Stergios Plataniotis is a quantitative researcher at Thira Labs, where he develops and tests machine learning algorithms for the firm's quantitative and data-driven projects. His work focuses on bridging cutting-edge academic research in machine learning with practical applications in quantitative finance, bringing the latest methodologies from academia into production ready systems. He holds an MEng in Electrical and Computer Engineering and an MSc in Computer Science and Engineering, both from the Technical University of Crete, Greece. During his graduate studies, he conducted research on efficient exploration and aggregation in Ensemble Deep Reinforcement Learning, resulting in conference publications and ongoing contributions to the field.
Outside of work, he enjoys reading mystery novels, playing board games, and experimenting with different coffee varieties and brewing methods.
1. S. Plataniotis, C. Akasiadis, and G. Chalkiadakis, "Value of Information-Enhanced Exploration in Bootstrapped DQN," Proceedings of the 2025 IEEE International Joint Conference on Neural Networks (IJCNN), Rome, Italy, July 2025.
2. G. Koresis, S. Plataniotis, L. Bakopoulos, C. Akasiadis, and G. Chalkiadakis, "Privacy-Aware Deep RL for Sequential Coalition Formation Decisions under Uncertainty," Proceedings of the 37th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2025.