Engineering Portfolio
Featured Projects
Deep dives into sub-1 GFLOP computer vision models, kernel-level eBPF package security in Rust, and real-time distributed IoT telemetry.
Edge AI & MLFeatured
MicroDet
Sub-1 GFLOP Real-Time Drone Detection Pipeline
Metric 1
<1.0 GFLOP Budget
Metric 2
96% mAP@0.5
Metric 3
30+ FPS INT8 Inference
Metric 4
vs 89% MobileNetV2 Baseline
Architectural Breakdown
Architected an ultra-lightweight object detection neural network designed specifically for compute-constrained drone hardware payloads.
Coupled an optimized NanoDet backbone with custom YOLO decoupled heads, achieving 96% mAP@0.5 on target benchmarks.
Applied TensorRT INT8 calibration and quantization, delivering 30+ FPS sustained on embedded accelerators, outperforming standard MobileNetV2-SSD baselines (~89% mAP) at equivalent FLOP budgets.
Presented and demonstrated at NIDAR 2025 autonomous robotics symposium.
Built With:TinyGradPyTorchTensorRT INT8DeepStream SDKCUDAC++Python
Systems & SecurityFeatured
Archon
Security-Hardened AUR Package Manager with eBPF Sandboxing
Metric 1
<5% Build-Time Overhead
Metric 2
Kernel-level eBPF Probes
Metric 3
Bubblewrap Isolated Jail
Metric 4
Libalpm Graph Resolver
Architectural Breakdown
Engineered a high-performance, security-focused AUR (Arch User Repository) package manager in Rust.
Implements dynamic dependency graph resolution reconciling official Arch repositories and untrusted AUR PKGBUILDs.
Sandboxes unverified compilation scripts via Bubblewrap and attaches kernel-level eBPF probes (using Aya) to monitor syscalls (execve, openat, socket connect) in real-time.
Detects malicious script injections and unauthorized exfiltration attempts with under 5% build-time overhead.
Built With:RusteBPF (Aya)BubblewraplibalpmLinux KernelSecurity Systems
IoT & Full-StackFeatured
Machine Guard
End-to-End Industrial IoT Predictive Maintenance Pipeline
Metric 1
<200ms Alert Latency
Metric 2
94% Anomaly Accuracy
Metric 3
Rs. 50,000 Hackathon Winner
Metric 4
Real-Time MQTT Stream
Architectural Breakdown
Architected a distributed IoT telemetry pipeline for industrial machine vibration and acoustic anomaly detection.
Embedded quantized TFLite models on edge sensor nodes achieving 94% anomaly detection accuracy.
Streamed high-frequency sensor readings over MQTT to Firebase and synchronized to a high-density React operational dashboard with <200ms end-to-end alert latency.
Official winning submission at Ti Forge 2026 Hackathon (awarded Rs. 50,000 cash prize).
Built With:KotlinPythonTensorFlow LiteMQTTFirebaseReact.jsIoT Sensors
Looking for more repositories and experimental code?
Check out open-source toolkits, algorithms, and systems experiments on GitHub.