Mengmeng Wang
Data Scientist | Applied ML & GenAI — LLM pipelines, predictive modelling, statistical analysis
What I do
I’m a data scientist working at the intersection of applied machine learning, generative AI, and statistical analysis. I currently work as a Data Scientist / Research Fellow at Orygen, Centre for Youth Mental Health, University of Melbourne — where I build predictive machine learning models (XGBoost, SHAP), run statistical analyses on large clinical and survey datasets, and develop LLM and GenAI pipelines for extracting and synthesising evidence from clinical literature and unstructured research data.
Background
My path here started in signal processing — I hold a PhD in Biomedical Engineering from the University of Melbourne, with a thesis on statistical signal processing methods for resting-state functional near-infrared spectroscopy (fNIRS), published in the Journal of Neuroscience Methods and presented at IEEE ICASSP.
Currently exploring
Local LLM deployment, retrieval-augmented generation, and how far applied GenAI can go in speeding up evidence synthesis from messy, real-world research data — while keeping systems explainable and privacy-preserving.