Russell Schwartz

Professor and Department Head Carnegie Mellon University

  • Pittsburgh PA

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

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Carnegie Mellon University

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Biography

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

Cancer Biology
Biophysics
Population Genetics
Computational Genomics
Machine Learning
Computational Biology
Artificial Intelligence
Algorithms
Phylogenetics

Media Appearances

Pittsburgh’s AI-Powered Renaissance

CMU News  online

2024-10-09

"There is tremendous excitement about the new generation of AI tools and nowhere could their impact be greater than in improving human health. AI tools seem to hold great promise for personalized medicine but also warrant caution as we see what they do not do well and how much we do not yet understand about how they work and how they might be improved. The big leaps in AI we are seeing now are neither the beginning nor the end of the field. They were built on a foundation of decades of research into AI fundamentals and their application to real-world problems in human health and disease, areas in which Pittsburgh researchers have long been world leaders. And there is much more to be done to fully realize their potential."

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Computer science professor Sorin Istrail wins award recognizing algorithms research

The Brown Daily Herald  online

2022-12-02

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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Media

Social

Industry Expertise

Research
Biotechnology

Education

Massachusetts Institute of Technology

Ph.D.

Computer Science

Articles

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

Bioinformatics

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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