Seminars

MAT/STP 591 Topic: Data-Oriented Mathematical and Statistical Sciences

Schedule: Mondays 1:30 - 2:30pm (see listing for location and details)

Description: This seminar series is part of the NSF-RTG Data-Oriented Mathematical and Statistical Sciences. Seminar speakers will include ASU faculty and post-docs, outside visitors, and students. The RTG seminar will focus on both research and professional development. Topics of interest include mathematical and statistical challenges related to data problems that have emerged in recent years.

The seminar is open to all ASU students and faculty. In addition, students may register for 1 credit hour (pass/fail) or 3 credit hours (standard grading). Students registering for 1 credit must attend all talks. Students registering for 3 credits must attend all talks and present two regular length seminar talks on pre-approved topics (or two parts of the same topic). Under special circumstances, the course instructor may propose a different set of requirements. RTG fellows are required to register for three credit hours.

Prerequisite: Degree- or nondegree-seeking graduate student. Registration for three credit hours requires instructor approval.

Spring 2021

April 12

Huan Liu, ASU CIDSE
Some Unique Problems with Social Media Data for AI and Data Science

https://asu.zoom.us/rec/share/Ye0HfZUTKc5Qn-MvN6aUFidUsgZihUmvMKqaHG34yU9g2S581oBpV0GZg7XbfFLh.yF-AJIYuywMig9qS
Passcode: ^IM2D%Jv

April 5

  1. Alex Reust, ASU SoMSS

Bayesian Regularized Linear Models

  1. Henrique Cheng, ASU SoMSS

Exploring Seasonal Data Registration and Its Effect on Overall Trends

https://asu.zoom.us/rec/share/fINdVLwj2aUesmIsCVlu5w87xea1S4wL4hYJ2ByNDQLk6tSV8ByKMp4Drh_ULiqA._MrFxbOoOq-A9-0x
Passcode: =WW2C0zd

March 29

Lalitha Sankar, ASU ECEE
Alpha-loss: A Tunable Class of Loss Functions for Robust Learning

https://asu.zoom.us/rec/share/c82_N-ehyrok96Opz0n35eeSesK9-PO-Giy1Azb4XkE4a9LnNAtaHj4kiY7uDech.wlyctsyqZgO1qXX2
Passcode: VSG7z%&L

March 22

Deb Agarwal, LBNL
Enabling Science Through Data Science at Berkeley Lab

https://drive.google.com/file/d/1JJiCCAvrknjA-tvcp7suWqOJa7g9N4Zu/view?ts=6063487e

March 15

Lisa Claus, LBNL
Non-overlapping block smoothers within geometric multigrid methods for the solution of the Stokes equations

https://asu.zoom.us/rec/share/b-kmG4KWlmWV9a5aocZRKZ49CjJGncvfZee3TaWfCsststgCgCVU0MVRfJn0BuM.Cz-S6k-rjM_QCKAJ
Passcode: *tE4BSW&

March 8

Suren Jayasuriya, ASU School of Arts, Media and Engineering
Computational Light Transport: Acquisition and Algorithms

https://asu.zoom.us/rec/share/ZsnI-9s4ABcZImW-BSJpQxjqYbzcAGxcIkLPLoxCheDleI_uIfdPoCi6QfN2eyes.s3DYpBwiHOEBSlzr
Passcode: &Rk9#TWP

March 1

Rodrigo Platte, ASU SoMSS
Node generation for high-order approximation and algorithms for subsample selection

https://asu.zoom.us/rec/share/eFBM-JadfgdaC-YwmJJ8BdLoB37iXvwZncbwp0ygvIwJm4qUyAZmQIha5-1N7ygG.scObUTBItHyUJzgx
Passcode: +hH4wE^E

February 22

Ana Kupresanin, Lawrence Livermore Laboratory
My Journey to a National Lab: A Perspective on Data Science

(Not recorded)

February 15

Maria Han Veiga, University of Michigan
Towards an arbitrary high-order method for the induction equation

https://asu.zoom.us/rec/share/JiiAeJCFRpql9DHYxcUjItMRtGGlq-HTXCQT6OBSCPW_yYmNis061L4wKKr5mNEv.cha4R39OtoqQXmvj
Passcode: pQ5.!=7j

February 8

Michael Wakin, Colorado School of Mines
Stable embeddings of manifold models: Dimensionality reduction for signals and systems

https://asu.zoom.us/rec/share/NQEKZyhWvojcfKcikC2R0-VV1gw5Igqbiky1klfnI0z1tpm4a94WtlLO_4cRMaKP.Qryahh6gEM9JSKCX
Passcode: !yn09I#+

