My work is characterized by tying applied problems in a national laboratory setting to their mathematical foundations, designing algorithms for these problems, and developing software for the algorithms that can maximally utilize state of the art platforms to deploy solutions at realistic scales. I have initiated interdisciplinary projects in electric power systems and cyber security, and at Sandia I led a Laboratory-wide initiative that aims to bring scientific rigor into cyber experimentation. My work  fits under the broad umbrella of network science. I have published over 100 peer-reviewed papers with over 5800 citations in leading journals and conferences, four of which received best paper awards. In 2023, I was selected a fellow of Society of  Industrial  and Applied Mathematics and in 2015, I was named an Association of Computing Machinery (ACM) Distinguished Scientist. I also consider myself to be a community builder, as I initiated the SIAM Workshop on Network Science and SIAM Conference on Mathematics of Data Science, which brought together data scientists across SIAM. I have co-chaired two major SIAM conferences, multiple workshops, and have served as an associate editor for two SIAM journals.

Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (UIUC)

M.S. in Computer Engineering and Information Science, Bilkent University, Ankara, Turkey

B.S. in Computer Engineering and Information Science, Bilkent University, Ankara, Turkey

Selected Journal Publications:

1. E. Sariyuce, C. Seshadhri, and A. Pinar, Local Algorithms for Hierarchical Dense Subgraph Discovery, Proc. VLDB Endowment, 12(1), 43-56, 2018.
2. J. Cheng, R. Chen, H. Najm, A. Pinar, C. Safta, and J. Watson, Distributionally Robust Optimization with Principal Component Analysis, SIAM J. Optimization, 28(2), 1817-1841, 2018.
3. E. Sariyuce and A. Pinar, Fast Hierarchy Construction for Dense Subgraphs, Proc. VLDB Endowment,10(3), 97-108, 2016.
4. E. Sariyuce, C. Seshadhri, A. Pinar, and U. Catalyurek, Nucleus Decompositions for Identifying Hierarchy of Dense Subgraphs, ACM T. Web, 11(3) 16:1-16:27, 2017.
5. 5. S. Aksoy, T. Kolda, A. Pinar, Measuring and Modeling Bipartite Graphs, J. Complex Networks, 5(4), 581-603, 2017.
6. C. Safta, R. Chen, H. Najm, A. Pinar, and J. Watson, Efficient Uncertainty Quantification in Stochastic Economic Dispatch, IEEE T. Power Systems, 32(4), 2535-2546, 2017.
7. J. Bennett, A. Bhagatwala, J. Chen, C. Seshadhri, A. Pinar, and M. Salloum, Trigger Detection forAdaptive Scientific Workows Using Percentile Sampling, SIAM J. Scientific Computing, 38(5), S240-S263, 2016.
8. R. Chen, N. Fan, A. Pinar, and J. Watson, Contingency-Constrained Unit Commitment with Post-Contingency Corrective Recourse, Annals of Operations Research, 1-27, 2014.
9. M. Jha, C. Seshadhri, and A. Pinar, A space efficient streaming algorithm for estimating transitivity and triangle counts using the birthday paradox, ACM T. Knowledge Discovery from Data, 9(3), 15:1-15:21, 2015.
10. J. Ray, A. Pinar, and C. Seshadhri, A stopping criterion for Markov chains when generating independent random graphs, J. Complex Networks 3(2), 204-220, 2015.
11. T. Kolda, A. Pinar, T. Plantenga, and C. Seshadhri, A Scalable Generative Graph Model with Community Structure, SIAM J. Scientific Computing, 36(5), C424-C452, 2014.
12. C. Seshadhri, A. Pinar, and T. Kolda, Wedge Sampling for Computing Clustering Coefficients and Triangle Counts on Large Graphs," Statistical Analysis and Data Mining special issue for Best of SDM 2013, 7(4), 294-307, 2014.
13. R. Chen, A. Cohn, N. Fan, and A. Pinar, Contingency-Risk Informed Power System Design, IEEE T. on Power Systems 29(5), 2087{2096, 2014.
14. T. Kolda, A. Pinar, T. Plantenga, C. Seshadhri, and C. Task, Counting Triangles in Massive Graphs with MapReduce, SIAM J. Scientific Computing, 36(5), S48-S77, 2014.
15. C. Seshadhri, A. Pinar, and T. Kolda, An In-Depth Analysis of Stochastic Kronecker Graphs, J. ACM, 60(2), 13:1-13:32, 2013.
16. 16. C. Seshadhri, T. Kolda, and A. Pinar, Community structure and scale-free collections of Erdos-Renyi graphs, Physical Review E, 85(5), 056109, 2012.
17. I. Stanton and A. Pinar, Constructing and uniform sampling of graphs with prescribed joint degree distribution using Markov Chains, ACM J. Experimental Algorithmics, 17(1), 2012.
18. E. Kayaaslan, A. Pinar, U.Catalyurek, and C.Aykanat, Partitioning Hypergraphs in Scientific Computing Applications through Vertex Separators on Graphs," SIAM Scientific Computing, 34(2), A970-A992, 2012.
19. A. P nar, J. Meza, V. Donde, and B. Lesieutre, Optimization Strategies for the Vulnerability Analysis of the Power Grid, SIAM J. Optimization, 20(4), 1786-1810, 2010;
20. S. Kamil, L. Oliker, A. Pinar, and J. Shalf, Communication Requirements and Interconnect Optimization for High-End Scientific Applications, IEEE T. Parallel and Distributed Computing, 21(2), 188-202, 2010.
21. A. Pinar, E. Tabak, and C. Aykanat, One dimensional partitioning for heterogeneous systems, J. Parallel and Distributed Computing, 68(11), 1473-1486, 2008.
22. V. Donde, V. Lopez, B. Lesieutre, A. Pinar, C. Yang, and J. Meza, Severe Multiple Contingency Screening in Electric Power Systems," IEEE T. Power Systems, 23(2), 406-417, 2008.
23. A. P nar and B. Hendrickson, Improving Load Balance with Flexibly Assignable Tasks, IEEE T. Parallel and Distributed Systems, 16(10), 956-965, 2005.
24.  A. P nar and C. Aykanat, Fast Optimal Load Balancing Algorithms for 1D Partitioning, J. Parallel and Distributed Computing, 64(8), 974-996, 2004.
25. C. Aykanat, A. P nar, and U. Catalyurek, Permuting Sparse Rectangular Matrices into Block-Diagonal Form, SIAM J. Scientific Computing, 25 (6), 1860-1879, 2004.

