Sivarit (Tony) Sultornsanee
Associate Teaching Professor, Mechanical and Industrial Engineering
Office
- 342B Snell Engineering
Research Focus
Internet of Things (IoT) and intelligent factory transformation; Price recommendation and optimization using machine learning models; Image search engines and semantic segmentation; Applications of artificial intelligence in business; and Biomedical signal processing for healthcare applications
About
Dr. Sivarit Sultornsanee is an Associate Teaching Professor of Mechanical and Industrial Engineering and Assistant Program Advisor of the Data Analytics Engineering program at Northeastern University (Boston, MA).
He has worked as a Data Analytics Professor for over ten years. He is adept in curriculum development, classroom instruction, and research activities. His experience has afforded him well-rounded data science and data analytics teaching and research skills.
He is also a member of the British Classification Society and the International Federation of Classification Societies. His research interests are in the Internet of Things (IoT), intelligent factory transformation, price recommendation using ML models, image search engines and semantic segmentation, biomedical signal processing for healthcare applications, and applications of AI in business.
Education
Doctor of Philosophy in Interdisciplinary Engineering (Data Analytics Engineering)
Northeastern University, 2012
Master of Science in Computer Engineering
University of Massachusetts, 2007
Professional Affiliations
- Member of Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology Association of Thailand (ECIT-Thailand)
- Member of Electrical Engineering Academic Association of Thailand
- Member of British Classification Society
- Member of the International Federation of Classification Societies (IFCS)
Research Overview
Internet of Things (IoT) and intelligent factory transformation; Price recommendation and optimization using machine learning models; Image search engines and semantic segmentation; Applications of artificial intelligence in business; and Biomedical signal processing for healthcare applications
Department Research Areas
Selected Publications
- Artameeyanant P., Sultornsanee S., and Chamnongthai K. (2017) “EEG-Based Feature Extraction Using Complex Network for Automated Epileptic Seizure Detection.” Expert Systems: The Journal of Knowledge Engineering, 34(3), e12211.
- Artameeyanant P., Sultornsanee S., and Chamnongthai K. (2016) “An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection.” SpringerPlus, 5:2101.
- Kamarthi S., Sultornsanee S., and Zeid A. (2016) “Recurrence quantification analysis to estimating surface roughness in finish turning processes.” The International Journal of Advanced Manufacturing Technology, 87, pp. 451-460.
- Radhakrishnan, S., Duvvuru, A., Sultornsanee, S., Kamarthi, S. (2016) “Phase synchronization based minimum spanning trees for analysis of financial time series with nonlinear correlations.” Physica A: Statistical Mechanics and its Applications, 444, pp. 259-270.
- Sultornsanee, S., Duvvuru, A., Radhakrishnan, S., Chowdhary, H., Kamarthi, S, (2013) “Phase Synchronization Based Minimum Spanning Trees for the Analysis and Visualization of Currency Exchange Markets.” Procedia Computer Science, 20, pp. 460-465.
- Duvvuru, A., Radhakrishnan, S., More, D., Kamarthi, S, Sultornsanee, S. (2013) “Analyzing Structural & Temporal Characteristics of Academic Research between USA and India.” Procedia Computer Science, 20, pp. 439-445.
- Sultornsanee, S., Radhakrishnan, S., Falco, D., Zeid, A., Kamarthi, S. (2011) “Phase Synchronization Approach to Construction and Analysis of Stock Correlation Network.” Procedia Computer Science, 6, pp. 52-56.
- Sultornsanee, S., Zeid, I., Kamarthi, S. (2011) “Classification of Electromyogram Using Recurrence Quantification Analysis.” Procedia Computer Science, 6, pp. 375-380.