About Me
As a Computer Engineering student at Brawijaya University, I enjoy bringing AI from concept to deployment. My journey began with autonomous drones, implementing object detection (YOLO) in constrained environments using ROS, and has since expanded to building context-aware LLM applications.
Currently, I am expanding my focus beyond model training to understand the full Machine Learning Lifecycle. I have been working on projects involving Model Optimization (TensorRT), RAG architectures, and building inference pipelines with Docker/FastAPI. I strive to write clean, maintainable code and am always eager to learn new technologies to solve engineering problems.
Experience
Technical Mentor - ROBOTIIK Quadcopter Team
2026 - PresentServed as a technical advisor for autonomous UAV systems, conducting architectural reviews for ROS integration and perception stacks. Guided team leads in design validation, complex debugging, and strategic technical planning.
Artificial Intelligence Engineer - AITF Komdigi x Universitas Brawijaya 2025
2025Developed a scalable Gambling Detection Pipeline using a hybrid Deep Learning approach (ViT & RT-DETR). Integrated OCR and containerized the entire system as a REST API using FastAPI and Docker for production readiness.
Programming Core Team - Kontes Robot Terbang Indonesia 2025
2025Configured a containerized ROS environment using Docker to manage dependencies and ensure system consistency. Integrated the vision stack with sensor fusion logic and ArduPilot within the Gazebo simulation.
Person In Charge (PIC) Programming - ROBOTIIK Quadcopter
2025Led the technical division with a focus on advanced navigation research, including computer vision integration, autopilot configuration (Ardupilot/PX4), and SLAM integration. Managed the team's GitHub repository and mentored junior members in UAV software development.
Computer Vision Support Team - Kontes Robot Terbang Indonesia 2024
2024Contributed to the programming division by implementing initial computer vision algorithms and assisting in the testing of vision-based autonomous flight capabilities.
Portfolio Showcase
Explore my journey through projects, certifications, and technical expertise. Each section represents a milestone in my continuous learning path.
Smart Legal & HR Assistant - RAG
A context-aware chatbot using LangGraph that implements conditional routing for legal vs. general queries. Features Multi-representation Indexing on PostgreSQL for retrieval precision and CRAG to grade document relevance automatically.
Gambling Detection Pipeline
A FastAPI-based system for content analysis that integrates computer vision and OCR. The architecture fuses ViT and text extraction for classification decisions, with RT-DETR utilized for specific UI element detection on gambling-related images.
RT-DETR Gambling Fine Tuning
Fine-tuned RT-DETR (ResNet-50) for detecting gambling website UI elements (Banner Promo, CTA Button, Logo, Navbar, Game Thumbnail). Achieved 75.37% mAP.
ViT Gambling Fine Tuning
Fine-tuned Vision Transformer (ViT) for high-precision gambling website classification. Achieves 96.8% accuracy using PyTorch & Hugging Face Transformers.
YOLOv5 TensorRT Drone Vision
Real-time UAV object detection on NVIDIA Jetson Orin Nano. Optimized YOLOv5s using TensorRT to achieve 31 FPS (2.27x speedup) for autonomous drone missions.
Wokwi ESP32 MQTT
A hybrid IoT safety system simulating edge automation with Wokwi (ESP32) and MQTT. Features real-time WebSocket dashboarding and network debugging via MQTTX.
Get In Touch
Please contact me directly at akhmdashdq@gmail.com or through this form.