February 1

The underlying topology of data
Jose Perea, Michigan State University

Link to stream a similar talk by Prof. Perea:
https://www.youtube.com/watch?v=_6Mdp0qNnG0&feature=youtu.be

January 25

The illusion of the illusion of sparsity
Hedibert Freitas Lopes, INSPER and ASU SoMSS

https://asu.zoom.us/rec/share/cF5ar4A_CXHp7Em3_t4vR3T1QurG0XP5NrRk-4ZpZnef4XTgcs5GcHL_yrVNuQx8.a4cdSOVJiXCQbfcK
Passcode: Xlqw2#+X

January 11

Geometric and topological methods in data analysis
Doug Cochran, ASU SoMSS

https://asu.zoom.us/rec/share/Ic0aelrkhWooLdvihOIckl2aQIUFTRKGkfoLS5K6bICCqF5R57R5gEHoty6DwZl_.l88a_mKjvYB-lIUp
Passcode: ?25h2oPn

Fall 2020

November 30

  1. Alex Reust, ASU SoMSS
    Sampling techniques for constrained Bayesian regression models
  2. Chelsea Krantsevich, ASU SoMSS
    Composite cognitive metrics for tracking Alzheimer's disease progression using causal inference

https://asu.zoom.us/rec/share/iZ7UwWvx51KLqppclKR6Cvsg2RW8i5Sjy_J8Cwv5C4BouMIWPoyoa5ryecQG02W8.EPSC8l4ZhJuAzRLp
Passcode: d0%QBDP4

November 23

  1. Henrique Cheng, ASU SoMSS
    Constructing a Disease State Variable for ALS
  2. Nicholas Chmielewsky
    Function Approximation and Quick QR Codes: A tale of optimal sampling

https://asu.zoom.us/rec/share/QvehsSZvEYERuBf0MP676XrHgIzd7IIQr1xTJcp7777q1xHfyrZ3FdYcnQXq2ynj.2mbJLlDYSa3IpWBX
Passcode: 1Lu@=8^7

November 16

Youzuo Lin, Los Alamos National Laboratory
Computational Methods for Large-scale Inverse Problems: Data-driven VS Physics-driven or Combined?

https://asu.zoom.us/rec/share/fi8BmkAbAkVx_gWspatR4nnp5WrwNBgBwBWNP9ZpPJP2_5AlfbwfHGSNZRHeqt6V.RvrJ4B97cd9GU1-v

Passcode: Yh#z^7zs

November 9

Andrew Herren, ASU SoMSS
Testing model explanation methods using insights from regression and experimental design

https://asu.zoom.us/rec/share/GLAulusldIjSnR_eiQjwskIVLxo3DqAr0atZkJIA1zc1ujvpMKGIH-vx2muuCZLA.tuoG91c40i81dAXL
Passcode: Q*.=gv7z

November 2

Safely reopening K-12 schools during the Covid-19 Pandemic
Courtney Shelley, Los Alamos National Laboratory

https://asu.zoom.us/rec/share/U2rQy-v39hRhDanFvWBNuCDT-etrFw6vMRyJBQ84zj8l99yXupJ4MJbIyAbnIuPz.61Mex0VrUw00od8B
Passcode: bB+@5ZvL

October 26

Information divergences for meta learning of prediction error in machine learning
Visar Berisha, ASU ECEE and Speech & Hearing Sciences

https://asu.zoom.us/rec/share/Xvgu5RDHEcWYvJJWw4JidvSLFC8BvZUeJ86XdsTE2tEl9e2QwqeOklIUujqcdF0.xYnrU3UcHH-_nDlJ
Passcode: p9Uy8k3@

October 19

An introduction to symplectic geometry and some applications
Clayton Shonkwiler, Colorado State University

https://asu.zoom.us/rec/share/DbhFfqANZZdwXrpjSmNkF_SIVgYKOa04EIxjwSmj4MUNEBZIAvmu-vlv6L7Aw19n.cnF2dS2RwV7JsmtM
Passcode: hfU$s25!

October 12

Ingredients matter: Quick and easy recipes for estimating clusters, manifolds, and epidemics
Dustin Mixon, The Ohio State University

https://asu.zoom.us/rec/share/LcGYpcqs-lRTGn8KYhd6PfBXuTsrMXyLEl9Dh2nujdUJx2fa7xo5T8Mj7MnJQyLj.ZohkpsGQBkbvepGy
Passcode: %gsNK639

September 28

Matching component analysis for synthetic aperture radar data domain adaptation
Theresa Scarnati, U.S. Air Force Research Laboratory

https://asu.zoom.us/rec/share/dxDe3JC-z87SuBQJWGuafBFtAaF8ATD98mqtyh3cs6LNNAgHVHnhuyChqch5VtKi.FPgrHmTozh5WgPWo
Passcode: pKnHB6#1

