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Scott C. Schmidler

Associate Professor of Statistical Science
Statistical Science

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


ABSEIL: A polypeptide helicity and ensemble prediction tool.

Journal Article J Mol Biol · February 5, 2026 Nascent helicity in polypeptides and unfolded proteins arises from local structure formation and represents one of the earliest events in a protein folding reaction. Nascent helicity may also influence the physical properties of intrinsically disordered re ... Full text Link to item Cite

The effect of osmolytes on peptide helicity: Experiments and predictions.

Journal Article Protein Sci · February 2026 Nascent helicity in polypeptides and unfolded proteins is a type of rapid local structure formation that could represent the earliest events in a protein folding reaction. Nascent helicity may also influence the physical properties of intrinsically disorde ... Full text Link to item Cite

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

Journal Article J Immunol · January 21, 2026 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 Link to item Cite
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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 University of California - Los Angeles · 2025 - 2026

Evolutionary dynamics of zoonotic malaria

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

View All Grants

Education


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

External Links


Personal site