AI Engineer | Machine Learning Engineer

I specialize in building end-to-end AI systems that transform research into real-world applications.

My expertise includes machine learning, deep learning, computer vision, and mobile AI, with experience deploying production-ready AI applications on Android.

I enjoy solving real-world problems through artificial intelligence and continuously exploring new technologies in machine learning and computer vision.

My projects range from deep learning research and image classification to Android AI applications, where I focus on writing clean, maintainable, and production-ready code.

I am also actively involved in AI research and enjoy sharing knowledge, learning from the community, and collaborating on innovative ideas.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• Deep Learning
• Mobile AI
• Data Science

An end-to-end machine learning project focused on developing, training, and optimizing deep learning models to achieve accurate and reliable dog breed classification across 120 breeds.

Models

• MobileNetV2
• EfficientNetB0
• NASNetMobile
• Random Forest

A deep learning project focused on optimizing neural network architectures and training strategies to achieve robust and accurate image classification performance.

Models

• MobileNetV2
• NASNetMobile
• VGG16
• XGBoost

A Data Science and Machine Learning pipeline for ADHD classification using neuroimaging and demographic data.

Models

• Logistic Regression
• Random Forest
• Gradient Boosting
• CatBoost

Highlights

• Hyperparameter Optimization
• Cross Validation
• Model Evaluation
• Fairness & Bias Analysis

An Explainable AI framework for an ADHD data science pipeline with a different modeling strategy.

Models

• Logistic Regression
• Random Forest
• XGBoost
• LightGBM

Highlights

• Kernel PCA
• SHAP Explainability
• Hyperparameter Optimization
• Cross Validation
• Model Interpretation

Android application powered by TensorFlow Lite for real-time dog breed recognition.

Available on Google Play

Dog Breed Identifier

Android application for flower recognition using lightweight deep learning models.

Available on Google Play

Flower App

Author of two AI research papers currently under peer review, focusing on lightweight CNN architectures, computer vision, and efficient deep learning models for mobile deployment.

• Python
• Kotlin
• Java
• PHP

• TensorFlow / Keras
• PyTorch
• Scikit-learn
• OpenCV

• MobileNetV2
• EfficientNetB0
• NASNetMobile
• VGG16

• Random Forest
• Logistic Regression
• Gradient Boosting
• XGBoost
• LightGBM
• CatBoost

• NumPy
• Pandas
• Matplotlib
• Plotly

• Android
• Jetpack Compose
• TensorFlow Lite

• Laravel
• SQL Server

• Git & GitHub
• Android Studio
• Visual Studio Code
• Jupyter Notebook

I'm always happy to connect with developers, researchers, and AI enthusiasts.

If you have questions about the projects or would like to discuss machine learning, computer vision, or AI research, feel free to reach out.

Email: dogbreedidentifier1995@gmail.com

LinkedIn:

Google Play Developer Account: View My Published AI Applications