Arturo Bronson graduated in Metallurgical Engineering in 1970 from The University of Texas at El Paso (UTEP), and completed his Masters at UTEP in 1972. In 1977, he received his Ph.D. from The Ohio State University in metallurgical engineering with an emphasis in high temperature thermodynamics and immediately joined the faculty at the University of Arizona. At the University of Arizona, he developed a corrosion research effort along with his undergraduate and graduate teaching in thermodynamics, kinetics and chemical metallurgy. After he moved to the University of Texas at El Paso in 1983, he developed a strong research group in corrosion and high temperature research and became the Director of the Materials Center for Synthesis and Processing from 1995 to 2000 with fifteen faculty and approximately 32 students/year. He was also program director in the Division of Human Resource Development of the Education Directorate at the National Science Foundation (NSF) in 1993. The main thrusts of Arturo Bronson’s research have focused in the chemical interactions of high temperature materials at 1000-1800°C. Professor Bronson also investigates corrosive wear, a relatively new research area, using the concepts of electrochemistry, surface deformation, and material microstructure, as well as infusing statistics and mathematical modeling through an interdisciplinary approach to aid in predicting material behavior. The National Science Foundation (NSF) has continually supported his research since 1984 with additional funds acquired from the Air Force Materials Laboratory, NASA, Army Research Office, Sandia National Laboratories, Los Alamos National Laboratory, and Air Force Office of Scientific Research.

Professor Arturo Bronson's teaching methodology and philosophy emphasize the development of the understanding of basic concepts as applied to solving current scientific and engineering challenges. Professor Bronson has used the materials research laboratory with undergraduate and graduate students working and learning together to reinforce the application of basic concepts (e.g., thermodynamics, kinetics and transport phenomena). An achievable goal is that for every graduate student, an undergraduate is associated with him or her to support university research -- a practice initiated by Professor Bronson at the university and now integrated into the university education of scientists and engineers. In 2003, he was awarded the Men in Science Award for his excellence in teaching and research in materials science and engineering from the Quality Education for Minorities Network.

Arturo, active in the Minerals, Metals and Materials Society, was 1997 chair of the editorial board for the premier publication in metallurgy, Metallurgical and Materials Transactions B. He is also active in the Electrochemical Society and the National Association of Corrosion Engineers (NACE) for which he served as symposium chair of the conference on “Techniques for Corrosion Measurement.” He has served on several NSF review panels primarily for the Division of Human Resource Development and Division of Materials Research. Arturo was appointed (1997-2000) to the Committee on Equal Opportunities in Science and Engineering (CEOSE), an Advisory Committee to the Director of NSF; in 1998, he served as chair of CEOSE. He was appointed to NSF’s Advisory Committee for Mathematical and Physical Sciences (10/1999-9/2002) and NSF awarded him with an outstanding service award. 

Omar Cedillos is an Assistant Professor of Practice in the Department of Mechanical Engineering at UTEP. He obtained his BS in Mechanical and Electrical Engineering from Instituto Tecnologico y de Estudios Superiores de Monterrey in 1997, his MS in Materials Science from Universidad Autonoma de Ciudad Juarez in 2010 and his PhD in Materials from Imperial College London in 2015. His research is focused in advanced processing and characterization of ceramics for extreme environments applications. This includes ultra-high temperature ceramics (UHTCs), high-entropy ceramics and MAX phases. His interests also includes microstructure-property relationships, measurement of mechanical properties, measurement of thermal properties at high temperatures and measurement of melting points at very high temperatures (>4000 K) using laser heating techniques. Process development using spark plasma sintering (SPS), hot-pressing (HP) and pressureless sintering (PS) techniques. 

Dr. Cabrera's research interests include image and volumetric data compression; signal processing applied to biomedical problems and instrumentation; theory and methods of sparse signal representations, compressive sensing, and superresolution; Magnetic Resonance Image reconstruction using Deep Learning; 3-D joint depth-color image processing. 

