Research Highlights

TradeMaster, a first-of-its-kind reinforcement learning based quantitative trading toolkit aims to help financial institutions develop, train, and evaluate reinforcement learning based quantitative trading  algorithms  The integrated TradeMaster platform features reinforcement learning based quantitative trading  algorithms and aims to deliver a high-quality, cost-effective, and easy-to-use Fintech toolkit, which is highly beneficial to financial institutions. It aims to achieve economies of scale by becoming an industry-wide platform and help to translate basic research into real-world applications.  

Funded by National Research Foundation (NRF)’s Smart Systems Strategic Research Programme, Industry Alignment Fund – Pre-positioning (IAF-PP) Funding Initiative

Project Period: July 2022 - June 2025

Artificial Intelligence and data science are at the core of Brain-computer interface technologies. In this project, we are developing deep learning algorithms to accurately decode motor, cognition, and emotion from the brain signals via various learning strategies. We will develop new digital health solutions for mental health and brain sciences based on these deep learning models as a holistic medtech solution for restoring and enhancing motor, cognitive, and emotional functions in one unified system. By collaborating with hospitals, we will also investigate the neural mechanism and biomarkers of brain function recovery.

Supported by RIE2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund

Project Period: October 2020 - October 2025