Foad Mahdavi Pajouh

• PhD (2012) Oklahoma State University (Industrial Engineering and Management)
• MS (2006) Tarbiat Modares University (Industrial Engineering)
• BS (2004) Sharif University of Technology (Industrial Engineering)

• Stevens Institute for Artificial Intelligence (SIAI), Member
• Center for Research toward Advancing Financial Technologies (CRAFT), Member
• Operations Management (MGT 657) Course Coordinator, Chair
• Sustainability Tracking, Assessment and Rating System (STARS) Committee, Member
• Business Intelligence and Analytics Programs Committee, Member
• Ph.D. Admissions Committee, Chair
• Machine Learning (ML) and Artificial Intelligence (AI) Curriculum Review Committee, Chair

• Journal of Combinatorial Optimization, Associate Editor

• ISM-GB – Institute for Supply Management-Greater Boston Member
• Alpha Pi Mu – Industrial Engineering Honor Society Member
• AIS – Association for Information Systems Member
• INFORMS – Institute for Operations Research and the Management Sciences Member
• IISE – Institute of Industrial and Systems Engineers Member

Book Chapter

1. Mahdavi Pajouh, F. (2023). Finding central cliques in network systems.. Encyclopedia of Optimization (3 ed.). Springer.
   https://link.springer.com/referenceworkentry/10.1007/978-3-030-54621-2_805-1.
2. Mahdavi Pajouh, F.; Veremyev, A.; Boginski, V. (2014). Analysis and design of robust network clusters with bounded diameter. Examining Robustness and Vulnerability of Networked Systems (pp. 141-160). IOS Press,.
   https://ebooks.iospress.nl/publication/36426.
3. Rysz, M.; Mahdavi Pajouh, F.; Krokhmal, P.; Pasiliao, E. (2014). On risk-averse weighted k-club problems. Examining Robustness and Vulnerability of Networked Systems (pp. 231-242). IOS Press.
   https://ebooks.iospress.nl/volumearticle/36431.
4. Balasundaram, B.; Mahdavi Pajouh, F. (2013). Graph theoretic clique relaxations and applications. Handbook of Combinatorial Optimization (2 ed., pp. 1559-1598). Springer.
   https://link.springer.com/referenceworkentry/10.1007%2F978-1-4419-7997-1_9.
5. Mahdavi Pajouh, F.; Balasundaram, B. (2011). Gradient-type methods. Wiley Encyclopedia of Operations Research and Management Science (vol. 3, pp. 2092-2099). John Wiley & Sons, Inc..
   https://onlinelibrary.wiley.com/doi/10.1002/9780470400531.eorms0363.
6. Bukkapatnam, S. T.; Yang, H.; Mahdavi Pajouh, F. (2009). Towards prediction of nonlinear and nonstation- ary evolution of customer preferences using local Markov models. The Art and Science behind Successful Product Launches (pp. 271-287). Springer.
   https://link.springer.com/chapter/10.1007%2F978-90-481-2860-0_15.

Conference Proceeding

1. Nasirian, F.; Mahdavi Pajouh, F.; Namayanja, J. (2017). Exact algorithm for the minimum cost vertex blocker clique problem. In: Proceedings of the Northeast Decision Sciences Institute 2017 Annual Conference (pp. 621-627).
   https://nedsi.decisionsciences.org/past-proceedings.
2. Mahdavi Pajouh, F.; Kamath, M. (2010). Applications of Queueing Models in Hospitals. In: Proceedings of the 2010 Midwest Association for Information Systems (MWAIS) Conference, paper 23.
   https://aisel.aisnet.org/mwais2010/23/.
3. Oztekin, A.; Mahdavi Pajouh, F.; Kong, Z.; Bukkapatnam, S. T. (2010). Determining the Optimum Number of RFID Readers for E cient Asset Tracking. In: Proceedings of the 2009 American Society of Mechanical Engineers (ASME) Conference (pp. 1323-1331).
   https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2009/1323/335564.

