I’m a software engineer specializing in artificial intelligence and machine learning, with a background in computational mechanics and spacecraft engineering. I'm data driven, passionate about learning, and strive to build a better world with technology.
We're building human-level AI. My focuses are,
Numenta aims to reverse-engineer the neocortex for machine intelligence. My role was mainly to prototype algorithms from raw theory, test and validate them through experiments, and develop them into production quality code.
Applied new approach to designing a re-entry system for on-demand return of scientific payloads from the International Space Station.
Provided energy company with a new approach (k-NN clustering) to better classify customers based on true energy usage patterns and investigate energy efficiency traits (see Publications below).
Designed experiments and testbed to investigate the use of nitrous oxide in hybrid propulsion engines. Ran rocket engine firing tests and analyzed results across a variety of performance metrics.
I dropped out. Why? I found most of the curriculum unfulfilling, and dedicating myself to learning engineering management skills through experience to be far more valuable.
Implemented varieties of CNN + LSTM architectures to investigate temporal dependencies in video data, specifically for predicting speed and steering for a self-driving car.
Integrated Pareto-optimality into traditional A* and D* search algorithms to provide improved methods for multi-objective path planning. I showed strong improvements over traditional methods across all optimization metrics in simulated environments, including in a Mars terrain case study.
Lunar rover project manager and lead of systems engineering.
For the task of classifying particle collision events as either signal (Higgs boson decay) or background noise, I implemented a gradient boosted classifier that performed only 4.63% behind the winning score, and almost 2x the score of the baseline naive Bayes model.
"Finite Element-based Structural Optimization of Large System Models Under Buckling Constraints"
Note there are additional arXiv papers linked in the above projects section.
Drawing inspiration from systems neuroscience, we introduce a probabilistic generative model for vision in which message-passing based inference handles recognition, segmentation, and reasoning in a unified way. The Recursive Cortical Network (RCN) outperforms state-of-the-art deep learning models on challenging text recognition benchmarks, while being 300x more data efficient. See our "Common sense, cortex, and CAPTCHA" blog post for more.
Models of neocortex largely consider the ventral pathway of the vision system, yet integration with the parallel dorsal pathway is necessary for sensorimotor processing. I present a ventral + dorsal computational model with an updated canonical microcircuit.
Propose a novel anomaly detection algorithm based on Hierarchical Temporal Memory (HTM), an online sequence memory algorithm derive from the neocortex.
An online textbook on Hierarchical Temporal Memory with Numenta.
Paper and oral presentation at 2015 IEEE International Conference on Machine Learning Applications
Journal paper in Energy Efficiency