Time-series forecasting • Optimisation foundations (LP/MILP concepts) • Python/R (pandas, scikit-learn, PyTorch) • SQL • BI (Power
BI/Tableau) • Cloud (AWS; quick to adopt Databricks/Spark) 5+ years turning messy, multi-…
Time-series forecasting • Optimisation foundations (LP/MILP concepts) • Python/R (pandas, scikit-learn, PyTorch) • SQL • BI (Power
BI/Tableau) • Cloud (AWS; quick to adopt Databricks/Spark) 5+ years turning messy, multi-source data into production-ready models,
tools, and dashboards that improve on-time performance–style KPIs, reduce manual reporting ~30%, and translate insights into clear
actions for operations and commercial leaders.
<p>Lead data scientist on a Queensland Cancer Registry project: built reproducible Python/R pipelines for cohort assembly, data validation, and feature engineering across large linked datasets.</p><p>Delivered survival/risk-stratification models to support treatment planning; documented assumptions, calibration, and fairness checks for decision-makers.</p><p>Shipped executive-ready dashboards and briefs (Power BI/R Markdown) that turned model outputs into clear pathway recommendations and measurable actions.</p><p>Established governance artifacts (data lineage, ethics/consent mapping, code reviews) to keep analytics compliant, auditable, and handover-ready.</p><p>Coordinated clinicians, statisticians, and consumer reps; ran short delivery cycles with defined milestones and stakeholder check-ins.</p><p></p>
<p>Lead data scientist on a Queensland Cancer Registry project.<br>
Built reproducible Python/R pipelines for cohort assembly, data validation, and feature engineering across large linked datasets.<br>
Delivered survival/risk-stratification models to support treatment planning.<br>
Documented assumptions, calibration, and fairness checks for decision-makers.<br>
Shipped executive-ready dashboards and briefs (Power BI/R Markdown) that turned model outputs into clear pathway recommendations and measurable actions.<br>
Established governance artifacts (data lineage, ethics/consent mapping, code reviews) to keep analytics compliant, auditable, and handover-ready.<br>
Coordinated clinicians, statisticians, and consumer reps.<br>
Ran short delivery cycles with defined milestones and stakeholder check-ins.</p>
<p>Built end-to-end pipelines for multi-source ICU data (EHR, bedside monitors, labs): ingestion → validation → feature store; improved downstream modelling speed and reliability.</p><p>Developed and validated ML/DL models for ICU outcomes (mortality/deterioration), HRV/ECG ectopic-beat detection, and readmission risk; focused on calibration, interpretability, and harm minimization.</p><p>Containerized training/inference (Docker) and automated retraining/reporting on AWS (S3, Athena, SageMaker); added logging/metrics for traceability and cost awareness.</p><p>Produced decision dashboards and “so-what” narratives for clinicians and operations, reducing manual reporting effort by ~30% and increasing trust in KPIs.</p><p>Implemented privacy-preserving workflows for clinical text (de-identification) and robust QA checks (schema tests, missingness/outlier monitoring).</p><p>Collaborated daily with engineers, clinicians, and product leaders; turned vague questions into measurable use-cases, features, and success criteria.</p><p></p><p></p>
<p>Assisted in the development of AI and machine learning models for ICU and oncology projects. Annotating unstructured pathology reports and building hierarchical knowledge-base ontologies using machine learning algorithms.</p><p>Conducted data preprocessing, feature engineering, and statistical analysis on large-scale clinical datasets, ensuring high data quality and reproducibility.</p><p>Supported grant and project proposal preparation by conducting literature review, research synthesis and technical documentation.</p><p>Work with multidisciplinary teams of clinicians, data scientists, and industry partners, bridging the gap between technical innovation and clinical application.</p><p>Delivered presentations and research dissemination at academic meetings and conferences and workshops, strengthening institutional visibility and collaboration opportunities.</p><p>Trained and mentored junior students in data science, coding, and research methods, fostering skill development within the research group.</p><p></p>
<p>Built end-to-end pipelines for multi-source ICU data (EHR, bedside monitors, labs): ingestion → validation → feature store.<br>
Improved downstream modelling speed and reliability.<br>