Selected Conference and Workshop Proceedings:

1. R. Laishram, A. Sariyuce, T. Eliassi-Rad, A. Pinar, and S. Soundarajan, Residual Core Maximization:An Efficient Algorithm for Maximizing the Size of the k-Core, SIAM Data Mining, 334{342, 2020.
2. T. Eden, S. Jain, A. Pinar, D. Ron, and C. Seshadhri, Provable and practical approximations for the degree distribution using sublinear graph samples, 27th Intl. Conf. World Wide Web, 449-458, 2018.
3. R.Laishram, S. Soundarajan, E. Sariyuce, T. Eliassi-Rad, and A. Pinar, Measuring and Improving the Core Resilience of Networks, 27th Intl. Conf. World Wide Web, 609-618, 2018.
4. E. Sariyuce and A. Pinar, Peeling Bipartite Networks for Dense Subgraph Discovery, 11th ACM Intl. Conf. Web Search and Data Mining, 504-512, 2018.
5. A.Pinar, C. Seshadhri, and V. Vishal, ESCAPE: Efficiently Counting All 5-Vertex Subgraphs," 26th Intl. Conf. World Wide Web, 1431-1440, 2017.
6. G. Ballard, T. Kolda, A. Pinar, and C. Seshadhri, Diamond Sampling for Approximate Maximum All-pairs Dot-product (MAD) Search, IEEE Intl. Conf. Data Mining, 11-20, 2015 (Best Research Paper).
7. E. Sariyuce, C. Seshadhri, A. Pinar, and U. Catalyurek, Finding Overlapping and Hierarchical DenseSubgraphs using Nuclear Decompositions, 24th Intl. Conf. World Wide Web, 927-937, 2015 (Best PaperSocial Networks Track).
8. M. Jha, A. Pinar, and C. Seshadhri, Path Sampling: A Fast and Provable Method for Estimating 4-Vertex Subgraph Counts, 24th Intl. Conf. World Wide Web, 495-505, 2015.
9. M. Jha, C. Seshadhri, and A. Pinar, A Space Efficient Streaming Algorithm for Triangle Counting using the Birthday Paradox, ACM Knowledge Discovery and Data Mining, 589-597, 2013 (Best Student Paper).
10. C. Seshadhri, A. Pinar, and T. Kolda, Triadic Measures on Graphs: The Power of Wedge Sampling, SIAM Data Mining, 10-18, 2013 (Best Research Paper).
11. 36. A. Pinar, C. Seshadhri, and T. Kolda, The Similarity of Stochastic Kronecker Graphs to Edge-Configuration Models, SIAM Data Mining, 1071-1082, 2012.
12. A. P nar, T. Tao, and H. Ferhatosmanoglu, Compressing Bitmap Indices by Data Reorganization, 21st Intl. Conf. Data Engineering (ICDE), 310-321, 2005.
13. A. P nar and B. Hendrickson, Graph Partitioning for Complex Objectives, 15th IEEE Intl. Parallel and Distributed Processing Symp. (IPDPS), 1232-1237, 2001.
14. L. Fleischer, B. Hendrickson, and A. Pinar, On Identifying Strongly Connected Components in Parallel, Lecture Notes in Computer Science, Vol. 1586, 505-511, 2000.
15. A. P nar and M. Heath, Improving Performance of Sparse Matrix-Vector Multiplication, Supercomputing 99, 30-39, 1999.

• Fellow, Society of Industrial and Applied Mathematics, 2023
• Employee Recognition Award for Individual Technical Excellence, Sandia National Laboratories, 2016
• Distinguished Member, Association of Computing Machinery (ACM) 2015
• Best Paper Award, Intl. Conf. Data Mining (ICDM) 2015.
• Best Paper Social Networks Track, Intl. Conf. World Wide Web (WWW) 2015.
• Best Research Paper Award, SIAM Intl. Conf. Data Mining (SDM), 2013
• Co-author for Best Student Paper Award, 19th ACM SIGKDD Conf. Knowledge Discovery and Data Mining (KDD), 2013
• Senior member, Institute of Electrical and Electronics Engineers (IEEE) (2012)