The central thesis that drives my research is “agriculture as socio-ecological complex systems.” To help address challenging problems such as food security and economically & environmentally sustainable agricultue, I train myself as a computational modeler in economics, machine learning and mathematics in general. Recently, I focus on leveraging the emerging data streams in precision agriculture for optimizing site-specific management.


I have been very fortunate to receive supportive and constructive advice from two distinct academics at UW-Madison:

Bayesian optimization

An optimization technique with two appealing features: sample efficiency and flexibility for complex objective functions. It has a lot of potential for applications to agricultural production, which usually takes time due to the seasonal production cycle. In other words, evaluating an agricultural production function or input response function is expensive, and therefore Bayesian optimizaton is a cost-efficient technique to identify good input combinations for profitability.

Bayesian optimization is still a novel technique in most scientific disciplines. The following videos may give you some idea.

Agent-based modeling

Agent-based modeling has become one of the standard approaches to studying complex systems and emergent behavior. In agent-based models, an observed macroscopic phenomenon emerges as a result of interaction among heterogeneous agents in a dynamically evolving environment. Agents typically follow simple decision rules and influence each other either directly or indirectly through the environment, which itself evolves according to its own rules and agent actions. Because the processes being explicitly modeled are complex, researchers use computer simulations to examine outcomes over a wide range of parameter values. In short, agent-based models are laboratory experiments conducted in silico. The idea is nicely captured by the following video (though we usually don’t have physical robot agents).