Software Engineering addresses the problem of how to produce quality software on time and within a specific budget. Systems work addresses the problem of how to build large computer systems that accomplish some specific purpose. Specific research problems in Software Engineering that we are investigating include: how best to describe what software systems are supposed to do (specification), how to ensure that computational systems behave correctly (verification), model checking of software and hardware systems with a particular emphasis on scalable formal verification of cyber-physical systems and quantitative computational models, how to parallelize systems for maximum efficiency, how to model, analyze, and optimize the performance of software, and how to build large, concurrent and distributed systems.
The Computational Modeling laboratory investigates new high-performance computing (HPC) algorithms for discovering, synthesizing and validating computational models of engineered and natural systems from extreme scale data repositories and expert insight. We strive to provide a theoretical computer science foundation for the analysis of computational models including stochastic, agent-based, equation-based, and hybrid models. The interdisciplinary research in our group includes the following topics:
• Discovering predictive models from extreme-scale data repositories.
• Verifying computational models against data and expert insight.
• Scalable petascale and exascale simulation algorithms for quantitative models.
• Software tools for facilitating use of next-generation HPC hardware.
The focus of the laboratory is on building computer-aided design tools that enable practitioners to understand extreme scale data and rapidly prototype predictive models of complex systems. The lab includes National Science Foundation Graduate Fellow Emily Rebecca Sassano and UCF Graduate Research Excellence Fellow Faraz Hussain. Our lab collaborates closely with researchers from other universities, industries, defense and national laboratories, and applies our ongoing research to practical problems in computational sciences including those in computational systems biology, cyber-physical systems, agent-based modeling, and computational finance.
Computational Modeling Laboratory
The CSE – S3 Lab explores problems related to the development of programming techniques and tools for scalable software design for modern multicore and exascale systems. Future high-performance computing systems will be qualitatively different from current leadership-class machines. The main driving force for the growth in computational power will be the significant increase of parallelism on-chip. Adapting software applications will be difficult, as the architectural complexity of HPC systems will be high in terms of their degree of concurrency and heterogeneity, sensitivity to communications and data movement, and requirements for locality. At the CSE-S3 Lab, Dr. Dechev leads the efforts for discovering novel fine-grained algorithms and data structures, which will allow parallel applications to strongly scale. Furthermore, the CSE-S3 Lab performs forward-looking research in developing new methodologies for automatic large-scale performance analysis and optimization of HPC software and systems. The lab’s applied research aims to engineer software tools that facilitate the automatic analysis and evolutionary migration of applications, rapid prototyping of alternative solutions and new programming practices, and accurate performance modeling on a variety of HPC architectures ranging from existing large-scale clusters to future exascale systems.
Scalable and Secure Systems Lab
The Data Systems Group at UCF conducts cutting edge research focusing on the following important aspects of data:
• Data Management
• Data Analysis
• Data Communication
• Data Security and Privacy
Over the years our research has been supported by numerous funding agencies both government and private, including National Science Foundation, NASA, Florida Department of Transportation, Symantec Inc., Intel Inc., Oracle Corp., etc. We pride ourselves in building industrial strength prototypes and also working with the UCF Office of Research and Commercialization to help commercialize our research.
Data Systems Group
The goal of the formal methods lab is to improve human understanding of programming and programming methods. In particular we are interested in ways to write, specify, and verify object-oriented and aspect-oriented programs so that they are correct and have other desirable properties (such as safety and reliability). The lab’s flagship product is the Java Modeling Language (JML), which is a formal specification language for Java. The lab’s research has been supported by grants from the US National Science Foundation.
Formal Methods Lab
University of Nottingham, 2010
Ph.D., Computer science; Pennsylvania State University, 1982
Power electronics, solar energy conversion circuits and systems, dc-to-dc conversion, dynamic and control of power converters, power factor correction.
Ph.D., Electrical Engineering, University of Illinois at Chicago, 1990
Distributed systems, network agents, ubiquitous computing, and knowledge representation
Ph.D., Purdue University, 2000
Cognitive radio networks, dynamic spectrum access, economic issues in networks, applied game and auction theories, ad hoc and sensor networks, resource management and QoS provisioning, mobile video delivery, subjective video quality assessment (QoE).
Ph.D., University of Texas at Arlington, 2002
Ph.D., Texas A&M, 2009
Computer Architechture, Intelligent Systems, Evolvable Hardwaredemara@ece.ucf.edu
Parallel computation, combinatorial computing, graph theory, network optimization algorithms, parallel algorithms/parallel data structures
Ph.D., Northwestern University, 1965
Computational complexity design and analysis of algorithms, graph email@example.com
Expertise in Java programming and Cisco enterprise networking equipment.
