I’m an artificial intelligence researcher and entrepreneur whose primary goal is to provide quality education to all students worldwide. In addition to core technical competencies in AI and software, I’ve cultivated soft skills in leadership, client development, and communication.
I have research experience in many flavors of Machine Learning, with a specialization in Reinforcement Learning and Reinforcement Learning Theory. The core of my doctoral work is on the application of machine learning and other innovations to educational problems to improve the scalability and equity of educational innovation.
I created the video course “Hands-On Artificial Intelligence for Small Businesses”, published through Packt. This course covers several main types of machine learning, and each section discusses a common business problem that can be solved using a technique from the course, including everything from understanding your customers better to making automated recommendations that improve over time. This course also introduces artificial neural networks, t-SNE embeddings, and Thompson sampling for multi-armed bandit processes.
- SIGCSE 2019: “Harnessing the Wisdom of the Classes: Classsourcing and Machine Learning for Assessment Instrument Generation”, a conference paper about combining bandit processes, information theory, and crowdsourcing in order to quickly generate statistically validated assessments.
- ArXiv 2018: “Personalized Education at Scale”, a brief survey paper summarizing some obstacles and opportunities for Reinforcement Learning to dramatically improve educational outcomes.
I interned as a Data Scientist at Udacity in 2017. My work there involved analysis of data from millions of students to provide better course recommendations.
I also have a thorough background in mathematics and probabilistic analysis. I published a few papers as an undergraduate:
- AAAI 2016: “A Tool to Graphically Edit CP-Nets”, a short conference paper on a tech demo for a web-based tool that allowed editing and analyzing an annotated directed-acyclic-graph structure called a CP-Net.
- AAMAS 2015: “Probabilistic Copeland Tournaments”, a short conference paper I contributed to dealing with the computational hardness of a particular probabilistic inference problem.
- J. Phys. A 2015: “Generating random walks and polygons with stiffness in confinement”, a journal paper describing sampling methods for random polygons in 3-space under conditions that model the position of DNA in a viral capsid. This was the culmination of several years of research started when I was 17 years old.
- Full-stack development using Ruby on Rails for two single-server projects.
- Professional experience with Anaconda (Python scientific computing collection), PyTorch (deep learning library), and implementations of a variety of deep learning and other machine learning techniques.
- Experience in Lisp, Visual Basic for Applications, and Racket.
- Knowledge of design patterns, software architecture design principles, and repository management using git.
- Hobbyist game designer/developer.
I’ve consulted on applications of AI for Speedio in Brazil, Special A Education in China, and companies in the US. I also manage an LLC, including finances and HR.
- Brown University: Co-designed and guest lectured a Machine Learning course.
- The Learning Center (Public High School in Fayette County, Kentucky): 3 years teaching programming as a volunteer. (Several students passed collegiate-level final exams.)
- Experience tutoring mathematics and programming since 2012.
Students or people I have mentored on significant projects are listed here. (H|U|M|D) indicates High School, Undergraduate, Masters, or Doctoral students. ‘*’ indicates ongoing collaboration
In no particular order:
- Evan Cater (U) * - AI research and entrepreneurship. Received a Summer Research Scholarship to work on learning under uncertainty with me.
- Madeline Griswold (U) * - AI research and mathematics. Presented a poster at the Brown CS Student Research Symposium.
- Eric Choi (U) * - Full Stack Development and Research
- Fumi Honda (M) * - AI Research. Coauthor on accepted RLDM 2019 poster “Exploration under State Abstraction via Efficient Sampling and Action Reuse”.
- Alberta Devor (U) - (briefly) Research
Ph.D. Student at Brown University 2016 - present
- Directs the Personalized Education @ Scale lab
Graduate of the University of Kentucky 2013 - 2016
- Graduated with Honors in 3 years with majors in Computer Science and Mathematics, and a minor in Physics.
Honors and Awards
- NSF Graduate Research Fellowship Program | Honorable Mention 2016
- Computing Research Association Outstanding Undergraduate Researcher | Finalist 2016
- Goldwater Scholar 2014
- Golden Apple Award for Service in Fayette County Public Schools | Co-recipient 2014-2015
Leo el español mejor de lo que lo hablo.
Travelled to six countries (so far).