Teaching & Mentorship

Experience in AI-driven pedagogy, educational technology, and mentoring at the intersection of physics and machine learning.

Overview

I teach and mentor at the intersection of first-principles reasoning and practical iteration. Whether building an ed-tech platform from scratch, coaching olympiad teams, or stress-testing AI models, I focus on helping learners (and models) build a repeatable process for solving hard problems.

I believe that well-designed AI tools can provide the scaffolding necessary to meet learners where they are, making high-quality education globally accessible and personalized.

What you can expect from my mentoring:

  • First-Principles Thinking: Breaking complex physics or code into fundamental components.
  • Scaffolding & Guided Discovery: Moving beyond rote hints to help learners build their own internal mental models.
  • Research Workflow: Iterating through baselines → hypotheses → experiments → ablations.
  • Methodical Debugging: Applying rigorous logic to both math and code.

AI & Educational Technology

Physics AI Model Validation Expert – Handshake MOVE

Remote | Aug 2025 – Present

  • Design complex physics reasoning problems and reference solutions to stress-test the limits of state-of-the-art AI models.
  • Evaluate model pedagogical correctness and write targeted feedback to improve AI reasoning capabilities and factual accuracy in scientific contexts.
  • Act as a bridge between high-level physics expertise and AI evaluation, ensuring models provide safe and effective explanations for learners.

Co-Founder – Nebula

Tbilisi, Georgia | 2016

  • Co-founded an online educational platform (team of 3) providing Khan Academy–style video lectures and interactive exercises for national university entrance exams.
  • Architected the automated test-delivery system and content management backend to support hundreds of concurrent learners.
  • Designed structured data schemas for several hundred original tests in critical reasoning and mathematics, implementing logic for dynamic difficulty progressions.
  • Authored and produced video lectures covering core STEM topics, focusing on making high-quality prep accessible to students regardless of location or socioeconomic background.

University Teaching

Teaching Assistant – Quantum Mechanics I

Binghamton University, NY | Aug 2023 – Dec 2023

  • Supported lectures and problem sessions; helped students develop intuition for core QM concepts through first-principles derivations.
  • Graded homework assignment and held office hours, providing feedback to students.


Teaching Assistant – Statistical Mechanics and Thermodynamics

Binghamton University, NY | Aug 2022 – Dec 2022

  • Led office hours and emphasized the connection between physical intuition and rigorous mathematical formalism.
  • Graded homework assignments and provided feedback to students.


Teaching Assistant – General Physics

Binghamton University, NY | Aug 2018 – Aug 2020

  • Held discussion sections and office hours for introductory physics courses, helping students build intuition through fundamental principles and real-world examples.
  • Head Teaching Assistant (2nd year): Mentored and onboarded new TAs, coordinating labs and recitations for introductory courses.
  • Earned 85% positive feedback with 67% student participation in evaluations.
  • Created supplementary course materials, including solution guides and review worksheets to reinforce key concepts.

Physics Olympiad Coaching

Physics Team Coach

42nd School of Physics and Mathematics, Tbilisi, Georgia | Sep 2012 – May 2018

  • Coached students for national and international competitions; students earned multiple medals in local and international contests.
  • Lead Coach at the 2016 International Zhautykov Olympiad (Almaty, Kazakhstan); team won two silver medals in physics and mathematics.
  • Developed a systematic misconception-modeling framework: using timed attempts and solution post-mortems to identify and close specific cognitive skill gaps.

Mentoring Philosophy

I mentor the way I like to learn: start from fundamentals, stay curious, and improve through practice and feedback. My goal is to help mentees build a process they can reuse across problems and projects.

I came up through competitive physics (bronze medals at IPhO 2012 and IZhO 2012), which shaped my approach: clean reasoning, strong intuition, and deliberate practice. Co-founding an ed-tech platform taught me that great educational content requires understanding your learners just as deeply as the subject matter. Now, in ML research engineering, I emphasize:

  • Start with a simple baseline and a clear goal.
  • Change one thing at a time; write down what you learned.
  • Debug methodically (math, code, evaluation).

I am committed to building AI-driven tools that are inclusive, evidence-based, and designed to augment human potential. My experience spans creating educational content, building learning platforms, and evaluating AI models—and I’m driven to bring this perspective to the next generation of AI-powered learning tools.