Ed.S., Nova Southeastern University, 1998
University of Central Florida (2013)
Ultra-low Power Brain-inspired (Neuromorphic), Non-Boolean and Boolean Computing Using Emerging Nanoscale Devices, Device/ Circuit/ Architecture Co-design for Ultra-low Power, High Performance System
Purdue University (2015)
Deep Learning, Computer VisionStevenLawrence.Fernandes@ucf.edu
Computer vision, computer graphics visualization, signal image processing, bayesian estimation & decision theory, multimedia communications stochastic processes optimization theory
Ph.D., INRIA, France, 1996
Ph.D. Texas A&M University College Station, 2005
Areas of Specialty:
Big Data, Data Analytics, Deep Learning, Artificial Intelligence, Innovation, Simulation, and Agent-Based Models, Complex Systems, Computational Economics, Evolutionary Computation, and Computational Social Sciences.
Computer vision, machine learning, domain adaptation, transfer learning, object and human activity recognition
Ph.D., University of Southern California, 2015
Artificial intelligence, Machine Learning, knowledge-based systems, automated diagnostics, intelligent simulations, validation and verification of knowledge based systems
Ph.D., University of Pittsburgh, 1979
MS, University of Wisconsin
Distributed systems, computer networks, security protocols, modeling and simulation, and computer graphics
Ph.D., University of Texas, 1970
Computer architecture, parallel computer architecture, active memory and I/O systems, scalable cache coherence protocols, system-area networks, multiprocessor simulation methodology, and hardware/software co-design.
Ph.D., Stanford University, 1998
University of Central Florida
Data mining, especially frequent pattern mining across massive biological networks; integrative approaches to identifying phenotype specific pathways; DNA motif discovery and regulatory network interface; gene/protein function prediction; integration of e
Ph.D., University of Southern California, 2006
Multimedia databases, multimedia information systems, multimedia communications, Internet computing, wireless networks, parallel and distributed systems
Ph.D., University of Illinois at Urbana Champaign, 1987
Mixed and virtual reality models of parallel and distributed computation theory of computation
Ph.D., Pennsylvania State University, 1970
Ph.D., Carnegie Mellon University, 2010
Ph.D., Brown University, 2005
Programming and specification language design and semantics, formal methods (program specification and verification), aspect-oriented languages, object-oriented languages, distributed languages, type theory, programming methodology, software engineering,
Ph.D., Massachusetts Institute of Technology, 1988
Capella University (2014)
Temporal databases, operating systems architecture
Ph.D., University of Central Florida, 1994
Computational vision, active vision and mobile robotics, visual modeling for graphics
Ph.D., University of Toronto, 1993
Parallel and distributed systems, computational sciences, quantum information processing
Ph.D., Polytechnic Institute Bucharest, 1976
Ph.D. University of Minnesota Twin Cities, 2012
Computer Architecture support for irregular problems, Parallel processing, Quantum Computing
Ph.D., Central University of Venezuela, 2004
Ed.D., Arizona State University, 2007
University of Nevada, Reno, 1998
Database systems, object oriented systems
Ph.D., Ohio State University, 1984
Realistic image synthesis and display, and visualization
Ph.D., Birla Institute of Science and Technology, 1993
Computer vision, gesture recognition, lipreading shape from shading, visual surveillance, visual motion, motion based recognition, optical flow
Ph.D. Computer Engineering, Wayne State University, 1986
Ph.D., University of Pennsylvania, 2002
The evolution of increasingly complex neural networks
Ph.D., University of Texas at Austin, 2004
multi-agent systems, machine learning, robotics
Ph.D., Carnegie Mellon University, 2007
Louisiana State University, 2014
Wireless ad hoc, sensor and vehicular networks, value of information and privacy in Internet of Things (IoT), big data in STEM education
Ph.D., University of Texas at Arlington, 2002
Computer Architecture, OS and High Performancejuwang@ece.ucf.edu
Research interests primarily focus on methods of natural computation, theory of coadaptive and coevolutionary computation, and application of coadaptive methods for multiagent learning.
George Mason University (2004)
Ph.D., University of Karlsruhe, 2003
Genetic algorithms, evolutionary computation visualization, machine learning
Ph.D., University of Michigan, 1995
Wireless circuit design for reliability, RF energy harvesting for biomedical applications, sensor interface circuits for internet of things, and secure integrated circuits using CMOS or emerging firstname.lastname@example.org
Computer Communication Networks, Wireless Systems, Optical Wireless, Spectrum Sharing, Network Economics and Architectures, and Network Managementmurat.email@example.com
Computational biology and bioinformatics
Ph.D., University of California, San Diego, 2007
Ph.D., University of Minnesota Twin Cities, 2015