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Algorithm Developer & Math Domain Expert
Third member of core team
Math Academy, May 2019 - present

Involved in all aspects of production of an intelligent tutoring system that seeks to become the ultimate online math learning platform. As the third member of the core team, my primary role involves developing algorithms and encoding domain-expert knowledge so that the system can behave like an experienced tutor.

Most notably, I created the Fractional Implicit Repetition (FIRe) algorithm, which generalizes discrete spaced repetition on independent tasks to fractional implicit spaced repetition on highly connected knowledge graphs. Math Academy uses FIRe to estimate student knowledge profiles and select personalized tasks that optimize knowledge persistence over time, striking an optimal balance between learning new topics (maximizing knowledge gain) and reviewing already-seen topics (minimizing knowledge loss).

The FIRe algorithm has been a massive success, speeding up learning by a factor of 4x while improving mastery: students learning via FIRe on Math Academy's personalized learning platform can complete AP Calculus BC in just 35 minutes per school day with improved AP exam scores as compared to an instructor-led course consisting of 12 hours per week (1 hour class and 1 hour homework per school day, plus an amortized 2 hours per week of studying for quizzes/tests and the AP exam itself).

In addition to developing FIRe and many other algorithms, I also wear many other hats including developing command-line tools for managing content and the knowledge graph, encoding massive amounts of domain-expert knowledge into the knowledge graph, and putting finishing touches on all lessons written by the content team.

Data Scientist - Analytics, R&D
Worked full time while simultaneously an undergraduate
Aunalytics, Jan 2016 - Jan 2018

Worked full time while simultaneously an undergraduate (transitioned to full-time after interning during sophomore year).
  • Built predictive churn models & segmented user data to discover sales insights for clients.
  • Developed prototypes of in-house data tools & migrated existing workflows onto Aunsight (custom data processing platform).
  • Evaluated the potential of topological data analysis for practical use in the data science pipeline.


Instructor - Computation & Modeling, Machine Learning, Intelligent Systems
Developed one of the most advanced high school applied math/CS sequences in the country
Math Academy (Non-Profit Division), May 2020 - present

Math Academy supports a highly accelerated math program (by the same name) within the Pasadena Unified School District. The program is the most advanced in the country; students take AP Calculus BC in 8th grade and study a full undergraduate math curriculum in high school.

In collaboration with Jason Roberts (founder of Math Academy), I developed and taught a special three-course math/CS sequence within the Math Academy program called Eurisko ( Eurisko is one of the most advanced high school math/CS sequences in the country.
  • The first course in the sequence, Computation & Modeling, is inspired by MIT's Introduction to Computer Science, and goes far beyond it. Among other things, students write their own machine learning algorithms from scratch (including polynomial and logistic regression, k-nearest neighbors and k-means, and parameter fitting via gradient descent).
  • The second course, Machine Learning, covers more advanced machine learning algorithms such as random forests and neural nets, and students also work together to implement Space Empires, an extremely complex board game that pushes their large-scale project skills (object-oriented design, version control, etc) to the limit. Again, students implement all algorithms from scratch before using any external libraries.
  • The third course, Intelligent Systems, involves using evolutionary algorithms to develop agents that behave intelligently in complex environments. Students reproduce Blondie24 and continue implementing Space Empires with the goal of designing artificially intelligent agents to play it.

Math Educator / Content Developer
Many organizations, Jan 2018 - May 2020

Tutored, taught, and developed math content both independently and for a variety of organizations.
  • May 2019 - May 2021 | Independent tutoring
  • Jan 2018 - May 2020 | Math Academy - developed 300+ tutorial videos, taught substitute/summer classes & TA sessions. (Tutorial videos were used in the early days but we've since moved away from them for efficiency reasons.)
  • Aug 2019 - May 2020 | Pilgrim School - taught AP Calculus AB, physics, first-semester engineering
  • Aug-Sept 2019 | IXL Learning - item writing
  • Jun-July 2019 | Math Academy - taught Research and Presentation in Mathematics, Mathematical Problem Solving, Drawing Mathematics with Desmos
  • Apr 2019 | Math Academy - substitute taught Linear Algebra / Multivariable Calculus
  • Mar 2018 - Mar 2019 | FLEX College Prep - weekend SAT/ACT math classes
  • 2018-19 | Wrote 3 textbooks (for fun)
  • Feb 2018 - May 2019 | LA Tutors 123 - tutored ~10 students
  • Jan 2018 - Mar 2020 | HBar Tutoring - tutored ~20 students
  • Jan-Jun 2018 | - item writing

Math Instructor
Mathnasium, Mar 2013 - Jan 2018

Tutored ~250 students in grades K-12 over the course of 5 years, working primarily with middle/high school students taking algebra through calculus. (Evenings & weekends, 20h/week)

Research Internships

Theoretical Neuroscience
Vural Lab, iCeNSA, University of Notre Dame, Aug 2014 - May 2016

Simulated cyclic Hodgkin-Huxley neural networks with spike-timing dependent plasticity and later proved results under simplifying assumptions in the special case of tree networks. Project was a self-directed investigation advised by Prof. Dervis Can Vural of the Many-Body Physics group.

Computational Neuroscience / Deep Learning
Synthetic Cognition Group (affiliated with LANL), New Mexico Consortium, May - Aug 2015

Implemented spiking neurons in a deep neural network in attempt to create an emergent phenomenon of brain oscillations during an image classification task. Worked in the synthetic cognition group, affiliated with Los Alamos National Lab.

Experimental Particle Physics
Finalist, Intel International Science/Engineering Fair, 2013
QuarkNet (affiliated with CERN), University of Notre Dame, June 2013 - May 2014
Levine Lab (affiliated with Fermilab), IU South Bend, Sept 2012 - May 2013

Improved data transmission in Fermilab and CERN particle detectors by reducing signal loss at the detector surface. Finalist in Intel's International Science/Engineering Fair, 2013. (Two separate projects, the first under Prof. Ilan Levine of IUSB and the second in collaboration with Notre Dame's QuarkNet lab.)


MS, Computer Science, Georgia Institute of Technology, 2020
Machine Learning track; simultaneously worked 60+ h/week

BS, Mathematics, University of Notre Dame, 2018
Full-ride Lilly Scholarship (under 4% selection rate, applies to any university in Indiana)
Glynn Honors Program; simultaneously worked 60h/week during junior/senior years


  • Justin Math: Linear Algebra (2019). print, pdf, digital
  • Justin Math: Calculus (2019). print, pdf, digital
  • Justin Math: Algebra (2018). print, pdf, digital
  • Algorithm Exercises: from Sorting to Strategic Agents (in preparation; expected summer 2023)
  • Supervised Learning (in preparation; expected summer 2023)

  • The Ultimate High School Computer Science Sequence: 9 Months In (2020). blog post
  • But WHERE do the Taylor Series and Lagrange Error Bound even come from?! (2019). blog post