Research in the SODALab @UofA focuses on developing new algorithms and markets in the general field of optimization and decision-making under uncertainty using mathematical tools from computer science, economics, operations research, and control. In particular, we study the interplay between algorithms (for optimization), learning (for prediction), and incentives (for modeling strategic behaviors) in online decision-making, aiming to create a common framework to optimize decision-making under different types of uncertainty (e.g., online data inputs and strategic agent behaviors).

On the practical side, our research is primarily driven by applications at the interface of multi-agent systems, economics and computation (e.g., auctions; mechanism design; algorithmic game theory; resource allocation and pricing; network economics). Examples of some recent applications include combinatorial auctions for cloud resource allocation, online mechanisms for electric vehicle charging, aggregation of demand-side flexibility in electricity markets, and real-time pricing for transactive energy control.

For more details, see our selected recent publications below.