Journal Article

1. Jenkins, D.; Mahdavi Pajouh, F.; Kirshen, P.; Eftekhar, M. (2024). Which Is More Rewarding in Managing Sea Level Rise and Hurricane Storm Surge Flooding: Mitigation or Response?. Production and Operations Management [FT-50 Journal List, AJG 2021: 4].
   https://doi.org/10.1177/10591478231224945.
2. Zhong, H.; Mahdavi Pajouh, F.; Prokopyev, O. (2023). On designing networks resilient to clique blockers. European Journal of Operational Research [AJG 2021: 4] (1 ed., vol. 307, pp. 20-32).
   https://www.sciencedirect.com/science/article/abs/pii/S0377221722007251?via%3Dihub.
3. Wei, N.; Walteros, . L.; Mahdavi Pajouh, F. (2021). Integer programming formulations for minimum spanning tree interdiction. INFORMS Journal on Computing [UTD-24 Journal List, AJG 2021: 3] (4 ed., vol. 33, pp. 1461-1480).
   https://pubsonline.informs.org/doi/10.1287/ijoc.2020.1018.
4. Zhong, H.; Mahdavi Pajouh, F.; Prokopyev, O. A. (2021). Finding influential groups in networked systems: the most degree-central clique problem. Omega [AJG 2021: 3] (vol. 101, pp. 102262).
   https://www.sciencedirect.com/science/article/pii/S030504831931059X.
5. Mahdavi Pajouh, F. (2020). Minimum cost edge blocker clique problem. Annals of Operations Research [AJG 2021: 3] (vol. 294, pp. 345-376).
   https://link.springer.com/article/10.1007/s10479-019-03315-x#citeas.
6. Nasirian, F.; Mahdavi Pajouh, F.; Balasundaram, B. (2020). Detecting a most closeness-central clique in complex networks. European Journal of Operational Research [AJG 2021: 4] (2 ed., vol. 283, pp. 461-475).
   https://www.sciencedirect.com/science/article/pii/S0377221719309464.
7. Yezerska, O.; Mahdavi Pajouh, F.; Veremyev, A.; Butenko, S. (2019). Exact algorithms for the minimum s-club partitioning problem. Annals of Operations Research [AJG 2021: 3] (1-2 ed., vol. 276, pp. 267-291).
   https://link.springer.com/article/10.1007/s10479-017-2665-2#citeas.
8. Nasirian, F.; Mahdavi Pajouh, F.; Namayanja, J. (2019). Exact algorithms for the minimum cost vertex blocker clique problem. Computers & Operations Research [AJG 2021: 3] (vol. 103, pp. 296-309).
   https://www.sciencedirect.com/science/article/pii/S0305054818303022.
9. Rysz, M.; Mahdavi Pajouh, F.; Pasiliao, E. L. (2018). Finding clique clusters with the highest betweenness centrality. European Journal of Operational Research [AJG 2021: 4] (1 ed., vol. 271, pp. 155-164).
   https://www.sciencedirect.com/science/article/pii/S0377221718303849.
10. Rysz, M.; Mahdavi Pajouh, F.; Krokhmal, P.; Pasiliao, E. L. (2018). Identifying risk-averse low-diameter clusters in graphs with stochastic vertex weights. Annals of Operations Research [AJG 2021: 3] (1 ed., vol. 262, pp. 89-108).
    https://link.springer.com/article/10.1007/s10479-016-2212-6.
11. Yezerska, O.; Mahdavi Pajouh, F.; Butenko, S. (2017). On biconnected and fragile subgraphs of low diameter. European Journal of Operational Research [AJG 2021: 4] (2 ed., vol. 263, pp. 390-400).
    https://www.sciencedirect.com/science/article/pii/S0377221717304484#!.
12. Mahdavi Pajouh, F.; Moradi, E.; Balasundaram, B. (2017). Detecting large risk-averse 2-clubs in graphs with random edge failures. Annals of Operations Research [AJG 2021: 3] (1 ed., vol. 249, pp. 55-73).
