🎓 Data Scientist / GIS Scientist — PhD (Transport Engineering, Monash 2026) | 📍 Melbourne, AU · Open to relocation

PhD-trained Data Scientist / GIS Scientist with deep expertise in spatial analysis, geospatial ML, and large-scale mobility data. Based in Melbourne, open to relocation across Australia.

Over my PhD at Monash University, I built end-to-end pipelines that:

• 🛰️ Process 1M+ GPS trajectories for shared-mobility analysis (Lime e-scooter & e-bike), including map-matching, speed modelling, and interpretable ML (XGBoost + SHAP)
• 🗺️ Apply spatial statistics at city scale — MGWR, GWR, spatial autocorrelation — to quantify how environment and infrastructure shape travel behaviour
• 🤖 Build NLP systems for geospatial text — BERT-based classifiers and NEM for extracting location-tagged events from noisy social media data (also patented)
• 📊 Translate complex analytics into decisions — cartographic outputs in ArcGIS Pro / QGIS, dashboards, and policy-facing reports

Currently looking for: GIS Analyst · Geospatial Data Scientist · Transport Data Analyst · Spatial ML roles.

📬 Reach out via LinkedIn or email.

🔗 Full PhD Portfolio — frameworks, workflows & results (visual portfolio with 25+ figures, papers, and posters)

• NLP for Active Travel — BERT pipeline extracting active-travel activity from social media; ~4k POI-level locations recovered from ungeotagged tweets across Greater Melbourne.
  Python · BERT · NER · spatial analysis

• Urban Heat Island × Active Travel — First spatial-heterogeneous assessment of UHI on active travel in Melbourne, using Multiscale GWR at suburb level.
  MGWR · spatial regression · ArcGIS Pro

• Shared E-Scooter Speed Modeling — Interpretable ML on Lime GPS trajectories; SHAP reveals non-linear thresholds for infrastructure design.
  XGBoost · CatBoost · SHAP · map-matching

• 📄 [Journal 2025] Assessing the spatial heterogeneous impacts of urban heat island effects on active travel by leveraging social media data
• 📄 [Journal 2019] Human hands-and-knees crawling movement analysis based on time-varying synergy and synchronous synergy theories
• 📄 [TRB 2024] Identifying active transport from spontaneous data source with natural language processing
• 📄 [TRB 2024] Investigate the influence of urban heat island effects on active travel behavior by leveraging social media data
• 📄 [ATRF 2024] Investigate the travel behaviour of e-scooter riders in Melbourne: A spatiotemporal analysis with PCA
• 💡 [Patent] A method for integrating geotagged location and text location information in social media

• Teaching Assistant (TA): Mentored Master’s students on computational modeling and spatial data interpretation at Monash University.
• Volunteer Experience: Actively participated in transport industry networking events and community-focused urban planning workshops.

"Evidence-based solutions are the building blocks of sustainable cities."