Developed and validated ML/DL models for ICU outcomes (mortality/deterioration), HRV/ECG ectopic-beat detection, and readmission risk.<br>
Focused on calibration, interpretability, and harm minimisation.<br>
Containerised training/inference (Docker).<br>
Automated retraining/reporting on AWS (S3, Athena, SageMaker).<br>
Added logging/metrics for traceability and cost awareness.<br>
Produced decision dashboards and “so-what” narratives for clinicians and operations.<br>
Reduced manual reporting effort by ~30%.<br>
Increased trust in KPIs.<br>
Implemented privacy-preserving workflows for clinical text (de-identification).<br>
Implemented robust QA checks (schema tests, missingness/outlier monitoring).<br>
Collaborated daily with engineers, clinicians, and product leaders.<br>
Turned vague questions into measurable use-cases, features, and success criteria.</p>
<p>Developed advanced queries using SQL to set up and configure an ETL framework to manage data on the data warehouse.<br>
Improved report loading time by 15%.<br>
Optimised server performance.<br>
Conducted programmatic validation and cleansing for data listing reported on clinical trial data.<br>
Introduced process and system improvements.<br>
Led to a 25% increase in accuracy and consistency of data within 3 months.<br>
Implemented and maintained SAS programs.<br>
Implemented and maintained safety analysis datasets, safety tables, and listings used for analysis and reporting of clinical trial data.</p>
<p>This project aims to fill key gaps in current colorectal cancer (CRC) care by integrating advanced analytics with patient-centred design. First, we will apply innovative statistical methods to large-scale cancer registry data to understand how comorbidities influence clinical outcomes. Second, we will develop advanced AI models to improve prognostic and predictive risk-stratification capabilities for CRC patients with multiple health conditions. Finally, we will engage patients, clinicians, and stakeholders to co-design the first clinical tool that extracts and transforms comorbidity and clinical information into an MDT-ready format, supporting more informed, personalised, and efficient treatment decision-making.</p>
<p>Developed an end-to-end framework for dynamic risk modelling in TBI during the initial ICU stay using deep learning techniques. First, mapped physiologic–clinical interdependencies using correlation-based network analysis coupled with graph neural networks to surface key variables and communities. Built a deep neural model for ectopic-beat detection from ECG and assessed generalisability with both internal and external test sets. Designed a hybrid prognostic model that combines a custom learned-weight module with a BiLSTM unit to capture temporal dynamics. Finally, delivered a continuously updating ANN that ingests hourly physiological time-series to generate real-time trajectories of 30-day mortality risk. Together, these components provide interpretable drivers and streaming risk estimates to support ICU decision-making.</p>
<p>This project aimed to develop an explainable, AI-driven clinical decision-support system for personalised cancer treatment planning. Many cancers, including breast cancer (BC) and colorectal cancer (CRC), demonstrated heterogeneous treatment responses due to complex patient- and therapy-related factors, meaning that treatment effects varied widely across individuals. Although patient-centred cancer care had advanced, clinical guidelines still largely prioritised tumour characteristics and often overlooked broader contextual factors. Existing AI tools offered promise but predominantly relied on opaque “black-box” models and were mainly focused on diagnosis, limiting their clinical usefulness because of ethical, interpretability, and trust concerns.</p><p> In this project, we built a transparent “open-box” AI system that learned from historical patient data and provided clinicians with clear, evidence-informed treatment recommendations. The system integrated a knowledge base, computational optimisation algorithms, and multimodal patient information—including cancer stage, pathology reports, comorbidities, and socio-demographic factors—to support personalised decision-making. Clinician expertise was embedded through co-design, ensuring that the system aligned with real-world decision pathways and continuously improved in performance and clinical relevance.</p>
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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