Duke Probability Theory and Applications
   Search Help Login Join pdf version printable version

Scott C. Schmidler, Associate Professor of Statistical Science and Computer Science

Scott C. Schmidler
Contact Info:
Office Location:  223D Old Chem, Durham, NC 27708-0251
Office Phone:  (919) 684-8064
Email Address: send me a message
Web Page:  http://www.stat.duke.edu/~scs/


Ph.D.Stanford University2002
B.A.University of California - Berkeley1995

Bayesian Statistics
MonteCarlo Methodology
Stochastic Processes
Graphical Models
Data Mining and Machine Learning
Research Interests: Bioinformatics, Monte Carlo Methods, Statistical Shape Analysis, Machine Learning, Computational Chemistry


Alanine • Algorithms • Amino Acid Sequence • Amino Acids • Animals • Arabidopsis • Bayes Theorem • beta-Lactamases • Biometry • Computer Graphics • Computer Simulation • Conserved Sequence • Dipeptides • DNA, Intergenic • Elasticity • Epigenesis, Genetic • Evolution, Molecular • Gene Expression Profiling • Gene Expression Regulation, Plant • Genetic Variation • Genome, Plant • Globins • Hemoglobins • Humans • Hydrogen-Ion Concentration • Hydrophobic and Hydrophilic Interactions • Markov Chains • Mathematical Computing • Microscopy, Atomic Force • Models, Chemical • Models, Genetic • Models, Molecular • Models, Statistical • Molecular Sequence Data • Molecular Weight • Monte Carlo Method • Osmolar Concentration • Peptides • Phycocyanin • Phylogeny • Protein Binding • Protein Conformation • Protein Folding • Protein Structure, Secondary • Proteins • Regression Analysis • Reproducibility of Results • Rhodophyta • RNA, Messenger • Sequence Alignment • Sequence Analysis, Protein • Software • Solvents • Stochastic Processes • Temperature • Thermodynamics • Water

Current Ph.D. Students  

  • Ben Cooke  
  • Merrill Liechty  
  • Ming Liao  
  • Jason Cooper  
  • Juliette Colinas  
Postdocs Mentored

  • Jeff Krause (2002)  
Recent Publications   (More Publications)

  1. Larson, G; Thorne, JL; Schmidler, S, Incorporating Nearest-Neighbor Site Dependence into Protein Evolution Models., Journal of Computational Biology : a Journal of Computational Molecular Cell Biology, vol. 27 no. 3 (March, 2020), pp. 361-375 [doi]  [abs]
  2. Larson, G; Thorne, JL; Schmidler, S, Modeling Dependence in Evolutionary Inference for Proteins, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10812 LNBI (January, 2018), pp. 122-137, Springer International Publishing, ISBN 9783319899282 [doi]  [abs]
  3. Darnell, CL; Tonner, PD; Gulli, JG; Schmidler, SC; Schmid, AK, Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis., Msystems, vol. 2 no. 5 (September, 2017) [doi]  [abs]
  4. VanDerwerken, D; Schmidler, SC, Monitoring Joint Convergence of MCMC Samplers, Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 26 no. 3 (July, 2017), pp. 558-568, Informa UK Limited [doi]  [abs]
  5. Cooke, B; Herzog, DP; Mattingly, JC; Mckinle, SA; Schmidler, SC, Geometric ergodicity of two-dimensional hamiltonian systems with a Lennard-Jones-like repulsive potential, Communications in Mathematical Sciences, vol. 15 no. 7 (January, 2017), pp. 1987-2025, International Press of Boston [doi]  [abs]