Upcoming Talks
Spring 2026
Applied Math Seminar
Title: AI and LLM in Industry/Agriculture/Aquaculture Applications
Yo-Ping Huang, PhD
Chair Professor
Department of Mathematics
National Taipei University of Technology
When: Friday, February 17th, 2026 at 4:00 PM to 5:00 PM
Where: Pickard Hall, Room 311
Abstract: The integration of artificial intelligence (AI) and the internet of things (IoT), known as artificial intelligence of things (AIoT), is driving significant advancements in the industry/aquaculture, offering solutions to longstanding challenges related to operational efficiency, sustainability, and productivity. With the advent of powerful GPU, AI-related research or AI-based applications have sprouted in every corner of the world. Originating from pure network connectivity, the Internet of Things (IoT) has become a structure that can collect every piece of data from physical devices, daily activities, images, or videos into a data reservoir. As a result, tons of data are automatically generated into an enterprise database in a single day. This creates research opportunities on integrating AI, IoT, big data, and LLMs to improve the quality of industrial production, agriculture or net-cage aquaculture. IoT sensors deployed across industry/agriculture/aquaculture systems continuously track critical parameters such as temperature, humidity, and workers’ behavior. AI algorithms process these data streams to provide predictive insights into working environment management, operations detection, etc. This talk will address the latest research studies in AIoT and LLMs within the industry/aquaculture, focusing on real-time environmental monitoring, data-driven decision-making, and automation.This talk will also highlight future directions for AIoT in industry/agriculture/aquaculture, emphasizing the potential for hybrid AI models, improved scalability for large-scale operations, and sustainable resource management. Yo-Ping Huang (Fellow, IEEE) received the Ph.D. degree in electrical engineering from Texas Tech University, Lubbock, TX, USA. He is a Chair Professor in the Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan, where he served as the Secretary General. He also serves as the President of Chinese Automatic Control Society. He was the President of National Penghu University of Science and Technology, Penghu, Taiwan. He was a Professor and the Dean of Research and Development, the Dean of the College of Electrical Engineering and Computer Science, and the Department Chair with Tatung University, Taipei. His current research interests include deep learning modeling, intelligent control, machine learning, and AIoT systems design. Dr. Huang received the FUTEX (Future Tech) Award and the Outstanding Research Award from the National Science and Technology Council, Taiwan. He is Fellows of IET, CACS, TFSA, and AAIA. He serves as the IEEE SMCS VP for Conferences and Meetings, and Chair of the IEEE SMCS Technical Committee on Intelligent Transportation Systems. He was the IEEE SMCS BoG, the President of the Taiwan Association of Systems Science and Engineering, the Chair of the IEEE SMCS Taipei Chapter, the Chair of the IEEE CIS Taipei Chapter, and the CEO of the Joint Commission of Technological and Vocational College Admission Committee, Taiwan.
Title: Data-Driven Kernel Matrix Computations: Geometric Analysis and Scalable Algorithms
Difeng Cai, PhD
Assistant Professor
Department of Mathematics
Southern Methodist University
When: Friday, February 13th, 2026 at 2:00 PM to 3:00 PM
Where: Pickard Hall, Room 311
Abstract: Dense kernel matrices arise in a broad range of disciplines, such as potential theory, molecular biology, statistical machine learning, etc. To reduce the computational cost, low-rank or hierarchical low-rank techniques are often used to construct an economical approximation to the original matrix. In this talk, we propose data-driven approaches for accelerating dense kernel matrix computations. We first provide a straightforward geometric interpretation that answers a central question: what kind of subset is preferable for skeleton low-rank approximations. Based on the geometric findings, we present scalable and robust hierarchical algorithms for black-box dense kernel matrix computations. The efficiency and robustness will be demonstrated through experiments for various datasets, kernels, and dimensions, including benchmark comparison to the state-of-the-art packages for N-body simulations.
