Scholars@Duke

• Background
• 

  Education, Training, & Certifications

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

  Previous Appointments & Affiliations

  • Assistant Professor in the Institute of Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 2006 - 2010
  • Assistant Professor in the Institute of Statistics and Decision Sciences, Statistical Science, Trinity College of Arts & Sciences 2002 - 2006
  • Lecturer, University, Statistical Science, Trinity College of Arts & Sciences 2000 - 2002
• Research
• 

  Selected Grants

  • Evolutionary dynamics of zoonotic malaria awarded by National Institutes of Health 2023 - 2027
  • Deep Topological Sampling of Protein Structures - non-competing renewal for Year 4 awarded by National Institutes of Health 2017 - 2022
  • Bioinformatics and Computational Biology Training Program awarded by National Institutes of Health 2005 - 2021
  • EMSW21-RTG: Geometric, Topological and Statistical Methods for Analyzing Massive Datasets awarded by  National Science Foundation 2011 - 2018
  • Advances in Scalable Monte Carlo Algorithms for Bayesian Statistics awarded by  National Science Foundation 2014 - 2017
  • Structural Biology and Biophysics Training Program awarded by National Institutes of Health 1994 - 2015
  • Mechanistic Studies of Complex Protein Folding Reactions awarded by National Institutes of Health 2008 - 2013
  • Bayesian Analysis of Shapes and Curves with Applications in Bioinformatics awarded by  National Science Foundation 2006 - 2007
  • Statistical Models of Biopolymer Sequence and Folding awarded by  National Science Foundation 2002 - 2005
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Wu, K., S. Schmidler, and Y. Chen. “Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling.” Journal of Machine Learning Research 23 (July 1, 2022).
    • Darnell, Cynthia L., Peter D. Tonner, Jordan G. Gulli, Scott C. Schmidler, and Amy K. Schmid. “Systematic Discovery of Archaeal Transcription Factor Functions in Regulatory Networks through Quantitative Phenotyping Analysis.” Msystems 2, no. 5 (September 2017): e00032–e00017. https://doi.org/10.1128/msystems.00032-17.
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    • VanDerwerken, D., and S. C. Schmidler. “Monitoring Joint Convergence of MCMC Samplers.” Journal of Computational and Graphical Statistics 26, no. 3 (July 3, 2017): 558–68. https://doi.org/10.1080/10618600.2017.1297240.
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    • Ben-Shachar, Rotem, Scott Schmidler, and Katia Koelle. “Drivers of Inter-individual Variation in Dengue Viral Load Dynamics.” Plos Computational Biology 12, no. 11 (November 2016): e1005194. https://doi.org/10.1371/journal.pcbi.1005194.
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    • Estrada, Rolando, Carlo Tomasi, Scott C. Schmidler, and Sina Farsiu. “Tree Topology Estimation.” Ieee Transactions on Pattern Analysis and Machine Intelligence 37, no. 8 (August 2015): 1688–1701. https://doi.org/10.1109/tpami.2014.2382116.
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    • Herman, Joseph L., Christopher J. Challis, Ádám Novák, Jotun Hein, and Scott C. Schmidler. “Simultaneous Bayesian estimation of alignment and phylogeny under a joint model of protein sequence and structure.” Molecular Biology and Evolution 31, no. 9 (September 2014): 2251–66. https://doi.org/10.1093/molbev/msu184.
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    • Daniels, Kyle G., Nam K. Tonthat, David R. McClure, Yu-Chu Chang, Xin Liu, Maria A. Schumacher, Carol A. Fierke, Scott C. Schmidler, and Terrence G. Oas. “Ligand concentration regulates the pathways of coupled protein folding and binding.” J Am Chem Soc 136, no. 3 (January 22, 2014): 822–25. https://doi.org/10.1021/ja4086726.
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    • Rodriguez, Abel, and Scott C. Schmidler. “BAYESIAN PROTEIN STRUCTURE ALIGNMENT.” The Annals of Applied Statistics 8, no. 4 (January 2014): 2068–95. https://doi.org/10.1214/14-aoas780.
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    • Wang, R., and S. C. Schmidler. “Bayesian multiple protein structure alignment.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8394 LNBI (January 1, 2014): 326–39. https://doi.org/10.1007/978-3-319-05269-4-27.
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    • Ji, C., and S. C. Schmidler. “Adaptive Markov chain Monte Carlo for Bayesian variable selection.” Journal of Computational and Graphical Statistics 22, no. 3 (December 17, 2013): 708–28. https://doi.org/10.1080/10618600.2013.819178.
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    • Challis, Christopher J., and Scott C. Schmidler. “A stochastic evolutionary model for protein structure alignment and phylogeny.” Molecular Biology and Evolution 29, no. 11 (November 2012): 3575–87. https://doi.org/10.1093/molbev/mss167.
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    • Wolpert, R. L., and S. C. Schmidler. “α-Stable limit laws for harmonic mean estimators of marginal likelihoods.” Statistica Sinica 22, no. 3 (July 1, 2012): 1233–51. https://doi.org/10.5705/ss.2010.221.
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    • Zheng, N., S. C. Schmidler, D. Marks, and D. Brady. “Computer experiment and global optimization of layered monocentric lens systems.” Optik 123, no. 14 (July 1, 2012): 1249–59. https://doi.org/10.1016/j.ijleo.2011.07.058.
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    • Cooke, Ben, Jonathan C. Mattingly, Scott A. McKinley, and Scott C. Schmidler. “Geometric Ergodicity of Two–dimensional Hamiltonian systems with a Lennard–Jones–like Repulsive Potential.” Arxiv Preprint Arxiv:1104.3842, 2011.
