Saikath Bhattacharya, Ph.D.
Assistant Professor
- Milwaukee WI UNITED STATES
Dr. Saikath Bhattacharya is an expert in secured software engineering, software reliability, and data-driven models.
Education, Licensure and Certification
Ph.D.
Tradespace Analysis incorporating Reliability, Availability, Maintainability, and Affordability
University of Massachusetts
2021
M. Tech
Communication System Engineering
Kalinga Institute of Industrial Technology
2011
Bachelor of Engineering
Electronics and Communication Engineering
Manoharbhai Patel Institute of Engineering and Technology Gondia
2009
Biography
Dr. Saikath Bhattacharya is an assistant professor in MSOE's Electrical Engineering and Computer Science Department. His areas of expertise include software reliability engineering, software security, cyber-physical systems, failure prediction, and the test and validation of AI systems.
Accomplishments
Research Associateship Program (RAP) U.S. Army Research Laboratory (US-ARL) Tradespace exploration incorporating PHM for RAMS+C
2020
Systems Engineering Research Center, Doctoral Students Forum, Travel Grant
2017
SRE Stan Ofsthun Best Student Paper Award
2020
UC Doctoral Scholarship, University of Canterbury
2013
Society of Risk Analysis Travel Grant
2017
Best Paper Award, Systems Engineering Tools and Processes Session, AHS 2017, Modeling, Analysis, and Optimization of Rotorcraft and Fleet Availability
2017
PHM Society, Doctoral Symposium Travel Grant
2016
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Affiliations
- IEEE : Student Member, IEEE Reliability Society, IEEE Computational Intelligence Society
- Indian Association of Science : Annual Member
- Prognostics and Health Management Society : Student Member
- Vertical Flight Society : Student Member
Selected Publications
Tradespace Exploration incorporating Reliability and Availability
Research in Engineering Design2018
Optimal Reliability and Cost of Non-Repairable Systems subject to Two Failure Modes considering Correlated Failures
International Journal of Reliability, Quality and Safety Engineering2018
A Comparative Approach For Location Management Using ARMA, Non-Linear Auto Regression And Neural Network For Cellular Networks
Far East Journal of Electronics and Communications2014
