Ashok Samraj Thangarajan

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I am a Research Scientist in Pervasive Systems department in Nokia Bell Labs, Cambridge. My research primarily focuses on hardware-software co-design for energy-aware ultra-low power embedded systems, ultra-low power wearable system, energy harvesting and near-sensor processing for industrial IoT.

Previously I was a postdoctoral researcher in the Department of Computer Science, KU Leuven, mentored by Prof. Danny Hughes. I have over 9 years of industry experience in the automotive sector as an architect and software engineer for infotainment, telematics and intelligent map based ADAS systems. I enjoy working in safety-critical systems and I’m always happy to see that the products that I am involved in some way, is saving peoples lives.

I hold a PhD in Computer Science from KU Leuven under the supervision of Prof. Danny Hughes. My PhD focused on energy-aware ultra-low power IoT sensors with near-sensor processing capabilities targeting Industry 4.0 applications. I hold a masters from Nanyang Technological University (NTU), Singapore and a Bachelors from SASTRA University, India.

news

Feb 17, 2025 We are excited to share that our paper, “BioPulse: Towards Enabling Perpetual Vital Signs Monitoring Using a Body Patch,” has been accepted for presentation at HotMobile 2025. Additionally, the accompanying poster, “Towards Enabling Perpetual Vital Signs Monitoring Using a Body Patch,” will be showcased during the poster session. Looking forward to engaging discussions and feedback at the conference!
Nov 14, 2023 Two of our papers “FaultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things” and “Enabling a Battery-free Energy Harvesting Ecosystem for the Internet of Things” have been accepted in MobiQuitous 2023.
Mar 1, 2023 Joined Nokia Bell Labs as a Research Scientist in the Pervasive Systems department.
Sep 9, 2022 Defended my PhD titled “Energy-Aware Processing for the Industrial Internet of Things”.
Jul 1, 2022 Our paper “Towards on-board learning for harvested energy prediction” has been accepted in 6th International Workshop on Embedded and Mobile Deep Learning.