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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


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

Finite sample complexity of sequential Monte Carlo estimators on multimodal target distributions

Journal Article Annals of Applied Probability · February 1, 2024 We prove finite sample complexities for sequential Monte Carlo (SMC) algorithms which require only local mixing times of the associated Markov kernels. Our bounds are particularly useful when the target distribution is multimodal and global mixing of the M ... Full text Cite

Computing the inducibility of B cell lineages under a context-dependent model of affinity maturation: Applications to sequential vaccine design.

Journal Article bioRxiv · October 17, 2023 A key challenge in B cell lineage-based vaccine design is understanding the inducibility of target neutralizing antibodies. We approach this problem through the use of detailed stochastic modeling of the somatic hypermutation process that occurs during aff ... Full text Link to item Cite
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Recent Grants


Evolutionary dynamics of zoonotic malaria

ResearchCo Investigator · Awarded by National Institute of Allergy and Infectious Diseases · 2023 - 2027

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

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

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