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Ahmed Sultan

NUST · 2026

ahmedsultanx2002@gmail.com

Phone

923095257547

https://www.linkedin.com/in/ahmed-sultan-b66225304/

GitHub

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Academic

Program

Bachelors of Software Engineering

CGPA

3.27

Year

2026

Education

School of Electrical Engineering and Computer Science

Address

Street #3, Phase #3. Bahria TownHouse #47 , Rawalpindi , Pakistan

DOB

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Career

Current role

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Target role

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Skills

Python, PyTorch, MONAI, React, Node.js, FastAPI, Docker, PostgreSQL, Deep Learning, Medical Imaging, Generative AI, LLMs, Elastic Weight Consolidation, Continual Learning, Explainable AI, Grad-CAM, Django REST, Google Cloud, Data Structures & Algorithms, RBAC, Agile

Verbatim text

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Ahmed Sultan
Cell: 923095257547 | Email: ahmedsultanx2002@gmail.com
LinkedIn: https://www.linkedin.com/in/ahmed-sultan-b66225304/
Address: Street #3, Phase #3. Bahria TownHouse #47 , Rawalpindi , Pakistan
PROFESSIONAL PROFILE
Aspiring AI Engineer and ﬁnal-year Software Engineering student focused on Deep Learning and Medical Imaging . specialized in
developing "Expert-in-the-Loop" diagnostic workﬂows, including an ongoing project on MRI tumor segmentation that utilizes Elastic
Weight Consolidation to prevent model degradation. Technical proﬁciency includes Python, PyTorch, and MONAI, backed by
practical experience in deploying scalable web solutions using React and Node.js.
EDUCATION
Bachelors of Software Engineering
School of Electrical Engineering and Computer Science , Islamabad , 3.27 (2026)
INTERNSHIP EXPERIENCE
O1
19-Jun-2025 - 05-Aug-2026
-Worked on full-stack development for restaurant technology solutions serving clients in the MENA region, Spain, and the UK. -
Implemented features such as CSV data export and role-based access control (RBAC). - Debugged and optimized existing
codebases using React, Node.js/Express, and PostgreSQL. - Collaborated in agile sprints, participating in stand-ups, code reviews,
and demos. - Gained hands-on experience with real-world software delivery and client-focused development.
FINAL YEAR PROJECT
An Expert-Guided Multimodal AI Ecosystem for Diagnostic Intelligence
Architectural Design: Developing an end-to-end medical diagnostic platform designed to overcome the rigidity of standard AI. Utilized
the MoME+ architecture and PyTorch/MONAI to handle multi-modal MRI data, implementing Continual Learning strategies (Elastic
Weight Consolidation, Replay Memory) to adapt to new tumor types (BraTS 2024 GLI & MEN) without catastrophic forgetting.
Optimization & Performance: Engineering eﬃcient training pipelines for 3D volumetric data (64³ voxel crops), achieving high-ﬁdelity
segmentation (Dice Score: ~0.82) while optimizing resource allocation for mid-tier hardware constraints. Generative AI Integration: Building a "Safety-First" automated reporting pipeline that ﬁne-tunes Large Language Models to generate radiological text.
Implemented a strict JSON-to-Text grounding mechanism to ensure reports are evidence-based and free from hallucinations.
Interpretability & Trust: Integrated Explainable AI (XAI) techniques utilizing Grad-CAM to visualize model attention maps,
transforming "black box" deep learning decisions into clinically interpretable insights for radiologists. Full-Stack Deployment:
Delivering a clinician-centric web application using React.js, Django REST, and Docker. The platform integrates the segmentation
engine, metric extraction, and visual dashboard into a unified, deployable workflow on Google Cloud.
TECHNICAL EXPERTISE
Python Programming & Ecosystem
Proﬁcient in leveraging Python for complex deep learning pipelines (PyTorch) and high-performance backend systems (FastAPI),
integrating libraries like MONAI
Generative AI & Deep Learning
Experienced in implementing LLMs (MedAlpaca), developing structured-data-to-text pipelines to minimize hallucinations, and
applying continual learning techniques like Elastic Weight Consolidation (EWC).
Data Structures & Algorithms

AI enrichment

Ahmed Sultan is a final-year Software Engineering student specializing in Deep Learning and Medical Imaging, with practical experience in full-stack development. He has worked on an AI diagnostic platform involving MRI segmentation and LLM integration, alongside an internship in restaurant technology solutions.

Skills (AI)

["Python", "PyTorch", "MONAI", "React", "Node.js", "Django REST", "FastAPI", "PostgreSQL", "Docker", "Deep Learning", "Medical Imaging", "LLMs", "Continual Learning", "Explainable AI"]

Status: ai_done

Provenance

Source file: SEECS - Software Engineering-2026(1).pdf
From job #260 page 113
Created: 1778138737