About Me
I'm an AI Researcher at Huawei Technologies Canada based in Edmonton. During my Masters in Computing Science at the University of Alberta, I focused on neurosymbolic programming and reinforcement learning, supervised by Levi Lelis and Sandra Zilles. Before that, I completed my B.Sc. in Computer Engineering at Amirkabir University of Technology.
View RésuméEducation
Supervisor: Levi Lelis and Sandra Zilles | GPA: 3.8
Major: AI; Minor: Networks | GPA: 18.75/20 (3.96/4)
Publications
Conference Publications
Amirhossein Rajabpour, Kiarash Aghakasiri, Sandra Zilles, Levi Lelis.
Amirmohsen Sattarifard, Sepehr Lavasani, Kunlin Zhang, Amirhossein Rajabpour, Hanlin Xu, Fengyu Sun, Negar Hassanpour, Chao Gao.
Workshops
Amirhossein Rajabpour, Kiarash Aghakasiri, Sandra Zilles, Levi Lelis.
Work Experience
- Optimize and deploy large language models on mobile NPUs and CPUs, focusing on compilation, sparsification, and hardware-aware performance tuning (LiteRT Framework).
- Deployed and configured Slurm-based GPU clusters, including partition setup, fair-share scheduling, and resource allocation policies for large-scale ML experiments.
1-month project on advanced AI/ML methods to enhance real-time intrusion detection and anomaly detection in high-speed network storage infrastructures.
- Localizing LEDs on monitors for quality control.
- Clustering LED light pixels using fuzzy C-means for luminance/wavelength extraction.
- Applying regression models per color to detect malfunctioning hardware.
Hands-on experience with Docker and cloud-native tooling.
Research Experience
- Refined neural policies (RL agents) through targeted architectural and training adjustments, enabling them to match or outperform programmatic policies in generalization on key benchmarks.
- Proposed new benchmarks highlighting strengths of programmatic policies (e.g., memory-based reasoning).
- Built end-to-end training/evaluation pipelines (Python, Bash, PyTorch, TensorFlow).
- Used Large Language Models to iteratively generate and refine interpretable policies in settings where RL underperforms.
- Work accepted at RLC 2025 Programmatic RL workshop.
Improved "Unveiling Options with Neural Decomposition" using hill-climbing search and genetic algorithms.
Developed a face-aging platform using generative models (e.g., CycleGAN, reversible models).
Technical Skills
Programming Languages
AI & Machine Learning
Backend & Dev Tools
Teaching Assistant
Graduate Teaching Experience | University of Alberta
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CMPUT 365: Introduction to Reinforcement Learning
Labs, grading assignments, and exams.
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CMPUT 366: Search & Planning in AI
Lectures, labs, and grading for all course components.
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CMPUT 291: Introduction to File & Database Management
Lectures and lab sessions, grading projects, assignments, and exams.
Undergraduate Teaching Experience | Amirkabir University of Technology
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Principles of AI
Designed and graded projects covering CSPs, search, and RL.
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Cloud Computing
Designed assignments and labs focused on APIs, cloud services, Docker, and Kubernetes.
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Internet of Things
Developed scenarios for sensor/actuator integration and Arduino programming assignments.
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Algorithm Design
Graded assignments on greedy algorithms, divide & conquer, and dynamic programming.
Projects
I maintain a variety of open-source projects ranging from Reinforcement Learning implementations to Computer Vision platforms.
Explore Projects on GitHubHonors & Awards
- Fully funded admission to UAlberta (CS, ECE, Radiology & Diagnostic Imaging), Western Ontario (CS) (2023).
- Nationwide Mathematics University Entrance Exam (Iran): Top 1% among 140,000+ applicants.
- 4-year scholarship, Amirkabir University of Technology.