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I’m Yessmine Chaabouni, a 2nd-year Geospatial Engineering student at Manouba School of Engineering in Tunis, specializing in machine learning, Earth Observation, and GEOINT. I enjoy turning satellite data and geospatial signals into reliable pipelines—whether that’s building quality-control workflows for Sentinel-2 imagery, developing anomaly detection systems, or designing AI-driven decision support that can actually be validated against real-world evidence. Across internships and technical projects, I’ve worked on automated data processing and modeling: from U-Net and YOLO-based satellite image analysis, to maritime GEOINT workflows combining AIS, SAR, and trajectory consistency, to climate intelligence systems using LightGBM with ERA5 and SHAP explainability. I’m particularly motivated by edge-ready, actionable geospatial intelligence—bridging robust engineering with interpretable AI so results are both accurate and explainable.…I’m Yessmine Chaabouni, a 2nd-year Geospatial Engineering student at Manouba School of Engineering in Tunis, specializing in machine learning, Earth Observation, and GEOINT. I enjoy turning satellite data and geospatial signals into reliable pipelines—whether that’s building quality-control workflows for Sentinel-2 imagery, developing anomaly detection systems, or designing AI-driven decision support that can actually be validated against real-world evidence. Across internships and technical projects, I’ve worked on automated data processing and modeling: from U-Net and YOLO-based satellite image analysis, to maritime GEOINT workflows combining AIS, SAR, and trajectory consistency, to climate intelligence systems using LightGBM with ERA5 and SHAP explainability. I’m particularly motivated by edge-ready, actionable geospatial intelligence—bridging robust engineering with interpretable AI so results are both accurate and explainable.

Yessmine Chaabouni

AI Engineer, Data Scientist, Data Engineer, +3

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I’m Yessmine Chaabouni, a 2nd-year Geospatial Engineering student at Manouba School of Engineering in Tunis, specializing in machine learning, Earth Observation, and GEOINT. I enjoy turning satellite data and geospatial signals into reliable pipelines—whether that’s building quality-control workflows for Sentinel-2 imagery, developing anomaly detection systems, or designing AI-driven decision support that can actually be validated against real-world evidence. Across internships and technical projects, I’ve worked on automated data processing and modeling: from U-Net and YOLO-based satellite image analysis, to maritime GEOINT workflows combining AIS, SAR, and trajectory consistency, to climate intelligence systems using LightGBM with ERA5 and SHAP explainability. I’m particularly motivated by edge-ready, actionable geospatial intelligence—bridging robust engineering with interpretable AI so results are both accurate and explainable.…I’m Yessmine Chaabouni, a 2nd-year Geospatial Engineering student at Manouba School of Engineering in Tunis, specializing in machine learning, Earth Observation, and GEOINT. I enjoy turning satellite data and geospatial signals into reliable pipelines—whether that’s building quality-control workflows for Sentinel-2 imagery, developing anomaly detection systems, or designing AI-driven decision support that can actually be validated against real-world evidence. Across internships and technical projects, I’ve worked on automated data processing and modeling: from U-Net and YOLO-based satellite image analysis, to maritime GEOINT workflows combining AIS, SAR, and trajectory consistency, to climate intelligence systems using LightGBM with ERA5 and SHAP explainability. I’m particularly motivated by edge-ready, actionable geospatial intelligence—bridging robust engineering with interpretable AI so results are both accurate and explainable.

Available to hire

I’m Yessmine Chaabouni, a 2nd-year Geospatial Engineering student at Manouba School of Engineering in Tunis, specializing in machine learning, Earth Observation, and GEOINT. I enjoy turning satellite data and geospatial signals into reliable pipelines—whether that’s building quality-control workflows for Sentinel-2 imagery, developing anomaly detection systems, or designing AI-driven decision support that can actually be validated against real-world evidence.

Across internships and technical projects, I’ve worked on automated data processing and modeling: from U-Net and YOLO-based satellite image analysis, to maritime GEOINT workflows combining AIS, SAR, and trajectory consistency, to climate intelligence systems using LightGBM with ERA5 and SHAP explainability. I’m particularly motivated by edge-ready, actionable geospatial intelligence—bridging robust engineering with interpretable AI so results are both accurate and explainable.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Language

Arabic

Fluent

French

Advanced

English

Intermediate

Work Experience

AI/ML Research Intern at KIET Deemed to be University (Center for Supercomputing, NVIDIA-powered)

July 1, 2026 - September 1, 2026

Developed an AI digital twin for a smart classroom integrating Bayesian Knowledge Tracing (BKT), learner-state estimation, and instructor-facing decision support. Evaluated BKT on 400 held-out students and 11,828 predictions, and added an optional LLM-based explanation layer to support interpretability for instructional decisions.

AI/ML Research Intern at KIET Deemed to be University

July 1, 2026 - September 1, 2026

Developed an AI digital twin for a smart classroom integrating Bayesian Knowledge Tracing (BKT) with learner-state estimation and instructor-facing decision support. Evaluated BKT using 400 held-out students and 11,828 predictions. Added an optional LLM-based explanation layer to help contextualize model outputs.

Earth Observation / AI Intern at Climate Innov — Tunisia

June 1, 2026 - Present

Engineered an automated Sentinel-2 L1C quality-control pipeline using seven rule-based filters, statistical anomaly detection, and ML. Validated against 200 real ESA acquisitions and developed a radiometric composite achieving 50% recall, 3% false positive rate, F1=0.60, and AUC=0.81.

Geospatial / Maritime Cyber GEOINT Developer (Intern) at Center of Excellence for Cyber Security (Wipro-powered)

January 1, 2026 - June 30, 2026

Built a maritime-cyber GEOINT pipeline combining AIS data, SAR, trajectory consistency, dwell-time analysis, and ST-DBSCAN to detect potential GNSS/AIS spoofing. Implemented Hungarian-algorithm vessel re-linking to correct systematic SAR–AIS association errors and validated the pipeline against documented incidents in the Persian Gulf and Indian waters.

AI/ML Research / Cyber Security (Center of Excellence) at Center of Excellence for Cyber Security — Wipro-powered

January 1, 2026 - December 31, 2026

Built a maritime-cyber GEOINT pipeline combining AIS data, SAR imagery, trajectory consistency, dwell-time analysis, and ST-DBSCAN to identify potential GNSS/AIS spoofing. Implemented Hungarian-algorithm vessel re-linking to correct systematic SAR-AIS association errors and validated the pipeline against documented incidents in the Persian Gulf and Indian waters.

Deep Learning & Remote Sensing Intern at Digital Research Center of Sfax

June 1, 2024 - August 31, 2024

Developed satellite-image analysis pipelines using U-Net semantic segmentation and YOLOv8 object detection. Achieved mAP greater than 0.85 and strong F1 performance through end-to-end model training and evaluation for remote-sensing tasks.