– Director Praveen Madiraju
The database lab is dedicated to conducting research in both theoretical and system aspects of databases. There are many potential areas which will benefit as a result of the research carried out in this laboratory, such as information systems, health care, bioinformatics and others. Current projects include
- Constraints in Databases – Different issues related to constraints such as representation, checking and optimization for heterogeneous databases including both relational and XML databases. 1 MS student is working on the project.
- Community Information Management (CIM) – Data management issues related to discovery and management of peers in a community. 2 MS students are working on the project. Once the initial results are in, we plan to submit for grant.
- Agent Module for a System of Mobile Devices – SyD (System of Mobile Devices) is a new middleware that enables rapid application development for heterogeneous, autonomous and mobile devices. Improving the agent module for SyD
Systems Lab - Director Dennis Brylow
The lab creates new tools and methods for building and studying complex computer systems. Our emphasis is on embedded, real-time, and network systems, with strong ties to the electrical and computer engineering community, and the computer science education community. Current projects include:
- Experimental Embedded Networking Platform. Creation of laboratory infrastructure and software for research and education in the area of embedded networking appliances, particularly wireless routers and IP telephony. Collaboration with Cisco Systems Advanced Research Division.
- Experimental Embedded Operating System Laboratory. Creation of laboratory infrastructure and software for research and education in area of embedded operating systems. Collaboration with University of Buffalo and University of Mississippi, with funding from the National Science Foundation.
- Embedded Software Transactional Memory. Exploration of an innovative transactional memory model for guaranteeing process synchronization in embedded operating systems. Collaboration with Intel Research.
Ubicomp Lab - Director Sheikh Iqbal Ahamed
This lab focuses on the research issues in pervasive/ubiquitous computing systems and applications. Current projects include:
- Middleware services for pervasive/ubiquitous computing systems and applications -developing energy efficient infra-structure less device discovery, secure service discovery, location detection and self healing services on wirelessly connected PDAs and Sensors. Using these services to build different applications such as assessment tool, asset tracking, home monitoring, and healthcare applications. Got some equipment from Microsoft research. 2 MS students and 2 undergrad students are working.
- Security, trust and privacy in pervasive/ubiquitous computing- developing initial results for writing grants. 2 MS students are working. NSF support is being requested.
- Pervasive healthcare - applying pervasive computing technologies for wellness monitoring, elderly care, cancer patient care. Collaborating with Medical college of Wisconsin, Milwaukee, WI and Marshfield Clinic, Wausau, WI. 1 undergrad and 1 MS student are working. Microsoft research grant has been requested. NIH support is being requested.
- RFID security - developing security solutions for RFID. Developing initial results for writing grants. 1 MS student is working.
Semigroup Theory - Director Peter Jones
In this lab, recent research has centered on the role of restriction
semigroups, within semigroup theory itself and as connected with topics
in language theory. The primary current goal is to attain a greater
understanding of the lattices of varieties of
these semigroups, in their two-sided and one-sided guises. A secondary
goal is to extend the relationship between varieties of restriction
semigroups and varieties of categories to the broader context of
semigroupoids, which in turn will require introducing
a broader class of semigroup-like structures.
Computational Algebra - Director Michael Slattery
Research in this area offers a rare cross-over between pure mathematics and computation. In the case of Dr. Slattery, it is concerned with designing and analyzing algorithms and data structures to compute information about groups. Two important computer algebra systems (CAS) used for group theory are GAP and MAGMA.
Applied Graph Theory – Director Kim Factor
This laboratory applies graph theory and combinatorics to a variety of areas, as such as network traffic in power sources and the use of graph theory to determine protocols for biosurveillance with an emphasis in animal diseases. This is a continually growing and changing area with many opportunities.
Graph Theory - Director John Engbers
This laboratory investigates extremal and probabilistic questions in graph theory and combinatorics, including graph coloring questions and games on graphs. Some specific topics currently being studied include independent sets, vertex colorings, and alternating sign matrices.
Computational Applied Mathematics
GasDay Lab - Director Ron Brown, with Richard Povinelli, George Corliss, Tom Quinn, and others.
GasDay forecasts natural gas consumption for about 30
energy delivery companies all over the US. We build mathematical models,
perform statistical analysis of data, develop software, deliver that
software to customer utilities, prepare custom
research reports, and provide on-going customer support. We function
like a small business within the University. Each day, we provide
forecasts for nearly 20% of the natural gas in the US for residential,
industrial, and commercial uses. We are a student
centered learning project, a research project, and the technology
transfer project. Students do research and publish in the areas of
mathematical modeling, data mining, statistical analysis, database
design, computer systems architecture, and software engineering.
GasDay supports 10 graduate students and 20+ undergraduate students.
Participating faculty come from MSCS, Engineering, and Business.