Beverley Argus-Calvo, Ph.D. is associate dean for graduate studies and research for the UTEP College of Education (CoED). Argus-Calvo currently serves as an associate professor in the CoED’s Educational Diagnostician program in the Department of Educational Psychology and Special Services. Argus-Calvo began her education career as a teacher for learning disabled students in 1983. Since then, she has worked with children with special needs in elementary and secondary schools in the United States and Mexico. She worked as an educational diagnostician in New Mexico from 1997-1999. Argus-Calvo’s research and professional interests include binational education, extended learning, early college high school programs, music and arts based programs for elementary children in underserved communities, and working with families of children with special needs along the United States-Mexico border. Her work has been published in respected journals such as the College Student Journal, Multiple Voices for Ethnically Diverse Exceptional Learners, and the Rural Special Education Quarterly, books and international scholarly publications. Argus-Calvo is currently collaborating with colleagues from UTEP and the UACJ, the UACH, the CCHEP, and the CIESAS on research projects addressing education and children in vulnerable settings. 

Dr. Cesar Carrasco is a professor for the Department of Civil Engineering, with experience in material science and engineering mechanics. He has been actively involved in the management of numerous projects dealing with the modeling of micro-meteoroid and orbital debris impacts on spacecrafts, the development of finite element linear and non-linear modeling tools, material constitutive modeling, and structural reliability, as well as nondestructive testing and evaluation of structures. Dr. Carrasco has been the PI and Co-PI on research and development grants/contracts from NSF, the Applied Physics Laboratory, Southwest Research Institute, NASA, the Boeing Company, Lockheed Martin, the Missile Defense Agency, Raytheon Company, the Federal Highway Administration and the New York Department of Transportation. 

Professor Fuentes' main research area is machine learning in science, in particular, the development of systems that can help scientists extract knowledge and insight from the large amounts of scientific data currently available. His is particularly interested in issues such as the use of unlabeled data, active learning, intelligent optimization feature selection and creation, noise-insensitive and noise-aware algorithms, and the use of prior knowledge. He has worked in applications in astronomy, optics, geology and biology. Dr. Fuentes received a B.S. degree in Industrial Engineering from the Instituto Tecnológico de Chihuahua, in Chihuahua, Mexico, in 1989, an M.S. degree in Computer Science from the University of Texas at El Paso, in 1991, and a Ph.D. degree in Computer Science from the University of Rochester, in 1997. From 1998 to 2005 he was a Professor in the Computer Science Department at the Instituto National de Astrofisica, Optica y Electronica. Dr. Fuentes was a co-organizer of the First Iberoamerican Workshop on Machine Learning for Scientific Data Analysis, held in conjunction with IBERAMIA 2004, in Puebla, November 22, 2004. 

I am a computational flow modeling engineer with broad expertise in high-performance computing and multiphase flows. Currently, I am a senior research scientist at the Center of Innovation for Flow through Porous media. I was a postdoctoral research associate at the University of Wyoming and worked with Dr. M Stoellinger to implement thermal radiation models in a legacy multi-phase flow solver. I contributed to multiphysics and multiscale laboratories of Professor V Kumar at the University of Texas at El Paso. I was a guest researcher at Sandia National Laboratories and worked with Dr. W Spotz to develop an interface to integrate MFiX with Trilinos. I have worked with several other researchers at Sandia National Laboratories to incorporate tools such as Trilinos, and Dakota with other opensource software. I did my graduate work in the computational fluid dynamics group of Professor S Mittal at the Indian Institute of Technology. To date, my research results have been published in high impact journals, including Physics of fluids, Fluid Engineering, Propulsion and Power, Shock Waves, Powder Technology, Metallurgical and Materials Transactions, Numerical Methods in Fluids, Super-computing, and others.

As a postdoc at UWyo, I designed and implemented advanced spectral models for thermal radiation in MFiX. The fossil fuel community widely uses MFiX suite for simulating flow in fluidized beds. For verification, the results with those models are compared with that from OpenFoam and ANSYS Fluent. The results with those models for chemically reacting flows such as gasification and combustion of coal/biomass are validated. The chemical thermo-kinetics obtained from Cantera and C3M are incorporated in MFiX simulations.

My postdoctoral training at UTEP allowed me to develop an interface to integrate the advanced linear solvers in Trilinos with MFiX. Trilinos provides a framework for simulating large-scale, sophisticated multi-physics engineering and scientific problems. The interface is written in Fortran and C/C++. The interface has been verified and validated on various fluid bed problems. Two fluid, discrete element and particle in cell approaches are used to simulate the flow in fluidized beds. I also tested the performance of the linear solvers, which are based on the Kokkos programming model, on various computer architectures. The iterative solvers in the integrated multiphase flow solver are relatively fast compared to the built-in solvers in MFiX. Further, I designed and developed a framework to integrate MFiX with Dakota for uncertainty quantification, sensitivity analysis, and optimization of multi-physics problems. However, I also developed an exascale capable pore-network simulator and combined it with Dakota.

