MD Shaad Mahmud

Phone: (603) 862-1326
Office: Electrical & Computer Engineering, Kingsbury Hall, Durham, NH 03824
Md Shaad Mahmud

The advancement in wireless body sensor network and micro fabrication techniques have created a great opportunity for using miniaturized wireless microelectronic devices in a variety of health and life science applications. With big data and machine learning exponential growth in healthcare and clinical treatments, accurate analysis of clinical data helps in early disease detection, patient care, and community services. Keeping this mind, our research interests lie in 1) designing and implementing novel sensors and remote health monitoring systems, (2) IoT based wearable biosensor for personalized mobile health, (3) reliable and secure data communication, and (4) early disease detection using machine learning.


  • Ph.D., Electrical and Communication Engineering (ECE), University of Massachusetts - Dartmouth
  • M.S., Electrical and Communications Engineering (ECE), University of Massachusetts - Dartmouth
  • B.S., Electrical and Electronics Engineering (EEE), American International University Bangladesh (AIUB)

Research Interests

  • Artificial Intelligence/Cybernetics
  • Big Data
  • Biochemical Engineering
  • Bioengineering
  • Human-Machine Interface
  • Medical Biotechnology Diagnostics (incl. Biosensors)
  • Mobile Health (mHealth)

Courses Taught

  • 715/815: Introduction to VLSI
  • 900A: R&D Concept to Communication 1
  • 900B: R&D Concept to Communication 2
  • 992: Adv Topics Electrical Engineer
  • 999: Doctoral Research
  • ECE 543: Intro to Digital Systems
  • ECE 583: Designing w/Programmable Logic
  • ECE 999: Doctoral Research

Selected Publications

Keesee, A. M., Pinto, V., Coughlan, M., Lennox, C., Mahmud, M. S., & Connor, H. K. (2020). Comparison of Deep Learning Techniques to Model Connections Between Solar Wind and Ground Magnetic Perturbations. FRONTIERS IN ASTRONOMY AND SPACE SCIENCES, 7. doi:10.3389/fspas.2020.550874

Mahmud, M. S., Fang, H., & Wang, H. (2019). An Integrated Wearable Sensor for Unobtrusive Continuous Measurement of Autonomic Nervous System. IEEE INTERNET OF THINGS JOURNAL, 6(1), 1104-1113. doi:10.1109/JIOT.2018.2868235

Mahmud, M. S., Wang, H., Kim, Y., & IEEE. (2019). Accelerated Prediction of Bradycardia in Preterm Infants Using Time-Frequency Analysis. In 2019 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC) (pp. 468-472). Retrieved from

Mahmud, M. S., Fang, H., Wang, H., Carreiro, S., & Boyer, E. (2018). Automatic Detection of Opioid Intake Using Wearable Biosensor.. In Int Conf Comput Netw Commun Vol. 2018 (pp. 784-788). United States. doi:10.1109/ICCNC.2018.8390334

Mahmud, M. S., Wang, H., & Fang, H. (2018). SensoRing: An Integrated Wearable System for Continuous Measurement of Physiological Biomarkers. In 2018 IEEE International Conference on Communications (ICC) (pp. 1-7). IEEE.

Mahmud, M. S., Fang, H., Carreiro, S., Wang, H., & Boyer, E. W. (2018). Wearables technology for drug abuse detection: A survey of recent advancement. Smart Health.

Mahmud, M. S., Wang, H., & Fang, H. (2017). Design of a Wireless Non-Contact Wearable System for Infants Using Adaptive Filter. In Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications (pp. 83-88). ICST (Institute for Computer Sciences, Social-Informatics and ….

Mahmud, M. S., Wang, H., Esfar-E-Alam, A. M., & Fang, H. (2017). A wireless health monitoring system using mobile phone accessories. IEEE Internet of Things Journal, 4, 2009-2018.

Mahmud, M. S., Wang, H., & Kim, Y. (2016). Real time non-contact remote cardiac monitoring. In 2016 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.

Mahmud, M. S., Wang, H., Alam, E. E., & Fang, H. (2016). A real time and non-contact multiparameter wearable device for health monitoring. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.

Most Cited Publications