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Ayesha Siddiqa

NUST · 2026

ayeshasiddiqa19as19@gmail.com

Phone

923001232612

https://www.linkedin.com/in/ayesha-siddiqa-447348256/

GitHub

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Academic

Program

Bachelor of Software Engineering

CGPA

3.88

Year

2026

Education

SEECS

Address

MOHALLAH BAKHSH E KHAIL P/O LAWA TEHSIL LAWADISTT. CHAKWAL , Lawa , Pakistan

DOB

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Career

Current role

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Target role

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Skills

Machine Learning, Remote Sensing, Satellite Imagery Analysis, Transformer-based Models, Land-use Change Detection, Deforestation Monitoring, Vision-Language Models, Python, ML Frameworks, Google Earth Engine, BIT, ScratchFormer, Point Cloud Data, Multimodal Learning, Geospatial Data Pipelines, EEG Brain Disorder Classification, Explainable AI, Neurologist Eye-tracking, Deep Learning

Interests / quote

I am a motivated Software Engineering undergraduate at NUST (CGPA 3.88) with a strong interest in machine learning and real-world problem solving. i have hands-on research experience in remote sensing, satellite imagery analysis, and transformer-based models, including work on land-use change detection, deforestation monitoring, and vision-language models. Comfortable working across the full ML pipeline data collection, preprocessing, model training, evaluation, and deployment using Python and modern ML frameworks. I have been recognized for consistent academic excellence and leadership, with the ability to translate theoretical concepts into reliable, well-engineered systems. Actively seeking roles where strong fundamentals in software engineering and applied machine learning can be used to solve complex, real-world problems.

Verbatim text

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Ayesha Siddiqa
Cell: 923001232612 |  Email: ayeshasiddiqa19as19@gmail.com
LinkedIn: https://www.linkedin.com/in/ayesha-siddiqa-447348256/
Address: MOHALLAH BAKHSH E KHAIL P/O LAWA TEHSIL LAWADISTT. CHAKWAL , Lawa , Pakistan
PROFESSIONAL PROFILE
I am a motivated Software Engineering undergraduate at NUST (CGPA 3.88) with a strong interest in machine learning and real-
world problem solving. i have hands-on research experience in remote sensing, satellite imagery analysis, and transformer-
based models, including work on land-use change detection, deforestation monitoring, and vision-language models. Comfortable
working across the full ML pipeline data collection, preprocessing, model training, evaluation, and deployment using Python and
modern ML frameworks. 
I have been recognized for consistent academic excellence and leadership, with the ability to translate theoretical concepts into
reliable, well-engineered systems. Actively seeking roles where strong fundamentals in software engineering and applied machine
learning can be used to solve complex, real-world problems.
EDUCATION
Bachelor of Software Engineering
SEECS , Islamabad , 3.88 (2026)
INTERNSHIP EXPERIENCE
Machine Vision and Intelligent Systems Lab, SEECS, NUST
11-Jun-2025 - 31-Aug-2025
Conducted research on remote sensing and satellite image analysis to monitor deforestation and urban expansion. Created custom
bi-temporal datasets using Google Earth Engine for 20 global regions to track urban expansion and land use changes.Trained and
optimized machine learning models and transformer architectures (e.g., BIT, ScratchFormer) for land cover change detection
Machine Vision and Intelligent Systems Lab, SEECS, NUST
01-Sep-2024 - 01-Aug-2025
Working on generating detailed textual descriptions of satellite imagery using pre-trained Vision-Language Models (VLMs).In parallel,
worked with point cloud data to analyze spatial structure and elevation-based features, supporting tasks such as urban expansion
analysis and scene understanding. This work emphasizes multimodal learning, geospatial data pipelines, and the practical challenges
of aligning visual, spatial, and textual representations for real-world remote sensing applications.
FINAL YEAR PROJECT
Gaze-Guided Explainable AI for EEG Brain Disorder Classification
This project is a human-aligned AI framework that integrates neurologist eye-tracking data with EEG signals to make deep learning
based clinical decisions transparent, veriﬁable, and clinically meaningful. The project captures where experts visually focus during
EEG interpretation and synchronizes this gaze information with EEG epochs to create multimodal datasets combining
electrophysiology, attention maps, and diagnostic labels. By training models to align their internal attention and explanations with
expert gaze patterns, the system addresses key limitations of black-box EEG classiﬁers , lowering cognitive load and enabling
clinicians to validate whether predictions are based on medically relevant waveform features rather than spurious correlations. The
outcome is an interpretable, trust-worthy AI system that bridges human expertise and machine intelligence, accelerating EEG
analysis while preserving clinical rigor and accountability.
TECHNICAL EXPERTISE
Machine Learning

AI enrichment

Ayesha Siddiqa is a Software Engineering undergraduate with a 3.88 CGPA, specializing in machine learning, remote sensing, and multimodal AI. She has research experience in transformer-based models, vision-language systems, and developing explainable AI frameworks for clinical applications.

Skills (AI)

["Python", "Machine Learning", "Deep Learning", "Remote Sensing", "Satellite Imagery Analysis", "Transformer Architectures", "Vision-Language Models", "Point Cloud Data", "Geospatial Data Pipelines", "Explainable AI", "EEG Signal Processing", "Google Earth Engine"]

Status: ai_done

Provenance

Source file: SEECS - Software Engineering-2026(1).pdf
From job #260 page 60
Created: 1778138736