Research projects in the center focus on the design of novel mathematical modeling and advanced theory and computational algorithms with a primary emphasis on applications to bio/medical and healthcare problems. Specific applications include large-scale bio/medical informatics, modeling, and computing for 1) genomic analysis; 2) predictive health, medical prediction and diagnosis; 3) optimal treatment design and drug delivery; 4) early detection, target intervention, monitoring and controlling of disease; 5) public health and medical preparedness, emergency response and disaster medicine; 6) health informatics, quality and operations advances in healthcare delivery systems.

Broadly medical/biomedical research conducted in the center can be classified into six areas: The collaborative research program of Operations Research in Medicine and HealthCare at Georgia Tech is the first of its kind in the operations research community. Students and faculty in the center work closely with medical and healthcare researchers on each research project

We would like to acknowledge research support by the National Science Foundation and the Whitaker Foundation.

Novel pattern recognition and classification algorithms for health risk analysis, early disease diagnosis and prediction, target therapeutic intervention, and disease monitoring.

Analysis of clinical treatment modalities and design of individualized (patient-centered) optimal treatment regimens.

Public health and medical preparedness, emergency response and disaster medicine, critical infrastructure defense.

Clinical outcome analysis, comparative effectiveness, and development of prediction rules for treatment effectiveness, and design of improved treatment regimens

Selected Projects Highlight

Biological modeling and large-scale computing for multi-modality optimal cancer treatment design

    MRS can identify regions within tumors (e.g., prostate tumors) that have denser populations of tumor cells.
  • NMR studies have indicated that choline is elevated in rapidly growing tissues such as tumors.
  • Choline metabolism may be related to tumor proliferation.
  • 1H MRS can be used to image location of prostate cancer in the gland: differentiates cancer from benign tissue by the ratio [Cho+Cr]/Cit (Cho=choline, Cr=creatine, Cit=citrate) of the respective peaks in the MR spectrum. The ratios are calculated on a spatial grid covering the prostate tissue.
  • Tumor control probability (TCP) is largely determined by the response of fast-proliferating, radioresistant cells in the tumor.
  • Goal: Incorporate biologic tumor information into planning procedures (e.g., brachytherapy, IMRT) in the treatment of prostate carcinoma
  • Conjecture: improved TCP may be obtained by emphasizing not only the clinical prescribed dose but also appropriate escalated dose within the prostate.
  • Challenges: morphing of various imaging information into treatment images, design of optimal treatment plans.

MRS data for a patient with Gleason score 7, PSA= 8 ng/ml. The spectral voxels correspond to the grid overlaid on the image.


Example of dose escalation around the tumor region.


Large-scale cancer modeling and biocomputing intensity-modulated radiation therapy

    To produce IMRT treatment plans, sophisticated optimization methods must be used for designing the optimal beam geometry, fluence pattern and delivery parameters. This research explores novel optimization and biological models, and large-scale parallel algorithms for real-time clinically relevant plan design. Furthermore, issues related to organ motion and continuous tumor shrinkage are investigated.


Genomic pattern recognition and prediction of epigenetic silencing phenomenon in human cancer

  • Aberrant methylation of normally unmethylated CpG islands occurs frequently in human cancers
  • Aberrant methylation leads to inappropriate gene silencing
  • Methylation-mediated silencing plays a role in progression of human caners by inactivating genes thought to suppress invasion/metastasis
  • TMS-1: Target of methylation induced silencing
    • TMS-1 has been shown to be silenced by aberrant methylation of CpG islands in breast carcinomas.
    • This silencing results in the loss of TMS-1 promoting activity in apoptosis
This project focuses on developing computational models and algorithms, based on artificial intelligence and machine learning techniques, to understand genomic structures of CpG islands and their role in epigenetic cancer. Specific goals include:
  • Explore importance of CpG island methylation in cancers at the genomics level
  • Develop predictive rules for methylation status associated with TMS-1 in human cancer
  • Potential for reactivating genes being silenced by reversing DNA methylation, thus providing an exciting molecular target for chemotherapeutic intervention
  • Develop novel treatment strategies aimed at blocking or reversing methylation status
  • Develop methylation markers for cancer prediction, treatment and prognosis


