Electronics engineer building hardware-to-ML pipelines for embedded sensing, health-IoT, and AI-driven RF design. First-author published research in embedded fall detection.
My work spans the full stack, PCB design and firmware up through model training and on-device deployment. Currently extending that foundation into computational electromagnetics and AI-accelerated antenna design.
- Wearable health-IoT: embedded ML for real-time sensor data (fall detection, activity recognition)
- AI-driven RF/antenna design: reinforcement learning for antenna optimization (see
rl-dipole-tuning)
Research collaboration in embedded AI, intelligent sensor systems, and AI in RF design optimization. Particularly interested in machine learning for resource-constrained platforms and AI-accelerated hardware design.
Khatri, S. K., Parajuli, D., Mane, P. M., Chaulagain, B., & Thapa, S. (2026). Fall Detection System for Elderly People Using LSTM.Journal of Engineering Issues and Solutions, 5(1), 210-220.
Bachelor in Electronics, Communication & Information Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University (First Division with Distinction). Registered Engineer, Nepal Engineering Council.
| Project | What it is |
|---|---|
rl-dipole-tuning | RL environment wrapping NEC2 for automated dipole antenna resonant-frequency optimization |
Fall-Detection-System | Wearable fall-detection device + double-layer LSTM model (97.8% accuracy) - collaborative project; companion code to the publication above; designed the wearable PCB, collected the training dataset |
Startracker-Simulator-for-Attitude-Determination-of-Spacecrafts | Synthetic star-image simulator and visualization layer for spacecraft attitude determination - collaborative project; built the image generation & visualization layer |
Languages:PythonC/C++VHDL
Embedded:STM32ESP32RP2040ML/DL:PyTorchTensorFlow
Hardware:KiCADNEC2
Cloud/Dev:AWSGitLinux