Projects

Funded Projects

2026–2029 PI

Slope Performance During Rainfall Events

Pennsylvania Department of Transportation $356,933.80 · 36 months

2026–2028 PI

Alternative Fill Standards for Arsenic

Pennsylvania Department of Transportation $238,896.68 · 24 months

2026–2028 PI

Characterizing Three-Dimensional Strain Localization in Granular Materials: From Particle Kinematics to Full Deformation Gradient Fields

Temple University $100,000.00 · 24 months

2026–2028 PI

Nuclear Exempt Gauge and Nuclear Gauge Compatibility

Pennsylvania Department of Transportation $300,729.76 · 30 months

2025–2027 Co-PI

Landslide inventory and susceptibility mapping for Cincinnati and northern Kentucky: improving hazard assessment in a landslide-prone urban region

United States Geological Survey $182,961.00 · 24 months

2024–2026 PI

Interpretation of the Wissahickon Formation

Pennsylvania Department of Transportation $257,377.67 · 24 months

2024–2026 PI

Integrating In-situ, Local, and Remote Sensing for Transportation Infrastructure Assessment

Pennsylvania Department of Transportation $178,555.41 · 24 months

2020–2021 Co-PI

Transforming Past into Present: A Registration Approach to Using Old and New Topographic Information to Improve the Fidelity and Value of Legacy Geologic Maps

United States Geological Survey $133,973.00 · 12 months

Research Areas

Micro- and Nano-Mechanics of Geomaterials

Multi-scale investigation of deformation and fabric evolution in sands and clays, combining synchrotron X-ray imaging, micro-computed tomography, and digital image correlation.

Risk Assessment and Management

Turning geohazard science into decisions: regional landslide inventory and susceptibility mapping, rainfall-driven slope performance, and risk-informed management of transportation infrastructure. Risk = Hazard $\times$ Vulnerability $\times$ Consequences

Computational Geosciences

Remote sensing, terrain analysis, and open-source software for characterizing the subsurface and the land surface, from legacy map georectification to integrated in-situ and satellite observation.

Probabilistic Machine Learning and Uncertainty Quantification

Probabilistic methods that make model confidence explicit — Bayesian networks, aleatory and epistemic uncertainty, and honest error bars on geotechnical prediction.