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Discrete-time Stochastic Systems: Estimation and Control (Advanced Textbooks in Control and Signal Processing)

This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.

Discrete Transforms

The analysis of signals and systems using transform methods is a very important aspect of the examination of processes and problems in an increasingly wide range of applications. Whereas the initial impetus in the development of methods appropriate for handling discrete sets of data occurred mainly in an electrical engineering context (for example in the design of digital filters), the same techniques are in use in such disciplines as cardiology, optics, speech analysis and management, as well as in other branches of science and engineering. This text is aimed at a readership whose mathematical background includes some acquaintance with complex numbers, linear differen­ tial equations, matrix algebra, and series. Specifically, a familiarity with Fourier series (in trigonometric and exponential forms) is assumed, and an exposure to the concept of a continuous integral transform is desirable. Such a background can be expected, for example, on completion of the first year of a science or engineering degree course in which transform techniques will have a significant application. In other disciplines the readership will be past the second year undergraduate stage. In either case, the text is also intended for earlier graduates whose degree courses did not include this type of material and who now find themselves, in a professional capacity, requiring a knowledge of discrete transform methods.

Discrete Wavelet Transformations: An Elementary Approach with Applications

Updated and Expanded Textbook Offers Accessible and Applications-First Introduction to Wavelet Theory for Students and Professionals The new edition of Discrete Wavelet Transformations continues to guide readers through the abstract concepts of wavelet theory by using Dr. Van Fleet’s highly practical, application-based approach, which reflects how mathematicians construct solutions to challenges outside the classroom. By introducing the Haar, orthogonal, and biorthogonal filters without the use of Fourier series, Van Fleet allows his audience to connect concepts directly to real-world applications at an earlier point than other publications in the field. Leveraging extensive graphical displays, this self-contained volume integrates concepts from calculus and linear algebra into the constructions of wavelet transformations and their applications, including data compression, edge detection in images and denoising of signals. Conceptual understanding is reinforced with over 500 detailed exercises and 24 computer labs. The second edition discusses new applications including image segmentation, pansharpening, and the FBI fingerprint compression specification. Other notable features include: Two new chapters covering wavelet packets and the lifting method A reorganization of the presentation so that basic filters can be constructed without the use of Fourier techniques A new comprehensive chapter that explains filter derivation using Fourier techniques Over 120 examples of which 91 are “live examples,” which allow the reader to quickly reproduce these examples in Mathematica or MATLAB and deepen conceptual mastery An overview of digital image basics, equipping readers with the tools they need to understand the image processing applications presented A complete rewrite of the DiscreteWavelets package called WaveletWare for use with Mathematica and MATLAB A website, www.stthomas.edu/wavelets, featuring material containing the WaveletWare package, live examples, and computer labs in addition to companion material for teaching a course using the book Comprehensive and grounded, this book and its online components provide an excellent foundation for developing undergraduate courses as well as a valuable resource for mathematicians, signal process engineers, and other professionals seeking to understand the practical applications of discrete wavelet transformations in solving real-world challenges.

