
|Sinkhorn Distributionally Robust Optimization
|-
|10/14
|[https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09 Virtual] and 911 Van Vleck
|[https://you.stonybrook.edu/reutergroup/ Matt Reuter] (Stony Brook University)
|Becoming a Ghost Buster
|-
|10/19 ('''Wednesday at 4 PM)'''
|[https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09 Virtual] and 911 Van Vleck
|Ying Li
|Industry talk
|-
|10/28
|911 Van Vleck
|[https://ylzhang2447.github.io/ Yinling Zhang] (UW-Madison)
|A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization
|-
|11/4
|911 Van Vleck
|Haley Kottler (UW-Madison)
|Gaussian Mixture Model Parameter Recovery
|-
|11/11
|911 Van Vleck
|[https://sites.google.com/wisc.edu/zinanwang/ Zinan Wang] (UW-Madison)
|Encountering Singularities of a Serial Robot Along Continuous Paths at High Precision
|-
|11/18
|911 Van Vleck
|Parvathi Kooloth (UW-Madison)
|
|
|-
|Bella Finkel (UW-Madison)
|11/25
|TBD
|NO TALK
|THANKSGIVING WEEK
|
|-
|12/2
|[https://uwmadison.zoom.us/j/99844791267?pwd=eUFwM25Hc2Roc1kvSzR3N2tVVlpLQT09 Virtual] and 911 Van Vleck
|Jenny Yeon (Applied Scientist at Amazon)
|Industry talk
|}
|}


==Abstracts==
==Abstracts==
'''February 2, Thomas Chandler (UW-Madison):''' Fluid anisotropy, or direction-dependent response to deformation, can be observed in biofluids like mucus or, at a larger scale, self-aligning swarms of active bacteria. A model fluid used to investigate such environments is a nematic liquid crystal. In this talk, we will use complex variables to analytically solve for the interaction between bodies immersed in liquid crystalline environments. This approach allows for the solution of a wide range of problems, opening the door to studying the role of body geometry, liquid crystal anchoring conditions, and deformability. Shape-dependent forces between bodies, surface tractions, and analogues to classical results in fluid dynamics will also be discussed.
'''March 8, Danyun He (Harvard University):''' The ability of birds to soar in the atmosphere is a fascinating scientific problem. It relies on an interplay between the physical processes governing atmospheric flows, and the capacity of birds to process cues from their environment and learn complex navigational strategies. Previous models for soaring have primarily taken advantage of thermals of ascending hot air to gain energy. Yet, it remains unclear whether energy loss due to drag can be overcome by extracting work from transient turbulent fluctuations. In this talk, I will present a recent work that we look at the alternative scenario of a glider navigating in an idealized model of a turbulent fluid where no thermals are present. First, I will show the numerical simulations of gliders navigating in a kinematic model that captures the spatio-temporal correlations of atmospheric turbulence. Energy extraction is enabled by an adaptive algorithm based on Monte Carlo tree search that dynamically filters acquired information about the flow to plan future paths. Then, I will demonstrate that for realistic parameter choices, a glider can navigate to gain height and extract energy from flow. Glider paths reflect patterns of foraging, where exploration of the flow is interspersed with bouts of energy extraction through localized spirals. As such, this work broadens our understanding of soaring, and extends the range of scenarios where soaring is known to be possible.
'''March 15, Xiaoyu Dong (University of Michigan, Ann Arbor):''' An $n \times n$ matrix with $\pm 1$ entries which acts on $\R^n$ as a scaled isometry is called Hadamard. Such matrices exist in some, but not all dimensions. Combining number-theoretic and probabilistic tools we construct matrices with $\pm 1$ entries which act as approximate scaled isometries in $\R^n$ for all $n \in \N$. More precisely, the matrices we construct have condition numbers bounded by a constant independent of $n$.
Using this construction, we establish a phase transition for the probability that a random frame contains a Riesz basis. Namely, we show that a random frame in $\R^n$ formed by $N$ vectors with  independent identically distributed coordinate having a non-degenerate symmetric distribution contains many Riesz bases with high probability provided that $N \ge \exp(Cn)$. On the other hand, we prove that if the entries are subgaussian, then a random frame fails to contain a Riesz basis with probability close to $1$ whenever $N \le \exp(cn)$, where $c<C$ are constants depending on the distribution of the entries.
'''March 22, Mengjin Dong (University of  Pennsylvania)''': Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes primarily in the elderly population. As the most prevalent form of dementia, it impacts millions of families globally. The pathological hallmarks of AD, such as abnormal protein build-up in the brain, can manifest decades before the onset of clinical symptoms. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging (MRI) play pivotal roles in studying disease progression and elucidating its underlying mechanisms.


