Experience

  1. Research Assistant

    Intelligent Robot Learning and RLAI Labs, University of Alberta
    Collaborating on a survey paper describing how RL can benefit from Foundation models.

  2. Content Development Support

    Alberta Machine Intelligence Institute (Amii)
    Developed educational content for an NLP course using Keras.

  3. Graduate Research Assistant

    Intelligent Robot Learning and RLAI Labs, University of Alberta
    • Developed POLIS, a coding assistant that improves programs based on desired output or behaviour of the program using bottom-up program synthesis.
    • Designed a Domain Specific Language and implemented an interpreter based on Python interpreter.
    • Conducted a systematic web-based user-study with 40+ participants.

  4. Research Engineer Intern

    Delphi Technology Corp (Mitacs Internship)
    • Collaborated with a team of 3 to implement an RL environment for flight maneuvers in the X-Plane simulator using Python and OpenAI Gym.
    • Trained an RL agent on flight maneuver tasks using Soft-Actor-Critic implemented in PyTorch.
    • Developed two classifiers for identify pilot errors and expertise levels achieving 80% accuracy rate.

  5. Volunteer Workshop Lecturer

    Oxford Machine Learning Summer School (OxML)
    Presented tutorials and managed RL-in-practice sessions with 70+ attendees.

  6. Graduate Teaching Assistant

    Department of Computing Science, University of Alberta
    Conducted labs for 150+ students, prepared and marked assignments for CMPUT 174 and CMPUT 201 courses (introduction to C and Python languages)

  7. Undergraduate Research Assistant

    Ferdowsi University of Mashhad
    • Trained a model to predict CO2 levels at different times of day using time-series data, and visualized the estimated values by an interactive heatmap.
    • Extracted and clustered features from C/C++ code using Code Prover enabling error detection prior to compilation.

  8. Undergraduate Teaching Assistant

    Ferdowsi University of Mashhad
    Prepared and graded assignments for Fundamentals of Computer Programming (C/C++) and Advanced Programming (Java).

Education

  1. MSc Computing Science

    University of Alberta University

    Thesis: Program Optimization with Local Search.
    Optimizing Python code with respect to an objective like program behavior, time, and memory usage. Co-supervised by and .

    Selected Courses: Reinforcement Learning (RL), Computational Linguistics

    Read Thesis

  2. BSc Computer Engineering

    Ferdowsi University of Mashhad

    Degree Project: Real-time Deep Facial Expression Recognition on Raspberry Pi.

    Selected Courses: Advanced Neural Networks, Computational Intelligence, Foundations of Computer Vision, Artificial Intelligence, Data Structure and Algorithm, Foundations of Data Mining.

Publications
S. Schoepp, M. Jafaripour, Y. Cao, T. Yang, Fatemeh Abdollahi, S. Golestan, Z. Sufiyan, O. R. Zaiane, M. E. Taylor (2025). The Evolving Landscape of LLM- and VLM-Integrated Reinforcement Learning. IJCAI 2025.
PDF Cite
Fatemeh Abdollahi, S. Ameen, M. E. Taylor, L. Lelis (2023). Can You Improve My Code? Optimizing Programs with Local Search. IJCAI 2023.
PDF Cite Code DOI
C. Kharyal, T. Sinha, S. K. Gottipati, Fatemeh Abdollahi, S. Das, M. E. Taylor (2023). Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning. AAMAS 2023.
PDF Cite DOI
Skills
Technical
Tensorflow
PyTorch
stable-baselines
Pandas
scikit-learn
NumPy
NLTK
Linux
Git
Programming
Python
JavaScript
Shell scripting
SQL
C
Java
R
Matlab
Prolog
Honors and Awards
Graduate Research Assistantship and Fellowship
Department of Computing Science, University of Alberta ∙ May 2020
Received a total of $92,000 in funding over 2 years.
Direct Entry to the MSc Program
Ferdowsi University of Mashhad ∙ May 2019
Ranked 3rd in the class of 100 students at FUM and qualified for direct entry to the MSc program.
Second Place in FUM Programming Contest
Ferdowsi University of Mashhad ∙ April 2016
Won 2nd place in the FUM Programming Contest for first-year undergraduate students.