Discrete Wavelet Transformations: An Elementary Approach with Applications

Updated and Expanded Textbook Offers Accessible and Applications-First Introduction to Wavelet Theory for Students and Professionals The new edition of Discrete Wavelet Transformations continues to guide readers through the abstract concepts of wavelet theory by using Dr. Van Fleet’s highly practical, application-based approach, which reflects how mathematicians construct solutions to challenges outside the classroom. By introducing the Haar, orthogonal, and biorthogonal filters without the use of Fourier series, Van Fleet allows his audience to connect concepts directly to real-world applications at an earlier point than other publications in the field. Leveraging extensive graphical displays, this self-contained volume integrates concepts from calculus and linear algebra into the constructions of wavelet transformations and their applications, including data compression, edge detection in images and denoising of signals. Conceptual understanding is reinforced with over 500 detailed exercises and 24 computer labs. The second edition discusses new applications including image segmentation, pansharpening, and the FBI fingerprint compression specification. Other notable features include: Two new chapters covering wavelet packets and the lifting method A reorganization of the presentation so that basic filters can be constructed without the use of Fourier techniques A new comprehensive chapter that explains filter derivation using Fourier techniques Over 120 examples of which 91 are “live examples,” which allow the reader to quickly reproduce these examples in Mathematica or MATLAB and deepen conceptual mastery An overview of digital image basics, equipping readers with the tools they need to understand the image processing applications presented A complete rewrite of the DiscreteWavelets package called WaveletWare for use with Mathematica and MATLAB A website, www.stthomas.edu/wavelets, featuring material containing the WaveletWare package, live examples, and computer labs in addition to companion material for teaching a course using the book Comprehensive and grounded, this book and its online components provide an excellent foundation for developing undergraduate courses as well as a valuable resource for mathematicians, signal process engineers, and other professionals seeking to understand the practical applications of discrete wavelet transformations in solving real-world challenges.

Discrete–Time Stochastic Control and Dynamic Potential Games: The Euler–Equation Approach (SpringerBriefs in Mathematics)

​There are several techniques to study noncooperative dynamic games, such as dynamic programming and the maximum principle (also called the Lagrange method). It turns out, however, that one way to characterize dynamic potential games requires to analyze inverse optimal control problems, and it is here where the Euler equation approach comes in because it is particularly well–suited to solve inverse problems. Despite the importance of dynamic potential games, there is no systematic study about them. This monograph is the first attempt to provide a systematic, self–contained presentation of stochastic dynamic potential games.

Discretization and Implicit Mapping Dynamics (Nonlinear Physical Science)

This unique book presents the discretization of continuous systems and implicit mapping dynamics of periodic motions to chaos in continuous nonlinear systems. The stability and bifurcation theory of fixed points in discrete nonlinear dynamical systems is reviewed, and the explicit and implicit maps of continuous dynamical systems are developed through the single-step and multi-step discretizations. The implicit dynamics of period-m solutions in discrete nonlinear systems are discussed. The book also offers a generalized approach to finding analytical and numerical solutions of stable and unstable periodic flows to chaos in nonlinear systems with/without time-delay. The bifurcation trees of periodic motions to chaos in the Duffing oscillator are shown as a sample problem, while the discrete Fourier series of periodic motions and chaos are also presented. The book offers a valuable resource for university students, professors, researchers and engineers in the fields of applied mathematics, physics, mechanics, control systems, and engineering.

Discretization and MCMC Convergence Assessment (Lecture Notes in Statistics #135)

The exponential increase in the use of MCMC methods and the corre­ sponding applications in domains of even higher complexity have caused a growing concern about the available convergence assessment methods and the realization that some of these methods were not reliable enough for all-purpose analyses. Some researchers have mainly focussed on the con­ vergence to stationarity and the estimation of rates of convergence, in rela­ tion with the eigenvalues of the transition kernel. This monograph adopts a different perspective by developing (supposedly) practical devices to assess the mixing behaviour of the chain under study and, more particularly, it proposes methods based on finite (state space) Markov chains which are obtained either through a discretization of the original Markov chain or through a duality principle relating a continuous state space Markov chain to another finite Markov chain, as in missing data or latent variable models. The motivation for the choice of finite state spaces is that, although the resulting control is cruder, in the sense that it can often monitor con­ vergence for the discretized version alone, it is also much stricter than alternative methods, since the tools available for finite Markov chains are universal and the resulting transition matrix can be estimated more accu­ rately. Moreover, while some setups impose a fixed finite state space, other allow for possible refinements in the discretization level and for consecutive improvements in the convergence monitoring.