September 21

Searching for dusty corners: Understanding the prediction of the cross section of returns
Robert McCulloch, ASU SoMSS

https://asu.zoom.us/rec/share/IXFs9cFjwiRfKZzKk0oQmQf9a8TpmljXGOO9-jTcGImchNFhZ133vlfGfJ-1-e67.6PdvUZqCqmXaPKZN
Passcode: ufbE&DM5

September 14

Krylov meets Bregman: Sparse image reconstruction with nonnegativity constraint
Mirjeta Pasha, ASU SoMSS

https://asu.zoom.us/rec/share/t5aVEJC-WVJVOkjqE2F2Cb6NPwxwkW_0nds-ONSCAIlsf_hQ8JO3x7M0wvlKyLQm.Ch8GlcHhznF9GmyV
Passcode: X!u4zP3v

August 31

Tracking the evolution of impact crater shapes on planetary bodies via standardized representation
Prasun Mahanti, ASU School of Earth and Space Exploration

https://asu.zoom.us/rec/share/5Y8lDujp0FNOT7fvs3vRBqIwRr_nT6a80SUXr_YNyEa4xuRQ-v7ah3zPD5SiFUNK
Passcode: 20$NTU%4

Spring 2020

April 6
Reliability and validity of speech data in clinical populations
Shira Hahn, Aural Analytics

March 30
The subspace clustering problem
Keaton Hamm, University of Arizona

March 23
Structure learning in graphical models: Foundations and some modern challenges
Gautham Dasarathy, ASU School of Electrical, Computer and Energy Engineering

March 2
Scaling up Bayesian inference using Autoencoder
Shiwei Lan, ASU SoMSS

February 24
Fluid dynamical modeling
Kyle Squires, ASU School for Engineering of Matter, Transport and Energy

February 17
Beyond Bayes rule: Simulation experiments for principled data science
Richard Hahn, ASU SoMSS

February 10
High-performance computing tutorial, Part III
Jason Yalim, ASU SoMSS

February 3
High-performance computing tutorial, Part II
Jason Yalim, ASU SoMSS

January 27
Statistical inference for large-scale data with incomplete labels
Hyebin Song, University of Wisconsin

January 13
High-performance computing tutorial, Part I
Jason Yalim, ASU SoMSS

Fall 2019

November 18
Understanding Asteroid 16 Psyche's composition through 3D hydrocode impact crater models
Wendy Caldwell, Los Alamos National Laboratory

November 4
Mining healthcare transactional data for characterizations of patient response to offered appointment delays
Esma Gel, ASU School of Computing, Informatics and Decision Systems Engineering

October 28
Scalable control of robotic swarms in uncertain environments
Spring Berman, ASU School for Engineering of Matter, Transport and Energy

October 21
Finding structure in perceptual spaces
Richard Gerkin, ASU School of Life Sciences and SoMSS

September 30
Optimal augmentation of screening designs and strategic subdata selection for big data
Abigael Nachtsheim, ASU SoMSS

September 23
Deblurring images with mathematical models
Malena Español, ASU SoMSS

September 16
Distributed algorithms for optimization in networks
Angelia Nedich, ASU School of Electrical, Computer and Energy Engineering

September 9
Geometric methods in video and movement analysis
Pavan Turaga, ASU School of Arts, Media, and Engineering
 

Spring 2019

April 8
Frames and erasure problems
Doug Cochran, ASU SoMSS

April 1
Monte Carlo-based inference for measles dynamic
John Fricks, ASU SoMSS

March 18
Linear mixed cluster-weighted model with incomplete data
Petar Jevtic, ASU SoMSS

March 11
Spatial patterns of mortality in the United States: A spatial filtering approach
Petar Jevtic, ASU SoMSS

February 18
Transfer learning in image classification
Sebastien Motsch, ASU SoMSS

February 4
Big social media data and its challenges for machine learning
Huan Liu, ASU School of Computing, Informatics and Decision Systems Engineering

January 28
Expected number and height distribution of critical points of smooth isotropic Gaussian random fields
Dan Cheng, ASU SoMSS

Fall 2018

November 5
Some thoughts on data-oriented math and stats through a parameter selection problem
Toby Sanders, ASU SoMSS

October 29
Evaluating the data-driven model
Sharon Crook, ASU SoMSS

October 22
Information theory approach to machine learning, neural computing and artificial intelligence: A new perspective for statistical inference and optimal design? Part II
Milan Stehlik, ASU New College of Interdisciplinary Arts and Sciences

