Few words about me:
Zhigang Yao is an assistant professor in the Department of Statistics and
Applied Probability at the National University of
Singapore (NUS). He is also a Faculty Affiliate of
the Institute of Data Science (IDS)
at NUS. He received his
Ph.D. in Statistics from University of Pittsburgh in 2011. His thesis
advisors are Bill Eddy at Carnegie Mellon and Leon Gleser at
University of Pittsburgh. Before
joining NUS, he has been working with Victor Panaretos as a postdoc researcher at the Swiss Federal Institute of
Technology (EPFL) from
20112014.
Few words about my work:
My main research area is statistical
inference for complex data. Broadly speaking, in terms of statistical
complexity, there are two categories of "Complex Data" that I
have been interested in. The first one arises because of temporal/spatial
structure in the collection of the data, often with added complexity
because data are incomplete, in terms of variables which may be missing,
or poorly measured. The second one has emerged as the data is gathered
with increasingly more dimensions while there are only tens or hundreds
of instances available for study or the data itself lies in nonEuclidean
space.
My main work in the first category
includes inverse problem from brain imaging (i.e., MEG) and
tomographic reconstruction (i.e., electron microscope). In MEG, the
problem is to localize the electrical source in the brain using the
extremely weak magnetic signal outside of the head; in tomography, the
problem is to obtain the complete 3D folding of the particle from the
partial knowledge (say, its 2D projections) recorded on the film.
My work in the second
category includes making statistical inference by exploiting useful
structure (i.e., sparse features) in highdimensional data where the
useful signal is rare and weak,
and by finding principal variation (principal flows or submanifolds) of
data lying on (or nearby) a lower dimensional manifold. For either of
such data, most conventional approaches fail.
