Jonathan Huang is an NSF Computing Innovation (CI) postdoctoral fellow
at the geometric computing group at Stanford University. He completed
his Ph.D. in 2011 with the School of Computer Science at Carnegie
Mellon University where he also received a Masters degree in 2008.  He
received his B.S. degree in Mathematics from  Stanford University in
2005.  His research interests lie primarily in statistical machine
learning and reasoning with combinatorially structured data with applications
such as analyzing real world education data.  His
research has resulted in a number of publications in premier machine
learning conferences and journals, receiving a paper award in NIPS
2007 for his work on applying group theoretic Fourier analysis to
probabilistic reasoning with permutations.