Discretization Methods and Iterative Solvers Based on Domain Decomposition (Lecture Notes in Computational Science and Engineering #17)

Domain decomposition methods provide powerful and flexible tools for the numerical approximation of partial differential equations arising in the modeling of many interesting applications in science and engineering. This book deals with discretization techniques on non-matching triangulations and iterative solvers with particular emphasis on mortar finite elements, Schwarz methods and multigrid techniques. New results on non-standard situations as mortar methods based on dual basis functions and vector field discretizations are analyzed and illustrated by numerical results. The role of trace theorems, harmonic extensions, dual norms and weak interface conditions is emphasized. Although the original idea was used successfully more than a hundred years ago, these methods are relatively new for the numerical approximation. The possibilites of high performance computations and the interest in large- scale problems have led to an increased research activity.

Discretization Methods for Stable Initial Value Problems (Lecture Notes in Mathematics #1044)

Discretization of Processes (Stochastic Modelling and Applied Probability #67)

In applications, and especially in mathematical finance, random time-dependent events are often modeled as stochastic processes. Assumptions are made about the structure of such processes, and serious researchers will want to justify those assumptions through the use of data. As statisticians are wont to say, “In God we trust; all others must bring data.” This book establishes the theory of how to go about estimating not just scalar parameters about a proposed model, but also the underlying structure of the model itself. Classic statistical tools are used: the law of large numbers, and the central limit theorem. Researchers have recently developed creative and original methods to use these tools in sophisticated (but highly technical) ways to reveal new details about the underlying structure. For the first time in book form, the authors present these latest techniques, based on research from the last 10 years. They include new findings. This book will be of special interest to researchers, combining the theory of mathematical finance with its investigation using market data, and it will also prove to be useful in a broad range of applications, such as to mathematical biology, chemical engineering, and physics.

Discriminant Analysis and Statistical Pattern Recognition (Wiley Series in Probability and Statistics #583)

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition." –Computational Statistics Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.

Discriminants, Resultants, and Multidimensional Determinants (Modern Birkhäuser Classics)

"This book revives and vastly expands the classical theory of resultants and discriminants. Most of the main new results of the book have been published earlier in more than a dozen joint papers of the authors. The book nicely complements these original papers with many examples illustrating both old and new results of the theory."—Mathematical Reviews

Discrimination and Disparities

An enlarged edition of Thomas Sowell's brilliant examination of the origins of economic disparitiesEconomic and other outcomes differ vastly among individuals, groups, and nations. Many explanations have been offered for the differences. Some believe that those with less fortunate outcomes are victims of genetics. Others believe that those who are less fortunate are victims of the more fortunate.Discrimination and Disparities gathers a wide array of empirical evidence to challenge the idea that different economic outcomes can be explained by any one factor, be it discrimination, exploitation, or genetics. This revised and enlarged edition also analyzes the human consequences of the prevailing social vision of these disparities and the policies based on that vision--from educational disasters to widespread crime and violence.

Disease Mapping: From Foundations to Multidimensional Modeling

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.

Disease Mapping: From Foundations to Multidimensional Modeling

Disease Mapping: From Foundations to Multidimensional Modeling guides the reader from the basics of disease mapping to the most advanced topics in this field. A multidimensional framework is offered that makes possible the joint modeling of several risks patterns corresponding to combinations of several factors, including age group, time period, disease, etc. Although theory will be covered, the applied component will be equally as important with lots of practical examples offered. Features: Discusses the very latest developments on multivariate and multidimensional mapping. Gives a single state-of-the-art framework that unifies most of the previously proposed disease mapping approaches. Balances epidemiological and statistical points-of-view. Requires no previous knowledge of disease mapping. Includes practical sessions at the end of each chapter with WinBUGs/INLA and real world datasets. Supplies R code for the examples in the book so that they can be reproduced by the reader. About the Authors: Miguel A. Martinez Beneito has spent his whole career working as a statistician for public health services, first at the epidemiology unit of the Valencia (Spain) regional health administration and later as a researcher at the public health division of FISABIO, a regional bio-sanitary research center. He has been also the Bayesian Hierarchical Models professor for several seasons at the University of Valencia Biostatics Master. Paloma Botella Rocamora has spent most of her professional career in academia although she now works as a statistician for the epidemiology unit of the Valencia regional health administration. Most of her research has been devoted to developing and applying disease mapping models to real data, although her work as a statistician in an epidemiology unit makes her develop and apply statistical methods to health data, in general.