Electrical Impedance Tomography - Director Sarah Hamilton
Electrical Impedance Tomography (EIT) is a non-invasive, portable, radiation-free, imaging modality used for monitoring hearth and lung function of patients in the ICU setting. The goal of EIT is to recover the internal structure of a body from electrical measurements taken at the surface. The conductivity and permittivity of biological tissues such as heart, lung, blood, and fat are different allowing a medical doctor to then use the EIT images for diagnostic/monitoring purposes. Additional applications include: detection and classification of breast tumors, stroke classification in brain imaging, non-destructive evaluation on concrete structures, detection of groundwater contamination, oil exploration and landmind detection. The reconstruction task is a highly ill-posed nonlinear inverse problem and requires the use of mathmatical techniques from functional and complex analysis as well as computational skills. Traditional methods based on least squares minimization or linearization are not sufficient for static imaging applications (breast cancer, stroke). Our lab focuses on solving the full nonliear problem directly (non-iteratively), in real-time, using any available information to improve the images.
Pulmonary Lab – Director Gary Krenz
Applies mathematical modeling to address basic science questions regarding nonrespiratory functions of the [mammalian] pulmonary circulation. Current projects include:
- Pulmonary disposition of quinones. The working hypothesis is that metabolic changes precede the remodeling that occurs in pulmonary hypertension. Quinones are used as probes to investigate changes in the pulmonary endothelium redox status/signaling. Work is in collaboration with Marquette Biomedical Engineering and Zablocki/Medical College of Wisconsin researchers.
- Structure/function studies of the pulmonary circulation. Investigation of how structural changes that result in several biological models of pulmonary hypertension (chronic hypoxia exposure, monocrotaline exposure, thoracic irradiation) impact upon the hemodynamic functioning of the pulmonary circulation. 1 Ph.D. student working on the impact of supernumerary vessel recruitment may have upon normal pulmonary function.
Bioinformatics Lab - Director Serdar Bozdag
This lab applies machine learning and algorithmic techniques to biologically motivated questions. Particularly, we are interested in building models of gene regulation from high-throughput biomedical datasets in tumor cells. Current projects include:
- Age-specific signatures of glioblastoma. Glioblastoma (GBM), one of the deadly types of brain cancer exhibits different survival rates among young and old patients. More specifically, old GBM patients survive significantly shorter than young GBM patients. In this project, we analyze high-throughput genomic, genetic, and epigenetic datasets of several hundreds of GBM patients to find age-specific signatures of GBM. Work in collaboration with Howard A. Fine at the NYU Cancer Institute.
- Modeling of gene regulation from high-throughput biological datasets. It is known that there are several factors that affect how genes are regulated in genomes. In this project, we apply machine learning tools that incorporate high-throughput heterogeneous genomic, genetic, epigenetic datasets to build models of gene regulation. Work in collaboration with Stefan Wuchty at the National Center for Biotechnology Information at NIH.
- Genomic analysis of stress response against arsenic in Caenorhabditis elegans. In this project, we apply FastMEDUSA on gene expression datasets of C. elegans that are exposed to different levels of arsenic. We aim to identify significant regulators in the stress response against arsenic in C. elegans. Work in collaboration with Dr. H. Nese Cinar at Food and Drug Administration.
- Significant regulators glioma stem cells (GSCs) in response to metformin treatment. Metformin is a drug that is being used to type 2 diabetes. We hypothesize that metformin might be used to treat some glioblastoma patients, too. In this project, we analyze time-series gene expression datasets of GSCs treated with metformin to identify significant regulatory differences between metformin-sensitive and metformin-resistant GSCs. Work in collaboration with Howard A. Fine at the NYU Cancer Institute.
- Significant pathways of gene regulation. In this project, we analyze gene regulatory models derived from FastMEDUSA to compute significantly overrepresented pathways that potentially play an important role in gene regulation.
Biomedical Imaging – Director Anne Clough
This laboratory designs, develops, and optimizes imaging systems and requisite experimental protocols and image analysis tools for investigating cardio-pulmonary physiology pathophysiology of small laboratory animals. Close collaboration with Prof. Krenz’s group as well as scientists and clinicians at Zablocki VA Medical Center and Medical College of Wisconsin. Ongoing funded projects include:
- Micro-Angiography Investigation of Pulmonary Hemodynamics - Following completion of this instrument, its hardware, experimental protocols, and image analysis tools are being modified to determine capillary flow, its regional distribution, and changes therein following lung injury or treatment in rats. Supported by NHLBI, Co-Investigator.
- Angiogenesis in the Bronchial Circulation using SPECT (single-photon emission computed-tomography) – Micro-CT and SPECT to monitor angiogenesis in the bronchial circulation of rats. Supported by NHLBI, Co-principal investigator.
