Current
Principal R&D Engineer, Halliburton Singapore
Applied AI · Signal Processing · Digital Twins
Prasad Anjangi, Ph.D.
I develop applied AI and signal-processing systems that turn noisy, limited data into dependable decisions across energy, industrial, and communication environments.
Background
Ph.D., Electrical & Computer Engineering, NUS
Interests
Telemetry, sensing, time-series models, edge data systems
About
Engineering intelligence from difficult signals.
I develop intelligent systems for environments where data are scarce, noisy, delayed, and costly to acquire. By combining applied AI, signal processing, communications, and physics-based modelling, I turn difficult measurements into reliable engineering insight and deployable technology.
Across academic research, deep-tech R&D, and industrial engineering in Singapore, I have carried ideas from mathematical formulation to field-tested systems. My work spans downhole telemetry, digital twins, real-time signal decoding, adaptive communications, sensor fusion and localisation, and underwater acoustic networks, with a consistent focus on solutions that perform beyond the laboratory.
Selected work
Projects and outputs
A few representative projects that show how the research moves between algorithms, field systems, software, and industrial deployment.
AI-augmented telemetry and digital twins
At Halliburton, I work on signal processing, time-series modelling, and physics-based simulation for downhole telemetry systems operating in high-noise drilling environments.
Cooperative reception for long-range links
A distributed receiver architecture developed for long-range underwater acoustic communication, where spatially separated devices share signal observations and combine them to recover transmitted data.
View patentCommunication, localisation, and sensing
Designed and implemented communication, localisation, and multisensor methods for difficult field environments, including diver systems and subsea operations.
Open-source networking tools and RFCs
Contributor to unetpy, fjagepy, and the unetsocket module.
Research themes
Applied AI and signal systems for the physical world
My interests are practical and systems-oriented. I like problems where models, sensors, communications, and deployment constraints all have to be considered together.
Time-series and telemetry
Signal decoding, anomaly detection, and measurement-driven modelling for telemetry systems with noise, attenuation, and limited bandwidth.
Digital twins and simulation
Physics-based simulators and synthetic signal generation for algorithm design, validation, and operational decision support.
Adaptive communications and edge systems
Adaptive links, cooperative reception, localisation, and robust data movement across bandwidth-, energy-, and compute-constrained networks.
Experience
Research, engineering, and deployment
My professional path has kept me close to both the mathematical and operational sides of engineering.
Principal R&D Engineer, Halliburton
Signal processing, applied AI, digital twins, and real-time decoding for downhole telemetry systems.
Senior Research Scientist, Subnero
Adaptive communications, signal processing, edge deployment, sensor fusion, and field-validated subsea systems.
Ph.D., National University of Singapore
Optimisation and dynamic programming for communication networks operating with long propagation delays and constrained bandwidth.
Embedded systems and firmware
Wireless stacks, platform abstraction layers, Zigbee applications, and smart energy devices at Atmel and STMicroelectronics.
Publications
Selected papers
A selection of peer-reviewed work spanning industrial telemetry, adaptive communication, learning-based link optimisation, and field-tested systems.
AI-Aided Real-Time Data Decoder for Mud Pulse Telemetry: A Case Study
P. Anjangi, R. D. Navarro, R. R. Sobhana, F. C. O. Chagas and B. Pillai, SPE Conference at Oman Petroleum & Energy Show.
Adaptive Modulation and Coding With Feedback Scheduling for an Underwater Acoustic Link
W. Shuangshuang, M. Chitre and P. Anjangi, IEEE Journal of Oceanic Engineering.
Monte Carlo Tree Search and Feedback Adaptation for Underwater Acoustic Link Tuning
W. Shuangshuang, M. Chitre, P. Anjangi, IEEE OCEANS.
Diver Communications and Localisation System Using Underwater Acoustics
P. Anjangi and M. Chitre, IEEE Global OCEANS.
Model-based Data-driven Learning Algorithm for Tuning an Underwater Acoustic Link
P. Anjangi and M. Chitre, Fourth Underwater Communications and Networking Conference.
Propagation Delay Aware Unslotted Schedules with Variable Packet Duration for Underwater Acoustic Networks
P. Anjangi and M. Chitre, IEEE Journal of Oceanic Engineering.
Invited papers on super-TDMA and unslotted transmission schedules
P. Anjangi and M. Chitre, IEEE Third Underwater Communications and Networking Conference.
Design and Implementation of Super-TDMA
Best Experimental Student Paper Award, ACM WUWNet, Washington DC.
Teaching and mentorship
Learning by building real systems
I enjoy teaching and mentoring through projects. My preferred mode is to connect theory to working prototypes, field data, testing, and clear technical communication.
Past teaching and workshop experience includes NUS signals and systems laboratories, communication and networking tutorials, software-defined modem workshops, and industry mentorship.
Topics I like teaching
- Intelligent sensing and real-time AI systems
- Signal processing and time-series modelling
- Embedded and edge data pipelines
- Adaptive communications and constrained systems
Contact