Departmental Unit № 1
Department of Geospatial Information Sciences (GIS) & Department of Sustainable Earth Systems Sciences
PhD in Geospatial Information Sciences
100% Full Funding Guaranteed (4-5 Years)
Funding Package: Full tuition waiver + 9-12 month living stipend (~$26,000–$30,000/yr) + health insurance covered via Teaching and Research Assistantships.
Fall Priority Deadline: January 15 (Fall Priority)
GRE Policy: Not required / Waived
English Minimums: TOEFL iBT 80 or IELTS 6.5
MS in Geospatial Information Sciences
Eligible for Teaching/Research Assistantships
Funding Reality: Qualified MS students can compete for assistantship appointments that provide tuition reductions and stipends.
Application Deadline: February 1
Admissions Advisory: If full funding is required and MS is self-funded, students are strongly advised to apply directly to the PhD track.
Vetted Matched Scholars (2 Profiles)
Prof. May Yuan
Ashbel Smith Professor of Geospatial Information Sciences
In Plain Terms · What This Scholar Does:
She is one of the world's most famous pioneers of 'Time in GIS'—designing computer models that treat environmental events (like moving wildfires and flood waves) not just as static flat maps, but as living, evolving 4D movies.
Core Research Topics: Temporal GIS, spatiotemporal data modeling, GeoAI, environmental dynamics, disaster trajectory modeling.
Recent 2024–2026 Works & Grants: Developing spatiotemporal ontological representations and deep learning graph neural networks to track dynamic environmental phenomena, such as wildfire spread and flood plume migration over continuous space and time (NSF IIS / USGS 2024–2026).
Laboratory Instruments & Methods: Python, graph neural networks (GNNs), spatiotemporal databases, GeoAI, ArcGIS Pro, spatial process modeling.
Recruiting Context: Former editor of the International Journal of Geographical Information Science (IJGIS); worldwide giant in GIScience.
Candidate Match:
GISGeoAIEnvironmental ModelingSpatial Statistics
Tailored Cold-Email Opening Hook:
"Discussing your interest in spatiotemporal GIS data representation, dynamic environmental modeling, and GeoAI architectures in your group."
Prof. David J. Lary
Professor of Physics and Geospatial Information Sciences
In Plain Terms · What This Scholar Does:
He builds autonomous robotic flying drones and robotic boats with laser scanners that fly into smoke plumes and cruise across lakes to build 3D artificial intelligence maps of pollution in real time.
Core Research Topics: Autonomous drone remote sensing, machine learning environmental monitoring, airborne and aquatic sensors, particulate matter pollution, spatial AI.
Recent 2024–2026 Works & Grants: Deploying fleets of autonomous aerial and aquatic robotic drones carrying calibrated laser and optical sensors to map micro-scale air and water pollution plumes in real time using machine learning (NASA / EPA 2024–2026).
Laboratory Instruments & Methods: Autonomous UAVs/USVs, machine learning (random forest, deep neural nets), optical particle counters, Python, C++, GIS telemetry.
Recruiting Context: Leader in combining robotic autonomous platforms with machine learning for high-resolution environmental mapping.
Candidate Match:
Remote SensingGeoAIEnvironmental PollutionWater QualityGIS
Tailored Cold-Email Opening Hook:
"Discussing your interest in autonomous UAV environmental sensing, machine learning for pollution tracking, and remote sensing in the Lary Lab."