A)    Research in Complex Adaptive Systems

1)      Big Data Analytics

   a.       Modeling and computational research in cybersecurity:

•                                                                       i.  (DoD funded- Army Research Office) Optimal cybersecurity analyst scheduling, Fairness in Cyber Security Operation Center (CSOC) effort allocation across organizations, analyst performance modeling, alert prioritization, Optimal allocation of sensors to analysts, vulnerability management, attacker-defender models, and adversary cost modeling.

•                                                                         ii.    (DoD funded- Office of Naval Research) Believable Fake Scientific Document Generation by Exploring Data Semantics

   b.      Modeling and computational research in health care:

•                                                                         i.       (NIH funded) Combining Geographical Information Systems and Simulation-based-Optimization for Organ Allocation (Liver Transplants in particular).

•                                                                         ii.      Optimal Desensitization and Integrated EHR to Support Kidney Paired Donation System

  c.       Modeling and computational research in transportation:

•                                                                           i.      Dynamic Airspace Configuration and Airport Taxi-time reduction and Airport Departure Optimization.

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

   a.       Using Operational Patterns to Influence Attacker Decisions on a Contested Transportation Network

   b.       Human Capital Management for Air Force.

   c.       Federal Air Marshal Allocation for the TSA.

   d.       Capital Budgeting for the Missile Defense Agency

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) 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+C 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

Papers – Published/Accepted for Publication

1. Shah, A., Ganesan, R. Jajodia, S. and Cam, H., "A two-step approach to optimal selection of alerts for investigation in a CSOC," IEEE Transactions on Information Forensics and Security, To appear.

2. Karuna, P., Purohit, H., Ganesan, R., and Jajodia, S., Generating Hard to Comprehend Fake Documents for Defensive Cyber Deception, IEEE Intelligent Systems, 2018, accepted.

3. Shah, A., Ganesan, R. Jajodia, S. and Cam, H., "Understanding trade-offs between throughput, quality, and cost of alert analysis in a CSOC," IEEE Transactions on Information Forensics and Security, To appear.

4. Ganesan, R., Shah, A., Jajodia, S., and Cam, H.,, “Optimizing Alert Data Management Processes at a Cyber Security Operations Center,” in Adversarial and Uncertain Reasoning for Adaptive Cyber Defense: Building the Scientific Foundations, S. Jajodia, G. Cybenko, P. Liu, C. Wang, W. Wellman, Eds. Springer, To appear.

5. Ganesan, R., Shah, A., and Cam, H.,, “A strategy for effective alert analysis at a cybersecurity operations center,” in Data and Application Security and Privacy: Status and Prospects, I. Ray, P. Samarati, Eds. Lecture Notes in Computer Science, Springer.  To appear.

6. Farris, K. Cybenko, G., Shah, A. Ganesan, R. Jajodia, S.,  , "VULCON - A system for vulnerability prioritization, mitigation, and management," ACM Transactions on Privacy and Security, Vol. 21, No. 4, May 2018, pages 16:1-16:28. DOI: 10.1145/3196884

7. Shah, A., Ganesan, R. Jajodia, S. and Cam, H., “Adaptive reallocation of cybersecurity analysts to sensors for balancing risk between sensors,” Springer Service Oriented Computing and Applications, Vol. 12, No. 2, 2018, pages 123-135. DOI: 10.1007/s11761-018-0235-3

8. Shah, A., Ganesan, R. and Jajodia, S.  H., "A methodology for ensuring fair allocation of CSOC effort for alert investigation," Springer International Journal of Information Security, To appear. DOI: 10.1007/s10207-018-0407-3

9. Shah, A., Ganesan, R. Jajodia, S. and Cam, H., “Optimal assignment of sensors to analysts in a cybersecurity operations center,” IEEE Systems Journal, DOI: 10.1109/JSYST.2018.2809506 To appear.

10.  Ganesan, R., Shah, A., and Cam, H., A strategy for effective alert analysis at a cybersecurity operations center,” in Data and Application Security and Privacy: Status and Prospects, Springer Lecture Notes in Computer Science, I. Ray, I. Ray, P. Samarati, (eds.), 2018 to appear.

11. DeGregory, K. W., Ganesan, R., Scheduling Federal Air Marshals Under Uncertainty, in Applied Risk Analysis for Guiding Homeland Security Policy and Decisions, S. Chatterjee, R.T. Brigantic, A.M. Waterworth (eds), Wiley, 2018, to appear.

