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AI Research Engineer with 4+ years in Computer Vision & LLMs
Disciplined and detail-oriented Computer Science Researcher and Artificial Intelligence (AI) Engineer with a strong foundation in computer vision, Machine Learning (ML), Deep Learning (DL), and programming (Python, C++). Skilled in building production-oriented ML/DL pipelines and translating complex AI concepts into clear, actionable solutions. Willingness to continuously learn and adapt to new technologies. Demonstrated experience in AI/ML/DL/Computer Vision research, data analysis, prompt engineering, and problem solving through hands-on projects, development of AI-powered applications, and designing DL models for complex problems. Committed to continuous learning, innovation, and delivering high-quality AI solutions for real-world complex scenarios that drive success.
Riphah International University
BSCS (Bachelor of Sciences in Computer Science) · CS, AI, ML, DL, Computer Vision
September 1, 2021 – July 1, 2025
F.G Quaid-e-Azam Degree College
HSSC (Higher Secondary School Certification) · Pre-Engineering
July 1, 2019 – September 1, 2021
Buraq Secondary School
SSC (Secondary School Certification) · Computer Science
March 1, 2017 – April 1, 2019
Riphah International University
RESEARCH & TEACHING FELLOW
August 1, 2025 – Present
Islamabad, Islamabad Capital Territory, Pakistan
Independent Researcher
REMOTE ASSISTANT RESEARCH ANALYST – AI, ML & DL
March 1, 2024 – August 1, 2025
UK
Riphah International University
ARTIFICIAL INTELLIGENCE (AI) LAB TEACHING ASSISTANT
September 1, 2023 – September 1, 2024
Islamabad, Islamabad Capital Territory, Pakistan
Wisdom Academy
COMPUTER SCIENCE TUTOR
September 1, 2023 – March 1, 2024
Islamabad, Islamabad Capital Territory, Pakistan
Riphah International University
PROGRAMMING LAB TEACHING ASSISTANT
September 1, 2022 – September 1, 2023
Islamabad, Islamabad Capital Territory, Pakistan
Explainable DL-based Breast Cancer Diagnosis Framework using Histopathological Image analysis
March 1, 2024 – June 1, 2026
A diverse dataset, BRACS, was employed to enhance classification robustness, containing a variety of breast cancer subtypes and challenges like staining inconsistencies and complex tumor morphology. Proposed a novel DL-framework, integrating a lightweight HFF-Net enhanced using attention blocks, and finetuned DenseNet201 on ImageNet, achieving 97% screening and 84% grading accuracy. Tackled challenges such as tissue staining variations, image artifacts, and noisy backgrounds, and implemented Grad-CAM for improved interpretability, enabling the visualization of localized lesions. Ongoing work includes further optimization through ablation studies, for peer-review validation, contributing to the development of a clinically interpretable AI-powered breast cancer diagnostic system.
An Explainable Multi-Model Deep Learning Framework for Breast Cancer Diagnosis from Histopathological Images
Informatica
June 1, 2026 – Present
HARIC-Net: An Explainable Attention Residual-Inception CNN for Diabetic Retinopathy Diagnosis using C-CLAHE-DoG Enhanced Fundus Images
Informatica
June 1, 2026 – Present
MSCNN: A Lightweight Hybrid Convolutional Neural Network for Vision-Based Tomato Plant Disease Classification
ACM ICPS
June 1, 2026 – Present
MSHFF-Net: An Explainable Multi-Scale Hierarchical Feature Fusion CNN for Breast Cancer Diagnosis from Histopathological Images
27th International Multitopic Conference (INMIC), IEEE
January 26, 2026 – Present
Forecasting Solar Project Capacity Trends Using Predictive AI: A Policy-Oriented Analysis of Urban vs. Rural Solar Deployment
ACM ICPS
October 20, 2025 – Present
HIRD-Net: An Explainable CNN-Based Framework with Attention Mechanism for Diabetic Retinopathy Diagnosis Using CLAHE-D-DoG Enhanced Fundus Images
MDPI, Life
September 8, 2025 – Present
Introduction to Python
Unknown
February 1, 2024 – Present
Introduction to Java
Unknown
March 1, 2022 – Present
Microsoft Office Specialist
Unknown
June 1, 2021 – Present
CPA: Programming Essentials in C++
Unknown
January 1, 2021 – Present
Cultural Fit Analysis
The candidate's academic background and extensive teaching/research assistant roles at a university suggest a strong fit for an environment that values continuous learning, mentorship, and academic rigor. Their project on breast cancer diagnosis and multiple publications in medical imaging demonstrate a commitment to impactful, real-world applications of AI. The breadth of skills covers core AI/ML, computer vision, and programming, indicating adaptability. However, the experience is primarily academic, and exposure to diverse industry projects or large-scale commercial deployments is not explicitly detailed, which might require some adjustment to a fast-paced industry research environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving, analytical thinking, attention to detail, and critical thinking skills, which are essential for a research-oriented role. Their experience as a teaching assistant and tutor indicates good communication, leadership, and mentoring abilities. The focus on reproducible research workflows and structured pipelines suggests an organized and methodical approach to work. However, the resume does not provide explicit details on stress handling or direct team collaboration in a corporate research setting, which would be beneficial for a senior role.