    https://link.springer.com/article/10.1007%2Fs10479-016-2279-0.
13. Mahdavi Pajouh, F.; Balasundaram, B.; Hicks, I. V. (2016). On the 2-club polytope of graphs. Operations Research [FT-50 Journal List, AJG 2021: 4*] (6 ed., vol. 64, pp. 1466-1481).
    https://pubsonline.informs.org/doi/abs/10.1287/opre.2016.1500?journalCode=opre.
14. Ma, J.; Mahdavi Pajouh, F.; Balasundaram, B.; Boginski, V. (2016). The minimum spanning k-core problem with bounded CVaR under probabilistic edge failures. INFORMS Journal on Computing [UTD-24 Journal List, AJG 2021: 3] (2 ed., vol. 28, pp. 295-307).
    https://pubsonline.informs.org/doi/10.1287/ijoc.2015.0679.
15. Mahdavi Pajouh, F.; Walteros, J. L.; Boginski, V.; Pasiliao, E. L. (2015). Minimum edge blocker dominating set problem. European Journal of Operational Research [AJG 2021: 4] (1 ed., vol. 247, pp. 16-26).
    https://www.sciencedirect.com/science/article/pii/S0377221715004270?via%3Dihub#!.
16. Mahdavi Pajouh, F.; Boginski, V.; Pasiliao, E. . (2014). Minimum vertex blocker clique problem. Networks [Impact Factor: 2.1] (1 ed., vol. 64, pp. 48-64).
    https://onlinelibrary.wiley.com/doi/abs/10.1002/net.21556.
17. Mahdavi Pajouh, F.; Miao, Z.; Balasundaram, B. (2014). A branch-and-bound approach for maximum quasi-cliques. Annals of Operations Research [AJG 2021: 3] (1 ed., vol. 216, pp. 145-161).
    https://link.springer.com/article/10.1007%2Fs10479-012-1242-y.
18. Mahdavi Pajouh, F.; Xing, D.; Zhou, Y.; Hariharan, S.; Balasundaram, B.; Liu, T.; Sharda, R. (2013). A specialty steel bar company uses analytics to determine available-to-promise dates. INFORMS Journal on Applied Analytics [AJG 2021: 2] (6 ed., vol. 43, pp. 503-517).
    https://pubsonline.informs.org/doi/abs/10.1287/inte.2013.0693.
19. Mahdavi Pajouh, F.; Balasundaram, B.; Prokopyev, O. A. (2013). On characterization of maximal independent sets via quadratic optimization. Journal of Heuristics [AJG 2021: 3] (4 ed., vol. 19, pp. 629-644).
    https://link.springer.com/article/10.1007%2Fs10732-011-9171-5.
20. Mahdavi Pajouh, F.; Balasundaram, B. (2012). On inclusionwise maximal and maximum cardinality k-clubs in graphs. Discrete Optimization [AJG 2021: 2] (2 ed., vol. 9, pp. 84-97).
    https://www.sciencedirect.com/science/article/pii/S1572528612000163#!.
21. Oztekin, A.; Mahdavi Pajouh, F.; Delen, D.; Swim, L. K. (2010). An RFID network design methodology for asset tracking in healthcare. Decision Support Systems [AJG 2021: 3] (1 ed., vol. 49, pp. 100-109).
    https://www.sciencedirect.com/science/article/pii/S0167923610000205#!.
22. Oztekin, A.; Mahdavi Pajouh, F.; Erande, K.; Kong , Z.; Swim, L. K.; Bukkapatnam, S. T. (2010). Criticality index analysis based optimal RFID reader placement models for asset tracking. International Journal of Production Research [AJG 2021: 3] (9 ed., vol. 48, pp. 2679-2698).
    https://www.tandfonline.com/doi/abs/10.1080/00207540903565006.
23. Sepehri, M. M.; Mahdavi Pajouh, F. (2010). Predicting web users viewing route using clickstream analysis. Iranian Journal of Electrical and Computer Engineering (4 ed., vol. 7, pp. 290-298).