Fall 2025
Colloquia
Title: Optimization Using Model Predictive Control Combined with iLQR and Neural Networks
Nguyen-Truc-Dao Nguyen, PhD
Assistant Professor
Department of Mathematics and Statistics
San Diego State University
When: Friday, September 12th, 2025 at 3:30 PM to 4:30 PM
Where: Pickard Hall, Room 110
Abstract: This talk is devoted to combining model predictive control (MPC) and deep learning methods, specifically neural networks, to solve high-dimensional optimization and control problems. MPC is a popular method for real-life process control in various fields, but its computational requirements can often become a bottleneck. In contrast, deep learning algorithms have shown effectiveness in approximating high-dimensional systems and solving reinforcement learning problems. By leveraging the strengths of both MPC and neural networks, we aim to improve the efficiency of solving MPC problems. The talk also discusses the optimal control problem in MPC and how it can be divided into smaller time horizons to reduce computational costs. Additionally, we focus on enhancing MPC through two approaches: a machine learning–based feedback controller and a machine learning–enhanced planner, which involve implementing neural networks and iLQR. Overall, this talk provides insights into the potential of combining MPC and deep learning methods to tackle complex control problems across various fields, with applications to robotics.
Spring 2025
Colloquia
Title: AdaBB: A Parameter-Free Gradient Method for Convex
Optimization
Shiqian Ma, PhD
Professor
Department of Computational Applied Mathematics and Operations Research
Department of Electrical and Computer Engineering
Rice University
When: Friday, April 11th, 2025 at 2.00 PM to 3.00 PM
Where:
Pickard Hall, Room 309
Abstract: We propose AdaBB, an adaptive gradient method
based on the Barzilai-Borwein stepsize. The algorithm is line-search-free and
parameter-free , and essentially provides a convergent variant of the
Barzilai-Borwein method for general unconstrained convex optimization.We
analyze the ergodic convergence of the objective function value and the
convergence of the iterates for solving general unconstrained convex
optimization. Compared with existing works along this line of research, our
algorithm gives the best lower bounds on the stepsize and the average of the
stepsizes. Moreover, we present an extension of the proposed algorithm for
solving composite optimization where the objective function is the summation
of a smooth function and a nonsmooth function. Our numerical results also
demonstrate very promising potential of the proposed algorithms on some
representative examples.
Title: Bayesian Scalable Precision Factor Analysis for
Gaussian Graphical Models
Noirrit Kiran Chandra, PhD
Assistant Professor
Department of Mathematical Sciences
The University of Texas at Dallas
When: Friday, February 14th, 2025 at 3.30PM to 4.20PM
Where:
Pickard Hall, Room 110
Abstract: We propose a novel approach to estimating a
multivariate Gaussian precision matrix that relies on decomposing them into a
low-rank and a diagonal component. Such decompositions are very popular for
modeling large covariance matrices as they admit a latent factor based
representation that allows easy inference. The same is however not true for
precision matrices due to the lack of computationally convenient
representations which restricts inference to low-to-moderate dimensional
problems. We address this remarkable gap in the literature by building on a
latent variable representation for such decomposition for precision matrices.
The construction leads to an efficient Gibbs sampler that scales very well to
high-dimensional problems far beyond the limits of the current
state-of-the-art. The ability to efficiently explore the full posterior space
also allows easy assessment of model uncertainty. Exact zeros in the matrix
encoding the underlying conditional independence graph are then determined via
a novel posterior false discovery rate control procedure. A near minimax
optimal posterior concentration rate for estimating precision matrices is
attained by our method under mild regularity assumptions. We evaluate the
method’s empirical performance through synthetic experiments and
illustrate its practical utility. We then extend the model to arbitrary
non-Gaussian distributed data with auto correlations using a matrix-Gaussian
copula approach for a novel application in resting state functional
connectivity analysis is the auditory subcortical region of the human brain.
Title: On crop vector-borne diseases: impact of virus
lifespan and contact rate on the travelling-wave speed of infective fronts
Michael Chapwanya, Ph.D.
Department of Mathematics and Applied Mathematics
University of Pretoria, South Africa
When: Friday, January 31, 2025 at 2 pm
Where:
Pickard Hall, Room 311
Abstract: Plant vector-borne diseases are the most
common mode of virus transmission in plants. These diseases occur when viruses
are carried and transmitted by vectors, which are usually living organisms,
such as insects, mites, nematodes, or even fungi. Among these, insects like
aphids, whiteflies, thrips, and leafhoppers are the primary culprits in
spreading plant viruses. However, despite their predictable importance in
improving food security, very few mathematical models have been documented in
the literature. We begin by reviewing some of our work on crop diseases
including Maize Lethal Necrosis disease, Cassava Mosaic Disease, and Grapevine
leafroll-associated virus 3 (GLRaV-3). We then present a generic mathematical
model of virus transmission in plants. We show that traveling waves may exist,
with the wave speed dependent on the virus lifespan and the contact rate
between plants and pests. Strategies for control will also be discussed.
