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.


Work Experience

Senior Research Engineer
Vicarious AI
October 2016 +

We're building human-level AI. My focuses are,

  • Building probabilistic graphical models (PGM) for robotic vision, with inspiration from primate visual cortex
  • Developing motion planning and trajectory optimzation algorithms for our robots
Senior Software & Research Engineer
November 2014 - October 2016

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.

  • Experimented with algorithms for natural language processing (NLP), and managed the development of a document classification application
  • Investigated and implemented algorithms to compare with HTM -- e.g., deep learning (LSTM), traditional time-series and Bayesian methods, word embeddings (word2vec) -- for sequence prediction, classification, and anomaly detection
  • Built and managed the open-source Numenta Anomaly Benchmark
  • Helped architect CI pipelines for using Docker
  • Numerous public speaking engagements at conferences, meetups, and workshops
  • Key role in all publishing, including writing chapters for our textbook "BaMI"
Research Associate
NASA Ames Research Center
Summer 2013

Applied new approach to designing a re-entry system for on-demand return of scientific payloads from the International Space Station.

Data Analytics Specialist
Northwestern University and Agentis Energy
June - December 2012

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).

Rocket Propulsion Intern
Technion Institute of Technology
Summer 2013

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.



Mechanical Engineering
Carnegie Mellon University
Class of 2014
  • computational mechanics
  • mobile robotics
  • artificial intelligence & machine learning
  • engineering computation
GPA: 4.0
Master of
Engineering Management
Duke University
2013 - never
  • software development management
  • commercializing technology innovation

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.

GPA: 3.9
  • Concentration in Technology Innovation
Mechanical Engineering
Cornell University
Class of 2012
  • spacecraft engineering
  • mechatronics
GPA: 3.1 (3.6 in '11-'12)
  • Satellite dynamics research in Space Systems Design Studio
  • College of Engineering Dean's List, 2011-2012
  • Two-time Global Fellow for engineering work in France and Israel
  • Men's varsity golf team, 4-year starter and team captain


Technical Skills


  • Python
  • C++
  • Java
  • Golang
  • Android
  • R

Data Sci and ML Tools

  • keras
  • scikit-learn
  • tensorflow
  • pandas
  • numpy
  • plotly
  • nltk
  • opencv
  • edward

Misc. SW

  • architecture
  • Docker
  • GitHub API
  • AWS
  • continuous integration
  • git
  • web apps
  • html
  • raspberry Pi
  • ros



Cornell U., College of Engineering Dean's List
2011 & 2012
Cornell U., Engineering Global Fellow
2010 & 2011


Additional Projects

Deep Learning for Self-Driving Vehicles
GitHub repo

Implemented varieties of CNN + LSTM architectures to investigate temporal dependencies in video data, specifically for predicting speed and steering for a self-driving car.

  • Implemented a scaled down version of NVIDIA's "End to End Learning for Self-Driving Cars" model.
  • Experimented with different feature pooling architectures as means of incorporating temporal info into CNNs.
  • Experimented using optical flow tracking to improve model accuracy.
Mobile Robotics Path Planning Algorithms

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.

Google Lunar XPrize
GLXP team Astrobotic

Lunar rover project manager and lead of systems engineering.

Kaggle Higgs Boson Machine Learning Challenge
Higgs Challenge

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.

Carnegie Mellon Master's Thesis

"Finite Element-based Structural Optimization of Large System Models Under Buckling Constraints"

  • Identified the core issues with applying standard optimization methods to the problem, and wrote a new algorithm to address convergence issues and computational efficiency.
  • arXiv paper and thesis presentation
Spacecraft Research at Cornell
  • Designed and built attitude control system for a multiple CubeSat configuration; utilized DC motor inertia in place of reaction wheels, saving on mass, power, and spending budgets.
  • Designed the allocation and layouts of CubeSat payloads for efficient dynamics.



Note there are additional arXiv papers linked in the above projects section.

Science, 2017

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.

"A Computational Model of Ventral & Dorsal Processing"

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.

Neurocomputing, 2017

Propose a novel anomaly detection algorithm based on Hierarchical Temporal Memory (HTM), an online sequence memory algorithm derive from the neocortex.

ebook, 2016

An online textbook on Hierarchical Temporal Memory with Numenta.

ICMLA, 2015

Paper and oral presentation at 2015 IEEE International Conference on Machine Learning Applications

Energy Efficiency, 2014

Journal paper in Energy Efficiency




  • Heinlein
  • Game of Thrones
  • spacetime and string theory
  • philosophy of physics
  • Dawkins

Health & fitness

  • half marathons
  • yoga
  • paleo-ish
  • meditation