Selected Publications &

Research Interests

Recent Selected Publications
A. Mohan, A. Turk, R. S. Gudimetla, S. Tikale, J. Hennesey, U. Kaynar, G. Cooperman, P. Desnoyers, and O. Krieger, “M2: Malleable Metal as a Service”, in IEEE IC2E, 2017.

D. Turkoglu, A. Turk, Edge-Based Wedge Sampling to Estimate Triangle Counts in Very Large Graphs, in IEEE ICDM, 2017. (Best paper runner up)

O. Tuncer, E. Ates, Y. Zhang, A. Turk, J. Brandt, V. J. Leung, M. Egele, and A. K. Coskun, Diagnosing Performance Variations in HPC Applications Using Machine Learning, in ISC HPC, 2017. Gauss Award Winner

R. Pienaar, A. Turk, J. Bernal-Rusiel, N. Rannou, D. Haehn, P. E. Grant and O. Krieger, CHIPS – A Service for Collecting, Organizing, Processing, and Sharing Medical Image Data in the Cloud, in VLDB DMAH, 2017.

A. Turk, H. Chen, A. Byrne, J. Knollmeyer, S. Duri, C. Isci, and A. K. Coskun “DeltaSherlock: Identifying Changes in the Cloud”, in IEEE BigData, 2016

J. Hennessey, S. Tikale, A. Turk, E. Kaynar, C. Hill, P. Desnoyers, O. Krieger, “HIL: Designing an Exokernel for the Data Center”, in SoCC, 2016.

G. B. Tran, A. Turk, B. B. Cambazoglu, “A Random Walk Model for Optimization of Search Impact in Web Frontier Ranking”, in SIGIR, 2015.

M. Zapater, A. Turk, J. M. Moya, J. L. Ayala, A. K. Coskun, “Dynamic Workload and Cooling Management in High-Efficiency Data Centers”, in IGSC, 2015.

A. Turk, O. R. Selvitopi, C. Aykanat, H. Ferhatosmanoglu, “Temporal Workload-Aware Replicated Partitioning for Social Networks”. IEEE TKDE, 2014.

A. Turk, K. Y. Oktay, C. Aykanat, “Query-Log Aware Replicated Declustering”, IEEE TPDS, 2012.

O. R. Selvitopi, A. Turk, C. Aykanat, “Replicated Partitioning for Undirected Hypergraphs”, JPDC, 2012.
Research Interests

In the future, all computation will move to the Cloud. Meaningful Cloud computing research is fundamentally challenging: in addition to standing on sound theoretical grounds, it has to be deployed on real systems at a realistic scale, requires significant engineering effort, and often involves large teams and industry collaboration. I find this kind of research compelling because the problems are complex, intellectually challenging, require cross-disciplinary (economics, legal, privacy, …) solutions and have very high-impact. During my time as a PhD student, startup founder, postdoc at Yahoo! research, and research scientist at the Massachusetts Open Cloud (MOC), I was fortunate to be involved in projects where I had the opportunity to experience and develop the skills necessary to succeed in this kind of research. 

My research interests lie in the design/development of cloud-based bigdata platforms and improving the performance, security, or efficiency of cloud frameworks using machine learning and combinatorial mechanisms. My research target going forward is to start initiatives for cloud-assisted big data solutions for emerging applications such as healthcare, renewable energy, and crypto finance systems, while building on the projects I helped get started at MOC.

I also have extensive expertise in designing algorithms for graph analytics. I proposed approximation algorithms for identifying graph features and developed partitioning, replication, scheduling tools for parallel computations on large scale graphs. I plan to keep exploiting (and contributing to) fundamental computer science theory to improve performance and efficiency of Cloud applications.


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