Research Interests
My broad research interests are in the folowing areas:
- Biomedical Image Reconstruction Algorithms and Image Processing
- Bioinformatics
- Application of soft computing methods such as neural networks, fuzzy logic, genetic algorithms etc.
- Graphics and Visualization
External Grant and Contracts
Current Support
Agricultural Research Initiative
Development of recognition software as an initial step to automate the assessment of seed quality
Role: PI ; $58,349 ; January 2009 - January 2010
National Science Foundation
IDBR: Excitation-Emission Matrix Fluorescence Detection for Capillary Electrophoresis (NSF DBI 0754837)
Role: Co-PI (PI: Dr. Timothy Corcoran); $177,564 ; May 2008 - June 2010
Past Support
L-3 Communications/Interstate Electronics Corporation
Advanced 3-D Locator Base Station Software (Subcontract of Advanced 3-D Locator for First Responders)
Role: PI ; $78,400 ; January 2007 - June 2008
National Science Foundation
Acquisition of a workstation network for research in parallel and distributed computing (NSF MRI 0321333)
Role: Co-PI (PI: Dr. Hairong Kunag); $159,658 ; September 2003 - August 2005
National Textile Center (US Department of Commerce)
Fuzzy Forecasting Model for Apparel Sales (NTC S01-PH10)
Role: PI; $300,000 ; June 2001 - May 2004
National Textile Center (US Department of Commerce)
Haptic Simulation of Fabric Hand (NTC S00-PH08)
Role: Co-PI (PI: Dr. Muthu Govindaraj); $400,000 ; June 2000 - May 2003
Collaborators (Current and Past) and Resarch Projects
Dr. David Still Cal Poly, Pomona
Planting high quality seed is the basis by which agriculture remains profitable. Every seed lot sold commercially has had an assessment of
seed quality which required germinating seeds under multiple environmental conditions. Final germination under benign (control) conditions
indicates the potential of the seed lot, but a sensitive indicator to seed germination is provided by germination rate, particularly if evaluated
under various stressful environments which reduce germination capacity. Typically seed companies and commercial seed testing laboratories evaluate
thousands to tens of thousands of seed lots each year. Further, in order to discover the genetic determinants of seed germination under environmental
stress and the release from dormancy, researchers typically work with large numbers of genetic populations, each of which must be evaluated under a
multitude of environmental conditions. A bottleneck exists in evaluating seed germination both for commercial and research purposes. The research we are
collaborating on seeks to develop software by which the evaluation of seed germination may be automated by developing algorithms that will count seeds in a
Petri dish using an image of the seeds and to develop algorithms that differentiate a germinated seed from an not-germinated seed from images of these seeds
taken at various time intervals.
Dr. Timothy Corcoran Cal Poly, Pomona
Fluorescent labels data collected into a data cube with dimensions excitation × emission × time. Using the method of parallel factor analysis, the abundance of each
smoothly-varying spectra of typical fluorescent labels can be extracted as a time series, even in the presence of unknown interferents. Compression of the data cube
to as little as 1% of its original size via well chosen multidimensional hybrid wavelet transforms, data analysis accelerates by factors as large as 50 without
compromising accuracy. The data acquisition software will be developed using LabView, a powerful programming environment well-suited to instrument data acquisition and
the parallel factor analysis implemented in some high level programming language.
Dr. Wely Floriano Cal Poly, Pomona
Cassandra: a new tool for virtual ligand screening. Cassandra will be a structure-based molecular design computational tool that
researchers can easily use to identify/design potential therapeutic compounds for any target protein with known or predicted 3D structure.
This tool will allow researches to seamlessly step through different structure-based molecular design applications, from binding site recognition
and affinity profiling, to pharmacophore-based searches and genome data mining. This will shorten the training period for new researchers, make our
in-house tools available for other laboratories and research groups to use and validate, and will also provide numerous new opportunities for
research and educational use.
Currently, QT is being used to develop an intutive multi-platform GUI for Cassandra.
Dr. Dennis Livesay Univ. of North Carolina, Charlotte
Catalytic site prediction from sequence and structure: This research is investigating ways to predict specific catalytic residues
from sequence and structure. We are using machine learning and soft computing techniques to predict catalytic sites solely from sequence-derived information (i.e. alignment conservation, phylogenetic motifs and
predicted secondary structure). At the same time, we are investigating structure-based predictions schemes as well. For example, we are currently
studying catalytic residue predictions from network models, which recast protein structures as graphs.
Dr. Koushik Adhikari, Kansas State University, Manhattan
Create a software tool based on neual networks and genetic algorithms to predict the sensory analysis of different food products such as
meat, cheese and ice cream. Sensory data sets are complex because they involves human senses and judgments, which can be considered unstructured as opposed to instrumental data.
One of the goals of this research is to provide improved understanding of the relationship between specific characteristics of variables that make a desirable and marketable product.
This software will also provide the stepping stool to the ultimate goal of reverse engineering of consumer products coming up with the values of independent variables, given the desired
values of the dependent variables.
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