Our research pervades diverse areas in the intersection of Computer Science and other disciplines. Much of our work is in Multiagent Systems, at the intersection of Computer Science and Economics (especially Game Theory). A particularly important and prevalent theme of research in the lab is applying game theory to a broad array of problems, such as optimal security resource allocation, robust machine learning, risk analysis of health data de-identification strategies, and game theoretic design of broadly neutralizing antibodies.

A brief overview of current projects can be found below:

Adversarial machine learning: Machine learning has come to be widely used, often in settings where one must discern malicious behavior from benign (or normal). Since such settings often involve malicious attackers who may attempt to subvert learning, our research investigates the design of machine learning algorithms that are robust to such subversion. A common type of adversarial attacks on learning algorithms are evasion attacks, to which we have thus far devoted much attention. Our work includes the design of algorithms that are robust to a large class of evasion attacks, a principled means to add randomization to classifiers to further increase robustness, and an experimental investigation of how humans actually evade a classifier-based spam filter.

Incident prediction and response: We have been fortunate to obtain high quality crime incident and police patrol data, which we are now using to develop crime forecasting and police patrol algorithms. We are also working to optimally allocate ambulances to minimize response times to traffic accidents.

Game theory and privacy: In collaboration with Brad Malin’s lab, I am involved in several efforts to develop a principled means of privacy risk analysis in data sharing settings, using game theory to reason about adversary’s behavior. In the context of privacy, adversary’s goal generally involves re-identification of published (and de-identified) data.

Game theory and security: A significant portion of our research falls into this rubric. We have in the past developed methods for plan interdiction as a way to proactively account for attacker circumvention of defensive techniques. We are also interested in understanding and modeling both attacker and computer user behavior in the context of cyber security.

Vaccination: We are applying insights from my past and current work at the intersection of security and game theory to develop new methods for designing vaccines. In particular, we are viewing pathogens (such as viruses) as adversaries who aim to evade vaccination (specifically, antibodies that are elicited by vaccination). Our goal is to design vaccines (antibodies) which are broadly neutralizing, not only with respect to known pathogen types, but also against “low-effort” pathogen mutations from these.

Security design in networked settings: There has been considerable recent literature on designing randomized security strategies in a variety of security domains, such as airport screening and federal air marshall schedules. The growing field of network science has made us aware that many settings can be well modeled using networks. In the context of security, a network carries decision externalities, that is, decisions at network nodes have consequences for other nodes to which they are connected (possibly indirectly). In this project, we focus on developing models to analyze and compute (or approximate) security strategies in such networked settings.

Decentralized decision making in complex systems:Several foundational models of complex systems have been proposed in the literature, the most prominent of which are SOC (self-organized criticality) and HOT (highly optimized tolerance). The SOC model invokes fixed rules embedded in entities in the complex system such that the complex interactions of such rules yield interesting emergent behavior that has properties of critical transition boundaries observed in numerous physical phenomena. The HOT paradigm paints a complex system as a product of optimizing behavior (possibly heuristic). Our model, NOT, conceives of a complex systems as a complex interaction of many decision making entities, each seeking to maximize its selfish gain, accounting for the decisions of others. Our model is thus fundamentally game theoretic, and yields interesting insights about the nature of complex systems, positive impact of negative externalities, and the evolution of cooperation.

Analysis of behavioral experiments on networks:Decentralized coordination has a long history, although only recently has it been studied in the context in which coordination must take place on a network (e.g., social network). This work analyzes a set of human subject experiments in networked decentralized coordination, showing that (a) the actual coordination problem has much to do with the ability of agents to coordinate, (b) behavioral features can have considerable relationship to agent influence, and (c) simple simulation models calibrated on behavioral data replicate the qualitative nature of these and other findings in networked coordination experiments.

Simulation-based and data-driven models of human dynamic behavior on networks: One of the core research problems that we are interested in is predicting behavior of autonomous agents (software or human). This is, in fact, a central question of policy-making and mechanism design: given a particular policy/design choice, how will agents react? We have made some progress in the context where agents are essentially game-theoretic (for example, by design). Our current thrust is to model human behavior based on data collected in experiments or in the field (for example, making use of the data set of solar PV adoption in San Diego county).