Long Hoa Chung

...telling impactful stories through data — bridging science and analytics.

About Me

Long Hoa Chung at NSW Parliament House, Science Week 2023

At the NSW Parliament House during Science Week 2023, showcasing my image of human liver tissue captured by mass spectrometry—analysed from over 20,000 data points.

My journey began in biomedical research, advancing lipidomics, proteomics, and mass-spectrometry imaging. I’ve since expanded into AI and data analytics across finance, retail, and healthcare. Each project reflects my passion for blending science, technology, and creativity to deliver data-driven insights and solve real-world challenges.

Portfolio

I bring a unique blend of expertise spanning biomedical research and modern data analytics. From pioneering DESI-MS imaging (laboratory + instrument-acquired big data) to building AI-driven models for financial, retail, and healthcare datasets, my work demonstrates how rigorous science and data innovation can deliver impact across industries.

Project Experience

AI & Analytics (2024–2025): Optimised AI models for ethical outputs; delivered EDA, dashboards, and predictive models across finance and retail.
Healthcare & Biomedical Research (2019–2024): Built data pipelines; advanced lipidomics, proteomics, and DESI-MS imaging.
Leadership & Operations: Directed large-scale research operations and cross-functional projects.

IBM Data Analyst Capstone – Stack Overflow Survey

Analysed Stack Overflow developer survey data and built IBM Cognos dashboards to reveal tech trends, emerging skills demand, and workforce implications.

Solution

Built dashboards using Python + Cognos to track technology adoption, highlight emerging skills, and guide hiring, training, and investment.

Loan Default Prediction (Top 92%)

Developed ML models to predict loan defaults using financial data. Ranked in the top 92% globally in Coursera’s IBM Data Science competition.

Solution

Applied feature engineering and classification models to identify at-risk borrowers, testing robustness on both ordinary and extreme cases.

Exploratory Data Analysis on Retail Data

Analysed online retail transactions to uncover sales trends, customer behaviour, and product performance insights.

Solution

Delivered insights on top products, seasonal demand, and customer segments to support marketing, forecasting, and retail strategy.

ReTimeML: Retention Time Predictor

Predicted lipid retention times in LC-MS using machine learning models, improving annotation accuracy and supporting lipidomics workflows.

Solution

Built predictive models with molecular descriptors and chromatographic conditions to estimate lipid retention times, reducing manual validation.

Customer Churn Prediction

Predicted customer churn for streaming businesses using logistic regression, random forest, and gradient boosting models.

Solution

Applied robust scaling across 21 variables, engineered features, and compared models using cross-validation and AUC scores to select the best performer.

Weather Forecast European City

A responsive API-powered web app delivering a 7-day forecast for major European cities using the 7Timer API. Includes dynamic icons and interactive UI.

Solution

- Integrated real-time weather data, dynamic background transitions,
- Beautiful template to adapt to other corners of the world

Skills

Data Analytics & Statistics: Turning complex data into meaningful insights through exploratory analysis, trend detection, and business-driven storytelling.

Programming & Tools: Experienced across Python, R, SQL, and interactive dashboards — using IBM Cognos, Looker, and Power BI to bring data to life.

Machine Learning & AI: Building predictive models with focus on reliability, interpretability, and real-world impact.

Experience

A journey from scientific discovery to data innovation — bridging research, analytics, and AI leadership.

Research Scientist

PhD scientist with expertise in lipidomics, proteomics, and DESI imaging, publishing novel findings and earning multiple research awards.

Data Scientist

Transitioned into data science and AI, delivering predictive analytics pipelines, business dashboards, and ranking Top-92 in a global Coursera challenge.

AI & Leadership

Current work spans AI model evaluation (Outlier AI), financial/operational analytics, and leadership roles integrating data strategy with ethical AI.

Publications

My research integrates big data in genomics, proteomics, and lipidomics with advanced computational and instrumental approaches. This combination has revealed new disease mechanisms in metabolic disorders, cancer, and neurodegeneration — supporting better diagnostics and healthier ageing. Key contributions include AI-driven proteomics pipelines and real-time lipidomics imaging.

Here is a selection of highlighted papers from my research in lipidomics, proteomics, and imaging mass spectrometry. Please find the full list of publications on my profile.

View my ORCID profile

ReTimeML: a retention time predictor that supports the LC–MS/MS analysis of sphingolipids

Scientific Report, 2024

I collaborated with other researchers to employ build a model that can predict the behaviours of Lipids inside a mass-spec instrument, improving accuracy and detection. The tool helps other researchers quickly reference and identify appropriate methods for their studies.

Read Paper

Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy

Nature Metabolism, 2022

I contributed to a global clinical study by providing analytical guidance and lipidomics methods, connecting omics data with human samples to reveal how lipids influence the dynamics of Type 2 diabetes, its prevention, and management.

Read Paper

Ablation of sphingosine kinase 2 suppresses fatty liver-associated hepatocellular carcinoma via downregulation of ceramide transfer protein

Journal of Oncogenesis, 2022

I applied advanced data analytics to uncover how changes in liver fat metabolism contribute to non-alcoholic liver cancer and developed a novel imaging approach using digital analysis rather than the traditional microscopy. This method provided clearer visual and quantitative insights directly on real huamn tissues to support early diagnosis and treatment planning.