Christian L. Müller, Ph.D.Visiting Scholar, CCM, Flatiron Institute
Christian L. Müller is a Visiting Scholar for Flatiron Institute’s Center for Computational Mathematics.
Müller joined the Simons Foundation in 2014 to work at the Simons Center for Data Analysis, developing high-dimensional statistics methods and algorithms with applications to microbial and regulatory networks. Müller held postdoctoral positions at ETH Zürich and NYU. Müller holds an M.S. in computer science from Uppsala University, Sweden, an M.S. in bioinformatics and computer science, as well as a university certificate in literature and poetry, from the University of Tübingen, Germany, and a Ph.D. in computer science from ETH Zürich, Switzerland.
I am interested in developing computational statistics methods that are applicable in data-driven research in biology and microbial ecology.
As one of the principal investigators in the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES) I am involved in developing statistical methods for ocean microbiome data analysis.
In Summer 2018 I will be visiting faculty at the Max-Planck/TU Dresden Center for Systems Biology.
On the computational side my primary research interests are:
- High-dimensional and robust statistics
- Compositional data analysis
- Black-box optimization and sampling
- Proximal algorithms and their application in statistics
- Statistical and spectral methods to analyze complex networks
My biological applications include:
- Microbiome data analysis
- Inference of microbial association networks from amplicon data
- Analysis of microbial time series data from metagenomics measurements
- Robustness of parameter spaces in biological systems
My CV can be found here (pdf).
In summer, I teach network inference as part of the summer course “Strategies and Techniques for Analysis of Microbial Population Structure” (STAMPS), at the Marine Biological Laboratory (MBL), Woods Hole.
I have co-organized and continue to organize a number of workshops and symposia. The principle aim of the workshops I help organize is to bridge the gap between research in optimization, statistics, and microbial data analysis.
- Flatiron Institute workshop on Operator Splitting in Data Analysis which will take place on the 20th-22th of March 2019 at the Flatiron Institute, Simons Foundation, New York. Participation is by invitation only. If you are interested, please contact me.
- ASA Conference on Statistical Learning and Data Science (SLDS)/Non-parametric Statistics which took place on the 4th-6th of June 2018 at Columbia University , New York. Information and registration can be found here. The conference was financially supported by the Simons Foundation.
- SAMSI workshop on Operator Splitting in Data Analysis which took place on the 21th-23th of March 2018 at SAMSI, Research Triangle, NC. Information and registration can be found here. This workshop was financially supported by the Simons Foundation.
- Mini-symposium on Proximal Algorithms for High-dimensional Statistics as part of the SIAM Conference of Optimization 2017 which took place on the 22th-25th of May 2017 at the Sheraton Vancouver Wall Centre, Vancouver. Information and registration can be found here.
- Second workshop on statistical and computational challenges in microbiome research which took place on the 16th-17th of February 2017 at the Broad Institute, Cambridge. Information and registration can be found here (#SACMDA2).
- First workshop on statistical and computational challenges in microbiome research on the 25th-26th of February 2016 at the Simons Foundation, New York. Further information can be found here.
Please find below a list of representative publications (more complete list is available on my ResearchGate profile). Some of the accompanying code can be found in my personal github repository. My Erdös number is 3 (Bernd Gärtner -> Pavel Valtr -> Paul Erdös).
Recent highlights in statistics and optimization
- Bien J, Gaynanova I, Lederer J, Müller CL. Prediction error bounds for linear regression with the TREX. TEST 2018 , https://arxiv.org/abs/1801.01394 [code]
- Combettes PL, Müller CL. Perspective Functions: Proximal Calculus and Applications in High Dimensional Statistics. J. Math. Anal. Appl., 2018 [code]
- Bien J, Gaynanova I, Lederer J, Müller CL. Non-convex Global Minimization and False Discovery Rate Control for the TREX. J. Comp. Graph. Stats. 2018 , arXiv preprint arXiv:1604.06815 [code]
- Asmus J, Müller CL, Sbalzarini IF. Lp-Adaptation: Simultaneous Design Centering and Robustness Estimation of Electronic and Biological Systems. Scientific Reports, 2017.
Recent highlights in microbial data analysis and modeling
- Tipton L, Müller, CL, Kurtz ZD, Morris A, Huang L, Kleerup E, Bonneau R, Ghedin E, Fungi Stabilize Connectivity in Lung and Skin Microbial Ecosystems. Microbiome, 2018
- Äijö T, Müller CL, Bonneau R, Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing. Bioinformatics 2018, http://biorxiv.org/content/early/2016/09/22/076836 [code]
Manuscripts under review/in preparation
- Combettes PL, Müller CL. Perspective M-estimation via proximal decomposition. arXiv preprint arXiv:1805.06098 [code]
- Müller CL, Bonneau R, Kurtz ZD. Generalized Stability Approach for Regularized Graphical Models. https://arxiv.org/abs/1605.07072 [code]
Representative older publications
- Hill SM et al., HPN-DREAM Consortium, Inferring causal molecular networks: empirical assessment through a community-based effort. Nature Methods, 2016.
- Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11(5):e1004226 (* joint first authors) [code]
- Lederer J, Müller CL. Don’t fall for tuning parameters: Tuning-free variable selection in high dimensions with the TREX. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. (* joint first authors). [code]
- Stich SU, Müller CL, Gärtner B. Optimization of convex functions with Random Pursuit. SIAM J Optim 2013;23(2):1284-1309.
- Müller CL, Baumgartner B, Sbalzarini IF. Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes. Proc. IEEE Congress on Evolutionary Computation (CEC), May 18-21, 2009; Trondheim, Norway. [code]
Jacob Bien, Data Sciences and Operations, USC Marshall School of Business, USA, High-dimensional statistics.
Patrick Combettes, NC State, USA, Proximal algorithms.
Zachary Kurtz, Lodo Therapeutics, New York, Microbiome data analysis.
Ivo F. Sbalzarini, Max-Planck-Institute for Cell Biology, Dresden, Germany, Markov Chain Monte Carlo, Optimization.
Thomas J. Fuchs, Memorial Sloan Kettering Cancer Center, New York, USA, Approximate Bayesian Computation .
Aditya Mishra, (PhD Statistics 2017, UConn)
Michelle Badri (Principal supervisors Blaser/Bonneau, NYU), Josh Fass (Principal supervisor John Chodera, MSKCC)
Sebastian Stich (completed 2014, with Dr. Bernd Gärtner, ETH Zürich) now at EPFL, Switzerland
Josefine Asmus (completed 2017, with Dr. Ivo F. Sbalzarini, MPI-CBG/TU Dresden)
Master/diploma theses (2006-2010, ETH Zürich):
Ana Tusek, Benedikt Baumgartner, Christian Fiegl, Georg Ofenbeck, Markus König, Daniel Zünd, (with Ivo F. Sbalzarini), Thomas Lampart (with Peter Widmayer)
Bachelor/semester theses (2006-2010, ETH Zürich):
Johannes Lederer, Yannick Misteli, Patrick Plattner (with Ivo F. Sbalzarini)
Mentoring of Graduate Students (since 2013):
Evan Baugh, Zachary Kurtz, Ramya Raviram (all NYU)