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Syed Affan Hussain

I build things for the AI

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Syed Affan Hussain

About Me

Artificial Intelligence Engineer

Hello! I'm Syed Affan Hussain, an AI Engineer with a strong foundation in machine learning, computer vision, and natural language processing. I hold a Bachelor's degree in Computer Science from Sir Syed University of Engineering and Technology, and have 3 years of hands-on experience in the AI field. Currently, I am enrolled in the Master's program in Artificial Intelligence at FAU Erlangen-Nürnberg, Germany, and I am actively seeking new job opportunities in Germany to further expand my skills and experience in AI.

My professional journey revolves around developing cutting-edge AI systems, including high-accuracy disease diagnosis and personalized medical information retrieval. I have worked on a wide array of AI applications, with a focus on computer vision, NLP, and optimizing machine learning models. I have also collaborated on building full-stack AI solutions, where I honed both technical and problem-solving skills.

Passionate about public speaking, I love sharing knowledge and inspiring others through my experiences. I am a strong advocate for continuous learning and thrive on applying AI innovations to solve real-world problems. I hold certifications from prestigious institutions such as IBM, Stanford University, and Microsoft. In addition, I have been honored with awards like the Valedictorian Award at Sir Syed University and the Best Public Speaker Award, which reflect my commitment to excellence and leadership.

My Journey

Experience

March 2023 - March 2025

Associate AI Engineer - HnH Soft Tech Solutions Pvt (Ltd)

Karachi, Pakistan

I have been pivotal in developing innovative AI solutions. My notable project includes building a disease diagnosis system with a 98% accuracy rate, which predicts diseases and recommends tests based on input symptoms, thus eliminating the need for initial doctor consultations. Additionally, I developed a personalized medical information retrieval system using vector databases, enabling efficient querying of relevant data from medical PDFs. My role involves rigorously testing, optimizing, and deploying AI models, and staying updated with the latest AI advancements to explore innovative solutions for ongoing projects.

Nov 2021 - Feb 2023

Junior AI Engineer - Digital Landscape

Karachi, Pakistan

I delivered various Computer Vision and NLP projects for global clients, focusing on optimizing model performance and building tailored datasets per the Software Requirements Specifications (SRS). I collaborated closely with front-end developers and UX designers to build comprehensive full-stack AI applications. My tenure at Digital Landscape honed my skills in AI and allowed me to contribute to cutting-edge AI solutions, ensuring seamless integration and functionality across multiple projects.

Education

April 2025 - Present

Master of Science in Artificial Intelligence

Friedrich-Alexander-Universität Erlangen-Nürnberg

Nuremberg, Germany
Jan 2020 - April 2024

Bachelor of Science in Computer Science

Sir Syed University of Engineering and Technology

Karachi, Pakistan

My Skills

AI Skills

Machine Learning90%

Deep Learning85%

Data Modeling90%

Python95%

Computer Vision Skills

Image Processing85%

Object Detection80%

Image Segmentation85%

OpenCV70%

NLP Skills

Text Analysis85%

Sentiment Analysis80%

NLP Libraries (NLTK, Spacy)75%

Generative AI Skills

GPT Models80%

Text Generation75%

Generative Adversarial Networks70%

My project

Brain Tumor Detection

This is a web-based brain tumor detection system that utilizes Convolutional Neural Networks (CNN) to achieve approximately 92% accuracy. Developed using Python and advanced machine learning techniques, this system analyzes MRI scans to accurately identify the presence of brain tumors. By leveraging the power of CNN, the detection process is both efficient and reliable, providing critical support in medical diagnostics.

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Early Disease Detection

This project focuses on early disease detection using the Multinomial Naive Bayes algorithm. The model is trained to classify diseases based on symptoms extracted from a provided dataset, encompassing more than 150 symptoms.

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Diet Recommendation System

This project uses a pre-trained machine learning model to recommend a diet based on user input such as weight, height, and gender. It utilizes k-means clustering for grouping food items into clusters and recommends a diet from the cluster predicted by the model.

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AI Gym Trainer

This project will guide you through three different exercises including Curl, Deadlift, and Squat using Mediapipe Pose Estimation and OpenCV. By leveraging these advanced technologies, you'll receive real-time feedback and guidance, allowing you to improve your form and perform these exercises more effectively on your own.

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Particle Swarm Optimization

In this project, Particle Swarm Optimization (PSO) was used on a dataset with approximately 17,000 data points to enhance feature selection for machine learning models. For Random Forest, the accuracy increased from 88% with all features to 93% using 1,093 PSO-selected features. Similarly, for Support Vector Machine (SVM), accuracy dramatically improved from 38% to 98% with the same PSO-selected features.

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Image Generation Using DALL-E

This application allows authenticated users to generate images using OpenAI's DALL-E model based on the provided prompt. The app includes an admin panel to view request logs and search functionality.

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