Disease Mapping with WinBUGS and MLwiN (Statistics in Practice)

Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages – such as WinBUGS and MLwiN – are now easy to implement in practice. Provides an introduction to Bayesian and multilevel modelling in disease mapping. Adopts a practical approach, with many detailed worked examples. Includes introductory material on WinBUGS and MLwiN. Discusses three applications in detail – relative risk estimation, focused clustering, and ecological analysis. Suitable for public health workers and epidemiologists with a sound statistical knowledge. Supported by a Website featuring data sets and WinBUGS and MLwiN programs. Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.

Disease Prevention and Health Promotion in Developing Countries

This book brings together two important discussions in public health in developing countries: an understanding of the burden of disease, health equity and social determinants of health; and biomathematical models, epidemiological studies and estimation of the direct and indirect cost of disease. The empirical chapters in the first part discuss aspects of disease prevention and health promotion in developing countries, with a particular focus on countries that are part of the World Health Organization’s Eastern Mediterranean Region and the African Region. Health equity and social determinants of health constitute a cornerstone of this book, with the widespread recognition that addressing the social determinants of health is crucial not only for improving general health but importantly for reducing unfair and remediable health inequalities. Using mathematical models, epidemiological studies and statistical estimation of costs, the second part of this book shows the opportunities that exist for developing countries to prevent disease and promote health by adopting cost-effective strategies and cost–benefit analyses.

Disease Surveillance: A Public Health Informatics Approach

An up-to-date and comprehensive treatment of biosurveillance techniques With the worldwide awareness of bioterrorism and drug-resistant infectious diseases, the need for surveillance systems to accurately detect emerging epidemicsis essential for maintaining global safety. Responding to these issues, Disease Surveillance brings together fifteen eminent researchers in the fields of medicine, epidemiology, biostatistics, and medical informatics to define the necessary elements of an effective disease surveillance program, including research, development, implementation, and operations. The surveillance systems and techniques presented in the text are designed to best utilize modern technology, manage emerging public health threats, and adapt to environmental changes. Following a historical overview detailing the need for disease surveillance systems, the text is divided into the following three parts: Part One sets forth the informatics knowledge needed to implement a disease surveillance system, including a discussion of data sources currently used in syndromic surveillance systems. Part Two provides case studies of modern disease surveillance systems, including cases that highlight implementation and operational difficulties as well as the successes experienced by health departments in the United States, Canada, Europe, and Asia. Part Three addresses practical issues concerning the evaluation of disease surveillance systems and the education of future informatics and disease surveillance practitioners. It also assesses how future technology will shape the field of disease surveillance. This book's multidisciplinary approach is ideal for public health professionals who need to understand all the facets within a disease surveillance program and implement the technology needed to support surveillance activities. An outline of the components needed for a successful disease surveillance system combined with extensive use of case studies makes this book well-suited as a textbook for public health informatics courses

