Software For Efficient Gaussian Process Regression (GPR)
Uses low-rank based approximation to speedup GPR prediction and training
Uses low-rank based approximation to speedup GPR prediction and training
We embrace a philosophy grounded in academic rigor, curiosity and customer satisfaction.
By fostering inclusivity and simplifying the intricacies of probabilistic machine learning, we aim to empower students, businesses, and researchers to harness the full potential of this transformative technology.
Emil Thomas holds a Ph.D. in Computer Engineering from Texas A&M University, College Station, and a Master's degree in Telecommunication Systems Engineering from Indian Institute of Technology, Kharagpur, India. He brings a wealth of experience, with three years dedicated to firmware development at Mindtree Ltd, India, and an additional two years in teaching within the field of electrical engineering in India. Dr. Thomas’ research interests include Gaussian Process Regression, Bayesian methods, Machine learning and Optimization.
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