World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
11371
World Ranking
11891
National Ranking
4845

Research.com Recognitions

  • 2014 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Eric Mjolsness is affiliated with the University of California, Irvine in the United States. Their research spans several interconnected domains within biochemistry, genetics, and molecular biology, focusing extensively on molecular biology and biophysics, as well as specialized areas such as artificial intelligence, signal processing, and computational theory and mathematics.

Their scientific contributions are reflected in numerous publications, with a significant number appearing in venues such as arXiv (Cornell University), Biophysical Journal, bioRxiv (Cold Spring Harbor Laboratory), Nature Cell Biology, and Molecular Systems Biology.

Frequent coauthors who have collaborated with Eric Mjolsness include Cory Braker Scott, Terrence J. Sejnowski, Thomas M. Bartol, Matthew Hur, and Jacob Kim.

The main topics addressed in their work encompass:

  • Gene Regulatory Network Analysis
  • Bioinformatics and Genomic Networks
  • Cell Image Analysis Techniques
  • Cellular Automata and Applications
  • Advanced Electron Microscopy Techniques and Applications
  • Neural dynamics and brain function
  • Computational Physics and Python Applications

Among their recent papers are:

  • Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing, 2020, Nature Cell Biology
  • SBML Level 3: an extensible format for the exchange and reuse of biological models, 2020, Molecular Systems Biology
  • Graph diffusion distance: Properties and efficient computation, 2021, PLoS ONE
  • Detection and prediction of a beam-driven mode in field-reversed configuration plasma with recurrent neural networks, 2020, Nuclear Fusion
  • Explicit Calculation of Structural Commutation Relations for Stochastic and Dynamical Graph Grammar Rule Operators in Biological Morphodynamics, 2022, Frontiers in Systems Biology

Eric Mjolsness was awarded the Fellow of the American Association for the Advancement of Science (AAAS) in 2014.

Best Publications

  • 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

  • New algorithms for 2D and 3D point matching: pose estimation and correspondence

    Steven Gold;Anand Rangarajan;Chien-Ping Lu;Suguna Pappu

  • A connectionist model of development

    Eric Mjolsness;David H. Sharp;David H. Sharp;John Reinitz

  • 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

  • SBML Level 3: an extensible format for the exchange and reuse of biological models

    Sarah M. Keating;Sarah M. Keating;Dagmar Waltemath;Matthias König;Fengkai Zhang

  • A robust point-matching algorithm for autoradiograph alignment

    Anand Rangarajan;Haili Chui;Eric Mjolsness;Suguna Pappu

  • Model for cooperative control of positional information in Drosophila by bicoid and maternal hunchback.

    John Reinitz;John Reinitz;Eric Mjolsness;David H. Sharp

  • Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

    Bruce E. Shapiro;Andre Levchenko;Elliot M. Meyerowitz;Barbara J. Wold

  • Modeling the organization of the WUSCHEL expression domain in the shoot apical meristem

    Henrik Jönsson;Marcus Heisler;G. Venugopala Reddy;Vikas Agrawal

  • New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence

    Steven Gold;Chien-Ping Lu;Anand Rangarajan;Suguna Pappu

  • Analysis of ultrasound images in the presence of contrast agent

    Howard Dittrich;Harold Levene;Eric Mjolsness

  • A novel optimizing network architecture with applications

    Anand Rangarajan;Steven Gold;Eric Mjolsness

  • Scaling, machine learning, and genetic neural nets

    Eric Mjolsness;David H Sharp;Bradley K Alpert

  • Algebraic transformations of objective functions

    Eric Mjolsness;Charles Garrett

  • A Lagrangian relaxation network for graph matching

    A. Rangarajan;E.D. Mjolsness

  • Learning with Preknowledge: Clustering with Point and Graph Matching Distance Measures

    Steven Gold;Anand Rangarajan;Eric Mjolsness

  • Translation-invariant mixture models for curve clustering

    Darya Chudova;Scott Gaffney;Eric Mjolsness;Padhraic Smyth

  • Optimization in model matching and perceptual organization

    Eric Mjolsness;Gene Gindi;P. Anandan

  • Towards learned traversability for robot navigation: From underfoot to the far field

    Andrew Howard;Michael J. Turmon;Larry H. Matthies;Benyang Tang

Frequent Co-Authors

Elliot M. Meyerowitz
Elliot M. Meyerowitz California Institute of Technology
Anand Rangarajan
Anand Rangarajan University of Florida
Terrence J. Sejnowski
Terrence J. Sejnowski Salk Institute for Biological Studies
Barbara J. Wold
Barbara J. Wold California Institute of Technology
Steve Chien
Steve Chien Jet Propulsion Lab
Andre Levchenko
Andre Levchenko Yale University
Michael Hucka
Michael Hucka California Institute of Technology
G. Wesley Hatfield
G. Wesley Hatfield University of California, Irvine
Larry Matthies
Larry Matthies Jet Propulsion Lab
John C. Doyle
John C. Doyle California Institute of Technology

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