holdenb
Sr. Software Engineer | Robotics & Control | Current MS-ECE @ Purdue University
Purdue UniversityLafayette, Louisiana
Pinned Repositories
holdenb
"About Me" page which includes my personal bio, experience, and education.
lpcv-2023-tiny-espresso-net
A tiny CNN for on-device disaster scene parsing and semantic segmentation for the lpcv.ai 2023 challenge.
modern-robotics
Modern Robotics Techniques & Algorithms. Based on Modern Robotics: Mechanics, Planning, and Control (Lynch & Park, 2017). The project contains various algorithms re-implemented in python.
mojo-modern-robotics
Experimental take on Modern Robotics Techniques & Algorithms using Mojo. Based on Modern Robotics: Mechanics, Planning, and Control (Lynch & Park, 2017).
mojo-num-analysis
Numerical analysis and algorithms for numerical linear algebra written in Mojo. This project is inspired by Justin Solomon's lectures on Mathematical Methods for Robotics, Vision, and Graphics.
professioneer
A World of Warcraft (classic) profession estimation system that simulates crafting and uses Monte Carlo estimation to predict viable crafting routes.
professioneer-web
Web app/server for the Professioneer sim. Professioneer is a World of Warcraft (classic) profession estimation system that simulates crafting and uses Monte Carlo estimation to predict viable crafting routes.
pycvss
A python module for the ULL High Performance Cloud Computing (HPCC) lab's Cloud Video Streaming Service (CVSS) architecture designed for research and service provision.
quantified-self-sensor-processing
Postprocessing and analysis of IMU data for health-related metrics and classification tasks. Reference: Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data - Mark Hoogendoorn & Burkhardt Funk
TD3-BC-from-scratch
Re-implementation of the original TD3+BC algorithm for offline reinforcement learning by Fujimoto & Gu, 2021.
holdenb's Repositories
holdenb/TD3-BC-from-scratch
Re-implementation of the original TD3+BC algorithm for offline reinforcement learning by Fujimoto & Gu, 2021.
holdenb/mojo-num-analysis
Numerical analysis and algorithms for numerical linear algebra written in Mojo. This project is inspired by Justin Solomon's lectures on Mathematical Methods for Robotics, Vision, and Graphics.
holdenb/lpcv-2023-tiny-espresso-net
A tiny CNN for on-device disaster scene parsing and semantic segmentation for the lpcv.ai 2023 challenge.
holdenb/mojo-modern-robotics
Experimental take on Modern Robotics Techniques & Algorithms using Mojo. Based on Modern Robotics: Mechanics, Planning, and Control (Lynch & Park, 2017).
holdenb/holdenb
"About Me" page which includes my personal bio, experience, and education.
holdenb/modern-robotics
Modern Robotics Techniques & Algorithms. Based on Modern Robotics: Mechanics, Planning, and Control (Lynch & Park, 2017). The project contains various algorithms re-implemented in python.
holdenb/professioneer
A World of Warcraft (classic) profession estimation system that simulates crafting and uses Monte Carlo estimation to predict viable crafting routes.
holdenb/professioneer-web
Web app/server for the Professioneer sim. Professioneer is a World of Warcraft (classic) profession estimation system that simulates crafting and uses Monte Carlo estimation to predict viable crafting routes.
holdenb/pycvss
A python module for the ULL High Performance Cloud Computing (HPCC) lab's Cloud Video Streaming Service (CVSS) architecture designed for research and service provision.
holdenb/quantified-self-sensor-processing
Postprocessing and analysis of IMU data for health-related metrics and classification tasks. Reference: Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data - Mark Hoogendoorn & Burkhardt Funk