Transforming Complex Problems into Intelligent Solutions
A comprehensive showcase of my work in applied machine learning, computer vision, and AI research. From autonomous systems to generative models, these projects demonstrate my expertise in developing end-to-end solutions that bridge theoretical concepts with practical implementations.
AI-powered access control system using advanced computer vision and deep learning. Implemented end-to-end pipeline from data collection through real-time deployment, featuring UNet for pixel-level segmentation and custom CNN for robust animal identification.
Project Highlights:
Advanced generative AI project implementing Wasserstein GAN with Gradient Penalty for high-fidelity synthetic image generation. Explored cutting-edge techniques in generator-critic optimization and training stability for photorealistic results.
Implementation Progression:
Full-stack computer vision solution for intelligent parking management. Led senior design team in developing comprehensive system integrating real-time CV analysis, web backend, and native iOS application with advanced perspective transforms and homography matrices.
Project Features:
Hardware-accelerated neural network implementation demonstrating expertise in hardware-software co-design. Translated Python MLP to optimized VHDL with focus on combinational logic reduction for FPGA deployment, achieving significant performance gains.
Implementation Details:
Deep understanding of fundamental ML/CV architectures and their practical implementations, from simple operators to complex systems. These frameworks represent my core competencies in translating theoretical concepts into production-ready solutions.
Fundamental understanding of complete CV pipelines from image acquisition through feature extraction, processing, and decision-making. Expertise in optimizing each stage for real-time performance.
Deep understanding of convolution operations, kernel design, and feature map generation. Implementation expertise from basic filters to complex hierarchical feature extractors.
State-of-the-art CNN architectures with residual connections, attention mechanisms, and multi-scale feature fusion. Proven implementation in production systems.
Complex multi-layer architectures with advanced activation functions, regularization techniques, and optimization strategies for superior generalization.
Advanced FPGA implementations and hardware simulations for neural network acceleration. Expertise in hardware-software co-design for edge AI applications.
End-to-end neural network design and optimization
Real-time image processing and analysis systems
FPGA and embedded ML implementations
Scalable ML architectures for production
Core contributor to Michigan Dearborn Autonomous Shuttle (MDAS) project. Developed real-time object detection and tracking systems using state-of-the-art deep learning models optimized for edge deployment.
Advanced machine learning research at Raytheon Technologies Research Center, developing novel algorithms for aerospace and defense applications with focus on robustness and real-time performance.
Graduate research in advanced signal processing techniques, including wavelet transforms, spectral analysis, and multi-resolution image processing for various applications.
Comprehensive reference materials covering fundamental concepts, architectures, and implementation details for neural networks and deep learning systems. These resources serve as quick references for both theoretical understanding and practical implementation.
Core concepts and mathematics behind neural networks, including backpropagation, activation functions, optimization algorithms, and network architectures.
In-depth exploration of deep learning concepts, advanced architectures, regularization techniques, and state-of-the-art implementations.
Essential deep learning concepts for beginners and practitioners, covering CNN architectures, RNNs, autoencoders, and practical implementation tips.
I'm always interested in discussing new opportunities in machine learning, computer vision, and AI research. Feel free to reach out for collaborations or to learn more about my work.
