I'm passionate about pushing the boundaries of technology and turning innovative ideas into reality. Below is a snapshot of my skills and projects.
I develop cutting-edge machine learning and deep learning models, from predictive analytics to computer vision. I excel in developing intelligent systems that learn and adapt, utilizing frameworks like PyTorch and TensorFlow alongside techniques such as Transfer Learning, Self-Supervised Learning, and Reinforcement Learning.
I have extensive experience building and refining computer vision models. My skills include advanced data augmentation, filtering, data cleaning, and sophisticated feature engineering and selection techniques to achieve high-accuracy object detection, semantic segmentation, and tracking.
I design and implement decision-making pipelines for autonomous vehicles, incorporating multi-model threading for real-time performance on edge devices. My work includes LiDAR-based detection and tracking, camera-based road segmentation, and applying Reinforcement Learning for intelligent control systems.
My expertise extends to the hardware level, including the development of custom computer chips like ASICs, floating-point processors, and tensor processors using Verilog HDL. I specialize in creating efficient, pipelined, and parallel chip architectures in Xilinx Vivado.
I am proficient in deploying complex algorithms on a range of embedded systems, including the Nvidia Jetson series, Raspberry Pi, ESP32, and Arduino platforms, with a focus on optimizing for real-time, low-latency applications.
From algorithmic optimizations in Python to system-level programming in C++, I tackle a wide variety of projects with precision and depth, building robust and efficient solutions.
