Currently the following themes are studied in my group:

o **Foundations
of Statistical Learning**, including theory and algorithms for:

1) Learning
time/space varying models and evolving structures

2) High-dimensional
inference under structured sparsity, complex conditions for variables, and complex
tasks (e.g., multi-attributes, multi-task, missing value, etc.)

3) Nonparametric
Bayesian inference, Posterior regularization

4) Latent
space inference (e.g., topic modeling, sparse coding, deep learning)

5) RKHS
embedding, nonparametric inference, and spectral methods for graphical models

6) Distributed
and online algorithms for optimization, approximate inference, and sampling on
Tara-scale data.

o **Large-scale
Information & Intelligent System**:

1) Development of general-purpose backend
system, programming interface, and infrastructure for large-scale machine
learning in distributed computing environment

2) Multi-view
latent space models, topics models, and sparse coding for image/text/relational
data mining

3) Evolving
structure, stable metrics, and prediction for dynamic social networks,
goal-driven network design and optimization

4) Web-scale
image understanding, search, prediction, and storyline synthesis

5) User
modeling, personalization, temporal analysis, and computational advertising

6) Information
visualization, indexing and storage, web/mobile app development

o **Computational
Biology**:

1) Understanding
genome-microenvironment interactions in cancer and embryogenesis via joint
analysis of genomic, proteomic, and pathway signaling data

2) Genetic
analysis of population variation, demography and evolution

3) Statistical
inference of genome-transcriptome-phenome association in complex diseases

4) Personalized
diagnosis and treatment of spectrum diseases via next generation sequencing and
computational "omic" analysis

5) Biological
image and text mining

Last updated 08/1/2013 |