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Dr. Manob Saikia - EECE

Dr. Saikia

Assistant Professor

(901) 678-3650ES 204Amsaikia@memphis.edu

Click here to learn more about Dr. Saikia's work

Bio

Dr. Manob Saikia is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Memphis. He is the Founder and Director of the Biomedical Sensors & Systems Lab (est. 2022), where he leads a research program dedicated to engineering "Intelligent Bio-Integrated Systems" to make healthcare proactive, predictive, and accessible.

Dr. Saikia’s research group is distinguished by its "full-stack" capability, pioneering end-to-end solutions that span from novel biosensor hardware and low-power electronics to advanced AI and signal processing. As a Principal Investigator, he has secured competitive research funding to support his lab's innovations and holds multiple patent disclosures.

Dr. Saikia has established a prolific research record, authoring 112 peer-reviewed publications (78 Journal, 34 Conference) with over 1,160 citations. Deeply committed to student success, he currently advises or has mentored 22 graduate students (5 Ph.D., 17 M.S.) and over 39 undergraduate researchers.

Prior to joining the University of Memphis, Dr. Saikia was an Assistant Professor at the University of North Florida (2022–2024). His extensive research background includes serving as a Research Associate at the Center for Applied Brain and Cognitive Sciences at Tufts University (2019–2022) and as a primary researcher on a major NSF EPSCoR grant at the University of Rhode Island (2016–2019). Earlier in his career, he served as a Senior Research Fellow at the prestigious Indian Institute of Science (IISc).

Dr. Saikia is a Senior Member of IEEE and a Full Member of Sigma Xi. He holds a Ph.D. in Electrical Engineering from the University of Rhode Island (2019), an M.Tech. in Bio-Electronics Engineering (2013), and a B.E. in Electronics and Communication Engineering (2009)

Visit Dr. Saikia's Google Scholar Page

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Education

  • Ph.D. in Electrical Engineering, University of Rhode Island, RI, USA (2019)
  • M.Tech. in Bio-Electronics Engineering, Tezpur University, Tezpur, India (2013)
  • B.E. in Electronics and Communication Engineering, Visvesvaraya Technological University, Belagavi, India (2009)

Professional Experience

  • Assistant Professor, Department of Electrical and Computer Engineering, The University of Memphis, TN, USA (Aug. 2024 – Present)
  • Assistant Professor, Department of Electrical Engineering, University of North Florida, FL, USA (Aug. 2022 – July 2024)
  • Research Associate/Associate Investigator, Center for Applied Brain and Cognitive Sciences, Tufts University, MA, USA (Sep. 2019 – July 2022)
  • Research Assistant, Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, RI, USA (Jan. 2016 – Aug. 2019)
  • Senior Research Fellow, Department of Physics, Indian Institute of Science, Bangalore, India (June 2012 – Dec. 2015)

Research Interests

Dr. Saikia’s research program is dedicated to engineering "Intelligent Bio-Integrated Systems" that make healthcare proactive, predictive, and accessible. His lab is distinguished by a unique "full-stack" innovation capability, meaning his team does not rely on off-the-shelf devices. Instead, they engineer complete, end-to-end solutions in-house, from designing novel biosensors and custom circuit boards (PCBs) to developing the advanced Causal AI algorithms that power them.

His research is organized into three synergistic thrusts:

  1. Next-Generation Neuro-Biosensors: Developing intelligent, hybrid sensing platforms (e.g., combined EEG-fNIRS) to enable clinical-grade brain research in real-world environments. A key focus of this thrust is equitable sensing, engineering autonomous hardware that adapts to diverse skin tones to eliminate bias in medical diagnostics.
  2. Causal AI & In Silico Diagnostics: Creating interpretable AI frameworks that fuse multi-modal data, such as MRI, physiological signals, and clinical text, to move beyond simple correlation to causal inference. This work aims to discover "composite biomarkers" for the early detection of complex conditions like pediatric cancer and epilepsy.
  3. Foundational Hardware for Human Digital Twins: Pioneering bio-adaptive, resource-aware hardware architectures capable of simultaneously capturing multi-system physiological interactions. This research lays the foundation for predictive "Human Digital Twins" that can forecast individual health trajectories in real-time.

