Research

Research Interest

Artificial Intelligence, Computational Game-theory, Mechanism Design, Machine Learning, Spatio-temporal Analysis, Large-scale and Robust Optimization, Security and Sustainability

Overview of Research

My research interests lie in the area of artificial intelligence, focusing on the intersection of computational game theory and machine learning, with strong connections to robust and large-scale optimization. Aiming to address real-world challenges in critical domains such as security, sustainability, and mobility, my work not only has strong theoretical contributions but also has led to applications that have fundamentally altered current practices in the domains I have worked in. Along the way, I have actively collaborated with researchers and practitioners in diverse disciplines, including computer science, operations research, psychology, economics, criminology, conservation biology and ecology.

My work has been featured in media including:

Projects

Detect Illegal Mining Sites from Satellite Imagery

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Protect Wildlife from Poaching

Poaching of endangered species is reaching critical levels as the populations of these species plummet to unsustainable numbers. The global tiger population, for example, has dropped over 95% from the start of the 1900s and has resulted in three out of nine species extinctions. Depending on the area and animals poached, motivations for poaching range from profit to sustenance, with the former being more common when profitable species such as tigers, elephants, and rhinos are the targets.

To counter poaching efforts and to rebuild the species’ populations, countries have set up protected wildlife reserves and conservation agencies tasked with defending these large reserves. Because of the size of the reserves and the common lack of law enforcement resources, conservation agencies are at a significant disadvantage when it comes to deterring and capturing poachers. Agencies use patrolling as a primary method of securing the park. Due to their limited resources, however, patrol managers must carefully create patrols that account for many different variables (e.g., limited patrol units to send out, multiple locations that poachers can attack at varying distances to the outpost).

PAWS takes basic information about the protected area and information about previous patrolling and poaching activities as input, and generates patrol routes as output. As the patrollers execute the patrol routes, more poaching data will be collected, and feed back to PAWS. The core algorithm of PAWS integrates learning poachers’ behavior model, game-theoretic reasoning and route planning. More specifically, PAWS learns the behavior models of the poachers from the crime data collected. Based on the poachers’ behavior model, PAWS calculates a randomized patrolling strategy, in the form of a set of patrol routes and the probabilities of taking each route. PAWS then suggests patrol routes sampled from this strategy to the patrollers.

A preliminary field test of PAWS was conducted in Uganda’s Queen Elizabeth National Park (QENP) in April 2014. PAWS patrols were outputted onto a GPS unit as a series of waypoints. Using the set of waypoints on the GPS as a directional guide, wildlife rangers executed their patrol and searched for signs of illegal activity.

[Check this project at CMU – Societal Computing Program website]

Patrol Strategies for Protecting Ferries

ferryescort

This project focuses on designing patrol strategies to protect the ferries that are moving between terminals with fixed schedules. Packed with hundreds of passengers, these may present attractive targets to attack (e.g., with a small boat packed with explosives that may be only detected once it gets close to the ferry). Small, fast patrol boats can provide protection to such ferries, but there are often limited numbers of patrol boats, i.e., they cannot protect the ferries at all times at all locations.

I propose a new game model for this problem with a discretized strategy space for the defender and a continuous strategy space for the attacker, and provided an efficient linear program-based solution that uses a compact representation for the defender’s mixed strategy, while accurately modeling the attacker’s continuous strategy using a novel sub-interval analysis method. I also propose heuristic methods of equilibrium refinement for improved robustness. This work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since 2013 and fundamentally altering previously used tactics.

[Check this project at CMU – Societal Computing Program website]

Patrol Strategies for Protecting Forest Land

forestpatrol

Illegal extraction of fuelwood or other natural resources from forests is a problem confronted by officials in
many developing countries. We focus on finding the best patrol strategy which distributes the patrols throughout the forest,
in space and time, in order to minimize the resulting amount of extraction that occurs or maximize the degree of forest protection.
[Check the project website]

We build a game model for this problem where the actions of adversary are taken over continuous space (due to the continous forest land) and provide an algorithm computing the optimal distribution of patrol effort.

Computational Game Theory for Protecting Fisheries

fish

Illegal, unreported, and unregulated (IUU) fishing is one of the major threats to the sustainability of ocean fish resources. It is impossible to maintain a 24/7 presence to prevent IUU fishing everywhere due to the limited asset patrolling resources. Hence the allocation of the patrolling resources becomes a key challenge for security agencies like USCG. Research within fishery protection project aims to address the problem of deriving accurate patrol schedules for the US Coast Guard. [Check the project website]

In our work, we focus on robust protection of fisheries against unknown a population of heterogeneous Lanchas of unknown type who fish frequently, and we have very little data about these Lanchas in general.

Graph Game

game_network
This is a part of the research project Algorithmic Experimental Game Theory.

We designed the graph game to simulate security game. Here is the example game. We ask human agents to play the game, help us understand how human behavior in a simple target select game,
and try to design better strategy to protect the facilities against real attackers.
Checkout performance here.