Skip to main content

Scott C. Schmidler

Associate Professor of Statistical Science
Statistical Science
Box 90251, Department of Statistical Science, Durham, NC 27708-0251
212 Old Chem, Durham, NC 27708-0251

Overview


Research Interests:

  • Monte Carlo methods; high-dimensional sampling algorithms; Mixing times of Markov chains; MCMC; Sequential Monte Carlo; probabilistic graphical models; Bayesian computation; probabilistic Machine Learning; Computational complexity of statistical inference.

  • Computational biology; Protein structure and folding; computational immunology; computational biophysics; statistical physics; computational statistical mechanics; molecular evolution.

Current Appointments & Affiliations


Associate Professor of Statistical Science · 2010 - Present Statistical Science, Trinity College of Arts & Sciences
Associate Professor in Computer Science · 2011 - Present Computer Science, Trinity College of Arts & Sciences

Recent Publications


Computing the inducibility of broadly neutralizing antibodies under a context-dependent model of affinity maturation: applications to sequential vaccine design.

Journal Article Journal of immunology (Baltimore, Md. : 1950) · September 2025 A key challenge in B-cell lineage-based vaccine design is understanding the "inducibility" of target neutralizing antibodies-the ability of these antibodies to be elicited by presentation of an immunogen. Induction relies on a combination of stochastic div ... Full text Cite

On Gibbs Sampling for Endpoint-Conditioned Neighbor-Dependent Sequence Evolution Models

Journal Article Journal of Computational and Graphical Statistics · January 1, 2025 In 2008, Hobolth described an algorithm for sampling paths in models of DNA sequence evolution with neighbor dependence. We show that this algorithm fails to sample the desired target distribution, and provide a simple Metropolis acceptance step correction ... Full text Cite

Efficient enumeration and visualization of helix-coil ensembles.

Journal Article Biophys J · February 6, 2024 Helix-coil models are routinely used to interpret circular dichroism data of helical peptides or predict the helicity of naturally-occurring and designed polypeptides. However, a helix-coil model contains significantly more information than mean helicity a ... Full text Link to item Cite
View All Publications

Recent Grants


DMS/NIGMS 1: Challenges in Stochastic Modeling and Computation for Sequential Vaccine Design

ResearchPrincipal Investigator · Awarded by National Science Foundation · 2024 - 2027

Evolutionary dynamics of zoonotic malaria

ResearchCo Investigator · Awarded by National Institute of Allergy and Infectious Diseases · 2023 - 2025

Deep Topological Sampling of Protein Structures - non-competing renewal for Year 4

ResearchSignificant Contributor · Awarded by National Institutes of Health · 2017 - 2022

View All Grants

Education, Training & Certifications


Stanford University · 2002 Ph.D.
University of California, Berkeley · 1995 B.A.

External Links


Personal site