Disease Surveillance: A Public Health Informatics Approach

An up-to-date and comprehensive treatment of biosurveillance techniques With the worldwide awareness of bioterrorism and drug-resistant infectious diseases, the need for surveillance systems to accurately detect emerging epidemicsis essential for maintaining global safety. Responding to these issues, Disease Surveillance brings together fifteen eminent researchers in the fields of medicine, epidemiology, biostatistics, and medical informatics to define the necessary elements of an effective disease surveillance program, including research, development, implementation, and operations. The surveillance systems and techniques presented in the text are designed to best utilize modern technology, manage emerging public health threats, and adapt to environmental changes. Following a historical overview detailing the need for disease surveillance systems, the text is divided into the following three parts: Part One sets forth the informatics knowledge needed to implement a disease surveillance system, including a discussion of data sources currently used in syndromic surveillance systems. Part Two provides case studies of modern disease surveillance systems, including cases that highlight implementation and operational difficulties as well as the successes experienced by health departments in the United States, Canada, Europe, and Asia. Part Three addresses practical issues concerning the evaluation of disease surveillance systems and the education of future informatics and disease surveillance practitioners. It also assesses how future technology will shape the field of disease surveillance. This book's multidisciplinary approach is ideal for public health professionals who need to understand all the facets within a disease surveillance program and implement the technology needed to support surveillance activities. An outline of the components needed for a successful disease surveillance system combined with extensive use of case studies makes this book well-suited as a textbook for public health informatics courses

Diseases of the Cardiac Pump (Biomathematical and Biomechanical Modeling of the Circulatory and Ventilatory Systems #7)

Together, the volumes in this series present all of the data needed at various length scales for a multidisciplinary approach to modeling and simulation of flows in the cardiovascular and ventilatory systems, especially multiscale modeling and coupled simulations. The cardiovascular and respiratory systems are tightly coupled, as their primary function is to supply oxygen to and remove carbon dioxide from the body's cells. Because physiological conduits have deformable and reactive walls, macroscopic flow behavior and prediction must be coupled to nano- and microscopic events in a corrector scheme of regulated mechanism. Therefore, investigation of flows of blood and air in anatomical conduits requires an understanding of the biology, chemistry, and physics of these systems together with the mathematical tools to describe their functioning in quantitative terms.The present volume focuses on macroscopic aspects of the cardiovascular and respiratory systems in pathological conditions, i.e., diseases of the cardiac pump, blood vessels, and airways, as well as their treatments. Only diseases that have a mechanical origin or are associated with mechanical disorders are covered. Local flow disturbances can trigger pathophysiological processes or, conversely, result from diseases of conduit walls or their environment. The ability to model these phenomena is essential to the development and manufacturing of medical devices, which incorporate a stage of numerical tests in addition to experimental procedures.

Disequilibrium and Self-Organisation (Mathematics and Its Applications #30)

Approach your problems from the right end It isn't that they can't see the solution. It is and begin with the answers. Then one day, that they can't see the problem. perhaps you will find the final question. G. K. Chesterton. The Scandal of Father 'The Hermit Clad in Crane Feathers' in R. Brown 'The point of a Pin'. van Gulik's The Chif1ese Maze Murders. Growing specialization and diversification have brought a host of monographs and textbooks on increasingly specialized topics. However, the "tree" of knowledge of mathematics and related fields does not grow only by putting forth new branches. It also happens, quite often in fact, that branches which were thought to be completely disparate are suddenly seen to be related. Further, the kind and level of sophistication of mathematics applied in various sciences has changed drastically in recent years: measure theory is used (non­ trivially) in regional and theoretical economics; algebraic geometry interacts with physics; the Minkowsky lemma, coding theory and the structure of water meet one another in packing and covering theory; quantum fields, crystal defects and mathematical programming profit from homotopy theory; Lie algebras are relevant to filtering; and prediction and electrical engineering can use Stein spaces. And in addition to this there are such new emerging subdisciplines as "experimental mathematics", "CFD", "completely integrable systems", "chaos, synergetics and large-scale order", which are almost impossible to fit into the existing classification schemes. They draw upon widely different sections of mathematics.

