Passionate AI Engineer specializing in Machine Learning, Computer Vision, and LLMs. Currently pursuing MS in AI at Friedrich-Alexander-Universität Erlangen-Nürnberg, with 3+ years of experience building innovative AI solutions.
I am Syed Affan Hussain, a dedicated Artificial Intelligence Engineer with expertise in machine learning, computer vision, and natural language processing. With a Bachelor's degree in Computer Science from Sir Syed University of Engineering and Technology and over 3 years of professional experience in AI development, I bring a comprehensive understanding of modern AI technologies and their practical applications. Currently pursuing a Master's degree in Artificial Intelligence at Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany, I am committed to advancing my expertise while contributing to innovative AI solutions.
Throughout my career, I have specialized in developing sophisticated AI systems for healthcare and security domains, including high-precision disease diagnosis models and intelligent security automation platforms. My technical expertise encompasses end-to-end AI pipeline development, from data preprocessing and model architecture design to deployment and optimization. I have successfully delivered solutions that achieved significant improvements in accuracy, efficiency, and scalability, working with technologies such as deep learning frameworks, computer vision libraries, and large language models.
Beyond technical excellence, I am passionate about knowledge sharing and continuous learning. I actively engage in professional development through certifications from leading institutions including IBM, Stanford University, and Microsoft. My academic achievements include receiving the Valedictorian Award at Sir Syed University and recognition as Best Public Speaker, demonstrating my commitment to both technical mastery and effective communication. I thrive on solving complex real-world challenges through innovative AI applications and collaborative problem-solving approaches.
Advanced web-based brain tumor detection system utilizing Convolutional Neural Networks achieving 92% accuracy. Analyzes MRI scans with deep learning techniques to provide reliable medical diagnostic support for healthcare professionals.
Intelligent disease classification system using Multinomial Naive Bayes algorithm. Analyzes patient symptoms from comprehensive dataset covering 150+ medical symptoms for early disease prediction.
Personalized nutrition recommendation system using k-means clustering and machine learning. Provides customized diet plans based on user's physical attributes, health goals, and dietary preferences.
Real-time AI fitness trainer using pose estimation technology. Provides instant feedback for exercises including Curls, Deadlifts, and Squats with computer vision-based form correction and performance tracking.
Advanced feature selection optimization using Particle Swarm Optimization on 17K+ data points. Enhanced Random Forest accuracy from 88% to 93% and SVM from 38% to 98% through intelligent feature engineering.
Secure AI-powered image generation platform utilizing OpenAI's DALL-E model. Features user authentication, admin dashboard for request monitoring, and advanced search capabilities for generated content management.