2014 - Fellow of the American Association for the Advancement of Science (AAAS)
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.
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.
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.
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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The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.
M. Hucka;A. Finney;H. M. Sauro;H. Bolouri;H. Bolouri.
Fast and globally convergent pose estimation from video images
C.-P. Lu;G.D. Hager;E. Mjolsness.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)
New algorithms for 2D and 3D point matching: pose estimation and correspondence
Steven Gold;Anand Rangarajan;Chien-Ping Lu;Suguna Pappu.
Pattern Recognition (1998)
An auxin-driven polarized transport model for phyllotaxis.
Henrik Jönsson;Marcus G. Heisler;Bruce E. Shapiro;Elliot M. Meyerowitz.
Proceedings of the National Academy of Sciences of the United States of America (2006)
A connectionist model of development
Eric Mjolsness;David H. Sharp;David H. Sharp;John Reinitz.
Journal of Theoretical Biology (1991)
Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy
Jonathan W Young;James C W Locke;Alphan Altinok;Nitzan Rosenfeld.
Nature Protocols (2012)
Machine learning for science: state of the art and future prospects.
Eric Mjolsness;Dennis DeCoste.
Animation of plant development
Przemyslaw Prusinkiewicz;Mark S. Hammel;Eric Mjolsness.
international conference on computer graphics and interactive techniques (1993)
A robust point-matching algorithm for autoradiograph alignment
Anand Rangarajan;Haili Chui;Eric Mjolsness;Suguna Pappu.
Medical Image Analysis (1997)
Model for cooperative control of positional information in Drosophila by bicoid and maternal hunchback.
John Reinitz;John Reinitz;Eric Mjolsness;David H. Sharp.
Journal of Experimental Zoology (1995)
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