Erdem Kus

Erdem Kus

AI Researcher and Engineer

I am completing my PhD in Computer Science at the University of St Andrews.

I have 6 years of industry experience at Nokia, working on Generative AI (GenAI), Quantum Machine Learning (QML), and AI-driven optimisation.

My research interests include frugal AI, active learning, algorithm selection, uncertainty quantification, statistical machine learning, quantum machine learning, AI for decision making, and constraint programming.

For research collaborations, industry projects, or consultancy, please feel free to contact me by email. kuserdem16@gmail.com

Email | CV | Google Scholar | LinkedIn | ORCID

Research

Frugal AI
Cost-Aware Learning and Algorithm Selection
Developing learning methods for settings where obtaining labels, running experiments, or collecting computational feedback is expensive.
Uncertainty
Uncertainty Quantification and Statistical Machine Learning
Using uncertainty estimates, surrogate models, and statistical learning methods to support efficient and reliable decision making.
Quantum ML
Quantum Machine Learning and Quantum Optimisation
Exploring quantum optimisation and resource-efficient training methods for quantum machine learning models.
GenAI
Generative AI and Agentic Systems
Developing retrieval-augmented generation, multi-agent, and tool-using AI systems for complex reasoning and decision-support tasks.
AI & Optimisation
AI for Decision Making and Constraint Programming
Developing learning and optimisation methods for complex decision-making and combinatorial search problems.

Publications

JAIR 2026
Frugal Algorithm Selection for Combinatorial Search
Erdem Kus, Lars Kotthoff, Ozgur Akgun, Nguyen Dang, and Ian Miguel
paper | data
CP 2026
On the Effect of Training Data Selection in Automated Algorithm Selection
Erdem Kus, Lars Kotthoff, Ozgur Akgun, Nguyen Dang, and Ian Miguel
paper | data
CP 2024
Frugal Algorithm Selection
Erdem Kus, Ozgur Akgun, Nguyen Dang, and Ian Miguel
paper | data
AutoML 2024
Cost-Efficient Training for Automated Algorithm Selection
Erdem Kus, Ozgur Akgun, Nguyen Dang, and Ian Miguel
paper | data

Industry Experience

Nokia
GenAI Engineer & Quantum Machine Learning Specialist
Developed large-scale enterprise GenAI, retrieval-augmented generation, and agentic AI systems, and led quantum machine learning and optimisation projects.
April 2020 – August 2026
Cliexa
Backend / Artificial Intelligence Engineer
Developed backend services, data-processing pipelines, and machine-learning models for healthcare applications and clinical decision support.
October 2019 – March 2020

Invited Talks

OIST 2026
Frugal Algorithm Selection for Combinatorial Search and its Adaptation to Frugal Training of Noise-Aware Quantum Neural Networks
Okinawa Institute of Science and Technology (OIST), Japan
July 2026
NoBel AI 2023
Quantum Machine Learning, QUBO Formulations and Hamiltonian-Based Optimisation
NoBel AI Community, Nokia Bell Labs
January 2023
NoBel AI 2021
Fair AI and Hybrid Group/Individual Fairness
NoBel AI Community, Nokia Bell Labs
July 2021

Reviewing

2026
AutoML Conference
Conference Reviewer
2025–Present
Information Sciences
Elsevier · Journal Reviewer
2024–Present
IEEE Transactions on Artificial Intelligence
Journal Reviewer
2023–Present
Engineering Applications of Artificial Intelligence
Elsevier · Journal Reviewer

Awards & Funding

Wellcome Trust
InFrame – £3M Multi-University Research Programme
Wellcome Trust-funded research programme involving the Universities of St Andrews, Edinburgh, and Glasgow.
November 2025 – December 2026
University of St Andrews
Handsel Scholarship
Awarded for academic excellence and research potential.
Bahcesehir University
Outstanding Achievement / Academic Excellence Scholarship
Awarded for outstanding academic achievement.