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Schedule Interview NowMy name is Sidra R. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Python, Machine Learning, Natural Language Processing, MATLAB, Deep Learning Modeling, etc.. I hold a degree in Bachelor of Engineering (BEng), Doctor of Philosophy (PhD), Master of Engineering (MEng). Some of the notable projects I've worked on include: Optimizing Multi-Layer LSTM for Vehicle Trajectory Prediction, Wireless Sensor Network based Machine Learning for Leakage Detection, Machine learning model for fault detection in pipelines, Survey of Routing Protocols for Vehicular Ad-hoc Networks, Machine learning model for distributed event detection in WSN, etc.. I am based in Islamabad, Pakistan. I've successfully completed 8 projects while developing at Softaims.
My passion is building solutions that are not only technically sound but also deliver an exceptional user experience (UX). I constantly advocate for user-centered design principles, ensuring that the final product is intuitive, accessible, and solves real user problems effectively. I bridge the gap between technical possibilities and the overall product vision.
Working within the Softaims team, I contribute by bringing a perspective that integrates business goals with technical constraints, resulting in solutions that are both practical and innovative. I have a strong track record of rapidly prototyping and iterating based on feedback to drive optimal solution fit.
I'm committed to contributing to a positive and collaborative team environment, sharing knowledge, and helping colleagues grow their skills, all while pushing the boundaries of what's possible in solution development.
Main technologies
4 years
2 Years
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2 Years
Potentially possible
SwanSea University UK
A non-local spatio-temporal interaction-based optimized long-short-term memory (NST-LSTM) model is introduced to predict future trajectories. The proposed model effectively captures high- order intera
A smart wireless sensor network leveraging onboard micro‑sensing and wireless communication to enable real‑time leak detection and size estimation in long‑range oil, gas, and water pipelines. By using
A distributed WSN approach that leverages transient pressure wave signatures and wavelet analysis on low‑power sensor nodes to detect and localize pipeline bursts and leakages in real time. Field depl
A comprehensive survey of prediction‐based routing protocols in Vehicular Ad‑hoc Networks (VANETs) from 2015–2020, classified by evaluation metrics and ITS applications using Kitchenham guidelines. It
Wireless Sensor Network for Distributed Event Detection based on Machine learning
Bachelor of Engineering (BEng) in Computer engineering
2008-01-01-2012-01-01
Doctor of Philosophy (PhD) in Computer science
Master of Engineering (MEng) in Computer engineering
2008-01-01-2012-01-01