AI/ML & Computer Vision Engineer (MEng, University of Limerick) with an IEEE-published research paper and hands-on experience deploying edge-AI models on embedded hardware. Strong full-stack foundation in the MERN stack. AWS Certified AI Practitioner, focused on building efficient, real-world deep learning systems — from facial recognition to fatigue detection.
- 🔭 Currently building and refining edge-AI/computer vision projects deployed on embedded hardware (ESP32-S3, OpenMV, Raspberry Pi4)
- 🌱 Deepening my skills in cloud deployment (AWS) and scalable full-stack systems
- 👨💻 All of my projects are available at github.com/Abhik8261
- 🌐 Portfolio: abhinavportfoilio.netlify.app
- 📫 Reach me at Abhik.tech8261@gmail.com
- 📄 Resume: View here
- Facial Recognition on ESP32-S3 (TinyML) — On-device MobileNetV2 facial recognition, 90.6% accuracy, 250ms inference, INT8 quantized, running fully within 512KB SRAM.
- Fatigue Detection System — Real-time yawn/fatigue detection using a custom CNN + FOMO on OpenMV Cam RT1062, 91.6% accuracy, no cloud dependency.
- Facial Recognition & Object Detection (Raspberry Pi) — HOG-based face recognition + TensorFlow Lite object detection, tuned for varied lighting conditions.
- E-Commerce Web App (MERN) — Full-stack platform with RBAC, JWT auth, Stripe payments, and an admin dashboard.
- MERN Task Manager (Redux) — Full-stack task management app with create/edit/complete workflows, built with efficient state management via Redux.
