Michael Linderman, PhD
- ASSISTANT PROFESSOR | Genetics and Genomic Sciences
Research Topics:Bioinformatics, Computational Biology
Linderman Research Webpage
Michael Linderman is a computer engineer and computational systems biologist building large-scale coherent models of disease. He earned his Ph.D. and M.S. from Stanford University in Electrical Engineering and his B.S. from Harvey Mudd College in Engineering. Michael did his postdoctoral work in Garry Nolan's laboratory at Stanford applying a GPU supercomputer and novel programming tools to cancer biology. Michael is currently a member of the Department of Genetics and Genomic Sciences and the Institute for Genomics and Multiscale Biology where he works on developing more powerful and efficient computer systems for systems biology.
Multi-Disciplinary Training AreasDesign Technology and Entrepreneurship [DTE], Genetics and Genomic Sciences [GGS]
BS, Harvey Mudd College
PhD, Stanford University
Computational Systems Biology
At the scale of 100-1000s of gigabytes of data per complete genome, computational systems biology is quickly reaching a scale of computing more typically associated with Google, Facebook, or Yahoo. This is the cutting-edge of “big-data”, and as such life scientists cannot be mere consumers of high-performance computing (HPC) technology but must also be innovators. Successfully extracting clinical insight from large-scale high-dimensional datasets will depend on us building new computer systems orders-of-magnitude bigger, faster and more efficient than anything in use today. My research focuses on delivering those improvements in performance, efficiency and usability.
My research program in the Institute of Genomics and Multiscale Biology addresses these challenges by: 1) building a more flexible, powerful and efficient supercomputer by combining traditional servers, cloud-based servers and GPUs; 2) developing new statistical methods that can take better advantage of those computing resources; and 3) developing software tools that make Institute’s computing infrastructure more accessible to its researchers and their collaborators.
Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman MD, Sachs K, Nolan GP, Plevritis SK. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nature biotechnology 2011 Oct;.
Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP. Computational solutions to large-scale data management and analysis. Nature reviews. Genetics 2010 Sep; 11(9).
Linderman MD, Bruggner RV, Athalye V, Meng TH, Bani Asadi N, Nolan GP. High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers. Proc. of the Intl. Conf. on Supercomputing 2010;: 95-104.
Linderman MD, Collins JD, Wang H, Meng TH. Merge: A programming model for heterogeneous multi-core systems. Proc. of Intl. Conf. on Architectural Support for Programming Languages and Operating Systems 2008;: 287-296.