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    • Wilson, Melanie A., Edwin S. Iversen, Merlise A. Clyde, Scott C. Schmidler, and Joellen M. Schildkraut. “BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES.” Ann Appl Stat 4, no. 3 (September 1, 2010): 1342–64. https://doi.org/10.1214/09-aoas322.
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    • Jia, B., J. Li, S. Huang Scott, C. Schmidler, and Y. K. Wu. “Electron Beam Energy Spread Measurements Using optical klystron Radiatio.” Phys. Rev. St Accel. Beams, no. 13 (2010): 080702.
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    • Wiehe, K., T. B. Kepler, and S. C. Schmidler. “P19-50. Simulation of an MPER peptide samples epitope conformations of two broadly neutralizing antibodies.” Retrovirology 6, no. SUPPL. 3 (October 22, 2009). https://doi.org/10.1186/1742-4690-6-S3-P370.
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    • Woodard, D. B., S. C. Schmidler, and M. Huber. “Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions.” Annals of Applied Probability 19, no. 2 (April 1, 2009): 617–40. https://doi.org/10.1214/08-AAP555.
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    • Woodard, D. B., S. C. Schmidler, and M. Huber. “Sufficient conditions for torpid mixing of parallel and simulated tempering.” Electronic Journal of Probability 14 (January 1, 2009): 780–804. https://doi.org/10.1214/EJP.v14-638.
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    • Cooke, Ben, and Scott C. Schmidler. “Statistical prediction and molecular dynamics simulation.” Biophysical Journal 95, no. 10 (November 2008): 4497–4511. https://doi.org/10.1529/biophysj.108.131623.
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    • Cooke, Ben, and Scott C. Schmidler. “Preserving the Boltzmann ensemble in replica-exchange molecular dynamics.” The Journal of Chemical Physics 129, no. 16 (October 2008): 164112. https://doi.org/10.1063/1.2989802.
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    • Valiaev, Alexei, Dong Woo Lim, Scott Schmidler, Robert L. Clark, Ashutosh Chilkoti, and Stefan Zauscher. “Hydration and conformational mechanics of single, end-tethered elastin-like polypeptides.” Journal of the American Chemical Society 130, no. 33 (August 2008): 10939–46. https://doi.org/10.1021/ja800502h.
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    • Colinas, Juliette, Scott C. Schmidler, Gil Bohrer, Borislav Iordanov, and Philip N. Benfey. “Intergenic and genic sequence lengths have opposite relationships with respect to gene expression.” Plos One 3, no. 11 (January 2008): e3670. https://doi.org/10.1371/journal.pone.0003670.
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    • Jiang, T., C. Tomasi, and S. C. Schmidler. “How to dispatch observers to track an evolving boundary.” 2007 1st Acm/Ieee International Conference on Distributed Smart Cameras, Icdsc, December 1, 2007, 305–12. https://doi.org/10.1109/ICDSC.2007.4357538.
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    • Schmidler, Scott C., Joseph E. Lucas, and Terrence G. Oas. “Statistical estimation of statistical mechanical models: helix-coil theory and peptide helicity prediction.” J Comput Biol 14, no. 10 (December 2007): 1287–1310. https://doi.org/10.1089/cmb.2007.0008.
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    • Valiaev, A., W. L. Dong, A. Chilkoti, S. Schmidler, and S. Zauscher. “Polypeptide-solvent interactions measured by single molecule force spectroscopy.” Materials Research Society Symposium Proceedings 898 (January 1, 2005): 92–97. https://doi.org/10.1557/proc-0898-l06-06-nn04-06.
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    • Schmidler, S. C. “Discussion of "A Statistical Approach to Modeling Genomic Aberrations in Cancer Cells" by Newton et. al.” Bayesian Statistics7, 2003.
    • Schmidler, S. C., J. S. Liu, and D. L. Brutlag. “Bayesian segmentation of protein secondary structure.” Journal of Computational Biology : A Journal of Computational Molecular Cell Biology 7, no. 1–2 (February 2000): 233–48. https://doi.org/10.1089/10665270050081496.
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    • Schmidler, S. C., J. S. Liu, and D. L. Brutlag. “Probabilistic modeling for protein structure prediction.” Faseb Journal 13, no. 7 (April 23, 1999): A1542–A1542.
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    • Schmidler, S. C., J. S. Liu, and D. L. Brutlag. “Bayesian protein structure prediction.” Case Studies in Bayesian Statistics 5 (1999): 363–78.
    • Wu, T. D., S. C. Schmidler, T. Hastie, and D. L. Brutlag. “Regression analysis of multiple protein structures.” Journal of Computational Biology 5, no. 3 (1998): 597–607.
    • Wu, T. D., S. C. Schmidler, T. Hastie, and D. L. Brutlag. “Regression analysis of multiple protein structures.” Journal of Computational Biology : A Journal of Computational Molecular Cell Biology 5, no. 3 (January 1998): 585–95. https://doi.org/10.1089/cmb.1998.5.585.
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    • Wu, T. D., S. C. Schmidler, T. Hastie, and D. L. Brutlag. “Modeling and superposition of multiple protein structures using affine transformations: analysis of the globins.” Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, January 1998, 509–20.
    • Wu, T. D., T. Hastie, S. C. Schmidler, and D. L. Brutlag. “Regression analysis of multiple protein structures.” Proceedings of the Annual International Conference on Computational Molecular Biology, Recomb, January 1, 1998, 276–84. https://doi.org/10.1145/279069.279132.
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  • Conference Papers