Short bio: Dr. Michael Chapwanya is a Professor of
Mathematics and the Graduate Advisor in the Department of Mathematics and
Applied Mathematics at the University of Pretoria, South Africa. He earned his
Ph.D. in Mathematics from the University of Limerick, Ireland. His research
focus is on mathematical modelling, numerical analysis and scientific
computation. The problems that he works on are drawn from a wide range of
sources with biological, medical, engineering, industrial and environmental
context.
Previous Talks
Colloquia
Title:
Mathematical Modeling of Drug Resistance in Cancer
Dr. Natalia Komarova
Department of Mathematics, UC San Diego, CA
When: Friday, November 8, 2024 at 2 pm
Where:
Pickard Hall, Room 311
Abstract: Resistance to drugs is one of the most
challenging problems in public health. In this talk, I will focus
on drug resistance in cancer, which is often associated with the
existence of resistant mutants in the evolving population of
malignant cells. I will start by showing the type of stochastic
modeling that has been used to quantify various aspects of
resistance generation, including multiple-drug resistance, the
role of cellular turnover, cellular quiescence, and the phenomenon
of cross-resistance. I will compare and contrast treatment
strategies such as cycling and simultaneous multi-drug treatment.
I will then focus on a specific case of Chronic Lymphocytic
Leukemia (CLL). This is the most common leukemia, mostly arising
in patients over the age of 50. The disease has been treated with
chemo-immunotherapies with varying outcomes, depending on the
genetic make-up of the tumor cells. More recently, a promising
tyrosine kinase inhibitor, ibrutinib, has been developed, which
resulted in successful responses in clinical trials, even for the
most aggressive chronic lymphocytic leukemia types. The crucial
questions include how long disease control can be maintained in
individual patients, when drug resistance is expected to arise,
and what can be done to counter it. Computational evolutionary
models, based on measured kinetic parameters of patients, allow us
to address these questions and to pave the way toward a
personalized prognosis and treatment.
Short Bio: Natalia Komarova holds an MS in
Theoretical Physics from Moscow State University and a PhD in
Applied Mathematics from University of Arizona. After being a
Member at the Institute for Advanced Studies in Princeton
(1999-2003), she became Assistant Professor at the Department of
Mathematics at Rutgers, and then worked at UC Irvine from 2004
until 2024, when she joined UCSD. Komarova is interested in
Applied Mathematics, and in particular, in Mathematical Biology,
evolution, and modeling of complex social phenomena.
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Title:
Dynamical Lie algebras
Dr. Bojko Bakalov
Department of Mathematics, North Carolina State University
When: Friday, November 1, 2024 at 2 pm
Where:
Pickard Hall, Room 311
Abstract: Quantum computers are physical machines
that process information using the principles of quantum
mechanics, which in turn is underpinned by linear algebra. The
talk will start with a review of Lie algebras (consisting of
matrices under the operation of commutator) and their role in
quantum mechanics. The dynamical Lie algebra (DLA) of a quantum
system is defined as the Lie algebra obtained by taking all real
linear combinations and nested commutators of the terms of the
Hamiltonian. The significance of the DLA is that the time
evolution of the system is given by elements of the associated Lie
group. The DLA determines the set of reachable states of the
system and its controllability, so it is relevant for designing
quantum circuits. In this talk, I will present a classification of
DLAs generated by 2-local Pauli interactions on spin chains and on
arbitrary interaction graphs. I will also discuss applications of
DLAs to variational quantum computing, including the problem of
barren plateaus. The talk will be accessible to all mathematics
graduate students; no prior knowledge of physics or quantum
computing is assumed.
Short Bio: Originally from Bulgaria, Bojko
Bakalov received his PhD from MIT and was a Miller Research Fellow
at UC Berkeley before joining the NC State Math Department in
2003. Currently, he is the Director of Graduate Programs in Math
and Applied Math, and has a leadership role in the NC State
Quantum Initiative. Bakalov’s research interests include
representation theory, quantum computing, mathematical physics,
signal processing, and integrable systems. In 2006, he was awarded
the Hermann Weyl Prize of the International Colloquia on Group
Theoretical Methods in Physics, for original work of significant
scientific quality in the area of understanding physics through
symmetries. Bakalov’s research has been funded by the US Air
Force, DOE, NSA, NSF, and the Simons Foundation.
