RESEARCH TOPICS

 

A) Research in Complex Adaptive Systems

1) Big Data Analytics

a. Modeling and computational research in cybersecurity: (DoD- Army Research Office funded MURI-2013-2018) Optimal cybersecurity analyst scheduling, Fairness in Cyber Security Operation Center (CSOC) effort allocation across organizations, analyst performance modeling, and adversary cost modeling.

b. Modeling and computational research in health care: (NIH funded 2010-2013) Combining Geographical Information Systems and Simulation-based-Optimization for Organ Allocation (Liver Transplants in particular). Big Data from UNOS.

c. Modeling and computational research in transportation: Dynamic Airspace Configuration and Airport Taxi-time reduction and Airport Departure Optimization. Big Data from FAA and BTS.

2) Stochastic Optimization using Approximate Dynamic Programming (ADP) (defense applications)

a. Human Capital Management for Air Force.

b. Federal Air Marshal Allocation for the TSA.

c. Capital Budgeting for the Missile Defense Agency

d.  A game theoretic approach to the Vehicle Routing problem for detection of Improvised Explosive Devices.

3) Optimal Control: Engineering Process Control using Diffusion Wavelets and ADP

a. Applications in Nano-manufacturing

B) Methodology Research in Stochastic Optimization and Simulation

1) Optimized Simulation Optimization: (NSF Funded 2012-2014) Methods to improve simulation efficiency within a given computational budget and solution quality of optimization in a combined setting.

2) Mitigating Curse of Dimensionality in Approximate Dynamic Programming using Diffusion Wavelets to achieve scalability to large-state and high-dimensional optimization problems.

C) Research in Quality and Statistics

1) Quality and Statistics: Feature detection and function approximation in high-dimensional data

a. Wavelet applications in statistical process monitoring and control.

b. Function approximation using Diffusion Wavelets in high-dimensional data.

2) Anomaly Detection using Diffusion Wavelet Analysis of Acoustic Emission Data in nano-manufacturing

D) Research in STEM Education

1) Improving Quality of STEM education in K-12, and preparing graduate students with deeper understanding of their field, ability to communicate research to public, and as advocates of STEM education in K-12

 

Publications, Papers in Review, and Reports

Journal Papers

  1. Ankit Shah, Rajesh Ganesan, Sushil Jajodia, Hasan Cam, “Adaptive Reallocation of Cybersecurity Analysts to Sensors for Balancing Risk between Sensors”, ACM Trans. on Information and System Security, In review, 2016

  2. Rajesh Ganesan, Sushil Jajodia, Ankit Shah, and Hasan Cam. 2016b. Dynamic Scheduling of Cybersecurity Analysts for Minimizing Risk Using Reinforcement Learning. ACM Trans. Intell. Syst. Technol. 8, 1, Article 4 (July 2016), 21 pages. DOI:http://dx.doi.org/10.1145/2882969

  3. Rajesh Ganesan, Sushil Jajodia, Ankit Shah, Hasan Cam, “Optimal Scheduling of Cybersecurity Analysts for Minimizing Risk” ACM Trans. on Intelligent Systems and Technology, Accepted. To appear 2016 pdf

  4. Rajesh Ganesan, Wen-Chi Hung, Tzu-Yi Peng, Chun-Hung Chen, Naoru Koizumi, “A Hybrid Liver-Candidate Transportation Approach to Improve Accessibility and Extend Organ Life in Liver Transplantation” Health Care Management Science, In review 2015

  5. Stimpson, D. Ganesan, R., A reinforcement learning approach to convoy scheduling on a contested transportation network, Optimization Letters, Vol 9(8) pp 1641-1657, 2015

  6. Morman, E., Ganesan R., An Approximate Dynamic Programming Approach to Financial Execution for Weapon System Programs, Management Science, In review, 2015

  7. Ganesan, R., and Balakrishna, P., Multiresolution Analysis for Value Function Approximation in Approximate Dynamic Programming, INFORMS Journal of Operations Research, In review, 2015.

  8. Koizumi, N., Gentili, M., Ganesan, R. Chen., C.H., Melancon, K. Waters, N., et al., “Mathematical Optimization and Simulation Analyses for Optimal Liver Allocation Boundaries” in Healthcare Data Analytics in the Wiley Series in Operations Research and Management Science. Edited by Yang Hui and Eva K. Lee (Accepted, Final copy editing stage at Wiley), John Wiley & Sons, Inc., 2014

  9. Kulkarni, S., Ganesan, R., and Sherry, L. Dynamic airspace configuration using approximate dynamic programming - an intelligence based paradigm, Transportation Research Record, vol 2266, 31-37, 2012

  10. Ganesan, R. Diffusion Wavelet for Efficient Real-time Monitoring of Acoustic Emission Signal in Nanomanufacturing, In Book: Wavelets: Classification, Theory and Applications, Editors: Manel del Valle, Roberto Muñoz Guerrero and Juan Manuel Gutierrez Salgado, Chapter 15, 2012.