12. Shah, A., Ganesan, R. Jajodia, S. and Cam, H., “Dynamic optimization of the level of operational effectiveness of a cybersecurity operations center under adverse conditions,” ACM Transactions on Intelligent Systems and Technology, Vol. 9, No. 5, 2018. DOI: 10.1145/3173457

13. Shah, A., Ganesan, R. Jajodia, S. and Cam, H. A methodology to measure and monitor level of operational effectiveness of a CSOC, International Journal of Information Security, Springer Computer Science: Communication Networks, vol 17(2), pp 121-134, 2018,  DOI: http://dx.doi.org/10.1007/s10207-017-0365-1

14.  Ganesan, R., Jajodia, S., Cam, H. Optimal scheduling  of cybersecurity analysts for minimizing risk," ACM Trans. on Intelligent Systems and Technology, Vol. 8(4), 32 pages, 2017, DOI:  http://dx.doi.org/10.1145/2914795 (Designated as Papers With Practical Content by ACM TIST for 2017) https://www.acm.org/publications/practical-content-papers

15.  Ganesan, R., Jajodia, S., Shah, A. and Cam, H., Dynamic Scheduling of Cybersecurity Analysts for Minimizing Risk Using Reinforcement Learning, ACM Trans. on Intelligent Systems and Technology, Vol 8 (1),  21 pages, 2016, DOI: http://dx.doi.org/10.1145/2882969

16.  Ganesan, R., Shah, A., Jajodia, S., and Cam, H., A novel metric for measuring operational effectiveness of a cybersecurity operations center,” in Network Security Metrics, P. Liu, S. Jajodia, C. Wang (eds.), Springer, pages 177-207, 2017.

17.  Koizumi, N., Gentili, M., Ganesan, R. Chen., C.-H., Melancon, K., and Waters, N., Mathematical Optimization and Simulation Analyses for Optimal Liver Allocation Boundaries, in Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, Yang Hui and Eva K. Lee (eds.), John Wiley & Sons, Inc., pp.451-475, 2016.

18. 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.

19. 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

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

21.  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.

22.  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.

23.  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, Volume 2052, pages 54-61, 2008.

24.  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.

 
25.  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.  

26.  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.

27.  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.

28.  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.

29.  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: Published/Accepted

30.  Karuna, P., Purohit, H., Uzuner, O., Ganesan, R., and Jajodia, S., Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers, Proceedings of the First International Workshop on Language Cognition and Computational Models, Santa Fe, New-Mexico, Association for Computational Linguistics, August 2018, pp. 31-40 http://aclweb.org/anthology/W18-4104

31.  Ganesan, R., Hung W-C., Peng T.-Y., Chen C.-H., Koizumi, N.,A Hybrid Liver-Candidate Transportation Approach to Improve Accessibility and Extend Organ Life in Liver Transplantation, Proceedings of the 28th Annual INCOSE International Symposium, Washington DC, July 2018, to appear.

32. Venkatesan, S., Albanese, M., Shah, A., Ganesan, R., and Jajodia, S. "Detecting stealthy botnets in a resource-constrained environment using reinforcement learning," Proceedings of the 4th ACM Workshop on Moving Target Defense (MTD 2017), Dallas TX, Oct. 2017. DOI: https://doi.org/10.1145/3140549.3140552

33. DeGregory, K.W., Ganesan, R., Approximate Dynamic Program for Scheduling Federal Air Marshals in Real-Time, Proceedings of the 2016 Industrial and Systems Engineering Research Conference, H. Yang, Z. Kong, and MD Sarder, (eds.), (ISERC Best Paper Award, National Security Track), Anaheim CA, pages 691-696, May 2016. https://www.xcdsystem.com/iise/2016_proceedings/papers/411.pdf

34. Koizumi, N., Ganesan, R. Gentili, M., Chen., C.-H., Melancon, K., and Waters, N.,  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, 2014, Vol. 61, pp 15-27, 2014, DOI 10.1007/978-3-319-01848-5__2

35. Kulkarni, S., Ganesan, R., and Sherry, L. Dynamic airspace configuration using approximate dynamic programming - an intelligence based paradigm, Proceedings of the Annual Meeting of the Transportation Research Board of the National Academies, Washington DC, 2012. https://doi.org/10.3141/2266-04

36.     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, Proceedings of the Frontiers in Education conference, IEEE-ASEE, Seattle WA, Oct 2012.

37. Kulkarni, S., Ganesan, R., and Sherry, L. Static Sectorization approach to Dynamic Airspace Configuration using Approximate Dynamic Programming, Proceedings of the 11th Integrated Communications Navigation and Surveillance Conference, IEEE Computer Society, Herndon VA, pages J2.1–J2.9, May 2011.

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

39.  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, Proceedings of the Frontiers in Education conference, IEEE-ASEE, Rapid City SD, Oct 2011.

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

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

42.  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, Proceedings of the Frontiers in Education conference, IEEE-ASEE, San Antonio TX, Oct 2009. (Acceptance Rate: 63%, 355 Papers Accepted).

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

44.  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), Proceedings of the 3rd International Conference on Research in Air Transportation, Fairfax VA, June 2008. (Acceptance Rate: 65%, 60 Papers Accepted).

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

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

47.  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, Proceedings of the ASEE Annual meeting, Pittsburgh PA, June 2008. (Acceptance Rate: 92%, 1381 Papers Accepted).

48.  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, Proceedings of the Frontiers in Education conference, IEEE-ASEE, Oct 2008. (Acceptance Rate: 52%, 413 Papers Accepted).

49.  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. Proceedings of the ASEE - Emerging Trends in Engineering Education, Portland, OR. June 2005. (Acceptance Rate: 90%, 1380 Papers Accepted).

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