- Micro-SPECT Hardware and Software Design – Construction of a flexible system for rapid, high-resolution imaging of small animals using radiopharmaceuticals targeted at specific molecular functions. Supported by the Keck Foundation.
- Redox Status of Lungs in Rats Exposed to Chronic Oxidative Stress with Micro-SPECT – Use molecular imaging radiopharmaceuticals and pharmacokinetic modeling to assess early lung injury. Supported by NHLBI, Co-Investigator.
- Reducing Patient Dose in Spiral and Conebeam CT – Simulations designed to determine optimal scanning parameters. Supported by NIBIB, Co-Investigator with U. of Iowa.
Biomedical Applications – Director Steve Merrill
This lab applies mathematical modeling and computational approaches to questions arising in laboratory, field, and clinic. Some current projects include:
- Quantitative and geometric description of prion (misfolded protein) accumulation in a yeast model.
- The role of UV light in cancer initiation and progression. Of interest is the interplay between sunlight and artificial light in melanoma and Human Papilloma Virus (HPV) associated cancers. This is joint with the Division of Biology, Chemistry, and Materials Science at CDRH at the FDA.
- Thyroid autoimmunity and association with thyroid cancer and diabetes. This work has collaborators in Italy and University of Wisconsin - Whitewater.
- Using fluoroscopic (2-D) images to obtain 3-D information in the heart. This project is joint with APN Health, LLC.
Applied and Computational Probability – Director Elaine Spiller
Randomness and nonlinearity are dominant features in many mathematical models. The interplay between the two often causes interesting behavior that we wish to understand and predict. Current projects include:
- Hazard Mapping - Devastation caused by pyroclastic (rapid, granular, volcanic) flows can be extreme for communities situated near volcanoes. We are devising methods to draw accurate hazard maps for use in civil protection and planning. Work is in collaboration with investigators at the State University of New York at Buffalo (math, volcanology), Duke University (statistics), and National Institute of Statistical Sciences. This project is supported by the NSF.
- Nonlinear Optics - In nonlinear, random systems interesting phenomena are often in the form of rare but important events. The performance of optical communication systems and mode-locked lasers is limited physically by noise, i.e., incoherent photons introduced during amplification. We seek to understand both how errors occur and how frequently they occur. Work is in collaboration with investigators at the State University of New York at Buffalo and Northwestern University.
- Data Assimilation - Data assimilation is a broad term for techniques that combine noisy observations with dynamic model based predictions. Our current work is in devising methods of data assimilation that work well in systems that are both nonlinear and high-dimensional. Work is in collaboration with investigators at the University of North Carolina at Chapel Hill (math, statistics) and University of California at Los Angeles (geosciences).
Elaine Spiller’s research work intersects with the Biomathematics group’s activities, but also interfaces with the Statistics group’s work.
Statistical Consulting and Training Center –
Co-Directors Naveen Bansal and Mehdi Maadooliat
Statistical Methods – Co-Directors Naveen Bansal and Hossein Hamedani
This group develops and applies new statistical methods in a variety of applications. Projects include Bayesian hypothesis testing using a decision theoretic approach. A doctoral student is developing a decision theoretic methodology for testing multiple and multi-sided hypotheses to produce more powerful test procedures. The idea is to incorporate prior information in order to produce more powerful tests which will have application in gene expression data analysis.
High Dimensional Data – Director Mehdi Maadooliat
My primary research interests lie in the developments of the statistical models in high-dimensional data structures with application to biological sciences, including, but not limited to genomics and proteomics.
In particular my doctoral research focuses on the dimension reduction and the functional data analysis in non-Gaussian frameworks, which has been applied in the context of analysis of high-dimensional gene expression data; and the main focus of my research during the postdoctoral program, was on the modeling of the large spherical data structures with an application in protein structure prediction and classification. My current research interests include:
- Machine Learning
- Functional Data Analysis and Skewed Distributions
- Nonparametric and Semiparametric Methods
Functional Magnetic Resonance Image Analysis – Director Daniel B. Rowe
The long-term goal of the Rowe Functional Magnetic Resonance Image Analysis Lab research efforts is to develop a unified mathematical model for functional magnetic resonance imaging (fMRI). This mathematical model is to include the fundamental physics of the nuclear magnetic resonance signal, the compensation for biological signals not of interest, the preprocessing of the MR images, the statistical modeling of complex-valued time series, and the statistically significant determination of focal brain activation. This mathematical model will extract the most information from the acquired data in the most efficient way possible. This unified model will contain important physiological information that may not be available by other means or is only available by more time-consuming elaborate means. This model will allow us to address important fundamental neuroscience questions with fMRI. The Rowe fMRI Analysis Lab's primary research efforts are focused on working deeper in the data acquisition and processing stream while expanding the unified model at each step.