I have used machine learning algorithms in various python libraries to predict pressure difference/flow rate in Hagen-poiseuille flow, friction factor, metrics for characterizing laser propagation in atmospheric turbulence, and depth of penetration of molten salt into a pore network. This work has been presented at an ASME conference. I also have experience in using deep learning and convolutional neural networks in TensorFlow.

During my doctoral studies, I developed a stabilized finite-element method with higher-order interpolation functions to solve turbulent flows via Reynolds-Averaged Navier Stokes equations in three dimensions. MPI library implemented in the code for interprocessor communications allows simulating engineering flow problems. I compared the performance of 3-noded linear and 6-noded quadratic triangular elements. In 3D, the relative performance is evaluated for 6-noded linear and 18-noded quadratic wedge elements. Numerical results are compared for the solutions to Euler, laminar, and turbulent flows at subsonic, transonic, and supersonic speeds. This in-house CFD code, which is written in Fortran and C, was used to simulate flow in various components of a ramjet engine.

Dr. Kumar’s research goal is to develop and integrate cutting-edge computational tools for complex engineering and science challenges by leveraging Exascale/High Performance Computing (HPC), machine learning and artificial intelligence, data analytics/bigdata concepts, Uncertainty Quantification (UQ), and leading-edge computational capabilities. His research group has focused on real-life and fundamental thermal-fluid applications coupled with crosscutting domains such as structure dynamics, solid mechanics, biomedical engineering, hypersonic. The group uses Computational Fluid Dynamics (CFD) and Fluid-Structure Interactions (FSI) with HPC algorithms on massively parallel supercomputers. Research activities include multiple interdisciplinary projects including developing flow conductance model porous core at pore level for CO2 sequestration, studying effects nanoparticles/coatings on thermal energy storage systems in Concentrating Solar Power (CSP) system, and FSI analysis Deep Venous Thrombosis (DVT), CFD-Discrete Element Modeling (CFD-DEM)/Multiphase Simulations, laser propagation characterization for Air Force Remote Sensing/Directed Energy applications, exascale computing, biomass gasification, long-term weather forecast, turbulence modeling, and FSI analysis of flexible membranes. 

Phase stability is one of the most fundamental phenomena in the universe, and prediction of structure and properties of materials using computers is nowadays one of the most important problems in applied science. Dr. Jorge Muñoz and his group study the quantum mechanical origins of the phase stability of materials by developing computational, data science, and machine learning methods and tools to extend the reach of first-principles atomistic simulations of materials. Changes induced by pressure, temperature, chemical ordering, etc. in the electronic structure and magnetism of elemental metals and metallic alloys and their effect on the phonons and phonon entropy are of particular interest. The group also develops machine learning pipelines and data science solutions for applied problems in physics. 

Dr. Paras Mandal is currently an Associate Professor of Electrical and Computer Engineering (ECE) and the Director of Power & Renewable Energy Systems (PRES) Laboratory within the ECE Department at UTEP. His research interests include electric power systems operations and markets, renewable energy integration and forecasting, machine learning applications to power grid, and smart grid. He has authored more than 100 scientific articles and proven technical, academic and leadership skill with various awards and honors. He is a recipient of best papers award by IEEE and Young Engineer award from IEEJ. He participates and assumes leadership roles in multiple professional groups within the IEEE Power and Energy Society (PES). He is a Senior Member of IEEE, Vice-Chair of IEEE PEEC award subcommittee, Secretary of IEEE New Product Development (NPD) Committee – Selection & Quality Control Subcommittee, and Member of various IEEE working groups and subcommittees. Dr. Mandal is a regular reviewer of journals and conferences and, serves as an Editorial Board of the journals and a session chair and panelist in IEEE PES conferences. 

Shirley Moore received her PhD in Computer Sciences from Purdue University in 1990. She has 20 years of research experience in parallel scientific computing, which is her current main research interest. 

Dr. Sharma's research focuses on developing numerical methods for solving differential equations with applications in material sciences and drug delivery systems. 

