Teaching & Service

Teaching Experience

At UNC-Chapel Hill, I have been serving as both Instructor of Record (Graduate Teaching Fellow) with full course responsibility and Graduate Teaching Assistant across undergraduate and graduate-level courses in optimization, decision science, and data modeling.

Instructor of Record (Graduate Teaching Fellow)

• STOR 113: Decision Models for Business and Economics (Summer 2025 — Sessions 1 & 2)
  Role: Primary Instructor
  Fully responsible for syllabus design, lectures, assignments, and exams. Taught algebra and calculus-based quantitative decision models, non-linear functions, marginal analysis, single/multivariate optimization (partial derivatives, Lagrange multipliers), and elasticity for economic decision-making.
• STOR 155: Introduction to Data Models and Inference (Summer 2024)
  Role: Primary Instructor
  Fully responsible for syllabus design, lectures, assignments, and exams. Covered exploratory data analysis, sampling/experimental design, probability distributions (binomial, normal, geometric), statistical inference (confidence intervals and hypothesis testing for means, proportions, and chi-square tests), and linear regression.

Graduate Teaching Assistant & Lab Instructor

• STOR 612: Foundations of Optimization (Ph.D. Level, Fall 2026)
  Led recitations and graded advanced graduate coursework covering convex analysis, duality theory, polyhedral geometry, linear/quadratic programming (simplex and interior-point methods), first-order & proximal algorithms, accelerated gradient methods, and stochastic optimization (SGD with variance reduction) applied to ERM, LASSO, and SVMs.
• STOR 512: Optimization for Machine Learning and Neural Networks (Spring 2026)
  Held weekly office hours and assisted with coursework grading. Guided students on optimization formulations in ML (nonlinear regression, SVMs, matrix factorizations), stochastic first-order algorithms (SGD, mini-batching, Adam), backpropagation and automatic differentiation, neural network training (PyTorch/TensorFlow), and minimax/GAN architectures.
• STOR 415 / STOR 415H: Introduction to Optimization (Standard & Honors) (Fall 2025)
  Held office hours and recitations on linear programming, simplex methods, network flows, and integer programming.
• STOR 120: Foundations of Statistics and Data Science (Spring 2024)
  Served as Lab Instructor for 3 lab sections (80+ students), guiding students through computational data analysis and statistical inference in Python.
• STOR 155: Introduction to Data Models and Inference (Summer 2023 — Sessions 1 & 2)
  Led recitation and tutorial sessions, graded coursework, and assisted students with probability theory, statistical inference for population parameters, linear regression modeling, and spreadsheet data analysis in Microsoft Excel.
• STOR 113: Decision Models for Business and Economics (Spring 2023)
  Conducted discussion sections and provided technical assistance on introductory algebra and calculus-based quantitative modeling, derivative rules, and optimization in business contexts.
• STOR 215: Foundations of Decision Science (Fall 2022)
  Guided students through foundational concepts in discrete mathematics, logic, counting methods, and proof-based decision modeling.

Professional Service

Peer Reviewer

Active reviewer for peer-reviewed journals and major international conferences in optimization, computational mathematics, and machine learning:

• International Conferences:
  • International Conference on Machine Learning (ICML) — Silver Reviewer Award (2026)
  • Neural Information Processing Systems (NeurIPS)
• Academic Journals:
  • Journal of Optimization Theory and Applications (JOTA) — Springer
  • EURO Journal on Computational Optimization — Elsevier
  • Optimization and Engineering — Springer
  • Journal of Computational and Applied Mathematics — Elsevier