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 wrote the book Education: The Next Hundred Years, available through Barnes and Noble and as a Kindle Book on Amazon. The book combines insights from my research, teaching, and consulting experience to provide a roadmap for transforming education over the next century using emerging pedagogical and computational technologies.
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.
My dissertation, Query Strategies for Directed Graphical Models and their Application to Adaptive Testing, brings together machine learning, computer science theory, and education research to develop scalable and effective methods for generating assessments for new topics. This is an important step forward in education research, as it allows us to easily create (and then use) rigorous measures of learning to evaluate educational interventions, pedagogy, and policy. My thesis defense is also available on YouTube.
- LAK 2020: “Applying Prerequisite Structure Inference to Adaptive Testing”, a conference paper about inferring prerequisite relationships between assessment items and leveraging those relationships for efficient adaptive testing.
- 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 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.
- Reviewer for IJCAI-PRICAI 2020.
- At IM DATA 2019, I delivered an hour-long talk on applications of AI to education, and then moderated a panel discussion on a similar topic.
- 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 that deals 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 Angular, Ionic (UI framework), and Firebase (backend database, functions, and authentication) for three interconnected web apps/services.
- 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 various organizations in the US. I also manage an LLC, including finances and HR.
- Freelance Teaching: Private math and programming instruction for a variety of courses, including “Intro to Programming through Games”, “Algorithms and Competitive Programming”, and “Intro to Machine Learning and Research”.
- Consultant: Ran a 3-day machine learning boot camp for the Woods Hole Oceanographic Institution.
- Online Courses: Ran an online course that served as a combined introduction to computer science and number theory.
- Brown University: Co-designed and guest lectured a Machine Learning course. Guest lectured in a Discrete Mathematics 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. Also, private coaching on reading, creating writing, public speaking, and teaching.
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 alphabetical order:
- Evan Cater (U) - AI research and entrepreneurship. Coauthor on our LAK 2020 paper.
- Eric Choi (U) - Full Stack Development and Research
- Alberta Devor (U) - (briefly) Research
- Kevin Du (U) - Machine Learning applications in adaptive assessment.
- Madeline Griswold (U) - AI research, NLP, and mathematics. Presented a poster at the Brown CS Student Research Symposium.
- Fumi Honda (M) - AI Research. Coauthor on accepted RLDM 2019 poster “Exploration under State Abstraction via Efficient Sampling and Action Reuse”.
- Mingxuan Li (M) * - AI research in reinforcement learning and planning under some state abstractions. Theory of Reinforcement Learning.
- Zhiyin Lin (H) * - Research in uncertainty modeling for Machine Learning models and medical decision-making. Coauthor on a recent workshop submission.
- Naveen Srinivasan (M) - Theory of Reinforcement Learning. Working on a project involving planning with access to a state partition and local options.
- Zora Zhang (H) - Research into explainable machine learning models.
Ph.D. Student at Brown University 2016 - 2021
- 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).