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Title:
Cardiac hemodynamics and congenital heart disease:
restoringnormal function
Sandra Rugonyi, Ph.D.
Oregon Health & Science University, Biomedical Engineering
Department, Portland, OR, USA
When: Friday, October 25, 2024 at 2 pm
Where:
Pickard Hall, Room 311
Abstract: In the normal heart, intracardiac blood
flow (hemodynamics) optimizes pumping efficiency by facilitating
the motion of blood in and out of the heart and closure of valves.
Abnormal blood flow patterns that occur due to heart disease can
be detrimental: both due to decreasing cardiac efficiency, and to
the sensitivity of heart cells to abnormal hemodynamic stresses,
which could exacerbate pathological progression of heart disease.
In this talk, we will focus on hypertrophic cardiomyopathy (HCM),
a congenital heart disease characterized by thickening of the left
ventricular wall (hypertrophy) that leads to altered cardiac flow
patterns. We will discuss how computational modeling approaches
can be used in the evaluation of HCM patients and the effect of
novel myosin inhibitor drugs to treat HCM. This work is in
collaboration with Dr. Ted Abraham (UCSF HCM Center of
Excellence).
Short Bio: Sandra Rugonyi, Professor of
Biomedical Engineering, Oregon Health & Science University
(OHSU), Portland, OR, USA. Prof. Rugonyi has expertise in
cardiovascular biomechanics and computational modelling. Her
career started in Argentina, where she got an MS-equivalent degree
in Nuclear Engineering from the Balseiro Institute. After working
for a nuclear power plant and then a steel company, she moved to
the USA, where she earned a PhD in Mechanical Engineering from MIT
that focused on advanced numerical methods for fluid-structure
interaction problems. In 2005 Dr. Rugonyi joined the Biomedical
Engineering department at OHSU, and since then she has applied
mechanical engineering principles to heart development and
congenital heart disease. Dr. Rugonyi has contributed to
fundamental understanding of hemodynamic regulation on heart
formation.
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Title:
Bayesian Inversion Using Level Sets in Diffuse Optical
Tomography
Dr. Taufiquar Khan
University of North Carolina at Charlotte
When: Friday, September 27, 2024, from 2-4 pm
Where:
Pickard Hall, Room 311
Abstract: In this talk, we will provide an
overview of the ill-posed inverse problem in Diffuse Optical
Tomography (DOT) at an introductory level. We will discuss several
regularization approaches to solve the ill-posed inverse problem
in DOT including deterministic, statistical, and machine learning.
Then we will present our most recent work using Bayesian Inversion
Using level sets for image reconstruction in DOT. The results of
image reconstruction will be demonstrated using synthetic data for
the recently proposed algorithm. This is joint work with Anuj
Abhishek (Case Western Reserve University) and Thilo Strauss
(Xi’an Jiaotong-Liverpool University).
Short Bio: Taufiquar Khan is currently a
Professor and the Chair of the Department of Mathematics and
Statistics, University of North Carolina at Charlotte (UNC
Charlotte). He was a Professor and an Associate Director of
Graduate Studies of the School of Mathematical and Statistical
Sciences, Clemson University, Clemson, SC, USA, before joining UNC
Charlotte. He is a recipient of the Humboldt Fellowship from
Germany. His research interests include machine learning, inverse
problems involving ordinary and partial differential equations.
His present and past research have been supported through the NSF,
DOD, Humboldt Foundation, and the industry.
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Seminars
Title:
Boundary Problems In Rough Domains With Data in Weighted Morrey
Spaces
Dr. Marcus Laurel
University of Texas at Arlington
When: Friday, October 11, 2024, from 3-4 pm
Where:
Pickard Hall, Room 305
Abstract: The goal of this talk is to present a
brief introduction to the method of layer potentials for solving
boundary value problems on rough domains. Specifically, we work
with the class of weakly elliptic, second-order, homogeneous,
constant (complex) coefficient systems in Euclidean space. We use
singular integrals of layer potential type, which themselves can
be defined on the class of uniformly rectifiable sets, the
geometric measure theoretic sharp analogue of Lipschitz images.