Disequilibrium Economics: Oligopoly, Trade, and Macrodynamics

This book discusses mathematical models for various applications in economics, with a focus on non-linear dynamics. Based on the author’s over 50 years of active work in the field, the book has been inspired by models from the period between 1920 and 1950. Following a brief introduction to economics for mathematicians and other modelers, it assembles a repository of useful specific functions for global dynamic modeling. Furthermore, twelve “research stubs” – outlined research agendas that have not yet been fully worked on – are suggested for further study and could even be expanded to entire research projects. The book is a valuable resource, particularly for young scientists who are skilled in mathematical and computational techniques and are looking for applications in economics.

A Disequilibrium Model of Real and Financial Accumulation in an Open Economy: Theory, Evidence, and Policy Simulations (Lecture Notes in Economics and Mathematical Systems #236)

This is the fourth version of a model that five years ago we set out to build and estimate along the lines of the continuous time approach clarified In chapter 1. Previous versions appeared in journal articles and conference proceedings, where the space is notoriously limited. Therefore we welcome the possibility of publishing a book-length treatment of this fourth version, so that we can describe its theoretical and empirical aspects in some detail. Although we have worked closely together and accept joint responsibility for the whole book, chs. 1 and 2 and appendix I have been written by G. Gandolfo, whilst chs. ] and 4 and appendix II have been written by P.c. Padoan. Different parts of this version of the model have been discussed In various lectures at the European University Institute (Florence) in 1984, In a seminar organized by the Bank of Italy (Sadiba, Perugia, Italy, February 16-18, 1984), in the second Viennese Workshop on Economic Applications of Control Theory (Vienna, May 16-18, 1984), and in the sixth annual Conference of the Society for Economic Dynamics and Control (Nice, France, June 13-15, 1984). In all of these we received helpful comments; similarly helpful were the comments of Clifford R .. Wymer, who, however, is absolved of any responsibility.

Disjunctive Programming

Disjunctive Programming is a technique and a discipline initiated by the author in the early 1970's, which has become a central tool for solving nonconvex optimization problems like pure or mixed integer programs, through convexification (cutting plane) procedures combined with enumeration. It has played a major role in the revolution in the state of the art of Integer Programming that took place roughly during the period 1990-2010. The main benefit that the reader may acquire from reading this book is a deeper understanding of the theoretical underpinnings and of the applications potential of disjunctive programming, which range from more efficient problem formulation to enhanced modeling capability and improved solution methods for integer and combinatorial optimization. Egon Balas is University Professor and Lord Professor of Operations Research at Carnegie Mellon University's Tepper School of Business.

Disk-Based Algorithms for Big Data

Disk-Based Algorithms for Big Data is a product of recent advances in the areas of big data, data analytics, and the underlying file systems and data management algorithms used to support the storage and analysis of massive data collections. The book discusses hard disks and their impact on data management, since Hard Disk Drives continue to be common in large data clusters. It also explores ways to store and retrieve data though primary and secondary indices. This includes a review of different in-memory sorting and searching algorithms that build a foundation for more sophisticated on-disk approaches like mergesort, B-trees, and extendible hashing. Following this introduction, the book transitions to more recent topics, including advanced storage technologies like solid-state drives and holographic storage; peer-to-peer (P2P) communication; large file systems and query languages like Hadoop/HDFS, Hive, Cassandra, and Presto; and NoSQL databases like Neo4j for graph structures and MongoDB for unstructured document data. Designed for senior undergraduate and graduate students, as well as professionals, this book is useful for anyone interested in understanding the foundations and advances in big data storage and management, and big data analytics. About the Author Dr. Christopher G. Healey is a tenured Professor in the Department of Computer Science and the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics, both at North Carolina State University in Raleigh, North Carolina. He has published over 50 articles in major journals and conferences in the areas of visualization, visual and data analytics, computer graphics, and artificial intelligence. He is a recipient of the National Science Foundation’s CAREER Early Faculty Development Award and the North Carolina State University Outstanding Instructor Award. He is a Senior Member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE), and an Associate Editor of ACM Transaction on Applied Perception, the leading worldwide journal on the application of human perception to issues in computer science.