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 founded Edapt Technologies, which incorporated in November 2021. Within 5 months, we developed a financial literacy assessment suite to help schools in Rhode Island meet a newly legislated graduation requirement, and we became the official state-recommended measure of proficiency. We are now working with multiple national organizations to deliver and improve financial literacy education at scale. Our efforts have been recognized by multiple non-profits and by the state treasurer.
Other Business Experience
I’ve consulted on applications of AI for Speedio in Brazil, Special A Education in China, and several organizations in the US. I’m a board member of the Franklin Center For Innovation (FCFI).
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 was on the application of machine learning and other innovations to educational problems to improve the scalability and equity of educational innovation. Some of the problems I’ve developed models for include adaptive assessment, spaced repetition, and content evaluation and recommendation.
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, consequently, 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.
- Interviewed by CEST (Centro de Estudos Sociedade e Tecnologia) about The Future of AI in Education in 2022.
- Invited speaker at the 1st Conference in the Middle East on AI in Higher Education, speaking on “AI and Data Privacy in Higher Education” in late 2021.
- Reviewer for AAAI 2022, IJCAI-PRICAI 2020.
- Invited to deliver an hour-long talk at IM DATA 2019 on applications of AI to education, and then moderate a panel discussion on a similar topic.
- 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 these 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 React, Ionic (UI framework), and Firebase (backend database, functions, and authentication) for three interconnected web apps/services using TypeScript.
- Full-stack development using Ruby on Rails for two single-server projects. Additional experience with Django and Angular.
- 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.
- 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.)
- Education Design: Co-creator of Minds Across Time, an educational trading card game aligned with more than 40 middle school educational standards in Kentucky.
- Experience tutoring mathematics and programming since 2012. Also, private coach for reading, creative writing, scientific 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) -Theory of Reinforcement Learning and research in planning under some state abstractions.
- Steven Li (H) - Game Design and Software Engineering. Created a custom game engine in Python and novel level-design formats.
- Zhiyin Lin (H) - Research in uncertainty modeling for Machine Learning models and medical decision-making. Coauthor on a AAAI workshop paper.
- Alvin Miao (H) * - Machine Learning. Working on a project involving brain-computer interfaces.
- Naveen Srinivasan (M) - Theory of Reinforcement Learning. Worked on a project involving planning with access to a state partition and local options.
- Yiming Song (H) - Applied Machine Learning. Advised on a project involving instrument segmentation from a mixed audio recording.
- Ryan Wong (H) - Applied Machine Learning. Combined a variety of techniques to predict needed repairs for bike sharing in New York. Culminated in a submission to a student research competition.
- Jack Xie (H) - Supervised Learning on images. Unsupervised learning for linguistics. Built a model for visually detecting sign language.
- Zora Zhang (H) - Research into explainable machine learning models.
Ph.D. in Computer Science from Brown University 2016 - 2021
- Directed the Personalized Education @ Scale lab.
- Served as the department Faculty-Graduate Liaison (FGL) 2019-2021.
B.Sc. from 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
- CFA Society Providence/FPA-RI Financial Literacy Coalition Leadership Award | 2022
- 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
- Eagle Scout - Boy Scouts of America | Rank Earned 2013
Leo el español mejor de lo que lo hablo.
Traveled to six countries (so far).