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Russell Schwartz - Carnegie Mellon University. Pittsburgh, PA, US

Russell Schwartz

Professor and Department Head | Carnegie Mellon University


Russell Schwartz works in computational biology and the use of algorithms, AI, machine learning, and simulation in biomedical science.


Russell Schwartz works broadly in computational biology and the use of algorithms, artificial intelligence, machine learning, and simulation in biomedical science. This has included work on problems in computational genomics, phylogenetics, population genetics, and biophysics. The largest area of his lab’s work in recent years has been cancer biology, with specific focus on clonal evolution in cancers and its role in disease progression. He is also active in bioinformatics education and improving quantitative and computational training in the biomedical field.

Areas of Expertise (9)

Cancer Biology


Population Genetics

Computational Genomics

Machine Learning

Computational Biology

Artificial Intelligence



Media Appearances (1)

Computer science professor Sorin Istrail wins award recognizing algorithms research

The Brown Daily Herald  online


Through the paper and its surrounding work, Istrail and colleagues made use of one of the pillars of computer science in genomics — recognizing that a genome can be modeled as a computational artifact and that, in doing so, one is able to access a powerful set of tools for analyzing it, according to Russell Schwartz, co-author of the paper and head of the computational biology department at Carnegie Mellon University.

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Industry Expertise (2)



Education (1)

Massachusetts Institute of Technology: Ph.D., Computer Science

Articles (5)

A Clonal Evolution Simulator for Planning Somatic Evolution Studies

Journal of Computational Biology

2023 Somatic evolution plays a key role in development, cell differentiation, and normal aging, but also in diseases such as cancer. Understanding mechanisms of somatic mutability and how they can vary between cell lineages will likely play a crucial role in biological discovery and medical applications. This need has led to a proliferation of new technologies for profiling single-cell variation, each with distinctive capabilities and limitations that can be leveraged alone or in combination with other technologies. The enormous space of options for assaying somatic variation, however, presents unsolved informatics problems with regard to selecting optimal combinations of technologies for designing appropriate studies for any particular scientific questions.

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Simulating the distortion of clonal fractions in ctDNA due to spatially heterogenous selection

Cancer Research

2023 Time-series circulating tumor DNA (ctDNA) sequencing has the potential to reveal emerging variants in the tissue in real time, and there is now substantial evidence that it can detect tumor growth and treatment-resistant mutations long before growth is visible. However, the reliability of ctDNA for profiling the primary tumor may be compromised by spatial heterogeneity in the tumor genetics, for example due to selective effects of differential drug penetration, immune infiltration, and oxygenation. We explore the influence of spatial factors on tumor genetics through a lattice-based branching process model of solid tumor evolution and ctDNA shedding with the goal of understanding where spatially variable selection pressure could lead to a significant difference between the clonal fractions in the blood and the tissue.

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Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations


2022 Motivation Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference.

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Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers

Nucleic Acids Research

2022 Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs.

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Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer


2022 Motivation Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples.

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