Ali Rizwan

As AI Research Scientist, accomplished the design and deployment of multimodal, generative, and spatio-temporal AI models powering self-healing and self-organizing intelligence in next-generation 5G networks. Successfully delivered a $400K-funded project, ensuring innovation in model architectures, performance optimization, and full-cycle technical documentation. Generative AI Modelling: Engineered deep learning and GAN-based models for data sparsity and fault diagnosis in 5G, leveraging >500K synthetically generated data samples to train and validate large-scale AI frameworks. AI Framework Development: Co-developed an integrated AI-based SON (Self-Organising Network) framework addressing three core challenges: data sparsity, intelligent automation, and mobility dynamics. AI-Enabled MDT Coverage Reconstruction: Designed GAN-based and classical interpolation methods to enrich sparse MDT maps, enabling accurate fault diagnosis under limited measurements, resulted in filing a patent. Network Fault Diagnosis Innovation: Developed a CDR-based auto-diagnosis framework using clustering (K-means) and classification (SVM), achieving 97.6% accuracy, resulted in filing a patent. Research Impact: Published 7+ peer-reviewed IEEE papers and filed 2 provisional patents in data enrichment and fault diagnosis for 5G/6G networks.

As applied AI researcher lead hands-on machine learning and deep learning initiatives across healthcare and telecom domains, driving end-to-end model development, data pipelines, and experimental validation. Delivered 5+ funded research projects in collaboration with international academic and industry partners. Atrial Fibrillation Detection (ADEK Award AARE18-203, Al Ain University, UAE): Developed CNN-LSTM models for ECG-based AF detection, achieving high accuracy for clinical applications. Cellular Network Classification (Fujitsu UK Collaboration): Designed ML algorithms to classify cellular network performance using large-scale CDR data. 5G Self-Optimisation (EPSRC Global Challenges, Ajman University): Built Spark/Python big data pipelines to support large-scale network optimisation tasks. Non-Invasive Optical Sensing (ADEK Award AARE17-019, UAE University): Developed ML models for hydration and skin sensing using non-invasive optical signals. Research Impact: Contributed to multi-institutional consortia, resulting in 10+ peer-reviewed publications and several patent applications in healthcare AI and telecom optimisation.

MSc Big Data Science