Srinivasan Radhakrishnan

Biography

Srinivasan Radhakrishnan is a Clinical faculty member in the Operations and Decision Sciences area. Prior to this role, he was an Associate Teaching Professor at Northeastern University, where his work integrated teaching, applied research, and academic leadership in data analytics, machine learning, artificial intelligence, and engineering education.

His research focuses on applying machine learning and artificial intelligence to real-world problems in healthcare, manufacturing, supply chains, and scientometrics. His scholarly contributions include work on physiological signal analysis for pain and blood pressure estimation, machine diagnostics using entropy and complexity measures, hard disk drive failure prediction, supplier-manufacturer network resiliency, and keyword co-occurrence networks for systematic literature analysis.

As an educator, he taught undergraduate and graduate students in engineering and analytics courses, with emphasis on data analytics, data mining, machine learning, data visualization, mathematical foundations for machine learning, and computation for analytics.

He also held major academic leadership roles at Northeastern University. As Associate Director and later Director of the MS in Data Analytics Engineering program, he led curriculum development, enrollment strategy, and program operations across multiple campuses in the United States and Canada. He helped launch Northeastern’s first fully online graduate engineering program in partnership with Coursera and contributed to curriculum design for MS programs in Data Analytics Engineering, Data Science, and Artificial Intelligence. His leadership also included work on ABET accreditation, curriculum assessment, student learning outcomes, and continuous program improvement.

His contributions have been recognized through awards such as the College of Engineering AI Fellow designation, the College of Engineering Dean’s Award for Faculty Research Team, the Outstanding Graduate Teaching Award, and the Outstanding Graduate Research Award. Through his teaching, research, and leadership, he has advanced the use of analytics and AI as practical tools for engineering decision-making, healthcare innovation, manufacturing intelligence, and scalable education.

Area

Primary Area : Operations and Decision Sciences

Contact

Email : srinivasanr@iima.ac.in

Phone : +91-79-7152 7893

Secretary : Shylaja Deepak

Phone : +91-79-7152 7911

Education

LEAD Program, Stanford Graduate School of Business, 2024

PhD in Industrial Engineering, Northeastern University, 2018

MS in Computer Systems Engineering, Northeastern University, 2010

BE in Mechanical Engineering, Mumbai University, 2006

Research Area

Machine learning and artificial intelligence applications in healthcare, including pain intensity estimation, physiological signal analysis, and biomedical informatics

Predictive analytics and explainable AI for manufacturing, including machine component fault diagnostics, tool wear estimation, and hard disk drive failure prediction

Supply chain resilience and network analysis, including mutualistic supplier–manufacturer networks and disruption modeling

Scientometrics and keyword co-occurrence network methods for systematic reviews of scientific literature

Academic Experience

Associate Teaching Professor, Northeastern University, 2023 – April 2026

Assistant Teaching Professor, Northeastern University, 2019 – 2023

Part-time Lecturer and Postdoctoral Research Associate, Northeastern University, 2019

Professional Experience

Director of Data Analytics Engineering, Northeastern University, 2023 – 2024

Associate Director of Data Analytics Engineering, Northeastern University, 2020 – 2022

Articles

Ozek, B., Lu, Z., Radhakrishnan, S., & Kamarthi, S. (2025). Influence of Pre- Existing Pain on the Body’s Response to External Pain Stimuli: Experimental Study. JMIR Biomedical Engineering, 10, e70938.

Li, W., Zhou, H., Radhakrishnan, S., & Kamarthi, S. (2025). Explainable time series features for hard disk drive failure prediction. Engineering Applications of Artificial Intelligence, 152, 110674.

Kasbekar, R. S., Radhakrishnan, S., Ji, S., Goel, A., & Clancy, E. A. (2025). Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure. Bioengineering, 12(5), 493.

Ozek, B., Lu, Z., Radhakrishnan, S., & Kamarthi, S. (2024). Uncertainty quantification in neural-network based pain intensity estimation. PLOS ONE, 19(8), e0307970.

Ozek, B., Lu, Z., Pouromran, F., Radhakrishnan, S., & Kamarthi, S. (2023). Analysis of pain research literature through keyword co-occurrence networks. PLOS Digital Health, 2(9), e0000331.

Pouromran, F., Radhakrishnan, S., & Kamarthi, S. (2021). Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. PLOS ONE, 16(7), e0254108.

Xu, M., Radhakrishnan, S., Kamarthi, S., & Jin, X. (2019). Resiliency of mutualistic supplier–manufacturer networks. Scientific Reports, 9(1), 1–10.

Radhakrishnan, S., Lee, Y. T., Rachuri, S., & Kamarthi, S. (2019). Complexity and entropy representation for machine component diagnostics. PLOS ONE, 14(7), e0217919.

Radhakrishnan, S., Erbis, S., Isaacs, J. A., & Kamarthi, S. (2017). Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLOS ONE, 12(3), e0172778.

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, 259–270.

Radhakrishnan, S., Lin, Y., Zeid, I., & Kamarthi, S. (2013). Finger-based multitouch interface for performing 3D CAD operations. International Journal of Human-Computer Studies, 71(3), 261–275.

Conferences

Boda, S. R. R., Kamarthi, V. S., Ozek, B., Lu, Z., & Radhakrishnan, S. (2025). Canonical Time Series Features for Pain Classification. Companion Proceedings of the 27th International Conference on Multimodal Interaction, 86–91.
Zhou, H., Li, W., Radhakrishnan, S., & Kamarthi, S. (2025). Feature Engineering Toolkit for Predictive Analytics in Engineering and Healthcare Informatics. 2025 Annual Reliability and Maintainability Symposium (RAMS), 1–7. IEEE.
Radhakrishnan, S., Lee, Y. T., & Kamarthi, S. (2017). Estimation of online tool wear in turning processes using recurrence quantification analysis (RQA). 2017 IEEE International Conference on Big Data, 1755–1759.
Radhakrishnan, S., & Kamarthi, S. (2016). Complexity-entropy feature plane for gear fault detection. 2016 IEEE International Conference on Big Data, 2057–2061.
Radhakrishnan, S., & Kamarthi, S. (2016). Convergence and divergence in academic and industrial interests on IoT based manufacturing. 2016 IEEE International Conference on Big Data, 2051–2056.

Book Chapters

Radhakrishnan, S., Li, W., & Kamarthi, S. (2021). Machine Component Fault Classification Using Permutation Entropy and Complexity Representation of Vibration Signals. In Industry 4.0 and Advanced Manufacturing (pp. 289–297). Springer, Singapore.
Radhakrishnan, S., Harris, B., & Kamarthi, S. (2018). Supply chain resiliency: a review. In Supply Chain Risk Management (pp. 215–235).

Patents Filed

Patent: Kamarthi, S., Li, W., Zhou, H., & Radhakrishnan, S. (2024). U.S. Patent Application No. 18656095 (filed).

Faculty & Research

Srinivasan Radhakrishnan