In this presentation, I will commence with an overview of AD fundamentals and recent research advancements. Subsequently, I will delve into my research, which utilizes deep learning techniques to longitudinally monitor and localize AD progression using MRI data.


==Past Semesters==
==Past Semesters==
*[[SIAM Fall 2023]]
*[[SIAM Spring 2023]]
*[[SIAM Seminar Fall 2022|Fall 2022]]
*[[SIAM Seminar Fall 2022|Fall 2022]]
*[[Spring 2022 SIAM|Spring 2022]]
*[[Spring 2022 SIAM|Spring 2022]]

Latest revision as of 19:28, 18 March 2024


Spring 2024

Date Location Speaker Title
2/2 VV911 Thomas Chandler (UW-Madison) Fluid–body interactions in anisotropic fluids
3/8 Ingraham 214 Danyun He (Harvard) Energy-positive soaring using transient turbulent fluctuations
3/15 VV911&Zoom Xiaoyu Dong (UMich) Approximately Hadamard matrices and Riesz bases in frames
3/22 VV911&Zoom Mengjin Dong (UPenn) Advancing Alzheimer's Disease Research: Insights and Innovations in MRI-Based Progression Tracking
4/5 VV911 Sixu Li (UW-Madison) TBD
4/12 VV911&Zoom Anjali Nair (UChicago) TBD
4/19 VV911 Jingyi Li (UW-Madison) TBD
5/3 Bella Finkel (UW-Madison) TBD

Abstracts

February 2, Thomas Chandler (UW-Madison): Fluid anisotropy, or direction-dependent response to deformation, can be observed in biofluids like mucus or, at a larger scale, self-aligning swarms of active bacteria. A model fluid used to investigate such environments is a nematic liquid crystal. In this talk, we will use complex variables to analytically solve for the interaction between bodies immersed in liquid crystalline environments. This approach allows for the solution of a wide range of problems, opening the door to studying the role of body geometry, liquid crystal anchoring conditions, and deformability. Shape-dependent forces between bodies, surface tractions, and analogues to classical results in fluid dynamics will also be discussed.

March 8, Danyun He (Harvard University): The ability of birds to soar in the atmosphere is a fascinating scientific problem. It relies on an interplay between the physical processes governing atmospheric flows, and the capacity of birds to process cues from their environment and learn complex navigational strategies. Previous models for soaring have primarily taken advantage of thermals of ascending hot air to gain energy. Yet, it remains unclear whether energy loss due to drag can be overcome by extracting work from transient turbulent fluctuations. In this talk, I will present a recent work that we look at the alternative scenario of a glider navigating in an idealized model of a turbulent fluid where no thermals are present. First, I will show the numerical simulations of gliders navigating in a kinematic model that captures the spatio-temporal correlations of atmospheric turbulence. Energy extraction is enabled by an adaptive algorithm based on Monte Carlo tree search that dynamically filters acquired information about the flow to plan future paths. Then, I will demonstrate that for realistic parameter choices, a glider can navigate to gain height and extract energy from flow. Glider paths reflect patterns of foraging, where exploration of the flow is interspersed with bouts of energy extraction through localized spirals. As such, this work broadens our understanding of soaring, and extends the range of scenarios where soaring is known to be possible.

March 15, Xiaoyu Dong (University of Michigan, Ann Arbor): An $n \times n$ matrix with $\pm 1$ entries which acts on $\R^n$ as a scaled isometry is called Hadamard. Such matrices exist in some, but not all dimensions. Combining number-theoretic and probabilistic tools we construct matrices with $\pm 1$ entries which act as approximate scaled isometries in $\R^n$ for all $n \in \N$. More precisely, the matrices we construct have condition numbers bounded by a constant independent of $n$.

Using this construction, we establish a phase transition for the probability that a random frame contains a Riesz basis. Namely, we show that a random frame in $\R^n$ formed by $N$ vectors with  independent identically distributed coordinate having a non-degenerate symmetric distribution contains many Riesz bases with high probability provided that $N \ge \exp(Cn)$. On the other hand, we prove that if the entries are subgaussian, then a random frame fails to contain a Riesz basis with probability close to $1$ whenever $N \le \exp(cn)$, where $c<C$ are constants depending on the distribution of the entries.


March 22, Mengjin Dong (University of Pennsylvania): Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes primarily in the elderly population. As the most prevalent form of dementia, it impacts millions of families globally. The pathological hallmarks of AD, such as abnormal protein build-up in the brain, can manifest decades before the onset of clinical symptoms. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging (MRI) play pivotal roles in studying disease progression and elucidating its underlying mechanisms.

In this presentation, I will commence with an overview of AD fundamentals and recent research advancements. Subsequently, I will delve into my research, which utilizes deep learning techniques to longitudinally monitor and localize AD progression using MRI data.

Past Semesters