* * * This page has been archived (June 2023). Although it was useful early on, this page now feels like a distraction. See specialties instead. * * *




Currently developing the AI that powers Math Academy's fully automated and personalized online learning system.

Previously applied math in a variety of contexts including working as a data scientist, modeling biological neural networks, and improving particle detectors.

Also taught, tutored, and developed math content in parallel for a decade, culminating in developing one of the most advanced high school applied math/CS sequences in the USA.

Click to expand most sections below. Summarized on LinkedIn.


Algorithm Developer
Developed all quantitative aspects of the product including the novel AI system that makes Math Academy fully automated, fully personalized, and 4x more efficient than a traditional math class. Solved every problem that Math Academy has faced while constructing an educational knowledge graph for all of 4th grade through university-level mathematics and building a fully automated & personalized learning system around it.
Math Academy, May 2019 - present

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.


Applied Math/CS Instructor
Developed one of the most advanced high school applied math/CS sequences in the USA
Math Academy (Non-Profit Division), May 2020 - June 2023

Math Academy supports a highly accelerated math program (by the same name) within the Pasadena Unified School District. The program has been recognized by the Washington Post as "America's most accelerated math program"; 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. During its operation from 2020 to 2023, Eurisko was one of the most advanced high school math/CS sequences in the USA. The sequence culminated in students doing masters/PhD-level coursework (reproducing academic research papers in artificial intelligence).
(2023) The Story of Math Academy's Eurisko Sequence

Math Educator
Many organizations, Mar 2013 - May 2021

Tutored, taught, and developed math content both independently and for a variety of organizations.
  • May 2019 - May 2021 | Independent tutoring
  • Jan 2018 - May 2021 | Math Academy - developed many hundreds of tutorial videos and fully scaffolded lessons; taught substitute/summer classes & TA sessions.
  • 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 | Study.com - item writing
  • Mar 2013 - Jan 2018 | Mathnasium - tutored ~250 students

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.
(2015) Shaping STDP Neural Networks with Periodic Stimulation: a Theoretical Analysis for the Case of Tree Networks

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

Helped improve 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.)
(2014) Optimizing Scintillation and Light Transmission for Use in a High Energy Particle Detector
(2013) Making a Matching Layer for Acoustic Sensors for a COUPP Dark Matter Detector


MS, Computer Science, Georgia Institute of Technology, 2020
Machine Learning track; simultaneously worked full time

BS, Mathematics, University of Notre Dame, 2018
Full-ride Lilly Scholarship (top 0.5% high school grads in Indiana; must attend college in state)
Glynn Honors Program (top 5% admits); simultaneously worked full time


Selected Posts