Gabriel A. Terejanu
Assistant Professor
Dept. of Computer Science & Engineering
College of Engineering & Computing
University of South Carolina
315 Main St, 3A50 Swearingen
Columbia, SC 29208
(803) 777-5872

Research Interests:
Model Validation
Uncertainty Quantification
Information Fusion
Nonlinear Filtering
Decision Making under Uncertainty
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My research interests lie primarily in the field of model validation and uncertainty
quantification, information fusion, and decision making under uncertainty.
These include topics such as:
- Bayesian inference and model validation of complex physical systems, including
optimal experimental design using information theoretic concepts.
- Improving the decision making under uncertainty using accurate uncertainty
representations and goal-oriented propagation of uncertainty through computational models.
- Near-real time data assimilation with applications to evolution of toxic clouds
in air as result of chemical and biological releases.
- Applications of machine learning concepts to reduce the dimensionality of
complex uncertainty quantification problems.
Recent/Upcoming Events:
- Grant: (PI) NSF-IUSE project focused on developing statistical models to track student knowledge, suggest remedial interventions, and guide future examinations in cornerstone engineering classes with high student-to-faculty ratios. Joint work with Juan Caicedo and Charles Pierce (Civil Engineering @ University of South Caroline).
- Grant: (Co-PI) NSF-DMREF project focused on linking experiments and theory in a rigorous manner using Bayesian statistics to accelerate the design and discovery of novel multimetallic catalysts for biorefinery industry. Joint work with Andreas Heyden and Salai Ammal (Chemical Engineering @ University of South Carolina) and Jesse Bond (Biomedical and Chemical Engineering @ Syracuse University).
- SPRING 2016: Teaching CSCE 590 Data Visualization,
See course syllabus.
- Our paper "Validating Predictions of Unobserved Quantities" has been accepted for
publication in Computer Methods in Applied Mechanics and Engineering.
- CONGRATULATIONS to Jiting Xu for successfully defending his M.S. thesis "Approximate Bayesian
Computation Based on Progressive Correction of Gaussian Components"
- FALL 2014: Teaching CSCE 883 Machine Learning in partnership with
EagleEye Analytics. Get the opportunity
to work on real-world insurance data and be selected for an internship interview.
See course flyer
- Our paper "Data partition methodology for validation of predictive models"
has been accepted for publication in Computers and Mathematics with Applications.
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