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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/

Teaching (Fall 2018):

  • STA 863.01, ADVANCED STATISTICAL COMPUTING Synopsis
    Old Chem 025, TuTh 08:30 AM-09:45 AM

Education:

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

Specialties:

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

Keywords:

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 (January, 2018), pp. 122-137, 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 [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, vol. 15 no. 7 (2017), pp. 1987-2025 [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]