  11. Ganesan, R., Balakrishna, P, Sherry, L. Improving Quality of Prediction in Highly Dynamic Environments Using Approximate Dynamic Programming, INFORMS Special Issue of the Journal of Quality and Reliability Engineering International, Vol 26, issue 7, 717-732, 2010.

  12. Balakrishna, P., Ganesan, R., and Sherry, L. Accuracy of Reinforcement Learning Algorithms for Predicting Aircraft Taxi-out Times- A case study of Tampa Bay departures, Journal of Transportation Research Part C, Special Issue on Air Transportation, Vol 18, 950-962, 2010.

  13. Balakrishna, P., Ganesan, R., and Sherry, L. Airport Taxi-out Prediction Using Approximate Dynamic Programming: An Intelligence-based Paradigm, Transportation Research Record, Journal of the Transportation Research Board of the National Academies, Washington, DC, Volume 2052, pages 54-61, 2008

  14. Ganesan, R., Das, T.K., and Rao A.N.V, A Multiscale Bayesian SPRT Approach for Online Process Monitoring, IEEE transactions on Semiconductor Manufacturing, 21(3), 399-412, 2008.

  15. Ganesan, R., Real-time Monitoring of Complex Sensor Data Using Wavelet-based Multiresolution Analysis, International Journal of Advanced Manufacturing Technology, 39, 543-558 Sept 2007.  

  16. Ganesan, R., Das, T. K., and Ramachandran, K., A Multiresolution Analysis-Assisted Reinforcement Learning Approach to Run-by-Run Control IEEE Transactions on Automation Science and Engineering Vol 4(2), 182-193, 2007.

  17. Das, T.K., Ganesan, R., Sikder, A., and Kumar, A., Online end point detection in CMP using SPRT of wavelet decomposed sensor data. IEEE Transactions on Semiconductor Manufacturing. Vol 18(3), 440-447, Aug. 2005.

  18. Ganesan, R., Das, T.K., and Venkataraman, V., Wavelet based multiscale process monitoring - A literature review. IIE Transactions on Quality and Reliability Engineering. Vol 36(9), 787-806, Sept. 2004.

  19. Ganesan, R., Das, T.K., Sikder, A., and Kumar, A., Wavelet based identification of delamination defect in chemical mechanical planarization (CMP) using nonstationary acoustic emission signal, IEEE Transactions on Semiconductor Manufacturing, Vol 16(4), 677-685, Nov. 2003.

Refereed Conference Proceedings: Technical Research

  1. DeGregory, K. Ganesan, R. Approximate dynamic program for scheduling federal air marshals in real-time, Submitted to IIE-ISERC, In review 2016

  2. Koizumi, N., Ganesan, R. Gentili, M., Chen., C.H., Melancon, K. Waters, N., et al., “Redesigning Organ Allocation Boundaries for Liver Transplantation in the United States”, Proceedings of the International Conference on Health Care Systems Engineering,  Springer Proceedings in Mathematics & Statistics Volume 61, 2013, pp 15-27

  3. Kulkarni, S., Ganesan, R., and Sherry, L. Dynamic airspace configuration using approximate dynamic programming - an intelligence based paradigm, Accepted in Proceedings of the Annual Meeting of the Transportation Research Board of the National Academies, Washington, DC, 2012.

  4. Kulkarni, S., Ganesan, R., and Sherry, L. Static Sectorization approach to Dynamic Airspace Configuration using Approximate Dynamic Programming, In Proceedings of the Integrated Communications Navigation and Surveillance Conference, May 2011, Herndon, VA, USA.

  5. Ganesan, R., Balakrishna, P., and Sherry, L. Taxi-out Time Analysis using Approximate Dynamic Programming, In Proceedings of IEEE SSCI-ADPRL Symposium, Paris, France April 2011,  pages 226-233.

  6. Balakrishna, P., Ganesan, R., and Sherry, L. Application of Reinforcement Learning Algorithms for Predicting Taxi-out Times, In proceedings of the Eighth USA/Europe Air Traffic Management Research and Development Seminar, FAA-EUROCONTROL, ATM June 2009. (Acceptance Rate: 47%, 69 Papers Accepted).