This requires a Calderón-Zygmund theory that works in such
rough geometries as well as on the function spaces we have in
mind. Specifically, we consider boundary problems where the
boundary datum is arbitrarily chosen from a Muckenhoupt-weighted
Morrey space (an offshoot of the scale of Muckenhoupt-weighted
Lebesgue spaces), in which integrals over balls are bounded by a
uniform constant multiplied by a specific power of the radii of
the balls. We will see the delicate interplay between harmonic
analysis, functional analysis, and geometry that leads to a
well-posedness result for the Dirichlet Problem. This is joint
work with Professor Marius Mitrea (Baylor University).
Short Bio: Dr. Marcus Laurel received his Ph.D.
in mathematics in 2024 from Baylor University under the guidance
of Professor Marius Mitrea. He works on the confluence of
geometry, harmonic analysis, and PDE. His interests lie in layer
potential methods to solve boundary value problems for elliptic
systems, as well as function space theory in rough geometric
settings. He, with Prof. Mitrea, recently published a book titled
*Weighted Morrey Spaces: Calderón-Zygmund Theory and
Boundary Problem.* Currently, Dr. Laurel is an assistant professor
of instruction at UT Arlington.
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Title: "Solving linear fractional differential
equations with random non-homogeneous parts"
Dr. Laura Villafuerte
The University of Texas at Austin
When: Friday, October 4, 2024 from 2-4 p.m.
Where: Pickard Hall, Room 311
Abstract: Experimental data and algorithms for
certain real-world phenomena have shown that fractional order
derivatives provide more efficient modeling than integer order
derivatives. In these scenarios, using fractional differential
equations rather than integer-order differential equations to
describe these phenomena seems more appropriate. In addition, to
consider the uncertainty arising from measurement errors and the
complexity of the phenomena analyzed, randomness is included in
the differential equations through their coefficients, initial
conditions, and non-homogeneous parts. In this work, we
investigate mean square solutions for some families of fractional
linear differential equations with random non-homogeneous parts.
This approach is based on the mean square Caputo derivative. For
the sake of generality, we assume that the initial conditions and
coefficients of the equations are random variables satisfying
certain mild conditions. For this class of equations, we construct
a generalized power series solution by using the mean square
Laplace transform. Then, assuming an exponential growth condition
on the force term, we show its mean square convergence. As a
consequence of the mean square convergence, the convergence of the
two first statistical moments, mean and variance, is guaranteed.
Several examples are discussed to compare the fractional and
integer order random differential equations utilizing its first
two moments.
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Title:
A Meta-analysis based Hierarchical Variance Model for Powering
One and Two-sample t-tests
Jackson Barth, PhD
Assistant Professor, Department of Statistical Science, Baylor
University
When: Friday, September 20, 2024 from 3:30-4:20 pm
Where:
Pickard Hall, Room 110
Abstract: Sample size determination (SSD) is
essential in statistical inference and hypothesis testing, as it
directly affects the accuracy and power of the analysis. We
propose a SSD methodology for one and two-sample t-tests that
ensures clinical relevance using a pre-determined unstandardized
effect size. Our novel approach leverages Bayesian meta-analysis
to account for the uncertainty surrounding the variance, a common
issue in SSD. By incorporating prior knowledge from related
studies via a Bayesian gamma-inverse gamma model, we obtain an
informative posterior predictive distribution for the variance
that leads to better decisions about sample size. For efficient
posterior sampling, we propose an empirical Bayes approach, which
is further combined with a quantile simulation approach to
facilitate computation. Simulations and empirical studies
demonstrate that our methodology outperforms other aggregate
approaches (simple average, weighted average, median) in variance
estimation for SSD, especially in meta-analyses with large
disparity in sample size and moderate variance. Thus, it offers a
robust and practical solution for sample size determination in
t-tests.
