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.
2020-2021
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
-
Alex Reust, ASU SoMSS
Bayesian Regularized Linear Models
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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
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
- Alex Reust, ASU SoMSS
Sampling techniques for constrained Bayesian regression models - 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
- Henrique Cheng, ASU SoMSS
Constructing a Disease State Variable for ALS - 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
2019-2020
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
2018-2019
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
2017-2018
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
2016-2017
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