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Ali Pinar is a Principal Member of Technical Staff in the Data Science & Cyber Analytics Department at Sandia National Laboratories in Livermore, California. His current research interests include modeling and analysis of networks, sampling algorithms on graphs, optimization problems in power systems, in situ analysis of simulation data, and data mining in general. His earlier work focused on combinatorial scientific computing with emphasis on load balancing for parallel computing, sparse matrix computations, interconnection networks, and communication libraries. Ali has been an author for three best paper awards in SDM13, KDD13, and ICDM15.
Currently, Ali is working on methods to infer global properties of a network from limited samples. The essence of this work is to apply techniques of sublinear algorithms in a practical setting. Such techniques can enable rapid analysis of extremely large data sets, since the algorithms aim to predict global properties of the network by looking at only a small portion of it. Another goal is to infer network properties when only limited data is available. Ali's work has produced several important results. He has co-authored a paper to compute triadic statistics in graphs with a small number samples, which won the best paper prize at the SIAM International Conference on Data Mining (SDM'13). An extension of this work applied similar techniques for triadic analysis on a streaming setting, which won the Best Student Paper Prize at 2013 ACM Knowledge Discovery and Data Mining Conference. More recently, Ali was involved in a project to predict large entries of the product of two matrices using sampling, and this work was recognized with a Best Paper Prize at 2015 IEEE International Conference of Data Mining.
Ali's on modeling and analysis of networks resulted in ability to predict community structure in a network using only triadic information. This method led to a Block Two Level Erdos Renyi (BTER) graph model and associated software, which can generate graphs with community stricture at extremely large scales. In other work, he was also involved in analyzing existing models such as Stochastic Kronecker Graphs (SKG), also known as R-MAT.
Currently, Ali is working on methods to infer global properties of a network from limited samples. The essence of this work is to apply techniques of sublinear algorithms in a practical setting. Such techniques can enable rapid analysis of extremely large data sets, since the algorithms aim to predict global properties of the network by looking at only a small portion of it. Another goal is to infer network properties when only limited data is available. Ali's work has produced several important results. He has co-authored a paper to compute triadic statistics in graphs with a small number samples, which won the best paper prize at the SIAM International Conference on Data Mining (SDM'13). An extension of this work applied similar techniques for triadic analysis on a streaming setting, which won the Best Student Paper Prize at 2013 ACM Knowledge Discovery and Data Mining Conference. More recently, Ali was involved in a project to predict large entries of the product of two matrices using sampling, and this work was recognized with a Best Paper Prize at 2015 IEEE International Conference of Data Mining.
Ali's on modeling and analysis of networks resulted in ability to predict community structure in a network using only triadic information. This method led to a Block Two Level Erdos Renyi (BTER) graph model and associated software, which can generate graphs with community stricture at extremely large scales. In other work, he was also involved in analyzing existing models such as Stochastic Kronecker Graphs (SKG), also known as R-MAT.
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Esref Yalcinkaya,Marco Bohnhoff,Ethem Gorgun,Hakan Alp, Stephen Bentz,Ali Pinar, Fatih Alver, Omer Kilicarslan,Burcin Didem Tamtas, Burcak Gorgun
BULLETIN OF THE MINERAL RESEARCH AND EXPLORATION (2023): 99-116
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IEEE Transactions on Dependable and Secure Computingno. 99 (2023): 1-14
Esref Yalcinkaya,Marco Bohnhoff,Ethem Görgün,Hakan Alp,Stephan Bentz,A. Pinar, Fatih ALVER, Ömer KILIÇARSLAN,Burçin TAMTAŞ, Burçak GÖRGÜN
Yerbilimlerino. 1 (2022): 37-60
GRADES-NDA '22: Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (2022): 1-10
Proposed for presentation at the Workshop on Cyber Experimentation and the Science of Security held November 9-10, 2021 in , (2021)
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OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2021)
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Proposed for presentation at the MILCOM 2021 held December 1-2, 2021 in , (2021)
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