Kaif Ul Imaan

Academic

Career

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Kaif Ul Imaan
Cell: 923174123955 |  Email: kaifulimaan@gmail.com
LinkedIn: https://www.linkedin.com/in/kaif-ul-imaan-32b56927a
Address: Off Lawrence Road , 50 b , Lahore , Pakistan
PROFESSIONAL PROFILE
Final year Computer Science student at the National University of Sciences and Technology (NUST) , Islamabad (H-12),
specializing in Artificial Intelligence with a strong academic foundation in machine learning, deep learning, and large language
models. Experienced in end-to-end data analysis, computer vision applications, and modern retrieval-augmented generation (RAG)
systems, leveraging tools such as PyTorch, Pandas, PySpark, and Databricks to derive actionable insights from complex datasets
and drive informed decision making.
Currently developing a final year project on  automated disease detection from microscopic cell images, targeting cross-modality
generalization across tissue histopathology and blood smear microscopy. The work combines an in-depth review of state-of-the-art AI
models and medical imaging platforms with hands-on experimentation using Hugging Face based PyTorch architectures, and
extends to end-to-end system design including GPU accelerated training, cloud deployment on AWS, FastAPI based backend
inference services, temporary tile storage pipelines, and explainable AI outputs tailored for clinician-friendly diagnosis.
Eager to apply both theoretical knowledge and practical skills to challenging problems in AI research and industry, while
continuing to expand expertise in emerging technologies and real-world applications.
EDUCATION
Bachelors in Computer Sciences
School of Electrical Engineering and Computer Sciences (SEECS), NUST, Pakistan. , Islamabad. , 2.93 (2026)
INTERNSHIP EXPERIENCE
Ministry of Planning, Development and Special Initiatives Pakistan
21-Jul-2025 - 01-Sep-2025
Designed and implemented a deep learning–based Signature Detection and Verification System, utilizing YOLO for automatic
signature detection from PDF documents and Siamese Networks for signature authenticity verification, forming an end-to-end
computer vision pipeline for localization and similarity matching. Gained hands-on experience with LangChain and LangGraph to
develop Retrieval-Augmented Generation (RAG) systems, including exposure to modern and modular RAG architectures, and
explored LLM tool integration and agentic workflows for reasoning over external knowledge sources and structured tools.
Cyberisk
03-Jun-2024 - 30-Aug-2024
I worked on data analytics and ETL pipelines using Python libraries, managed cloud resources through the AWS Console, and
performed data processing and analysis with PySpark on Databricks.
FINAL YEAR PROJECT
Automated Disease Detection Using Microscopic Cell Images for Cross-modality generalization across two
microscopy domains (tissue histopathology and blood smear microscopy)Automated Disease Detection
Using Microscopic Cell Images for Cross-modality generalization across two microscopy domains (tissue
histopathology and blood smear microscopy)
This project tackles the challenge of cross-modality generalization in medical microscopy, bridging two distinct diagnostic domains:
histopathology and hematology, across both standard microscopic images and whole-slide images. We leveraged Hugging Face-
based foundation models that were pretrained specifically on microscopy data to establish a strong starting point. Our approach
unfolded in three phases. In Phase I, we applied self-supervised domain adaptation using a DINOv2 student-teacher framework,
allowing the model to learn robust representations from our datasets without requiring labeled data. This step was crucial for building

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