Muhammad Athar

Academic

Career

Verbatim text

Muhammad Athar
Cell: 923269552003 |  Email: atharrizwan234@gmail.com
LinkedIn: https://www.linkedin.com/in/muhammad-athar-51450524a
Address: H. no. 153C , Iqbal avenue phase 3 , Lahore , Pakistan
PROFESSIONAL PROFILE
With a passion for mathematics and computer science, I am determined to bring about a positive change in the world through
research and development by making things easier and more convenient for the general public. I am also interested in sharing my
knowledge with anyone who is interested and believe that knowledge sharing is one of the key things that have led to the exponential
growth of technology.
EDUCATION
Bachelors of Engineering in Software Engineering
SEECS , Islamabad , CGPA: 3.9/4 (2026)
INTERNSHIP EXPERIENCE
TUKL-NUST R&D Center
03-Jun-2024 - 06-Sep-2024
Designed and Implemented a CNN + Transformer-based Deep Learning Architecture in PyTorch for the classification of EEG Signals
into normal and abnormal which achieves State-of-the-Art performance on NMT Dataset collected in Military Hospital Rawalpindi.
RheinMain University of Applied Sciences, Wiesbaden, Germany
10-Jun-2025 - 06-Sep-2025
Performed independent research on weather forecasting and devised a multi-modal flood forecasting framework which utilizes ERA5
weather data and global river discharge maps, trained on a Sentinal-1 satellite imagery based flood dataset.
FINAL YEAR PROJECT
EEGWriter: Multimodal Deep Learning Framework for Automated EEG Diagnostic Report Generation
Electroencephalography (EEG) is a critical diagnostic tool in neurology, widely used for identifying abnormalities such as epilepsy,
seizures, and other brain disorders. Interpreting EEG signals, however, requires expert neurologists and is a time-consuming
process. This project aims to bridge the gap between raw EEG data and automated clinical interpretation using large language
models (LLMs). EEGWriter proposes the development of a multi-modal architecture that can generate structured EEG diagnostic
reports from time-series EEG signals, patient metadata (e.g., age, gender, symptoms), and technician's notes. The generated reports
follow a medical report format, including Factual Report and Impression sections. The system consists of deep learning models that
extract temporal and spatial features from raw EEG data using deep learning (e.g., CNN, Transformer models), and an LLM (e.g.,
Qwen-3 8B) that produces natural language text conditioned on these features. The final model will aim to simulate how a neurologist
writes EEG interpretations, potentially supporting telemedicine platforms, automated triage systems, and AI-assisted diagnostics.
TECHNICAL EXPERTISE
C++
Honed my C++ skills through regular competitive programming on platforms like CodeForces and LeetCode.
Python
Extensively used Python throughout many of my development as well as research-based projects.
Git
Collaborated with many individuals via GitHub by utilizing Git. https://github.com/AtharRizwan

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