Armin Schwartzman

Armin Schwartzman is a Professor at the Halıcıoğlu Data Science Institute and Department of Biostatistics at the University of California, San Diego (UCSD). He holds a PhD in Statistics from Stanford University, an MSc in Electrical Engineering from the California Institute of Technology, and a BSc in Electrical Engineering from Technion-Israel Institute of Technology. His research focuses on statistical methods for signal and image analysis with applications in biomedical and environmental domains, including spatial inference, high-dimensional data analysis, and random field theory.

Education:

• PhD in Statistics, Stanford University, 2006
• MSc in Electrical Engineering, California Institute of Technology, 1996
• BSc in Electrical Engineering, Technion-Israel Institute of Technology, 1995

Research Interests:

Dr. Schwartzman develops statistical methodologies for analyzing complex data, particularly in neuroimaging, climate science, and genomics. Key areas include spatial inference for image analysis, multiple testing in high-dimensional data, and applications of random field theory. His work bridges theoretical statistics and practical challenges in biomedical and environmental research.

Grants & Funding:

• NIH R01EB026859 (2019–2023): Spatial inference methods for image analysis
• NIH R21EB013795 (2012–2015): Voxelwise analysis of imaging response in neuro-oncology
• NIH R01CA157528 (2012–2019): Multiple testing methods for random fields

Advising:

Current advisees include Sam Davenport, Anubhav Singh-Sachan, and others. Former students have transitioned to roles at institutions like Moderna, University of Florida, and Cruiser.

Labs & Collaborations:

His research group works on projects such as:

• Random field theory for noise modeling in images
• Spatial inference in neuroimaging and climate data
• Machine learning for medical imaging (e.g., glaucoma diagnosis)
• Mountain glacier monitoring via satellite imagery