Ali Akoglu
Associate Professor, Electrical and Computer Engineering
Associate Professor, BIO5 Institute
Primary Department
Department Affiliations
(520) 621-2434
Work Summary
Ali Akoglu is an Associate Professor in the Department of Electrical and Computer Engineering and the BIO5 Institute at the University of Arizona. He received his Ph.D. degree in Computer Science from the Arizona State University in 2005. He is the site-director of the National Science Foundation (NSF), Industry-University Cooperative Research Center on Cloud and Autonomic Computing regarding the design and development of architectures for achieving self-management capabilities across the layers of cloud computing systems. Dr. Akoglu is an expert in high performance scientific computing and parallel computing with a primary focus on restructuring computationally challenging algorithms for achieving high performance on parallel hardware architectures. His research projects have been funded by the National Science Foundation, Defense Advanced Research Projects Agency, Office of Naval Research, US Air Force, NASA Jet Propulsion Laboratories, Army Battle Command Battle Laboratory, and industry partners such as Nvidia and Raytheon.
Research Interest
Ali Akoglu is an Associate Professor in the Department of Electrical and Computer Engineering and the BIO5 Institute at the University of Arizona. He received his Ph.D. degree in Computer Science from the Arizona State University in 2005. He is the site-director of the National Science Foundation (NSF), Industry-University Cooperative Research Center on Cloud and Autonomic Computing regarding the design and development of architectures for achieving self-management capabilities across the layers of cloud computing systems. Dr. Akoglu is an expert in high performance scientific computing and parallel computing with a primary focus on restructuring computationally challenging algorithms for achieving high performance on field programmable gate array (FPGA) and graphics processing unit (GPU) hardware architectures. Recently he has contributed to the scientific computing domain with: 1) design and development of novel computational methods on T-Cell Receptor (TCR) synthesis for studying the immune systems of complex organisms, which led to reducing the time scale of determining all possible ways (several trillions of sequences) in which proteins can be encoded from 62 months scale to 19 hours on a single GPU; 2) investigation of electrophysiological behavior of the heart by coupling tissue and cell models in a such a way that through physics aware programming (PAP) paradigm 3D heart simulations become parallelizable without sacrificing model accuracy, which led to reducing the time scale of such simulations from 453 hours to 1.66 seconds with a simulation accuracy of 99.9% Dr. Akoglu has been involved in many crosscutting collaborative projects with the goal of solving the challenges of bridging the gap between the domain scientist, programming environment and emerging highly-parallel hardware architectures. His research projects have been funded by the National Science Foundation, Defense Advanced Research Projects Agency, Office of Naval Research, US Air Force, NASA Jet Propulsion Laboratories, Army Battle Command Battle Laboratory, and industry partners such as Nvidia and Raytheon.

Publications

Liu, H., Rajavel, S. T., & Akoglu, A. (2013). Integration of Net-Length Factor with Timing-and Routability-Driven Clustering Algorithms. ACM Transactions on Reconfigurable Technology and Systems (TRETS), 6(3), 12.
Vincent, B., Buntzman, A., Hopson, B., McEwen, C., Cowell, L., Akoglu, A., Zhang, H., & Frelinger, J. (2016). iWAS-A novel approach to analyzing Next Generation Sequence data for immunology. Cellular Immunology, 299, 6-13. doi:http://dx.doi.org/10.1016/j.cellimm.2015.10.012
Nimmagadda, V. K., Akoglu, A., Hariri, S., & Moukabary, T. (2012). Cardiac simulation on multi-GPU platform. The Journal of Supercomputing, 59(3), 1360--1378.
Song, Y., & Akoglu, A. (2013). An adaptive motion estimation architecture for H. 264/AVC. Journal of Signal Processing Systems, 73(2), 161--179.
Machovec, D., Khemka, B., Kumbhare, N., Paricha, S., Maciejewski, A. A., Siegel, H. J., Akoglu, A., Koenig, G., Hariri, S. A., Tunc, C., Wright, M., Hilton, M., Rambhoros, R., Blandin, C., Fargo, F., Louri, A., & Imam, N. (2016). Utility-Based Resource Management in an Oversubscribed Energy-Constrained Heterogeneous Environment Executing Parallel Applications. Journal of Parallel Computing.