What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Algorithm
- Artificial intelligence
His main research concerns Mathematical optimization, Numerical analysis, Solver, Magnetic resonance imaging and Artificial intelligence.
His research integrates issues of Partial differential equation, Bayesian probability, Applied mathematics and Frequentist inference in his study of Mathematical optimization.
His Numerical analysis research incorporates themes from Quadratic programming, Sequential quadratic programming, Newton's method, Nonlinear system and Optimization problem.
His Solver research incorporates elements of Grid, Boundary value problem, Inverse problem and Computational science.
George Biros has researched Magnetic resonance imaging in several fields, including Machine learning, Preoperative care and Medical imaging.
The concepts of his Artificial intelligence study are interwoven with issues in Atlas, Brain tumor and Computer vision.
His most cited work include:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- A kernel-independent adaptive fast multipole algorithm in two and three dimensions (383 citations)
- Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part I: The Krylov--Schur Solver (221 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Algorithm, Discretization, Artificial intelligence, Applied mathematics and Solver.
His Algorithm research is multidisciplinary, incorporating elements of Kernel method, Kernel and Inverse problem.
As a part of the same scientific family, George Biros mostly works in the field of Discretization, focusing on Integral equation and, on occasion, Mechanics.
George Biros combines subjects such as Brain tumor, Machine learning and Computer vision with his study of Artificial intelligence.
His Applied mathematics research includes elements of Partial differential equation and Mathematical optimization, Constrained optimization.
His Solver study also includes fields such as
- Preconditioner which connect with Elliptic partial differential equation,
- Fast Fourier transform, Multigrid method, Supercomputer, Parallel computing and Distributed memory most often made with reference to Computational science.
He most often published in these fields:
- Algorithm (18.63%)
- Discretization (16.67%)
- Artificial intelligence (14.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Applied mathematics (14.22%)
- Artificial intelligence (14.71%)
- Hessian matrix (7.35%)
In recent papers he was focusing on the following fields of study:
George Biros mainly investigates Applied mathematics, Artificial intelligence, Hessian matrix, Glioblastoma and Algorithm.
His work is dedicated to discovering how Applied mathematics, Artificial neural network are connected with Hierarchical matrix and other disciplines.
Artificial intelligence and Machine learning are commonly linked in his work.
His biological study spans a wide range of topics, including Initial value problem and Inverse problem.
George Biros has included themes like Discretization and Residual in his Ode study.
His study in Optimization problem is interdisciplinary in nature, drawing from both Segmentation, Solver and Compressed sensing.
Between 2018 and 2021, his most popular works were:
- The 2019 mathematical oncology roadmap. (65 citations)
- ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs (61 citations)
- ANODEV2: A Coupled Neural ODE Evolution Framework (19 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Algorithm
- Artificial intelligence
George Biros focuses on Applied mathematics, Ode, Solver, Discretization and Residual.
His Solver research includes themes of Image registration, Diffeomorphic image registration, Computational science and Algorithm, Optimization problem.
The various areas that he examines in his Computational science study include Image, Fast Fourier transform and Interpolation.
George Biros interconnects Initial value problem and Norm in the investigation of issues within Algorithm.
His Optimization problem study combines topics from a wide range of disciplines, such as Parallel algorithm, Segmentation and Parameterized complexity.
The Discretization study combines topics in areas such as Artificial neural network and Generalization.
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