Dr. Noh's research specializes in deducing causal inference for time series microscopy images and developing statistical methods for genomic data analysis. Currently, he collaborates with peers in the Green Center for Systems Biology and in the Lyda Hill Department of Bioinformatics to develop statistical methods to resolve complex biomedical data.

We seek to understand how cancer cells harness the cytoskeleton to promote tumor growth, drug resistance and cancer metastasis through non-genetic, morphologic signaling programs.

Our aim is to develop computational methods to unveil the hidden biological circuitries behind the data, from understanding sequence-based regulations to the evolution of genomes and their impact to diseases.

Our lab combines normative theories and biologically plausible neural circuit models to study the principles of neural information processing, in order to answer how perception, cognition, and behavior emerge from neural circuits. 

Our team is interested in developing computational models to predict patient outcomes, which will allow clinicians to tailor treatment plans for individual patients.

The Saunders Lab aims to advance our understanding of the bacterial domain of life using high throughput genetics to map the molecular interactions that underly cellular physiology.

The Dean Lab aims to develop and apply cutting-edge microscopy instrumentation and analyses to gain insight into otherwise intractable biological problems.

The Danuser lab develops computer vision methods and mathematical models in combination with live cell imaging approaches to unveil non-genetic mechanisms of cancer metastasis and drug resistance.
Specialty areas: Computer Vision, Computational Biology, Live Cell Imaging

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The Lin lab develops theoretical models and uses computational tools to find the performance limits of complex biological systems.