Staff Profiles

Theo Knijnenburg, Ph.D.

Director, Computational Biology & Machine Learning

Theo joined the Allen Institute for Cell Science in August 2019. He leads the Modeling Team which develops computational models that facilitate visual and quantitative understanding of dynamic cellular processes. Theo is an electrical engineer by training, specialized in digital signal processing, information theory, statistics and machine learning. He worked and studied at Delft University of Technology, the Netherlands Cancer Institute and the Institute for Systems Biology. Over the last 15 years, he has performed research in the field of computational biology as part of various interdisciplinary teams. Theo's work is motivated by the observation that computational models, however powerful in detecting patterns in large and complex biological data, are best interpreted within a specific research paradigm, thereby allowing experts to overlay their domain knowledge and suggest novel hypotheses and experiments. His current research interest is to describe cellular states by integrating transcriptomics and imaging data of cell state transitions using machine learning frameworks that embed concepts from dynamical systems theory. 

Selected Publications

A deep generative model of 3D single-cell organization

PLoS Computational Biology
January 18, 2022

Donovan-Maiye RM, Brown JM, Chan CK, Ding L, Yan C, Gaudreault N, Theriot JA, Maleckar MM, Knijnenburg TA, Johnson GR