R. Paul Wiegand

Natural Computation & Coadaptive Systems Lab Advanced Research Computing Center

Phone: (407) 882-0313
E-mail: ceh@cs.ucf.edu

R. Paul Wiegand is an Research Associate Professor at the University of Central Florida's Institute for Simulation and Training.  He conducts research in machine learning and optimization, teaches for the Modeling & Simulation graduate program, and directs the Advances Research Computing Center.  He also holds a secondary joint appointment with the Department of Computer Science

Dr. Wiegand runs the Natural Computation & Coadaptive Systems lab. His research interests primarily focus on methods of natural computation, theory of coadaptive and coevolutionary computation, application of coadaptive methods for multiagent learning, and high performance / high throughput computing. More generally, he is interested in designing and applying effective learning algorithms and representations for generating and modeling robust heterogeneous, multiagent team behaviors.


Biologically Inspired Approaches for Reasoning on Complex Functional Networks

The NCCS lab has worked with geographers at the University of Wyoming on grants from the National Geospatial Intelligence Agency to investigate efficient methods to learn patterns of behaviors of entities operating within a network that is geospatially embedded (e.g., people walking along streets in a town). This includes investigation of robust graph-based, probabilistic methods for matching behaviors of entities operating on such networks under different types of uncertainty, which allows one to recognize when two routes within a network are similar, even when those routes share little explicit path information as well as a way to detect anomalous activity patterns on the network in near real-time. 

Coevolutionary Peer Instruction

The NCCS lab works with educational specialists and instructors of introductory computer science courses at the Citadel, University of South Florida, and Ramapo on NSF-funded work to examine machine learning methods for extracting comparison geometries from student-problem interactions for both categorization of student challenges, as well as development of improved problem sets.  These include leveraging ideas from the theory of coevolutionary computation to to identify programming concepts that are informatively easy or hard for students to master.  Our approach is different from more traditional analyses such as item response analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student-problem performance data rather than average performance measures. This method permits us to uncover not just the problems on which students are struggling, but the variety of difficulties different students face. The results of our analysis not only have implications for how to scale up and improve adap- tive tutoring software, but also have the promise of contributing to the construction of a concept inventory for introductory programming.

RICHES Mosaic Interface

The NCCS lab consults with the Regional Initiative for Collecting Histories, Experiences and Stories (RICHES) project from the UCF Department of History by providing machine learning expertise and advice in guiding the Connections feature of an archive of digitized historical documents, imagery, and audio, the RICHES Mosaic Interface (MI).  Connections uses a multi-criteria connections algorithm to make item selection recommendations and browse through the MI. The Connections algorithm allows researchers to examine a selected artifact and nearest related items in the archive based on multiple criteria from the metadata contained in the artifact of interest.

Nature-Inspired Coordinated Control Systems for Multi-Agent/Robot Systems

The NCCS lab has investigated the use of particle and swarm inspired systems for multiagent control.  In particular, we have examined how complex, heterogeneous multi-agent/robot systems can be constructed such that the growth of the parameter space scales reasonably with the different types and specializations within the team.  This is useful for robust, cooptimized learning methods.  Moreover, we have examined how the dichotomy of the nature of such models to both predict simulated natural behavior and provide control instructions to the underlying agents can complicate representation and machine learning methods for behaviors.  These complications are even more sophisticated when team members include human agents that are not under the direct control of the nature-based system.

High Performance Benchmarking for Practical, Distributed Applications

The NCCS lab has worked with a number of researchers both to establish robust benchmarking methods for various practical, distributed applications, as well as to implement computationally efficient practical solutions to key engineering tasks.  These include benchmarks for distributed constructive simulation for the DoD, as well as implementation of solvers for large-scale, sparse FEM/FVM solvers for GPU, and hybrid GPU/distributed systems on applications relevant to NASA and JPL.