Hamna Moieez

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I am currently looking for full-time Machine Learning Developer/ Data Scientist roles.

I, recently, did my graduate studies from Sapienza University of Rome, Italy in engineering in computer science. I was fortunate to be advised by Dr. Simone Scardapane. My research focused on continual learning systems, specifically looking at the problem of continual self-supervised learning for Earth Observation (EO). I worked on designing systems capable of learning to segment continually from sparsely labelled remotely sensed data.

Before that I did my undergraduate studies in computer science from National University of Sciences & Technology (NUST), Islamabad working in TUKL-NUST R&D Lab working with Dr. Faisal Shafait. My undergraduate thesis work studied the problem of slum mapping and localization from remotely sensed imagery.

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I am mainly interested in applied AI, specifically machine learning and I am looking for roles in the ML industry.

I am currently working as an Associate Machine Learning Developer at Applied AI Lab, AltaML. My work revolves around automating functioning of industrial plant’s operational cycle. Majority of the industrial plants require manual descriptive configuration files (often in proprietary formats understandable to only plant operators) to perform certain operations. I work to design machine learning pipelines to auto-generate these configuration files and auto-complete different operation modules using large-language models (LLMs).

This is a full-time contract position and my contract ends on September 1, 2023

We consider the problem of semantic segmentation, a problem that lends itself to various remote sensing applications. To this end, we build on the work done in the domain and introduce a new algorithm, CBT-ER.

This work was my graduate thesis, and the abstract got accepted to to flagship conference of IEEE Geoscience and Remote Sensing Society (GRSS).

In this work, I worked on understanding how slums form and change right inside major metropolitan cities. In addition to collecting dataset from satellite imagery, which included manual markings of slum settlements, I developed a pipeline to monitor, segment and localize slums in several areas of the cities. Such an automated pipeline is important to inform revelant authorities so as to redirect their rehabilitation efforts and resources appropriately.

Implemented and tested Self-Attention Networks (SAN) for graduate course in machine learning. The project was a tensorflow implementation of pair-wise and patch-wise self attention network for image recognition.

We looked at the problem of driver activity monitoring to detect any distraction that the driver maybe indulged in whilst operating a motor vehicle. We learned a fully functional, real-time and responsible system built-on the state-of-the-art machine vision stack capable of examining the in-vehicle activity via live dash-cam feed and responding as required.

This design is taken from ✩