Room #: 203 Dow
Phone: 906 487 2045
- Bachelors in Civil Engineering from M. S. Ramaiah Institute of Technology, India in 1999
- Masters in System Engineering from University of Alaska-Fairbanks, USA in 2007
- Ph. D. in Geotechnical and Geoenvironmental Engineering from Tufts University, USA in 2010
Teaching is an opportunity to share knowledge and experience with students from different disciplines, which can lead to new research ideas. In contrast, research provides new perspectives and solutions to global challenges that affect existence, which make teaching more exciting. Performed together, teaching and research integrate to produce finer results than when done alone.
Although geohazards/natural hazards cannot be prevented fully, their impact can be minimized by better engineering and management strategies aided by the latest technological developments. Over the years GIS and aerial/satellite based remote sensing have become valuable in providing information on earth surface characteristics and processes. Remote sensing provides the advantage of providing a regional perspective of the problem, monitor the disaster over time for its precursors if available, and rapid access to the disaster site for damage assessment.
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. Some examples of current and past research activities are:
- 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
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.