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Graduate Research Assistant (PhD Researcher)

University of Vermont

Doctoral research in computer vision–based geolocalization, spanning sensor networks, indoor positioning, and large-scale visual geolocalization from images.

• Research theme: geolocalization using visual and sensor data with an emphasis on robustness, uncertainty modeling, and deployability

• GPS-free outdoor sensor network geolocalization: designed a spatio-temporal learning framework using Graph Neural Networks and BiLSTMs to localize large-scale IoT sensor networks from noisy radio range measurements; achieved meter-level accuracy and published the results in a peer-reviewed IoT journal — Paper

• Reliable indoor geolocalization: developed a GNN-based regression model for indoor positioning and integrated conformal prediction to produce statistically valid confidence regions with calibrated uncertainty guarantees; demonstrated empirical coverage closely matching target confidence levels — Project

• Visual geolocalization under severe appearance changes: studied robustness under weather, illumination, and seasonal shifts; extended the Sample4Geo baseline with improved training strategies and robustness-oriented evaluation — Code

• Efficient fine-grained cross-view geolocalization: designed an efficient framework aligning ground-level and satellite imagery by decoupling heavy visual encoding from fast geometric refinement; achieved sub-meter accuracy with significantly reduced inference cost; currently under review at a top-tier computer vision venue — Project

• Ongoing research on vision-language models for geolocalization: exploring semantic and spatial reasoning using VLMs, including prompt engineering and parameter-efficient fine-tuning (LoRA).

• All research implemented primarily in PyTorch and PyTorch Geometric, with extensive experimentation, ablation studies, and reproducibility practices

Senior Data Analyst

Orange Jordan

Data-driven performance analysis and automation for large-scale telecom services.

• Led nationwide service performance analysis using large-scale operational telecom datasets

• Designed and deployed automated analytics pipelines using Python, SQL, Pandas, and NumPy to analyze service KPIs

• Managed and analyzed data generated by software-based service testing robots to detect performance degradation

• Developed automated Python-based alerting systems to trigger alarms when KPIs deviated from defined thresholds

• Built real-time monitoring and executive dashboards using Power BI, Tableau, and Grafana

• Led agile automation initiatives that reduced repetitive manual reporting and improved operational efficiency

• Collaborated closely with engineering and operations teams to enable data-driven service optimization

Radio Frequency Engineer

Orange Jordan

Radio network planning, optimization, and performance analysis for cellular systems.

• Worked on planning and optimization of large-scale 4G and 5G radio access networks

• Analyzed RF measurements, drive-test data, and network KPIs to identify coverage, capacity, and interference issues

• Supported radio network optimization using industry-standard RF planning and monitoring tools

• Developed strong intuition for wireless propagation, noisy measurements, and real-world system constraints

PhD

University of Vermont

Computer Science

PhD researcher specializing in computer vision and geolocalization, with a focus on uncertainty-aware learning and cross-view visual reasoning.

• Research focus: visual geolocalization, cross-view image understanding, uncertainty modeling

• Graduate coursework: Advanced Machine Learning, Deep Learning, Natural Language Processing, Large Language Models, Data Mining, Data Science, Advanced Algorithm Design

BSc

University of Jordan

Electrical Engineering

Strong foundation in signal processing, wireless communications, and applied mathematics, with early exposure to distributed systems and algorithm design.

• Coursework: Signals & Systems, Digital Signal Processing, Wireless Communications, Probability & Statistics

• Graduated with honors

• Graduation Project — Design of a Channel Management System for Cognitive Radio:

• Designed a fully distributed channel management framework enabling cognitive users to dynamically access spectrum while satisfying heterogeneous QoS requirements

• Developed novel channel allocation and coordination algorithms implemented in C++

• Used MATLAB for system-level simulation, performance evaluation, and visualization of network behavior