Suleman Hamdani Email & Phone Number

Islamabad, PK

Islāmābād, Pakistan

• Maintained and improved a Retrieval-Augmented Generation model (SiteJetAI) by fixing a bug that limited its contextual capabilities. Applied techniques like query decomposition improving its capability to answer.
• Enhanced SiteJetAI performance and deployed using Docker, Postgres, Flask, ElasticSearch and OpenAI API decreasing the response time by 80%.
• Migrated 40+ Django-based endpoints to FastAPI, seamlessly integrating the new FastAPI-based flow with both the existing and new front-end systems. Additionally, converted 15+ database tables from Django ORM to.
• Developed and maintained a FastAPI-based backend supporting dynamic API routing, handling multi-model database interactions, and optimized for scalability and low-latency processing.
• Designed and implemented RESTful APIs for MCQ creation and management, improving efficiency in educational content handling by 40%.
• Engineered a custom XML parser for Microsoft Word documents using lxml, automating data extraction, reducing manual processing time.

• Used satellite imagery and machine learning algorithms to geo-locate air pollution assets in South Asia.
• Developed an AWS-based pipeline from scratch to process and detect brick kilns in over a million Sentinel-2 tiles.
• Utilized AWS EC2 instances, S3 buckets, and parallel processing to iterate through 1.2 million square kilometers of the IGP region, reducing processing time by 72-fold.
• Maintained APAD’s AWS account, including requesting increased resources and writing detailed use cases to ensure the allocation of up to 72 vCPUs, which significantly enhanced processing capabilities.
• Developed a pipeline using SentinelHub to automate the mosaicking and retrieval of Sentinel-2 imagery for the entire region of Bangladesh, removing any sort of manual labour which mosaicking requires
• Used Google’s Static Maps API to build a dataset of brick kilns in Pakistan and India. Trained YOLOv8 models using the collected PNG images, followed by conversion to TIFF format for accurate geolocation.

Islamabad, Pakistan

• Led a classification project employing multi-spectral Landsat8 Satellite Imagery to semantically segment wheat, rice/paddy, and soybean crops across 20+ cities, covering 12 million hectares of cultivated land.
• Analyzed segmented crops’ health through vegetation indices, achieving 98% classification accuracy and 94% Kappa score.
• Leveraged Google Earth Engine to annotate sentinel 2 satellite imagery from 10 plantation sites, covering 1 square kilometer area and 4 unique classes with an accuracy of 93%.
• Preprocessed 1350+ multispectral images to 256x256 dimensions and generated corresponding CDL masks, improving mask quality through Dilation, Opening and Closing techniques.
• Trained a UNET-based segmentation model using RGB, Near Infrared band and NDVI index to accurately classify wheat and non-wheat pixels, achieving 94% classification accuracy.

Islāmābād, Pakistan

• Conducted detailed analysis on a dataset exceeding 100,000 data points to derive insights regarding CO2 emissions.
• Identified and correlated pivotal factors influencing CO2 emissions, providing valuable information for targeted environmental strategies.
• Developed and implemented advanced Machine Learning (ML) and Deep Learning (DL) models to automate the data collection process. This led to a significant enhancement in the accuracy of measuring R22 gas levels in Air.

Islamabad, Pakistan

• Teaching Assistant for Fundamentals of Computer Programming (CS-110)
• Held 100+ office hours to teach 45 students fundamental Python concepts including Loops, Functions, Arrays.Teaching Assistant for Object Oriented Programming (CS-220)
• Taught 45+ students introductory concepts relating to C++, including Classes. Inheritance and Polymorphism.

Islamabad, Pakistan

• Conducted comprehensive literature reviews to inform research direction.
• Developed and implemented a General Regression Neural Network (GRNN) model to predict IoT immeasurable water quality parameters like 5 Day BOD, Nitrates and Phosphates.
• Utilized measurable factors such as pH, Temperature, Total Dissolved Solids, and Electric Conductivity for accurate predictions.
• Enabled real-time monitoring of Islamabad’s local reservoir's water quality, contributing to real-time environmental insights.

• Assisted 3 Computer Vision Specialists to develop an object tracking module for autonomous vehicles using YOLOV5, to achieve 95% test accuracy on the experimental video dataset.
• Optimized company resources by overlooking GPU acquisitions for workstations, resulting in 30% cost reductions and maintaining 100% of workstations’ processing capabilities.

Islāmābād, Pakistan

Bachelor Of Science - Bs, Computer Science, 3.73/4.0

O/A Levels, Mathematics And Computer Science, 9 A*S, 2As