Daniel Wooten

Programming Languages: Python 2/3, C/C++, R, FORTRAN

Tools: Pandas, Numpy, RegEx, Scikit-learn, Matplotlib, PyTorch, TensorFlow, BERT

Machine Learning: Reinforcement learning, recommender systems, NLP, transfer learning

UC Berkeley - Dec '19
Ph.D. in Nuclear Engineering with a Designated Emphasis in Computational and Data Science Engineering

North Carolina State University - May '13
B.S. in Nuclear Engineering - summa cum laude

AI/ML Software Engineer - Perceptronics Solutions - Falls Church, Virginia - August 2020 to Current
- Designed and deployed multi-hypothesis tracker for assessing ethical considerations in shoot/no-shoot scenarios
- Built a particle filter based Bayesian inference engine for assessing human cognitive and emotional states solely from behavioral data
- Worked closely with a Human Factors psychologist to put into algorithms the state-of-the-art in behavioral inference
- Presented progress and findings at bi-weekly and quarterly government update meetings
- Co-wrote government funding proposals with a multi-member team

AI Fellow - Insight Data Science - New York City, New York - Jan 2020 to Mar 2020
- Built a reinforcement learning based recommender system in python to capture users' hybridized sequence and timing preferences with regards to streaming content
- Deployed this recommender system on AWS as part of a scaleable pipeline
- Organized and led training sessions on software development best practices

Researcher - UC Berkeley - Berkeley, California - Aug 2013 to Dec 2019
- Deployed a web-based computer vision system for emotion recognition in human faces
- Designed and built ADER, a 30k line modular addition to an industry code creating a first-of-its-kind reactor optimization tool
- Created a linear optimization routine accounting for economic, chemical, and nuclear constraints in reactor fuel cycles

Reactor Safety Intern - TerraPower - Belleveue, Washington - May 2015 to Aug 2015
- Developed an object-oriented reactor-safety-physics package in Python
- Integrated this package with the company wide design-software enabling live updates to safety parameters reflecting real-time changes in the system design
- Reduced the time and labor required for reactor safety analysis by 20 hours per week