Novel disease prediction via microvascular networks

    Recent development of fluorescence micro- angiography offers a cost-effective, imely technique for imaging the functional microvasculature in tissues and in multiple organs with superb resolution
  • Challenges: Quantitative analysis of large amounts of complex capillary patterns that may allow the identification of discriminatory patterns to provide fingerprints distinguishing between normal and perturbed microvascular perfusion.
  • Goals:
    • To develop automated fingerprinting algorithms through large-scale imaging analysis followed by predictive modeling for microvascular networks that could be used to investigate the potential perturbing effects of conditions such as cardiovascular disease, aging, genetic deficiency, diabetes, and cancer on the microvascular structure in relevant tissues.
    • “Angioprinting” will contribute to the understanding of angiogenic mechanisms, and could potentially be utilized in the early diagnosis of microvascular deficiencies, as well as for monitoring and allowing for design of a better therapeutic regimen.
    • The study offers novel approaches for
      • Early disease diagnosis and intervention
      • Treatment monitoring and prognosis
      • Novel therapeutic approach
After Ischemic condition is induced After Ischemic condition is induced and rescued using bone marrow transplant
MMP-9 KO day 0 -> 232 Branches MMP-9 KO day 14 -> 173 Branches MMP-9 KO + Transplant day 14 -> 358 Branches


Evolutionary models, DNA sequencing and genomic analysis

    Goals: To develop efficient mathematical models and robust algorithms for large-scale sequence analysis problems arising in computational genomics, protein structural analysis, and evolutionary biology.
    Specifically, key issues under investigation involve designing models and algorithms that are:
    • computationally practical and capable of returning optimal solutions
    • can be tailored to wide variety of real applications
    • provide flexibility to incorporate complex operations and parameters within the analysis




Decision support system for mass dispensing for pandemics and bioterror attacks

    Goal: To develop a computerized decision support system to help U.S. state, city and county healthcare departments create and test more efficient plans for emergency response logistics and treatment of infectious illness, whether it’s a natural or man-made outbreak. The software, 'RealOpt©' , allows for operational and strategic planning and offers the capability to:
    • Design customized and efficient clinic/dispensing facility floor plans for regional needs via an automatic graph-drawing tool.
    • Determine optimal labor resources required and provides the most-efficient placement of staff at individual stations within the facility. The results maximize the number of people who can be treated, minimize the average time patients spend in the clinic, and equalize utilization across clinic stations.
    • Determine the best location for emergency clinics based on population density and road accessibility, the most efficient facility layout, the number of health care professionals needed in certain areas, the number of medical countermeasures and supplies (e.g. vaccinations, antibiotics) needed and the time it will take to treat patients.
    • Process data in real time as the emergency treatment occurs, and determine dynamic changes when necessary.
    • Perform disease propagation analysis and derive dynamic response strategies to mitigate casualties.
    • Provide a framework for emergency healthcare managers to assess current resources and determine minimum needs to prepare for readiness in emergency situations for their regional population.
    • Allow large-scale virtual drills and performance analysis, and assist in the study, training and enhancement of emergency response, planning and treatment from terrorism, infectious disease outbreaks, and natural disasters.

    In collaboration with the Centers for Disease Control and Prevention, the development and testing of RealOpt© began in 2002. Since 2004, RealOpt© has been used for planning of Anthrax emergency exercises, and actual vaccinations for seasonal flu, and for Hepatitis A booster shots. The system currently includes clinic layouts for emergencies involving Anthrax, Flu pandemic, Hepatitis, and Smallpox. It is used by thousands of public health and emergency coordinators across the 50 states for infectious disease planning and biodefense exercise drills.
    Request RealOpt© Usage


Comprehensive analysis of patient triage in urban pediatric emergency departments