

My group works on stochastic models and algorithms for complex,
highdimensional, dynamical systems arising from the physical or
biological sciences. My theoretical interests lie at the intersection of
applied probability, Bayesian statistics, statistical mechanics,
and theoretical computer science.
Applications: I have particular interests in structural biology,
biophysics, and physical chemistry. We currently work on
problems related to protein folding, vaccine design, and molecular
engineering. Recently I have also expanded into problems in systems
biology, and stochastic epidemic models.
Theory: Stochastic processes, dynamical
systems theory, mixing times of Markov chains, randomized
algorithms, computational complexity.
Some ongoing projects and themes are described below, followed by a
list of Collaborations.
Monte Carlo Algorithms

Algorithms for efficient sampling of complex probability distributions
arising in Bayesian statistics and statistical mechanics.
 Adaptive Monte Carlo algorithms Monte
Carlo sampling algorithms that "learn" from their history to improve
future performance.
 Convergence rates of Markov chains  Theoretical
analysis of Markov chains, esp as relates to running times of Monte
Carlo algorithms.
 Computational complexity in Bayesian
statistics  Applying notions from theoretical
Computer Science to computational problems more relevant to statistical practice.
 
Bayesian shape analysis

Data collected on geometric shapes arise in many diverse fields,
including computer vision, archeology, astronomy, CAD design, anatomy
and morphology, and molecular and cellular biology. Statistical
analysis and comparison of shapes is an important challenge throughout
these applications. I've been one of a small number of researchers
bringing Bayesian ideas to problems in shape analysis, focusing
especially on the 'unlabeled landmark' problem.
 
Computational and Theoretical Biophysics

Simulation and modeling of biological systems especially at the
molecular level. We have worked especially on prediction of molecular
structure of proteins, and simulation of protein folding.
 Bayesian protein structure prediction
 Bayesian protein structure alignment
 Molecular dynamics simulation of proteins


Collaborations:
Some of my recent and ongoing collaborations:
 Terry Oas (Biochemistry, and Chemistry) ... (multiple
projects)
 Terry's group and mine have weekly joint meetings.
 Jack Keene (Molecular Genetics and Microbiol): Modeling
posttranscriptional regulation by RBPs.
 Tom Kepler (Duke Human Vaccine Institute): Structural biology of
HIV bnAbs.
 Part of the larger HIV Vaccine group led by Bart Haynes

 Jonathan Mattingly (Mathematics):
 Stephen Teitsworth (Physics):
 Ying K. Wu (Physics, FEL):

 Stefan Zauscher (Mech Eng & Materials Sci):
Analysis of singlemolecule AFM force spectroscopy experiments
 Carlo Tomasi (CS): Probabilistic boundary tracking
