George Biros

About

George Biros is a professor holding the W.A. "Tex" Moncrief, Jr. Endowment in Simulation-based Engineering Sciences Endowed Chair No. 2 at the University of Texas at Austin. He earned his Ph.D. in Computational Science and Engineering from Carnegie Mellon University and held a post-doctoral appointment at NYU's Courant Institute of Mathematical Sciences. He has also held faculty positions at the University of Pennsylvania and Georgia Institute of Technology. His research group focuses on computational mathematics and the invention of new parallel algorithms for physics-based simulations and large-scale data analysis. His work aims to develop technologies for discovery and innovation that will harness upcoming breakthroughs in high performance computing, including exascale platforms. Current research topics include parallel algorithms, numerical algorithms for integral and differential equations, inverse problems, data assimilation, computational statistics, biological complex fluids, blood rheology, soft tissue and cardiovascular mechanics, and medical image analysis. George Biros's contributions have been recognized through awards such as the Association for Computing Machinery's Gordon Bell Prize and the Early Career Young Investigator Award from the U.S. Department of Energy. He is also an associate editor of the Society for Industrial and Applied Mathematics Journal on Scientific Computing.

Research topics

• Artificial Intelligence
• Computer Science
• Medicine
• Medical physics
• Mathematics
• Computer vision
• Parallel computing
• Risk analysis (engineering)
• Algorithm
• Biology

Selected publications

• Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities

  Journal of Clinical Medicine · 2020 · 90 citations

  the integration of clinically relevant, patient-specific, mathematical models and optimal control theory.

  DOI

• Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

  Annual Review of Biomedical Engineering · 2020 · 63 citations

  Senior authorCorresponding

  ) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

  DOI

• Fast GPU 3D diffeomorphic image registration

  Journal of Parallel and Distributed Computing · 2020 · 25 citations

  DOI

• KNN-DBSCAN: a DBSCAN in high dimensions

  ACM Transactions on Parallel Computing · 2024-10-24 · 3 citations

  articleOpen accessSenior author

  Clustering is a fundamental task in machine learning. One of the most successful and broadly used algorithms is DBSCAN, a density-based clustering algorithm. DBSCAN requires ϵ-nearest neighbor graphs of the input dataset, which are computed with range-search algorithms and spatial data structures like KD-trees. Despite many efforts to design scalable implementations for DBSCAN, existing work is limited to low-dimensional datasets, as constructing ϵ-nearest neighbor graphs can be expensive in hig…

  PublisherOA PDFDOI

• A single-snapshot inverse solver for two-species graph model of tau pathology spreading in human Alzheimer disease.

  PubMed · 2025-02-24 · 2 citations

  preprintOpen accessSenior author

  We propose a method that uses a two-species ordinary differential equation (ODE) model to characterize misfolded tau (or simply tau) protein spreading in Alzheimer's disease (AD) and calibrates it from clinical data. The unknown model parameters are the initial condition (IC) for tau and three scalar parameters representing the migration, proliferation, and clearance of tau proteins. Driven by imaging data, these parameters are estimated by formulating a constrained optimization problem with a s…

  PublisherOA PDFDOI

Recent grants

• SHF: Small: Algorithms and Software for Scalable Kernel Methods

  NSF · $486k · 2018–2021

• ITR: Collaborative Research - ASE - (sim+dmc): Image-based Biophysical Modeling: Scalable Registration and Inversion Algorithms and Distributed Computing

  NSF · $329k · 2004–2009

• Collaborative Research: Petascale Algorithms for Particulate Flows

  NSF · $438k · 2007–2009

Frequent coauthors

• Christos Davatzikos

  University of Pennsylvania

  37 shared

• Denis Zorin

  New York University

  30 shared

• Andreas Mang

  University of Houston

  22 shared

• Hari Sundar

  Madras Medical Mission

  21 shared

• Shashank Subramanian

  National Energy Research Scientific Computing Center

  21 shared

• Omar Ghattas

  The University of Texas at Austin

  20 shared

• Chenhan D. Yu

  Yuan Ze University

  19 shared

• Bryan Quaife

  Florida State University

  18 shared

Labs

• Advanced Power Systems and Control Laboratory (APSCL)PI

Education

• Ph.D., Computational Science and Engineering

  Carnegie Mellon University

• Other

  New York University's Courant Institute of Mathematical Sciences

Awards & honors

• Association for Computing Machinery's Gordon Bell Prize
• Early Career Young Investigator Award from the U.S. Departme…

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