Arkajyoti Saha

Arkajyoti Saha is an Assistant Professor in the Department of Statistics at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information & Computer Sciences. His research integrates statistical modeling with artificial intelligence/machine learning (AI/ML), focusing on addressing data dependence in AI/ML methods to enhance scalability while preserving domain-specific structures. Key applications include environmental science, biomedical research, oceanography, finance, data privacy, and algorithmic fairness.

Professor Saha collaborates with domain scientists to solve interdisciplinary challenges, such as evaluating oceanic carbon levels to inform climate policy. His work emphasizes scalable methods for spatial and temporal data, leveraging tools like nearest-neighbor Gaussian processes and ensemble learning frameworks. Notable contributions include the RandomForestsGLS R package and methodologies for spatial probit models.

His publications span 2015–2025, with recent trends emphasizing spatial statistics, geospatial analysis, and dependent data modeling. He has developed novel clustering algorithms and statistical frameworks for handling complex dependencies. His work bridges theoretical advancements with real-world applications, such as air quality monitoring in Baltimore.

While no formal awards are listed, his research demonstrates significant contributions to statistical methodology and interdisciplinary collaboration. Advising/mentoring details and grants are not explicitly provided in the text. He is affiliated with the Center for Statistical Consulting and maintains an office in DBH 2228.