Teaching

I have extensive experience mentoring undergraduate and graduate students on open-ended, project-based computer science and AI systems, with an emphasis on problem formulation, system design, evaluation, and research integrity. My teaching and supervision focus on training students to operate in research-like environments, where requirements are ambiguous and rigor matters.

Since 2020, I have served as instructor or co-instructor for large undergraduate capstone courses and graduate-level AI and data science courses, guiding student teams from initial scoping through implementation and presentation. Across these roles, students consistently highlight my structured guidance, fairness, availability, and high engagement, particularly in technically complex and interdisciplinary projects.

Selected Teaching & Supervision Roles (Chronological)

• Spring 2026 – present
  1 - Computer Science I (CS 1337, UT Dallas) — instructor; two sections
  2 - Big Data and Data Science (CSCE 5200, UNT) — TA (graduate; ~50 students)

• Fall 2025
  1 - Senior Design / Capstone (CS 4485, UT Dallas) — instructor of record; supervised ~60 senior students working on full-stack, real-world systems
  2 - Big Data and Data Science (CSCE 5200, UNT) — Teaching Assistant (graduate; ~50 students)

• Summer 2025
  Machine Learning (CSCE 5215, UNT) — Teaching Assistant  (graduate; ~40 students); delivered lectures on AI ethics and responsible deployment

• Spring 2024
  Big Data and Data Science (CSCE 5200, UNT) — Teaching Assistant (graduate; ~83 students)

• Spring 2023
  Software Development for AI (CSCE 5214, UNT) — co-instructor (graduate)

• Fall 2022
  Introduction to Artificial Intelligence (CSCE 5210, UNT) — Teaching Assistant (graduate)

• 2020 – 2022
  Computer Science I & II (CSCE 1030 / CSCE 1040, UNT) — Lead Teaching Assistant; lab instruction and supervision for large undergraduate cohorts (C/C++)
  
  

Selected Evidence of Effectiveness

Instructor-of-record evaluation (UT Dallas capstone, Fall 2025): 4.8 / 5.0 overall rating; 100% recommendation rate
Graduate AI courses (UNT, 2022–2026): ~4.8 / 5.0 mean rating across 100+ students
Recurrent themes in student feedback: 'clear project structure', 'strong mentorship', 'fairness', 'responsiveness', and 'high engagement'.

Selected Student Feedback

“The professor’s guidance was most helpful in structuring the project from an engineering perspective. The feedback on scope, deliverables, and breaking the system into clearly defined components helped our team stay organized.”
— Senior Capstone Student, UT Dallas, Fall 2025

“Always enthusiastic to help or guide — never got the impression teaching felt like a chore. Extremely understanding and supportive throughout our team’s work and meetings.”
— Capstone Student, UT Dallas, Fall 2025

“I felt a lot more comfortable presenting and asking for guidance. The mentorship made a big difference in how confidently we approached the project.”
— Senior Capstone Student, UT Dallas, Fall 2025

“I have one class left to graduate and Poli has been the best TA so far. Extremely engaged with students — some TAs don’t even know your name, but she knows every student. Fairness and equality are clearly important to her.”
— Graduate Student, UNT, Fall 2025

“Extremely pushy, but in the most positive way. You quickly realize it’s because she wants you to succeed.”
— Graduate Student, UNT, Fall 2025

“Never said no to a meeting for resolving doubts. Her knowledge over the subject is commendable, and I never needed follow-up sessions.”
— Graduate Student, Software Development for AI, Spring 2023

“The way she explains things is amazing. Her exam structure prevents cheating and actually measures how much we learned.”
— Graduate Student, Big Data & Data Science, Fall 2025

“Very kind and understanding. I really enjoyed working on the project and hope to continue seeing it deployed in the real world.”
— Capstone Student, UT Dallas, Fall 2025