Thomas Oommen

Thomas Oommen



Assistant Professor, Geological and Mining Engineering and Sciences

Adjunct Assistant Professor, Civil and Environmental Engineering

  • PhD, Geotechnical and Geoenvironmental Engineering, Tufts University
  • MS, System Engineering, University of Alaska-Fairbanks
  • BS, Civil Engineering, M. S. Ramaiah Institute of Technology


Dr. Oommen’s research efforts focus on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types. To achieve his research interests, he has adopted an inter-disciplinary research approach from two main areas, specifically: aerial/satellite based remote sensing for obtaining data, and artificial intelligence/machine learning based methods for data processing and modeling.

Dr. Oommen is expanding his research to investigate future applications of satellite remote sensing and machine learning for geological engineering in the fields of geohazards and georesource characterization. His immediate goal is to verify the applicability of remote sensing techniques such as Differential Interferometric Synthetic Aperture Radar (DinSAR) and Light Detection and Ranging (LiDAR) as sustainable operational strategies for monitoring land subsidence. Land subsidence is often the surface expression of a variety of subsurface mechanisms such as lowering of water table, drainage, lateral flow, loading, vibration, and tectonic activity. Quantifying subsidence is critical for land use and infrastructure planning, health monitoring of engineered structures as well as for understanding the subsurface conditions.

Links of Interest

Teaching Interests

  • Computer Methods in Geomechanics
  • Computational Geoscience
  • Remote Sensing for Geotechniques
  • Geostatistics and Data Analysis

Research Interests

  • Liquefaction susceptibility evaluation at local and regional scales using in-situ measurements and remote sensing observations
  • Estimating liquefaction induced damage such as lateral spread displacement
  • Active learning to identify data gaps in empirical models
  • Documenting earthquake induced damages, especially liquefaction using aerial/satellite images that are sensitive to surficial moisture
  • Spatial statistics for predictive modeling of georesource prospectivity

Recent Publications

  • T. Oommen, L. G. Baise, R. M. Vogel, "Sampling bias and class imbalance in maximum likelihood logistic regression." Mathematical Geosciences 43: 1. 99-120. 2011 Read More
  • T. Oommen, D. Misra, A. Prakash, S. Bandopadhyay, S. Naidu, J. J. Kelley, "Multiple regressive pattern recognition technique: An adapted approach for improved georesource estimation." Natural Resources Research 20: 1. 11-24. 2011 Read More
  • T. Oommen, L. G. Baise, "Model development and validation for intelligent data collection for lateral spread displacements." ASCE Journal of Computing in Civil Engineering 24: 6. 467-477. 2010 Read More
  • T. Oommen, L. G. Baise, R. M. Vogel, "Validation and application of empirical liquefaction models." ASCE Journal of Geotechnical and Geoenvironmental Engineering 136: 12. 1618-1633. 2010 Read More
  • D. Misra, T. Oommen, A. Agarwal, S. K. Mishra, A. Thompson, "Application and analysis of support vector machine based simulation for runoff and sediment yield." Biosystems Engineering 103: 4. 527-535. 2009 Read More
  • T. Oommen, D. Misra, N. K. C. Twarakavi, A. Prakash, B. Sahoo, S. Bandopadhyay, "An objective analysis of support vector machine based classification for remote sensing." Mathematical Geoscience 40: 4. 409-424. 2008 Read More
  • T. Oommen, A. Prakash, D. Misra, J. J. Kelley, S. Naidu, S. Bandopadhyay, "GIS based marine platinum exploration, Goodnews Bay, Southwest Alaska." Marine Georesources & Geotechnology 26: 1. 1-18. 2008 Read More