William H. Hsu

William H. Hsu is an associate professor of Computing and Information Sciences at Kansas State University. He received a B.S. in Mathematical Sciences and Computer Science and an M.S.Eng. in Computer Science from Johns Hopkins University in 1993, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1998. His dissertation explored the optimization of inductive bias in supervised machine learning for predictive analytics. At the National Center for Supercomputing Applications (NCSA) he was a co-recipient of an Industrial Grand Challenge Award for visual analytics of text corpora. His research interests include machine learning, probabilistic reasoning, and information visualization, with applications to cybersecurity, education, digital humanities, geoinformatics, and biomedical informatics. Published applications of his research include structured information extraction; spatiotemporal event detection for veterinary epidemiology, crime mapping, and opinion mining; analysis of heterogeneous information networks. Current work in his lab deals with: data mining and visualization in education research; graphical models of probability and utility for information security; developing domain-adaptive models of large natural language corpora and social media for text mining, link mining, sentiment analysis, and recommender systems. Dr. Hsu has over 50 refereed publications in conferences, journals, and books, plus over 35 additional publications.

Publications

Predictive Analytics of Social Networks: A Survey of Tasks and Techniques
Ming Yang, William H. Hsu, Surya Teja Kallumadi. © 2016. 37 pages.
In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and...
Information Visualization Techniques for Big Data: Analytics Using Heterogeneous Data in Spatiotemporal Domains
William H. Hsu. © 2016. 16 pages.
This chapter presents challenges and recommended practices for visualizing data about phenomena that are observed or simulated across space and time. Some data may...
Foreword
William H. Hsu. © 2015. 3 pages.
This Foreword is included in the book Enhancing Qualitative and Mixed Methods Research with Technology.
Creating Open Source Lecture Materials: A Guide to Trends, Technologies, and Approaches in the Information Sciences
William H. Hsu. © 2015. 28 pages.
This chapter surveys recent and continuing trends in software tools for preparation of open courseware, in particular audiovisual lecture materials, documentaries...
Creating Open Source Lecture Materials: A Guide to Trends, Technologies, and Approaches in the Information Sciences
William H. Hsu. © 2015. 27 pages.
This chapter surveys recent and continuing trends in software tools for preparation of open courseware, in particular audiovisual lecture materials, documentaries...
Information Visualization Techniques for Big Data: Analytics using Heterogeneous Data in Spatiotemporal Domains
William H. Hsu. © 2014. 14 pages.
This chapter presents challenges and recommended practices for visualizing data about phenomena that are observed or simulated across space and time. Some data may...
Emerging Methods in Predictive Analytics: Risk Management and Decision-Making
William H. Hsu. © 2014. 425 pages.
Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular...
Continuous-Time Infinite Dynamic Topic Models: The Dim Sum Process for Simultaneous Topic Enumeration and Formation
Wesam Elshamy, William H. Hsu. © 2014. 36 pages.
Topic models are probabilistic models for discovering topical themes in collections of documents. These models provide us with the means of organizing what would...
Predictive Analytics of Social Networks: A Survey of Tasks and Techniques
Ming Yang, William H. Hsu, Surya Teja Kallumadi. © 2014. 37 pages.
In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and...
Creating Open Source Lecture Materials: A Guide to Trends, Technologies, and Approaches in the Information Sciences
William H. Hsu. © 2013. 28 pages.
This chapter surveys recent and continuing trends in software tools for preparation of open courseware, in particular audiovisual lecture materials, documentaries...
Mitigation Strategies for Foot and Mouth Disease: A Learning-Based Approach
Sohini Roy Chowdhury, Caterina Scoglio, William H. Hsu. © 2013. 28 pages.
Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected...
Mitigation Strategies for Foot and Mouth Disease: A Learning-Based Approach
Sohini Roy Chowdhury, Caterina Scoglio, William H. Hsu. © 2011. 35 pages.
Prediction of epidemics such as Foot and Mouth Disease (FMD) is a global necessity in addressing economic, political and ethical issues faced by the affected...
Handbook of Research on Computational Methodologies in Gene Regulatory Networks
Sanjoy Das, Doina Caragea, Stephen Welch, William H. Hsu. © 2010. 740 pages.
Recent advances in gene sequencing technology are now shedding light on the complex interplay between genes that elicit phenotypic behavior characteristic of any...
Incorporating Graph Features for Predicting Protein-Protein Interactions
Martin S.R. Paradesi, Doina Caragea, William H. Hsu. © 2009. 19 pages.
This chapter presents applications of machine learning to predicting protein-protein interactions (PPI) in Saccharomyces cerevisiae. Several supervised inductive...
Evolutionary Computation and Genetic Algorithms
William H. Hsu. © 2009. 6 pages.
A genetic algorithm (GA) is a method used to find approximate solutions to difficult search, optimization, and machine learning problems (Goldberg, 1989) by...
Genetic Programming
William H. Hsu. © 2009. 6 pages.
Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It...
Genetic Programming
William H. Hsu. © 2008. 15 pages.
Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It...
Evolutionary Computation and Genetic Algorithms
William H. Hsu. © 2005. 5 pages.
A genetic algorithm (GA) is a procedure used to find approximate solutions to search problems through the application of the principles of evolutionary biology....
Genetic Programming
William H. Hsu. © 2005. 5 pages.
Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It...
Control of Inductive Bias in Supervised Learning Using Evolutionary Computation: A Wrapper-Based Approach
William H. Hsu. © 2003. 28 pages.
In this chapter, I discuss the problem of feature subset selection for supervised inductive learning approaches to knowledge discovery in databases (KDD), and...