October 15
Information theory approach to machine learning, neural computing and artificial intelligence: A new perspective for statistical inference and optimal design? Part I
Milan Stehlik, ASU New College of Interdisciplinary Arts and Sciences

October 1
Pattern recognition and machine learning, Part II
Robert Skeel, ASU SoMSS

September 24
Pattern recognition and machine learning, Part I
Robert Skeel, ASU SoMSS

September 17
Function approximation, the curse of dimensionality, and sampling strategies, Part III
Rodrigo Platte, ASU SoMSS

September 10
Function approximation, the curse of dimensionality, and sampling strategies, Part II
Rodrigo Platte, ASU SoMSS

August 27
Function approximation, the curse of dimensionality, and sampling strategies, Part I
Rodrigo Platte, ASU SoMSS
 

Spring 2018

April 9
Variable selection in non-linear regression models: a parsimony-utility approach
Robert McCulloch, ASU SoMSS

April 2
Discrete-time approach to stochastic parameterization of spatiotemporal chaos
Kevin K. Lin, University of Arizona Department of Mathematics

March 26
Computer experiments
Jason Kao, ASU SoMSS

March 19
Advanced techniques for regularization in partial differential equations and imaging
Theresa Scarnati, ASU SoMSS

March 12
An introduction to inverse problems and synthetic aperture radar imaging
Toby Sanders, ASU SoMSS

February 26
Uncertainty assessment via iterated simulated learning
Richard Hahn, ASU SoMSS

February 19
Pattern theory, Part II
David Kaspar, ASU SoMSS

February 12
Pattern theory, Part I
David Kaspar, ASU SoMSS

February 5
Version control with Git
Adeline Kornelus, ASU SoMSS

January 29
Distributed decision problems, Part II
Lauren Crider, ASU SoMSS

January 22
Distributed decision problems, Part I
Doug Cochran, ASU SoMSS 

Fall 2017

November 6
Parenclitic networks: How to uncover new functions and structural information in biological data
Stefano Boccaletti, CNR Institute for Complex Systems, Florence

October 23
Motor-cargo complexes and stochastic simulation 
John Fricks, ASU SoMSS

October 16
Models of hormone treatment for prostate cancer: can mathematical models predict the outcomes? 
Yang Kuang, ASU SoMSS

October 2
Data Analytics to support efficient soybean variety development
Dieter Armbruster, ASU SoMSS
Esma Gel, ASU School of Computing, Informatics and Decision Systems Engineering

September 25 
Functional brain imaging and some of its design issues
Jason Kao, ASU SoMSS

September 18 
X-ray diffractive imaging of finite crystals 
Joe Chen, ASU Department of Physics

September 11 
More data with more noise, or less data with less noise: In the context of image reconstruction
Toby Sanders, ASU SoMSS

August 28
Function approximation from discrete data and electron microscopy 
Rodrigo Platte, ASU SoMSS
 

Spring 2017

April 3
Trees in machine learning, Part II

Robert McCulloch, ASU SoMSS

March 27
Trees in machine learning, Part I

Robert McCulloch, ASU SoMSS

March 20
An introduction to design of experiments, Part II

John Stufken, ASU SoMSS

March 13
An Introduction to design of experiments, Part I

John Stufken, ASU SoMSS

February 27
Series on analytic methods in dimensionality reduction - Introduction to lossless coding

Doug Cochran, ASU SoMSS

February 20
Series on analytic methods in dimensionality reduction - Data-adaptive compression: "Best basis" algorithms, vector quantization and other symbol library methods

Doug Cochran, ASU SoMSS

February 13
Series on analytic methods in dimensionality reduction - Foundations of transform coding: orthonormal bases, wavelets and frames, wavelet packets

Doug Cochran, ASU SoMSS

January 30
Series on inverse and imaging problems: Compressed sampling and L1 regularization

Rodrigo Platte and Toby Sanders, ASU SoMSS

January 23
Series on inverse and imaging problems: Ill-conditioned inverse problems

Rodrigo Platte, ASU SoMSS

Fall 2016

November 14
Optimal design and subdata selection for big data: Part II

John Stufken, ASU SoMSS

November 7
Optimal design and subdata selection for big data: Part II

John Stufken, ASU SoMSS

October 17
Fourier analysis and wavelets

Al Boggess, ASU SoMSS

October 3
On the path to precision medicine: Finding coherence in chaos by embracing cancer’s complexity

Ken Buetow, ASU School of Life Sciences

September 19
Sensing, statistics, and closed-loop data collection

Doug Cochran, ASU SoMSS

September 12
An introduction to the mathematics of computed tomography

Toby Sanders, ASU SoMSS

August 29
Principal component analysis with application to data analysis

Rodrigo Platte, ASU SoMSS