Header image  

Associate Professor of Statistical Science
and Computer Science

Computational Biology & Bioinformatics
Structural Biology & Biophysics
Duke Center for Systems Biology

Duke University

My group works on stochastic models and algorithms for complex, high-dimensional, 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


    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 post-transcriptional 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 single-molecule AFM force spectroscopy experiments
    • Carlo Tomasi (CS): Probabilistic boundary tracking