Hi, I’m Engr. Eesha Khan, a PEC Level 2 Software Engineer specializing in Machine Learning, AI, and Data Science. I build intelligent systems and data-driven solutions that address complex, real-world challenges, including predictive models, AI-powered applications, and scalable data pipelines.I actively engage in hands-on projects and competitive platforms, exploring new technologies to enhance the effectiveness and reliability of AI systems. My goal is to contribute to innovative solutions that combine technical rigor with practical impact, while continuously growing in the evolving AI landscape.

Deep Learning & NLP: BERT, DeBERTa, CNNs, RNNs
LLMs & GenAI: LangChain, Ollama, Groq API

Problem: Users cannot reliably query and extract information from large PDF documents using standard chatbots Approach: Applied Retrieval-Augmented Generation to retrieve and generate relevant document context Outcome: Enabled accurate, context-aware question answering over uploaded documents Link: https://github.com/EngrEeshaKhan/rag-chatbot

Problem: Writing professional outreach emails is time-consuming for job seekers and freelancers Approach: Used a large language model to generate structured cold emails from user input Outcome: Reduced email drafting time while improving message clarity and professionalism Link: https://github.com/EngrEeshaKhan/AI-Powered-Cold-Email-Generator-Job-Client-Outreach

Problem: Early indicators of problematic internet use in children are difficult to detect manually Approach: Built a predictive model using behavioral and physical activity data Outcome: Supported early identification and intervention for healthier digital habits Link: https://github.com/EngrEeshaKhan/Child-Mind-Institute-Problematic-Internet-Use

Problem: Manual identification of protein complexes in cryo-electron tomography data is slow and inefficient Approach: Applied deep learning models to classify protein structures from 3D tomographic data Outcome: Enabled scalable and automated biological structure analysis Link: https://github.com/EngrEeshaKhan/CZII-CryoET-Object-Identification

Problem: Early melanoma detection from skin images is challenging due to subtle visual differences Approach: Applied DIP and handcrafted feature extraction followed by ML classification Outcome: Improved accuracy and reliability of melanoma detection Link: https://github.com/EngrEeshaKhan/Automatic-Melanoma-Detection-using-Hybrid-Features-and-Machine-Learning-Models

Problem: Manual essay grading is time-consuming and inconsistent Approach: Used NLP-based models to evaluate essays based on structure, coherence, and semantic quality Outcome: Produced automated scores closely aligned with human evaluation Link: https://github.com/EngrEeshaKhan/Learning-Agency-Lab---Automated-Essay-Scoring-2.0

Role: ML Intern
Organization: Ezitech Institute Rawalpindi

Duration: June 2025 – August 2025

Responsibilities / Achievements:

• Designed and implemented end-to-end ML pipelines for data preprocessing, feature engineering, model training, and evaluation
• Developed an essay scoring system using Python, TensorFlow, Scikit-learn, Groq, and Streamlit, improving automated evaluation accuracy
• Built, fine-tuned, and validated machine learning models for real-world applications
• Prepared detailed reports and presented results to the supervising team, ensuring reproducibility and robustness of models

Role: Web Developer Intern
Organization: EzeeSol Technology Rawalpindi

Duration: [June 2024 – August 2024]

Responsibilities / Achievements:

• Assisted in front-end and back-end development for web applications.
• Gained experience in [e.g., HTML, CSS, JavaScript, PHP].

Research Area: Computer Vision, Machine Learning
Methods: Support Vector Machines (SVM), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN)
Status: Published

Research Area: Artificial Intelligence, Healthcare Analytics
Methods: CatBoost, XGBoost, LightGBM, Ensemble Learning
Status: Final Academic Research Manuscript (Unpublished); IEEE Format

Research Area: Digital Twin Technology, Biomedical Signal Processing
Methods: Sparse Identification of Nonlinear Dynamics (SINDy), Physics-Informed Neural Networks (PINN)
Status: Draft Academic Research

Research Area: Natural Language Processing, Educational AI
Methods: Linear Regression, XGBoost, LightGBM, LSTM, BERT
Status: Academic Research Paper

Research Area: Computer Vision, Medical Imaging
Methods: YOLO-based Deep Learning, 3D Tomographic Analysis
Status: Academic Research Paper

1. Hybrid Blockchain–AI Framework for Real-Time Semantic Data Integrity and Access Control in 6G-Enabled IoT Networks

2. AI-Based Electricity Billing Forecasting and Consumer Classification Using Behavioral Markers

3. Artificial Intelligence-Based Patient Triage System (PTS) in Healthcare Using Natural Language Processing

• CZII – CryoET Object Identification: Ranked 536 / 931
• ISIC 2024 – Skin Cancer Detection with 3D-TBP: Ranked 2597 / 2739
• BirdCLEF 2024: Ranked 333 / 974
• Learning Agency Lab – Automated Essay Scoring 2.0: Ranked 2137 / 2706

• Machine Learning Intern – Ezitech Institute
• Web Development Intern – EzeeSol Technologies
• Machine Learning & Data Science Intern (Demo Training Program) – Edureka

• Kaggle Profile: Active participation in ML, NLP, medical imaging, and audio classification challenges

• Email: [email protected]
• GitHub: github.com/EngrEeshaKhan
• Kaggle: kaggle.com/eeshakhanzadi