What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Gene
- Machine learning
His primary areas of study are Artificial neural network, Artificial intelligence, Regulation of gene expression, Machine learning and Meristem.
His Artificial neural network research includes elements of Simulated annealing, Algorithm, Cell cycle control and Affine transformation.
His biological study spans a wide range of topics, including Sketch, Simple and Computer vision.
His Regulation of gene expression research is multidisciplinary, incorporating elements of Evolutionary biology and Gene expression.
His Meristem study combines topics in areas such as Arabidopsis, Auxin and Computational biology.
Eric Mjolsness performs integrative Abstraction and Theoretical computer science research in his work.
His most cited work include:
- The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. (2637 citations)
- Fast and globally convergent pose estimation from video images (701 citations)
- An auxin-driven polarized transport model for phyllotaxis. (494 citations)
What are the main themes of his work throughout his whole career to date?
Eric Mjolsness spends much of his time researching Artificial intelligence, Artificial neural network, Theoretical computer science, Algorithm and Meristem.
In his research, Outlier is intimately related to Computer vision, which falls under the overarching field of Artificial intelligence.
Eric Mjolsness interconnects Computational biology and Relaxation in the investigation of issues within Artificial neural network.
His research in Theoretical computer science intersects with topics in Graph, Parameterized complexity, Probabilistic logic, Modeling language and Semantics.
The Meristem study combines topics in areas such as Arabidopsis thaliana, Biological system, Arabidopsis and Cell division.
His work carried out in the field of Cell biology brings together such families of science as Cell and Regulation of gene expression.
He most often published in these fields:
- Artificial intelligence (23.70%)
- Artificial neural network (19.08%)
- Theoretical computer science (17.34%)
What were the highlights of his more recent work (between 2012-2021)?
- Differential equation (5.78%)
- Statistical physics (3.47%)
- Theoretical computer science (17.34%)
In recent papers he was focusing on the following fields of study:
Eric Mjolsness mainly focuses on Differential equation, Statistical physics, Theoretical computer science, Multigrid method and Probability distribution.
As a member of one scientific family, he mostly works in the field of Differential equation, focusing on Applied mathematics and, on occasion, Basis function.
Within one scientific family, Eric Mjolsness focuses on topics pertaining to Modeling language under Theoretical computer science, and may sometimes address concerns connected to Computation and Development.
The concepts of his Multigrid method study are interwoven with issues in Artificial neural network, Algorithm and Hierarchy.
Eric Mjolsness has included themes like Beam and Convolutional neural network in his Artificial neural network study.
His work is dedicated to discovering how Iterative method, Artificial intelligence are connected with Finite element method and other disciplines.
Between 2012 and 2021, his most popular works were:
- Are microtubules tension sensors (57 citations)
- SBML Level 3: an extensible format for the exchange and reuse of biological models (42 citations)
- Analysis of cell division patterns in the Arabidopsis shoot apical meristem (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- Programming language
His primary areas of investigation include Statistical physics, Differential equation, Cell division, Division and Training.
The various areas that Eric Mjolsness examines in his Statistical physics study include Parameterized complexity, Markov chain, Ordinary differential equation and Product.
His Differential equation research is multidisciplinary, incorporating perspectives in State variable, Graph, Reduction and Constant coefficients.
His Cell division research incorporates themes from Arabidopsis thaliana, Cytoplasm, Meristem, Biological system and Edge.
His Division research integrates issues from Orientation, Live cell imaging, Perpendicular, Botany and Arabidopsis.
His Training studies intersect with other disciplines such as Artificial intelligence, Multigrid method, Iterative method, Artificial neural network and Hierarchy.
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