    • Larson, Gary, Jeffrey L. Thorne, and Scott Schmidler. “Incorporating Nearest-Neighbor Site Dependence into Protein Evolution Models.” In Journal of Computational Biology : A Journal of Computational Molecular Cell Biology, 27:361–75, 2020. https://doi.org/10.1089/cmb.2019.0500.
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    • Larson, G., J. L. Thorne, and S. Schmidler. “Modeling Dependence in Evolutionary Inference for Proteins.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10812 LNBI:122–37, 2018. https://doi.org/10.1007/978-3-319-89929-9_8.
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    • Wang, R., and S. C. Schmidler. “Bayesian multiple protein structure alignment.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8394 LNBI:326–39, 2014. https://doi.org/10.1007/978-3-319-05269-4_27.
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• Teaching & Mentoring
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  Recent Courses

  • CBB 510S: Computational Biology Seminar 2023
  • CBB 511: Journal Club 2023
  • CBB 540: Statistical Methods for Computational Biology 2023
  • CBB 591: Independent Study 2023
  • CBB 700: Internship 2023
  • STA 611: Introduction to Mathematical Statistics 2023
  • STA 613: Statistical Methods for Computational Biology 2023
  • STA 790-1: Special Topics in Statistics 2023
  • CBB 510S: Computational Biology Seminar 2022
  • CBB 511: Journal Club 2022
  • STA 571: Advanced Stochastic Modeling and Machine Learning 2022
  • STA 693: Research Independent Study 2022
  • STA 831: Probability and Statistical Models 2022
  • CBB 510S: Computational Biology Seminar 2021
  • STA 611: Introduction to Mathematical Statistics 2021
  • STA 863: Advanced Statistical Computing 2021
  • STA 995: Internship 2021

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