I work across business transformation, AI, data, and Web3 product development. As an independent consultant and product manager, I help teams improve processes, build practical systems, and turn ideas into working products. My Web3 product work also involves proprietary AI research, and I deliver data and machine-learning training.
My work sits between business needs and technical delivery: understanding how a team works, identifying what can be improved, and shaping solutions that people can actually use. Depending on the problem, that may involve process redesign, product management, automation, data systems, applied AI, or Web3 technology.
Alongside consulting and product work, I am a research assistant at Monash University Malaysia. My research covers mathematical modelling, multi-agent reinforcement learning, market microstructure, quantitative finance, and environmental systems.
Start Here
- Bio for my professional background and current areas of work.
- Notes for working explanations, derivations, and technical clarifications.
- Blog for essays, event write-ups, and less formal pieces.
Current Work
- Business transformation, process improvement, and practical systems
- AI, data, and Web3 product development
- Data and machine-learning training
- Information design and credit assignment in multi-agent cooperation
- Optimal execution with predictive alpha signals
Selected Publications
Abstract
A full-factorial experiment in the Public Goods Game shows that information regime and incentive strength explain 85.8% of cooperation-rate variance; algorithm choice accounts for just 3.8%. Agents with the least information cooperate most (83% vs 42% under full observation), attributable to state-space compression. TreeSHAP and Shapley-variance decomposition confirm information structure, not algorithm selection is the primary design lever for cooperation.
Abstract
The execution of large portfolio transactions requires balancing market impact and adverse price drift. The Almgren-Chriss (2001) framework provides a meanvariance trade-off for martingale price processes, but practitioners often utilize short-term alpha signals. This paper re-evaluates the optimal liquidation problem using Stochastic Optimal Control. By incorporating a mean-reverting alpha signal into the price dynamics, we derive a closed-form solution using the Hamilton-Jacobi-Bellman (HJB) equation. The resulting optimal trading rate is an affine function of the current inventory and the predictive signal. This results in a trajectory that adjusts execution speed to capture transient alpha. This work provides a transparent and additive framework for institutional execution desks.
Cite
@misc{lai2026execution,
title = {Optimal Execution with Alpha Signals},
author = {Lai, Jien Weng},
year = {2026},
howpublished = {SSRN Working Paper},
doi = {10.2139/ssrn.6323159},
url = {https://ssrn.com/abstract=6323159}
}
Pet Projects
Chains AI generation stages together so a whole video series can be produced from start to finish with very little manual work in between. Still rough.
A CRM for internal use at Quandatics. Off-the-shelf tools did not fit how they work, so this one is built around their actual workflow.
A hardware build: a small satellite and its ground station. Mostly assembly and getting the radio link to behave.
A fork of kepler.gl, tied to Monash work, for geospatial analysis and open-data sharing around the Klang Valley. Still in progress.