Eduardo Izquierdo

Eduardo Izquierdo Torres

Dr. Eduardo Izquierdo specializes in NeuroRobotics and Bio-inspired Artificial Intelligence. His research aims to develop artificial systems—robots, computers, and machines—that exhibit the adaptivity, flexibility, and robustness of living organisms. He has authored or co-authored 18 journal articles, 26 papers in top peer-reviewed conferences, 3 peer-reviewed non-archival papers, 3 editorial pieces, and numerous abstracts and posters. His work has been supported by several funding awards, including the prestigious NSF CAREER award. Dr. Izquierdo’s research and teaching are highly interdisciplinary, bridging Artificial Intelligence, Cognitive Science, Neuroscience, Robotics, and Electrical and Computer Engineering. This multidisciplinary approach is one of the ways he contributes to the excellence of education at Rose-Hulman. Outside the classroom, he enjoys climbing, tennis, swimming, running, hiking, camping, and cooking outdoors. Learn more on his personal webpage.

Academic Degrees

  • Ph.D. in Computer Science and AI (2008). Centre for Computational Neuroscience and Robotics. University of Sussex, Brighton, UK.
  • Master of Science in Intelligent Systems (2004). University of Sussex, Brighton, UK.
  • Bachelor of Science in Computer Engineering (2002). Universidad Simon Bolivar, Venezuela.

Teaching Interests

  • Evolutionary Robotics
  • Artificial Life
  • Bio-inspired Artificial Intelligence
  • Complex Systems

Research Interests

My research interest is in Evolutionary and Adaptive Systems, including Evolutionary Robotics, Cognitive Science, Artificial Life, Evolutionary Computation, Morphological Computation, Embodied Intelligence, Evolutionary Hardware, Neuromorphic Engineering, BioRobotics, NeuroRobotics, and Biologically-Inspired Artificial Intelligence. I have two complementary research goals: To better understand intelligence in living organisms; and to develop artificial systems with the robustness, flexibility, and adaptivity of living organisms.

Select Publications & Presentations

  • Yoder JA, Anderson CB, Cehong W, Izquierdo EJ (2022) Reinforcement learning for central pattern generation in dynamical recurrent neural networks. Frontiers in Computational Neuroscience 16:818985.
  • Campbell CM, Izquierdo EJ, Goldstone RL (2022) Partial copying and the role of diversity in social learning performance. Collective Intelligence, 1:1, 1-15.
  • Olivares E, Izquierdo EJ, Beer RD (2021) A neuromechanical model of multiple network rhythmic pattern generators for forward locomotion in C. elegans. Frontiers in Computational Neuroscience 15:572339.
  • Ikeda M, Matsumoto H, Izquierdo EJ (2021) Persistent thermal input controls steering behavior in Caenorhabditis elegans. PLOS Computational Biology 17(1), e1007916.
  • Candadai MV, Izquierdo EJ (2020) infotheory: A C++/Python package for multivariate information theoretic analysis. Journal of Open Source Software 5(47), 1609.
  • Rodriguez N, Izquierdo EJ, Ahn YY (2019) Optimal modularity and memory capacity of neural reservoirs. Network Neuroscience 3(2):551–566. Candadai MV, Setzler M,
  • Izquierdo EJ, Froese T. (2019) Embodied dyadic interaction increases complexity of neural dynamics: A minimal agent-based simulation model. Frontiers in Psychology 21;10:540.
  • Izquierdo EJ (2019) Role of simulation models in understanding the generation of behavior in C. elegans. Special Issue: Systems biology of model organisms. Current Opinion in Systems Biology 13:93–101
  • Izquierdo EJ, Beer RD (2018) From head to tail: An integrated neuromechanical model of forward locomotion in C. elegans. Philosophical Transactions of the Royal Society B: Biological Sciences 373(1758): 20170374.
  • Izquierdo EJ, Beer RD (2016) The whole worm: brain-body-environment models of C. elegans. Current Opinion in Neurobiology 40:23–30.

Awards & Honors

  • NSF CAREER: From connectome to behavior: computational models of multifunctional neural circuits in C. elegans (2019-2025). NSF/IIS 1845322. $882,772.00. PI.
  • NSF Workshop grant: Functional logic of neural circuits: diamonds in the rough (Part 2). NSF 2234198 (2022-2023) $50,000.00. Co-PI.
  • NSF Workshop grant: Functional logic of neural circuits: diamonds in the rough (Part 1). (2021-2022) $50,000.00. Co-PI.
  • NSF Supplemental grant: Reinforcement learning in dynamical recurrent neural networks (2021).
  • NSF 2114455. $50,683.00. PI.
  • NSF Robust Intelligence: An ensemble of neuromechanical models of C. elegans forward locomotion (2015-2018)
  • NSF/IIS 1524647. $492,189.00. Co-PI. NSF Robust Intelligence: The whole worm: a brain-body-environment model of nematode chemotaxis (2012-2015)
  • NSF/IIS 1216739, $489,440.00. Lead contributor.