CSIS Department Funded Grants (Current)

Dr. Rocio Guillen, in collaboration with Dr. Miguel Alonso Jr. from Miami Dade College and Dr. Andres Figueroa from UT Pan American:

  • NSF under the Broadening Participation in Computing (BPC)
    • for three years {2011-2014)
    • in the amount of over $1,000,000
    • to Scale and Adapt CAHSI Initiatives (SACI). SACI will advance the goals of increasing the number of Hispanic, female, and other underrepresented students who enter and complete degrees in computing areas and developing and sustaining competitive academic and research programs.

Dr. Youwen Ouyang, in collaboration with Dr. Kathy Hayden of the School of Education, College of Education, Health & Human Services:  

  • Three  NSF grants
  • CyberTEAM ($250,000 for 2008-2010 Completed)
  • iQUEST ($1.5 million for 2009-December 2012)
  •         CyberQUEST ($1 million for January 2012 to December 2014)
  • focus on helping local middle schools to integrate technology in science education. Both projects provide CSIS students with opportunities to provide technical supports for science teachers, be a role model for middle school students, work in summer camps, and develop software applications for middle school science classrooms.

 Dr. Xiaoyu Zhang, in collaboration with Dr. Betsy Read of Biological Sciences:

  • NIH SC3 GM092765-02  
  • 7/01/10 – 6/30/2014
  • Budget: $75,000/year

    Identify and characterize miRNA precursors and mature miRNAs in E. huxleyi by computationally analyzing the short RNA sequences; computationally predict and characterize targets of E. huxleyi miRNAs and validate the predictions with experimental evidences.

Dr. Ahmad Hadaegh, in collaborate with Dr. Sunil Kumar of SDSU:

  • From NIH
  • $200,000 with 48% overhead
  • August 2010-August 2012
To help in the design and development of potent and mechanistically novel HIVIN inhibitors. Our studies on HIV-1 protease (HIVPR) inhibitors have established that the lateral validation of multiple linear regression (MLR) analysis based Quantitative Structure-Activity Relationship (QSAR) models via comparative QSAR is a novel approach. These studies have also demonstrated that a hybrid QSAR approach using the artificial neural network (ANN) and MLR based QSAR models can provide much better predictability as well as mechanistic interpretation for modeling drug-receptor interactions.