A domain-theoretic approach to Brownian motion and general continuous stochastic processes. - Paul Bilokon; Abbas Edalat (Theor. Comput. Sci., 2017)

Identification and validation of Triamcinolone and Gallopamil as treatments for early COVID-19 via an in silico repurposing pipeline. - Méabh MacMahon; Woochang Hwang; Soorin Yim; Eoghan MacMahon; Alexandre Abraham; Justin Barton; Mukunthan Tharmakulasingam; Paul Bilokon; Vasanthi Priyadarshini Gaddi; Namshik Han (CoRR, 2021)

Benchmarking Specialized Databases for High-frequency Data. - Fazl Barez; Paul Bilokon; Ruijie Xiong (CoRR, 2023)

Exploring the Advantages of Transformers for High-Frequency Trading. - Fazl Barez; Paul Bilokon; Arthur Gervais; Nikita Lisitsyn (CoRR, 2023)

C++ Design Patterns for Low-latency Applications Including High-frequency Trading. - Paul Bilokon; Burak Gunduz (CoRR, 2023)

A compendium of data sources for data science, machine learning, and artificial intelligence. - Paul Bilokon; Oleksandr Bilokon; Saeed Amen (CoRR, 2023)

Applying Deep Learning to Calibrate Stochastic Volatility Models. - Abir Sridi; Paul Bilokon (CoRR, 2023)

Transformers versus LSTMs for electronic trading. - Paul Bilokon; Yitao Qiu (CoRR, 2023)

Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading. - Koti S. Jaddu; Paul Alexander Bilokon (CoRR, 2023)

From Deep Filtering to Deep Econometrics. - Robert Stok; Paul Bilokon (CoRR, 2023)

Evolution-Bootstrapped Simulation: Artificial or Human Intelligence: Which Came First? - Paul Alexander Bilokon (CoRR, 2024)

Semi-static conditions in low-latency C++ for high frequency trading: Better than branch prediction hints. - Paul Alexander Bilokon; Maximilian Lucuta; Erez Shermer (J. Parallel Distributed Comput., 2025)