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Schedule Interview NowMy name is Talha S. and I have over 2 years of experience in the tech industry. I specialize in the following technologies: Data Analysis, Data Visualization, Python, R, Data Science, etc.. I hold a degree in Associate's degree, Bachelor's degree. Some of the notable projects I've worked on include: Explainable Car Damage Severity Grading with ResNet18,YOLO - Python, Physics-Guided Synthetic rPPG: Converting PPG to rPPG for AF Detection, RegionBased Music Classification with Time-Domain, MFCCs, Spectrograms, Time Series Analysis of the US Industrial Production Index (1990–2023), Python, Industrial-Scale ECG Disease Classification Using EfficientNet, etc.. I am based in Lahore, Pakistan. I've successfully completed 28 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
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fiverr
This project trains a ResNet18, YOLO model to classify car damage severity (Minor, Moderate, Severe) from COCO-style annotated images and then explains its decisions. It builds per-image severity labe
This project addresses data scarcity in non-contact cardiac monitoring by generating synthetic rPPG data from the MIMIC PERform AF Dataset. Leveraging 20 minute ECG and PPG recordings from 35 critical
This project focuses on music classification from WAV files by extracting meaningful audio features. It combines raw time-domain signals, MFCCs for timbre, and spectrograms for time–frequency analysis
This project aims to analyze time series data by identifying trends and patterns using R. We use statistical methods and visualization tools, including log-transformation, trend extraction, and ARMA m
This project presents a robust, industrial-ready pipeline for multi-label ECG disease and severity classification. Leveraging improved 1D EfficientNet and residual neural architectures, the system int
Associate's degree in CCNA - Enterprise and CyberOps Associate
2020-01-01-2021-01-01
Bachelor's degree in
2021-01-01-2025-01-01