AI Research Scientist with 10+ years’ experience developing machine learning models and algorithms for complex, high-dimensional data, with a strong track record of translating research into real-world decision systems. …
AI Research Scientist with 10+ years’ experience developing machine learning models and algorithms for complex, high-dimensional data, with a strong track record of translating research into real-world decision systems. Published 50+ peer-reviewed papers with 855+ citations and h-index 18, demonstrating sustained research impact. Expertise in designing scalable AI models, experimental frameworks, and optimisation-driven systems across multimodal datasets. Proven ability to collaborate with cross-functional teams and deliver commercially relevant AI solutions aligned with risk modelling, predictive analytics, and large-scale data-driven decision-making.
Designed machine learning models for high-stakes prediction tasks (e.g. clinical risk prediction), achieving up to 92% accuracy and AUC 0.92, improving decision reliability over traditional statistical baselines
Developed end-to-end ML pipelines (data ingestion → feature engineering → modelling → evaluation), enabling reproducible experimentation and scalable deployment across multiple projects
Built deep learning and multimodal models (CNNs, GNNs, self-supervised learning), improving classification accuracy and robustness for complex datasets (e.g. imaging, structured clinical data)
Applied reinforcement learning and optimisation techniques to sequential decision-making problems, improving system performance in dynamic environments (e.g. scheduling, resource allocation)
Designed anomaly detection and predictive modelling systems for large-scale datasets, supporting early risk identification and decision support
Collaborated with domain experts, engineers, and stakeholders to translate research into deployable solutions aligned with operational constraints
Designed machine learning models for high-stakes prediction tasks (e.g. clinical risk prediction), achieving up to 92% accuracy and AUC 0.92, improving decision reliability over traditional statistical baselines, Developed end-to-end ML pipelines (data ingestion → feature engineering → modelling → evaluation), enabling reproducible experimentation and scalable deployment across multiple projects, Built deep learning and multimodal models (CNNs, GNNs, self-supervised learning), improving classification accuracy and robustness for complex datasets (e.g. imaging, structured clinical data), Applied reinforcement learning and optimisation techniques to sequential decision-making problems, improving system performance in dynamic environments (e.g. scheduling, resource allocation), Designed anomaly detection and predictive modelling systems for large-scale datasets, supporting early risk identification and decision support, Collaborated with domain experts, engineers, and stakeholders to translate research into deployable solutions aligned with operational constraints
Led research programmes in machine learning, deep learning, and AI, producing 50+ peer-reviewed publications across high-impact journals
Developed novel algorithms in areas including self-supervised learning, graph neural networks, and ensemble modelling, improving performance over baseline methods
Supervised large-scale research projects involving multimodal datasets and experimental design, ensuring rigorous evaluation and reproducibility
Delivered advanced AI systems integrating NLP, computer vision, and structured data for real-world applications
Led research programmes in machine learning, deep learning, and AI, producing 50+ peer-reviewed publications across high-impact journals, Developed novel algorithms in areas including self-supervised learning, graph neural networks, and ensemble modelling, improving performance over baseline methods, Supervised large-scale research projects involving multimodal datasets and experimental design, ensuring rigorous evaluation and reproducibility, Delivered advanced AI systems integrating NLP, computer vision, and structured data for real-world applications
Developed machine learning models integrating heterogeneous structured and high-dimensional data for risk prediction and time-to-event modellingApplied statistical modelling, deep learning, and survival analysis to capture temporal dynamics and uncertainty in prediction tasksDesigned evaluation pipelines improving model robustness and generalisation across datasetsFramework transferable to financial risk modelling, credit scoring, and portfolio risk estimation
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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