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Colloquia
Title: Interplay of Linear Algebra, Machine Learning, and High
Performance Computing
Dr. Xiaoye Sherry Li
Lawrence Berkeley National Laboratory
When: Friday, April 5, 2024 from 3-4 pm
Where: Pickard Hall, Room 110
Abstract: In recent years, we have seen a large
body of research using hierarchical matrix algebra to construct
low complexity linear solvers and preconditioners. Not only can
these fast solvers significantly accelerate the speed of
large-scale PDE-based simulations, but also they can speed up many
AI and machine learning algorithms, which are often
matrix-computation-bound. On the other hand, statistical and
machine learning methods can be used to help select the best
solvers or solvers' configurations for specific problems and
computer platforms. In both of these fields, high-performance
computing becomes an indispensable cross-cutting tool for
achieving real-time solutions for big data problems. In this talk,
we will show our recent developments in the intersection of these
areas.
Short Bio: Dr. Xiaoye S. Li is a Senior Scientist
in the Computational Research Division, Lawrence Berkeley National
Laboratory. Dr. Li earned her Ph.D. in Computer Science from UC
Berkeley in 1996, MS in Math & Computer Science from Penn
State Univ., and B.S. in Computer Science from Tsinghua Univ. She
has worked on diverse problems in high-performance scientific
computations, including parallel computing and sparse matrix
computations. She has authored over 130 publications, and is the
lead developer of SuperLU, a widely-used sparse direct solver, and
has contributed to the development of several other mathematical
libraries, including LAPACK and XBLAS. She has served on the
editorial boards of ACM Trans. Math. Software, IJHPCA, and SIAM J.
Scientific Comput., as well as many program committees of
scientific conferences. She is a Fellow of SIAM and a Senior
Member of ACM.
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Title: "How do Immune Cells Kill Tumor Cells?"
Ami E. Radunskaya, PhD
Lingurn H. Burkhead Professor of Mathematics at Pomona College, CA
When: Friday, February 16, 2024 from 10-11 am
Where: Pickard Hall, Room 311
Abstract: The immune system is able to fight
cancer by mustering and training an army of effector
“killer” cells. Mathematical models of tumor-immune
interactions must describe the proliferation, recruiting, and
killing rates of immune cells. Earlier work surprisingly showed
that the functions describing the kill rates distinguish between
two types of immune cells. The mechanisms behind these differences
have been a mystery, however. In an attempt to unravel this
mystery, we have created a cell-based fixed-lattice model that
simulates immune cell and tumor cell interaction involving MHC
recognition, and two killing mechanisms. These mechanisms play a
big role in the effectiveness of many cancer immunotherapies.
Results from model simulations, along with theories developed by
ecologists, can help to illuminate which mechanisms are at work in
different conditions.
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Seminars
Title: "What is Liutex: Examples of Hurricane and Tornado Vortex
Visualization using Liutex"
Oscar Alvarez
University of Texas at Arlington
When: Friday, February 2, 2024, from 2-3 pm
Where:
Pickard Hall, Room 311
Abstract: A vortex is a common phenomenon
that occurs in fluid flow, especially when studying turbulence. It
is to say, understanding vortices is essential knowledge for
scientists, researchers, and engineers doing work in the field of
fluid mechanics. In the past, scientists such as Helmholtz (1858)
had a desire to understand the physical nature of vortices and
popularized the idea of using vorticity to define a vortex in a
fluid field. Vorticity became the default for studying vortices
and vortex identification methods were created that were built on
the idea of vorticity. This went on for a long time until only
recently, Dr. Chaoqun Liu discovered the Liutex method in 2018.
The Liutex method started a new generation of vortex
identification methods. Liutex is based directly on the rotation
of a fluid as opposed to vorticity which also contains shear. It
has been shown that vorticity cannot distinguish between shear and
rotation. An immediate counterexample for the invalidity of
vorticity to define a vortex can be found near the boundary wall
of a boundary layer where vorticity is large but there is no
rotation or no vortex. Liutex has now become a well-known vortex
identification method. Researchers around the world agree that
Liutex mathematically defines what a vortex is. In this
presentation, I hope to explain what Liutex is. I will also show
some examples of Liutex being applied using real/experimental
hurricane data provided by the National Oceanic and Atmospheric
Administration (NOAA) and simulated high-resolution tornado data
with 250 billion grid points provided by a senior research
scientist at the University of Wisconsin.
Short Bio: Oscar Alvarez is a Research
Scientist 1 at the University of Texas at Arlington Research
Institute (UTARI) in Fort Worth, Texas. He also works under the
supervision of Dr. Chaoqun Liu as a mathematics PhD student
studying Liutex.
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