Research Topics

I am broadly interested in multi-agent systems, game theory, online learning and decision theory. My research has been primarily driven by the following intriguing question:

How can we design effective incentive mechanisms/information structures/learning algorithms to help stakeholders make better decisions in the presence of uncertainty and strategic behavior?

I believe solutions to this problem can help us gain insights into how to tackle many societal challenges that include traffic congestion, climate change, misinformation propagation and cyber attacks.

Research Projects

Mechanism Design with Thresholding Agents (2016 – ongoing)
In many real-world scenarios, individual agents’ interests are often not fully aligned, in fact, they can even be conflicting with a principal’s objectives. The principal needs to take measures to influence agents’ decisions or behavior to achieve desirable system-wide outcomes. A powerful tool for motivating self-interested agents to cooperate is to offer incentives for their efforts (e.g. cooperation or sacrifices) by committing to some allocation and payment rules. This approach to implementing the incentive rules is called mechanism design. Despite its promising prospects in addressing some of the most challenging societal issues, mechanism design has not leveraged its full potential due to assumptions such as full rationality, direct preference revelation, and no group manipulation. In this project, we introduce a unified framework called mechanism design with thresholding agents (MDTA) to relax some of those unrealisticassumptions. The proposed approach integrates a series of new techniques that include modeling agents’ decision-making with cutoff policies, indirect preference revelation, and using contests to increase competition among agents to counter group manipulations. Our work extends traditional mechanism design by providing a systematic approach to influencing agents’ behavior for desirable objectives.


Monitoring and Predictive Maintenance of Buildings and Building Systems (2014 – 2018)
Fault detection and diagnosis (FDD) for heating, ventilation and air conditioning (HVAC) equipment significantly impact energy consumption in both residential and commercial buildings. In this research, we integrates tradditional model-based methods and data-driven approaches to help accurately predict and diagonize faults in building systems.


Regulating Systems of Autonomous Machines for Social Good (2011 – 2017)
Systems of independent autonomous machines or agents, are rapidly becoming an important part of much of the world’s critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in ensuring that autonomous machine systems meet societal objectives. However, to date, a careful study of interactions between a regulator and autonomous machine systems is lacking. In this project, we report a series of user studies that give insights into how to design systems that allow people, acting as the regulatory authority, to effectively interact with autonomous machine systems.