Data Science, Big Data Analytics, Machine Learning, Data Mining, Protein Crystallization, Bioinformatics, Querying & Indexing, Computer Vision, Video & Image Processing, Robotics, Multimedia Systems, Multimedia Information Retrieval, Multimedia Databases, Multimedia Networking & Synchronization
Dr. Ramazan S. Aygun has joined Kennesaw State University as a Director of Research Computing and an Associate Professor in the Department of Computer Science in 2020 with joint appointment in School of Data Science and Analytics. He has published or presented over 100 refereed international journal/conference/workshop papers and book chapters in various aspects of data science including data mining, data modeling, data communications, data compression, data presentation, data retrieval, data indexing, data querying, and data fusion. He has performed research on protein crystallization analysis, bioinformatics/biochemistry, data mining, machine learning, computer vision, image & video processing, information retrieval, spatio-temporal indexing & querying, multimedia synchronization, and multimedia databases. Dr. Aygun served as a program co-chair of IEEE International Symposium on Multimedia in 2012 and 2018. He served on the organization and program committees of around 60 conferences and workshops. He also served as an Associate Editor of IEEE Transactions on Multimedia from 2018 to 2020. He is a co-author of the book titled "Data Analytics for Protein Crystallization" published by Springer in 2017.
- STTR Phase II/Macromolecule Crystallization Screening Results Analysis, (Role: PI at UAH), iXpressgenes (through NIH), $300,000, 2016-2018
- REU Site: Fundamental Research Topics Related to Unmanned Systems, (Role: Co-PI), NSF, $297,792, 2014-2017
- STTR/Macromolecule Crystallization Screening Results Analysis, (Role: PI at UAH), iXpressgenes (through NIH), $50,000, 2015-2016
- STTR Phase II/Fluorescence Intensity-based Scoring of Macromolecule Crystallization Plates, (Role: PI at UAH), iXpressgenes (through NIH), $300,000, 2013-2015
- STTR/ Fluorescence Intensity-based Scoring of Macromolecule Plates, (Role: PI at UAH), iXpressgenes (through NIH), $34,970, 2010
- III-COR-Small: Developing Novel Mosaic Generation Methods for Object-Based Multimedia Information Systems, NSF, (Role: PI), NSF, $189,941, 2008-2010
 T. X. Tran and R. S. Aygun, “WisdomNet: trustable machine learning toward error-free classification,” Neural Comput. Appl., Jul. 2020, doi: 10.1007/s00521-020-05147-4.
 T. X. Tran, M. L. Pusey, and R. S. Aygun, “Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images,” J. Fluoresc., vol. 30, pp. 637–656, 2020.
 M. Shrestha, T. X. Tran, B. Bhattarai, M. L. Pusey, and R. S. Aygun, “Schema Matching and Data Integration with Consistent Naming on Protein Crystallization Screens,” IEEE/ACM Trans. Comput. Biol. Bioinform., 2019.
 K. M. Paramkusem and R. S. Aygun, “Classifying Categories of SCADA Attacks in a Big Data Framework,” Ann. Data Sci., vol. 5, no. 3, pp. 359–386, 2018.
 R. Aygun and W. Benesova, “Multimedia Retrieval that Works,” in 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), Apr. 2018, pp. 63–68, doi: 10.1109/MIPR.2018.00019.
 N. Henderson and R. Aygun, “Human Action Classification Using Temporal Slicing for Deep Convolutional Neural Networks,” in 2017 IEEE International Symposium on Multimedia (ISM), Dec. 2017, pp. 83–90, doi: 10.1109/ISM.2017.22.
 S. Dinc, F. Fahimi, and R. Aygun, “Mirage: an O (n) time analytical solution to 3D camera pose estimation with multi-camera support,” Robotica, pp. 1–19, 2017.
 M. L. Pusey and R. S. Aygün, Data Analytics for Protein Crystallization. Springer International Publishing, 2017.
 T. Tuna et al., “User characterization for online social networks,” Soc. Netw. Anal. Min., vol. 6, no. 1, p. 104, Dec. 2016, doi: 10.1007/s13278-016-0412-3.
 M. S. Sigdel, M. Sigdel, S. Dinç, I. Dinc, M. L. Pusey, and R. S. Aygün, “FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure,” IEEE/ACM Trans. Comput. Biol. Bioinform., vol. 13, no. 2, pp. 326–340, Mar. 2016, doi: 10.1109/TCBB.2015.2459685.