About Vaibhav Mehandiratta Vaibhav Mehandiratta Postdoctoral Research Fellow (former), Statistics Stochastic fractional differential equations Metric graphs numerical analysis optimal control problems Vaibhav Mehandiratta was a Postdoctoral Research Fellow in the CEMSE Division, Statistics Program, at the King Abdullah University of Science and Technology (KAUST), supervised by Prof. David Bolin. He received his Ph.D. in Mathematics from the Indian Institute of Technology Delhi in November 2022 and joined KAUST as a Postdoctoral Fellow in January 2023. He held this position until September 2024. He is currently an Assistant Professor in the Department of Mathematics at BITS Pilani, K K Birla Goa Campus, India. Articles Related News June 2025 Efficient and Accurate Inference for Matérn Gaussian Processes on Intervals 1 min read · Mon, Jun 16 2025 News Gaussian processes statistical inference A new study by researchers at KAUST introduces a breakthrough method for statistical modeling with Matérn Gaussian processes, a popular tool in spatial statistics, machine learning, and the natural sciences. While Gaussian processes with stationary Matérn covariance functions (Matérn processes) are valued for their flexibility, their use with large datasets has been severely limited by the high computational cost of standard methods. The team has developed the first generally applicable approach that enables fast, linear-cost inference and prediction for Matérn processes on bounded intervals
Efficient and Accurate Inference for Matérn Gaussian Processes on Intervals 1 min read · Mon, Jun 16 2025 News Gaussian processes statistical inference A new study by researchers at KAUST introduces a breakthrough method for statistical modeling with Matérn Gaussian processes, a popular tool in spatial statistics, machine learning, and the natural sciences. While Gaussian processes with stationary Matérn covariance functions (Matérn processes) are valued for their flexibility, their use with large datasets has been severely limited by the high computational cost of standard methods. The team has developed the first generally applicable approach that enables fast, linear-cost inference and prediction for Matérn processes on bounded intervals
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