Summary
Shi Tang is a data scientist with 11 years’ experience and a PhD in computer science from NTU, blending deep academic research in reinforcement learning and generative models with hands-on ML engineering in finance and Web3. At UOB he helped build award-winning deep learning forecasting and portfolio-optimization models for a $200M AUM mandate that produced measurable economic value and outperformed benchmarks by ~200bps. His doctoral work on sample-efficient RL and GAN-based policy distributions has been published in top-tier conferences, and he has translated that research into production-ready backtesting frameworks and modular agent simulation. Previously he reported directly to the CEO of a German Web3 startup, driving blockchain data mining, analytics and ML-driven strategic insights across the NFT ecosystem. Known for bridging rigorous research with practical impact, he’s equally comfortable designing advanced algorithms and shipping data pipelines that influence corporate investment decisions.
11 years of coding experience
10 years of employment as a software developer
Australian National University
Doctor of Philosophy, School of Computer Science and Engineering, Artificial Intelligence, CAP/GPA: 4.88/5.00, Doctor of Philosophy, School of Computer Science and Engineering, Artificial Intelligence, CAP/GPA: 4.88/5.00 at Nanyang Technological University
Master of Technology - MTech (Knowledge Engineering), Artificial Intelligence, CAP/GPA: 4.79/5.00, Master of Technology - MTech (Knowledge Engineering), Artificial Intelligence, CAP/GPA: 4.79/5.00 at National University of Singapore
English, Chinese, Malay, Japanese