  7. Balakrishna, P., Ganesan, R., Sherry, L, and Levy, B. Estimating Taxi-Out Times With A Reinforcement Learning Algorithm, In Proceedings of the 27th Digital Avionics Systems Conference, published by IEEE, St. Paul, MN (Awarded the Best Paper in both Air Traffic Management Track and Session). Pages 3.D.3-1 - 3.D.3-12 Oct 2008. (Acceptance Rate: 60%, 150 Papers Accepted).

  8. Balakrishna, P., Ganesan, R., and Sherry, L. Accuracy of Reinforcement Learning Algorithms for Predicting Aircraft Taxi-out Times (A Case-study of Tampa Bay Departures), In Proceedings of the 3rd International Conference on Research in Air Transportation, Fairfax, VA, 2008. (Acceptance Rate: 65%, 60 Papers Accepted).

  9. Balakrishna, P., Ganesan, R., and Sherry, L. Airport Taxi-out Prediction Using Approximate Dynamic Programming: An Intelligence-based Paradigm, In Proceedings of the Annual Meeting of the Transportation Research Board of the National Academies, Washington, DC, 2008. (Acceptance Rate: 25%, 2814 Papers Accepted).

Refereed Conference Proceedings: Research in Engineering Education

  1. Henning, P., Ganesan, R. and Sterling, D. Work-In-Progress: STEM Capacity Building and STEM Outreach in India: A report on a recent NSF sponsored GK-12 George Mason Trip to India In Proceedings of the Frontiers in Education conference, IEEE-ASEE, Seattle WA, Oct 2012.

  2. Ganesan, R., Sterling, D., and Henning, P., Work-In-Progress: SUNRISE: Schools, University ‘N’ (and) Resources In the Sciences and Engineering-A NSF/GMU GK-12 Fellows Project, In Proceedings of the Frontiers in Education conference, IEEE-ASEE, Rapid City, Oct 2011.

  3. Ganesan, R., Sterling, D., and Henning, P., Impact of a University-School Division Partnership on Professional Development of Graduate Students, In Proceedings of the ASEE Annual meeting, 2010. (Acceptance Rate: 92%, 1399 Papers Accepted).

  4. Ganesan, R., Sterling, D., and Henning, P., Work-In-Progress: SUNRISE: Schools, University ‘N’ (and) Resources In the Sciences and Engineering-A NSF/GMU GK-12 Fellows Project, Accepted In Proceedings of the Frontiers in Education conference, IEEE-ASEE, Oct 2009. (Acceptance Rate: 63%, 355 Papers Accepted).

  5. Ganesan, R., Sterling, D., and Henning, P., Partnerships for building the nation’s STEM educational enterprise: A NSF GK-12 Fellows Project, In proceedings of the ASEE Annual meeting, 2008. (Acceptance Rate: 92%, 1381 Papers Accepted).

  6. Shrestha, M., Morris, K., Ganesan, R., and Sterling, D. An Impact Study of the Implementation of an Information Technology Rich Physical Science Module at the Fourth Grade Level, In Proceedings of the ASEE Annual meeting, 2008. . (Acceptance Rate: 92%, 1381 Papers Accepted).

  7. Ganesan, R., Sterling, D., and Henning, P., Work-In-Progress: SUNRISE: Schools, University ‘N’ (and) Resources In the Sciences and Engineering-A NSF/GMU GK-12 Fellows Project, In Proceedings of the Frontiers in Education conference, IEEE-ASEE, Oct 2008. (Acceptance Rate: 52%, 413 Papers Accepted).

  8. Martin-Vega, L.,  Ganesan, R., Das, T.K.,  Edwards, C., Okogbaa, O. G., Centeno, G., Kumar, A., Hunnicutt, L., and Project Fellows., The STARS GK-12 program at the University of South Florida. In Proceedings of the ASEE - Emerging Trends in Engineering Education, Portland, OR. June 2005. (Acceptance Rate: 90%, 1380 Papers Accepted).

  9. Ganesan, R., Das, T.K., Edwards, C., and Okogbaa, O.G., Challenges in enhancing science education in elementary classrooms through university-school district partnerships. In Proceedings of the Frontiers In Education Conference, T2D-3 – T2D-7, Savannah, GA. (published by IEEE and ASEE), Oct 2004.  (Acceptance Rate: 58%, 410 Papers Accepted).