Hello! I'm Kooshan Maleki, a senior Computer Engineering student at Amirkabir University of Technology. My research focuses on hybrid quantum-classical algorithms — from multi-objective architecture search for quantum neural networks (QNAS, accepted at IEEE IJCNN 2026) to deep learning-enhanced quantum linear solvers. I'm broadly interested in how quantum computing can advance machine learning and tackle hard optimization problems.
Location: Tehran, Iran
Researcher at eBRAIN Lab (NYUAD) and QuCAL (AUT). QNAS accepted at IEEE IJCNN 2026; arXiv preprint forthcoming, code on GitHub.
B.Sc. GPA 18.84/20 (4.0/4.0), top 5%, with thesis on HHL vs. classical linear solvers and linear regression.
Head TA and TA across advanced programming, quantum computing, architecture, and networking courses.
Built medical imaging solutions for cardiac ultrasound analysis and measurement workflows in an industrial setting.
Institution: Amirkabir University of Technology (2021 – Present)
GPA: 18.84/20 (4/4), Top 5%
Bachelor Thesis: Analysis and Comparison of the HHL Algorithm with Classical Methods for Solving Linear Systems and Its Application in Linear Regression
Institution: Allameh Helli High School (NODET) (2017 – 2021)
GPA: 19.40/20
Supervisors: Prof. Muhammad Shafique and Dr. Alberto Marchisio
Website: ebrain4everyone.com
Research on hybrid quantum-classical neural network optimization. Developed QNAS, a multi-objective framework for automated quantum neural architecture search using NSGA-II, accepted at IEEE IJCNN 2026 (WCCI 2026).
Supervisor: Prof. Negar Ashari Astani
Website: qucal.aut.ac.ir
Research on enhancing the Hybrid HHL algorithm through deep learning-based Pauli decomposition techniques.
Multi-objective framework for automated architecture search of hybrid quantum-classical neural networks. Uses NSGA-II to jointly optimize accuracy, circuit cost, and wire-cutting partitionability, with checkpoint-based correlation analysis for early stopping.
Website: https://mfp.co.ir
Trained students for the Iranian Physics Olympiad, focusing on deep conceptual understanding.
Organizer: NYU Abu Dhabi (International Hackathon for Social Good in the Arab World)
Focus: Quantum Computing, Quantum Machine Learning
Institution: The Coding School (QxQ)
QNAS accepted at IEEE IJCNN 2026 — First-author paper at the IEEE World Congress on Computational Intelligence.
NYUAD Hackathon 2025 — Selected participant; worked on quantum algorithms for social good at NYU Abu Dhabi.
Top 1% — Iranian Universities Entrance Examination — Ranked among the top 1% of over 142,000 participants (2021).
QubitXQubit Scholarship — Awarded course scholarship by The Coding School (MIT/Stanford-run program).
32nd Physics Olympiad of Iran — National-level competitor.
SamCode Programming Competition (2017) — Winner in Data Analysis among 200 NODET students.
Mother tongue (native proficiency).