Hatef Monajemi

Hatef Monajemi 

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About Me

I am currently a postdoctoral fellow in the Department of Statistics at Stanford working with David Donoho. I am also an instructor at Stanford co-teaching two new Data Science courses: Theories of Deep Learning (STATS385) and Massive Computational Experiments, Painlessly (STATS285).
I am interested in various aspects of Data Science, in particular the role that massive computation will play in the future of science. On the theoretical side, my current activities involve deeper understanding of deep learning, classification and sparse reconstruction problems. On the applied side, I am interested to apply undersampling theories to speed up MR imaging/spectroscopy where the long sampling time required can be a great challenge. I also develop computer abstractions and tools that can facilitate ambitious data science projects involving million-CPU-hour computational experiments. ClusterJob is the result of my research in this area, which is currently being used by several researchers at Stanford. To see a list of my present and past projects, please check my research page.


New Publications

H Monajemi and DL Donoho, ‘‘Sparsity/Undersampling Tradeoffs in Anisotropic Undersampling, with Applications in MR Imaging/Spectroscopy.’’
H Monajemi, DL Donoho, JC Hoch and AS Schuyler, ‘‘Incoherence of Partial-Component Sampling in multidimensional NMR.’’

Contact info (Office)

Room 223
Department of Statistics
Stanford University
Sequoia Hall
390 Serra Mall
Stanford, CA 94305
E-mail: monajemi AT stanford DOT edu

Check out my blog for my thoughts and recent news