Bryan Tripp

Professor Tripp uses computational models to study how the brain processes information. He integrates neurobiological models and deep learning to study visuomotor processes. He is also interested in applying these models in challenging robotics tasks, to better understand how the brain deals with the complex physical world. Recent progress in his lab includes: The first deep-network architecture that is based quantitatively on a large cortical network (Tripp, 2019); the most comprehensive model of a higher cortical representation (Rezai et al., 2018); the largest dataset of human-demonstrated robotic grasps (Iyegar et al., 2018); the only robotic head that has movement capabilities on par with humans (including saccade velocity, stereo baseline, and range of motion) (Huber et al., 2018); and the first spiking neural network model of the planning of complex actions (Blouw et al., 2016).

• Computational neuroscience

• Deep learning

• Robotics

• Medical AI

• BIOL 487 - Computational Neuroscience
  • Taught in 2023, 2026
• BME 261 - Prototyping, Simulation and Design
  • Taught in 2021
• BME 355 - Physiological Systems Modelling
  • Taught in 2021, 2026
• BME 461 - Biomedical Engineering Design Workshop 2
  • Taught in 2023, 2024
• BME 462 - Biomedical Engineering Design Workshop 3
  • Taught in 2024, 2025
• BME 530 - The Healthcare System
  • Taught in 2025
• SYDE 552 - Computational Neuroscience
  • Taught in 2023, 2026
• SYDE 577 - Deep Learning
  • Taught in 2024, 2025
• SYDE 599 - Special Topics in Systems Design Engineering
  • Taught in 2022, 2023

* Only courses taught in the past 5 years are displayed.