Research

Robust Systematic Conservation Prioritization

An R package for conservation planning under uncertainty — generating solutions robust to climate projections, species distribution models, and ecosystem service estimates.

Robust vs partially robust conservation solutions
R C++ CRAN

CRAN downloads

Risk Modelling for UK Tree Planting

Decision-support framework for resilient tree-planting strategies under compounding climate and economic uncertainties, informing national net-zero land use policy.

tree planting uncertainty 1 tree planting uncertainty 2

Presented at EAERE 2022. Media coverage: The Guardian, Phys.org, and others.

R Matlab CPLEX SQL

Flexible Koala Conservation Decisions

Stochastic optimization model showing that adaptive conservation strategies can substantially reduce costs and mitigate climate-driven extinction risk for koalas on private land.

koala conservation strategies koala conservation outcomes
Julia R Gurobi

VESDIO — Supply Chain Ecosystem Risk

A web app for quantifying how ecosystem service disruptions propagate through global supply chains, helping businesses assess and disclose nature-related financial risk.

VESDIO icon VESDIO screenshots
Python Dash EXIOBASE

Monte Carlo Landscape Decisions

Spatially-explicit Monte Carlo simulations of land-use decision models, examining how parameter uncertainty propagates through landscape-scale policy outcomes.

LDS MC 1 LDS MC 2

Presented at BIOECON 2023.

Julia R

Remote Sensing of Post-Typhoon Effects

Geospatial analysis of typhoon-induced landscape change using satellite imagery, combining Google Earth Engine and ArcGIS to map recovery patterns.

ESRI YSA

ESRI China Young Scholars Award 2019 — Second Runner-Up.

ArcGIS Google Earth Engine Stata

ahpsurvey — Analytic Hierarchy Process for R

CRAN package for the Analytic Hierarchy Process, with tools for aggregation, consistency checking, and sensitivity analysis of survey preference data.

ahpsurvey 1 ahpsurvey 2

35K+ downloads. Cited in multiple peer-reviewed articles.

R CRAN

CRAN downloads

Current Opportunities

PhD Scholarship Monash University

Decision AI for Biodiversity Conservation

Supervised by Prof. Iadine Chades, Dr Lily Xu (Columbia University) & Dr Frankie Cho

Conservation decisions must balance learning (surveying, monitoring, reducing uncertainty) and acting (managing threats, reducing extinction risk) — yet existing approaches too often optimise one at the expense of the other. This PhD will develop Decision AI methods that explicitly integrate information-gathering and management objectives, enabling decision-makers to learn about complex ecological systems while delivering effective conservation outcomes.

The project sits at the intersection of: decision-making under uncertainty; sequential and adaptive decision processes; multi-objective optimisation; Markov Decision Processes; reinforcement learning; value of information; and biodiversity conservation. The successful candidate will join the Environmental Informatics Hub.

Decision AI Reinforcement Learning MDPs Value of Information Conservation
PhD / Honours University of Newcastle

Evaluating Protected Area Expansion Strategies for Biodiversity Conservation in Australia

Supervised by Dr Brooke Williams (UoN) & Dr Frankie Cho (Monash)

Australia has committed to expanding its protected area network, but the effectiveness of newly designated areas remains uncertain. This project will evaluate how well new protected areas contribute to biodiversity conservation, compare business-as-usual expansion with prioritisation-driven approaches, and identify strategies to better achieve national conservation targets.

Spatial Planning GIS Protected Areas Conservation Policy

Funding is not attached — interested applicants should reach out to Dr Brooke Williams to discuss ideas and potential funding.

PhD / Honours University of Newcastle

Mapping Monitoring Gaps: Aligning Species Data with Biodiversity Observation Effort in Australia

Supervised by Dr Brooke Williams (UoN) & Dr Frankie Cho (Monash)

Observation efforts often fail to align with expert knowledge of species distributions. This project will compare expert-derived species range maps with existing monitoring data to identify spatial gaps and mismatches in survey effort, highlighting priority regions where targeted, low-cost monitoring could rapidly improve biodiversity knowledge and inform conservation decision-making.

Spatial Planning GIS Species Monitoring Biodiversity Data

Funding is not attached — interested applicants should reach out to Dr Brooke Williams to discuss ideas and potential funding.

About

I am a Research Fellow in Data Science and Artificial Intelligence at Monash University. My research develops computational tools — combining optimisation, machine learning, and geospatial analysis — to guide environmental decisions under uncertainty. I work at the intersection of biodiversity conservation, climate adaptation, and AI.

Previously, I completed a joint PhD in Environmental Economics at the University of Exeter and the University of Queensland, and hold a bachelor's and master's degree in Geography from the University of Hong Kong.

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