Hamza Riaz

PROFESSIONAL PROFILE I am a final-year Computer Science student with research experience in NLP and multimodal machine learning. My primary interest lies in Generative AI, with a focus on LLM-based systems. I have built end-to-end pipelines that combine prompt design, retrieval- augmented generation, and structured evaluation, and I am comfortable taking a system from an initial prototype to reproducible results. Additionally, I have certifications of Machine Learning and Deep Learning Specializations from Coursera. I have also taken all AI-focused electives throughout my degree. This has given me a strong theoretical and practical foundation to work on SOTA problems in this domain. Beyond model development, I can also build full stack applications to deploy and showcase these systems outside a notebook setting. I am looking to continue this work in professional research and development environments as well. EDUCATION BS Computer Science School of Electrical Engineering and Computer Science (SEECS) , Islamabad , 3.62/4.0 (2022) INTERNSHIP EXPERIENCE Research Intern, TUKL-NUST R&D Center (NUST x Technical University of Kaiserslautern collaboration) 03-Jun-2024 - 18-Aug-2025 - Co-developed a multimodal deep learning triage system to classify patients by the Korean Triage Acuity Scale (KTAS) using both structured signals and unstructured clinical text. - Benchmarked multiple model families (RCNN, ACNN, BERT variants) and found that smaller language models performed better than larger ones for this setting. - Tested LLM-based few-shot imputation to predict missing pain scores from context, reaching performance comparable to supervised approaches. - Reframed acuity prediction as an ordinal classification task and reported quadratic weighted kappa (QWK) alongside accuracy and macro-F1 for more meaningful evaluation. - Built a cross-modality feature fusion module and achieved 78% validation accuracy and 0.76 macro-F1, with SHAP- based explanations to verify clinically sensible feature importance. FINAL YEAR PROJECT XMedFusion: Agentic Cross-Modality Radiology Report Generation - Building a modular, agent-based pipeline for radiology report generation that separates vision understanding, knowledge grounding, retrieval, drafting, and refinement into clear components to output a precise and clinically accurate X-ray/CT scan report. - Implementing structured intermediate representations, in the form of knowledge graphs, to make generation more controllable and easier to validate than end-to-end free-form generation. - Designing retrieval-augmented workflows to pull k-most similar reports for achieving stylistic consistency, and to reduce unsupported or inconsistent statements. - Developing robust evaluation utilities that check output validity and measure both structure quality and end-task quality using various metrics from recent literature. - Creating a full stack demo to showcase the system, including a fully functional backend API and a deployed web interface for interactive testing. TECHNICAL EXPERTISE Programming Languages Python, C/C++, SQL. Strong fundamentals in data structures, algorithms, and writing clean, modular code. Deep Learning Frameworks PyTorch, TensorFlow. Model training, fine-tuning, custom modules, debugging training issues, and performance tuning. Generative AI Tools and Techniques

I am a final-year Computer Science student with research experience in NLP and multimodal machine learning. My primary interest lies in Generative AI, with a focus on LLM-based systems. I have built end-to-end pipelines that combine prompt design, retrieval- augmented generation, and structured evaluation, and I am comfortable taking a system from an initial prototype to reproducible results. Additionally, I have certifications of Machine Learning and Deep Learning Specializations from Coursera. I have also taken all AI-focused electives throughout my degree. This has given me a strong theoretical and practical foundation to work on SOTA problems in this domain. Beyond model development, I can also build full stack applications to deploy and showcase these systems outside a notebook setting. I am looking to continue this work in professional research and development environments as well.