Dr. Tandon’s research centers on the sustenance of community requires resilient infrastructure that enhances productivity, minimizes disruptions under stressed conditions, extends the life of existing infrastructure, and minimizes the frequency of maintenance. His main expertise lies in the development and evaluation of innovative, sustainable materials for highway infrastructure that generate minimal carbon footprint, including sustainable modification of existing materials like asphalt, aggregate, asphalt concrete, soils, and PCC, and development of new materials like geopolymer. He also has expertise in the evaluation of the impact of extreme climate events on transportation infrastructure (like flooding), the use of computational science for evaluating complex infrastructure materials, and the use of sustainable development approaches for smart cities. He has received major funding through the Texas Department of Transportation (TxDOT), as well has from sources like Federal Highway Administration, National Cooperative Highway Research Program, US Department of Transportation. Dr. Tandon has successfully collaborated with faculty within the department, outside the department, and from other universities to promote sustainable development. 

Dr. Tosh is an assistant professor in Computer Science at the University of Texas at El Paso. His research interests include Blockchain technology, cybersecurity, data provenance mechanisms, security of Internet of Battlefield Things (IoBT) environments, distributed system security, cyber-threat information sharing, cyber-insurance, cyber-risk assessment, game theory and mechanism design, and nature-inspired optimization techniques. He has been actively working with researchers from Air Force Research Lab (AFRL), Rome, NY and Army Research Lab (ARL), Adelphi, MD on the cybersecurity and blockchain research. Although the traditional centralized computing paradigm works well at present, the trust, privacy, and security issues are the main bottlenecks, which were overlooked. With a growing connectedness in our communities and increasing standards of cyber-crimes, security challenges are the must things to be addressed. With these motivations, Dr. Tosh has aligned his research focus in designing secure, decentralized, and scalable solutions for both civilian application (e.g. Internet of Things, Cloud and Edge computing platforms) and military/battlefield environments. 

Dr. Son-Young Yi is currently an Associate Professor of Mathematical Sciences and a core faculty of the Computational Science Ph.D. Program at the University of Texas at El Paso (UTEP). She received her M.S. degree in Mathematics from Seoul National University, South Korea. She then came to Purdue University, IN, where she received her Ph.D. degree in Mathematics under the guidance of late Dr. Jim Douglas, Jr.Dr. Yi’s research interests are in the field of Numerical Analysis and Scientific Computing, with a particular focus on numerical methods for partial differential equations. Specifically, her research includes the development and analysis of finite element methods for solid mechanics, flow and transport processes in deformable porous media, and fluid-solid interaction problems, the study of locally-conservative numerical schemes for advection-diffusion equations, and the multiscale modeling and analysis of flow in heterogeneous media. 

My research goals are towards understandings the properties of molecules, nanocrystals and materials using density functional theory. Major efforts of my group are devoted towards the development of density functional based code and the self-interaction-free density functional approximations. The code development projects are UTEP-NRLMOL electronic structure code that allows density functional calculations upto 50000 basis functions; FLOSIC (Fermi-Lowdin Orbial Self Interaction Corrected (FLOSIC)) code for self-interaction free density functional calculations and DFTSW (Density functional theory in the South West) code for density functional calculations.

I always have a passion for solving partial or stochastic differential equations that arise in emerging application areas using novel analytical or numerical tools. Particular problems that I am interested in include shock hydrodynamics, fluid-structure interaction, multi-material flows, and tumor growth modeling. While analytical approach is always my first choice of investigation, such as by proving the well-posedness of the mathematical model, computational methods are usually indispensable for solving these practically complex problems. To this end, I am also an expert in high-performance computing related to numerical simulation of physical and engineering systems.I am well prepared to achieve my research goals. Particularly, the undergraduate study at Peking University provided me solid mathematical background, the graduate study at Stanford University brought me to the frontier of computational mathematics, and the postdoctoral training at Duke University got me familiarized with cutting-edge high-performance computing technologies.Currently, I focus on three research subjects: firstly, a novel superconvergent hybrid-variable discretization framework for hyperbolic and parabolic partial differential equations; secondly, a computational framework for multi-physics problems that involve more than two mutually different physical entities; and finally, a mathematical and numerical model for tumor growth prediction as well as associated treatment plan studies.  

Rao Gudimetla received the Ph. D in Applied Physics from Oregon Health and Sciences University in the topic of the effects of the turbulent atmosphere on laser and speckle propagation. He has published several papers covering the areas of turbulent atmosphere effects, satellite tracking, thermal blooming, adaptive optics and atmospheric compensation. He is currently a senior research physicist at AFRL/Directed Energy Directorate, Maui branch.