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Industry advisory board member for the Data Science Institute / College of Engineering

★ Built and directed a multidisciplinary team to create industry-leading tech solutions, supporting $7B operations for real-time command centers (D&C, OPS) and developed analytics for OPEX reduction.★ Modernized reservoir forecasting systems, auto-forecasting for 4K+ oil wells with <2% error.★ Championed automation to boost productivity, cut costs, and optimize operational activities, while recruiting 100+ experts in tech, geosciences, and engineering.★ Spearheaded business transformations that cut GHG methane emissions by 30%, advocating for sustainable technology practices in the E&P industry.★ Directed development of supply chain solutions for a $2B water, sand and waste management operations and pioneered R&D in Distributed Acoustic and Temperature Sensing.

★ Instrumental in the overall growth of the data-driven approaches in Airline retail merchandizing, creating customer segmentation and choice behavior models, as well as travel recommender systems and subsequent product offerings for flights, hotels, and ancillary travel services.★ Ensured operational efficiency by managing flight arrival and departure forecasting tools and monitoring the organization’s revenue management system, demand analysis alerts, and operating systems.★ Increased the travel product conversion rate through digital advertising campaigns by designing game-theory inspired controlled experiments.

★ Developed broadband Marine Seismic Processing algorithms, multiple estimation and attenuation, and predictive filtering.★ Lead several projects in developing Machine learning algorithms for seismic facies recognition aiding seismic interpretation, and statistical reservoir characterization (collaboration with University of Oklahoma).★ Developed production-ready code in SeisSpace / ProMAX development environment utilizing JAVA, C++, C and FORTRAN.★ Implemented optimized algorithms/work-flows for large-scale cluster computations.

★ Led research in Geophysical Machine learning leading to computational program development in the Earth and Energy programs. Also led industry projects, co-supervising Ph.D. dissertation/M.S. thesis work in Seismic attributes for prospect identification and reservoir characterization (C++, JAVA, FORTRAN).★ Development of multi-scale 3D Geophysical image & interpretation models for Lithosphere Imaging utilizing integrated geophysical data. ★ Developed statistical models for reservoir characterization and production forecast models including case studies with shale plays.★ Worked on LiDAR, SAR, InSAR processing algorithms for generating DEMs and airborne gradiometry.★ Implement algorithms for Parallel Architectures, GPUs for large-scale computations.

★ Development of high-performance PET detectors, which measures depth-of-interaction (DOI) information with focus on detector response functions to improve PET image quality in novel instrumentation R&D (C++, Qt framework, FORTRAN based simulator GATE).★ Novel geometrical design in PET instrumentation and simulations of preclinical and clinical scan in Emission Tomography.★ Led the development of algorithms for Tomographic Imaging & statistical Image Reconstruction methodologies utilizing ordered subset Expectation-Maximization (OSEM) and Maximum Likelihood Expectation Maximization (MLEM) approach.

Developed Machine Learning Hyperspectral Classifiers under reduced dimensionality with applications to material identification in Earth observing spaceborne and airborne imagery.

Worked with Computational Science and Technology Research (CSTR) group on high dimensional machine learning regressors for image processing and modeling applications.

Joint Compression Classification (Machine Learning) in Hyperspectral Imagery

Development and Design of Multi-rate Filterbanks with Advanced Systems Division.

Seismic filtering and Signal Processing for pavement analyzers