Keywords / Core Areas

  • Bio-Instrumentation & Medical Devices
  • Wearable Bioelectronics & IoT
  • Neuro-Engineering (fNIRS / EEG)
  • Causal AI & Deep Learning
  • Multi-Modal Sensor Fusion
  • Digital Twins & Predictive Modeling

Selected Publications 

  • (Selected from 112+ peer-reviewed works)

    • Bulusu, G., Vidyasagar, K. E. Ch, Mudigonda, M., & Saikia, M. J.(2025). Cancer Detection Using Artificial Intelligence: A Paradigm in Early Diagnosis. Archives of Computational Methods in Engineering, 32, 2365–2403. https://doi.org/10.1007/s11831-024-10209-0 (Corresponding Author) [IF 12.1]
    • Shankar, A., Saikia, M. J., Dandapat, S., & Barma, S. (2024). Focal Cortical Dysplasia (Type II) Detection with Multi-modal MRI and a Deep-Learning Framework. npj Imaging, 2, 31.https://doi.org/10.1038/s44303-024-00031-5 (Corresponding Author) [Nature Partner Journal]
    • Saikia, M. J.(2023). Smart Fabric and e-Textile Sensor Technology for Wearables to Measure High Pressure. IEEE Transactions on Instrumentation and Measurement, 72, 1–9. https://doi.org/10.1109/tim.2023.3312487 (Solo Author) [IF 5.9]
    • Saikia, M. J.(2023). K-means Clustering Machine Learning Approach Reveals Groups of Homogeneous Individuals with Unique Brain Activation, Task, and Performance Dynamics using fNIRS. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 2535–2544. https://doi.org/10.1109/TNSRE.2023.3278268 (Solo Author) [IF 5.2]
    • Saikia, M. J., Besio, W. G., & Mankodiya, K. (2019). WearLight: Toward a Wearable, Configurable Functional NIR Spectroscopy System for Noninvasive Neuroimaging. IEEE Transactions on Biomedical Circuits and Systems, 13(1), 91–102.https://doi.org/10.1109/TBCAS.2018.2876089 (First & Corresponding Author) [IF 5.23]
    • Saikia, M. J.(2021). A spectroscopic diffuse optical tomography system for the continuous 3D functional imaging of tissue - a phantom study. IEEE Transactions on Instrumentation and Measurement, 70. https://doi.org/10.1109/TIM.2021.3082314 (Solo Author) [IF 5.9]
    • Purkayastha, B. B., Barma, S., & Saikia, M. J.(2025). A Resource-Efficient Cardiac Arrhythmia Detection Using Nonlinear Dynamics in Optimized Delay State Networks. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2025.3605297 (Corresponding Author) [IF 4.5]
    • Daurai, B., Gogoi, M., & Saikia, M. J.(2025). A Point-of-Care Optical Biosensor for alpha-amylase Estimation using CdS/ZnS Quantum Dots. IEEE Transactions on NanoBioscience. https://doi.org/10.1109/TNB.2025.3604755 (Corresponding Author) [IF 4.4]
    • Vinti, M., Saikia, M. J., Donoghue, J., Mandigout, S., Compagnat, M., & Kerman, K. L. (2023). Broader estimates of gastrocnemius activity generated a more representative cocontraction index: a study in pediatric population. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 4382–4389.https://doi.org/10.1109/TNSRE.2023.3329057 (Corresponding Author) [IF 5.2]

    View Complete List on Lab Website: Publications

Teaching

  • EECE 3270: Intro to Microprocessor (Spring 2026, Fall 2025)
  • EECE 3201: Circuit Analysis II (Fall 2024)