Duke Probability Theory and Applications
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Scott C. Schmidler, Associate Professor of Statistical 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/

Teaching (Spring 2019):

    Perkins 059, TuTh 08:30 AM-09:45 AM
    Perkins 060, F 08:45 AM-12:00 PM
    (also cross-listed as STA 613.01)

Ph.D.Stanford University2002
B.A.University of California at 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, 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 (2018), pp. 122-137, Springer International Publishing, ISBN 9783319899282 [doi]  [abs]
  2. 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]
  3. 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]
  4. Cooke, B; Herzog, DP; Mattingly, JC; McKinley, 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 (2017), pp. 1987-2025, International Press of Boston [doi]  [abs]
  5. Ben-Shachar, R; Schmidler, S; Koelle, K, Drivers of Inter-individual Variation in Dengue Viral Load Dynamics., edited by Ferguson, NM, Plos Computational Biology, vol. 12 no. 11 (November, 2016), pp. e1005194 [doi]  [abs]