My research interest is in artificial intelligence, where I develop methods based on decision theory, constraint programming, and heuristic search and apply them on agent-based applications such as smart home automation and edge computing systems. The word cloud above has been obtained using Wordle on the abstracts of my papers.
A distributed constraint optimization problem (DCOP) is a problem where multiple agents coordinate with each other to take on values such that the sum of the resulting constraint costs, that are dependent on the values of the agents, is minimal. DCOPs are a popular way of formulating and solving multi-agent coordination problems such as the distributed scheduling of meetings, distributed coordination of unmanned air vehicles and the distributed allocation of targets in sensor networks. Privacy concerns in the scheduling of meetings and the limitation of communication and computation resources of each sensor in a sensor network makes centralized constraint optimization difficult. Therefore, the nature of these applications call for a distributed approach.
In goal recognition problems, the goal is to identify the goal of an observed agent as quickly as possible before they reach their goal. In goal recognition design problems, the objective is to identify the best ways to modify the underlying environment of the agents in such a way that they are forced to reveal their goals as early as possible. These problems can be applied to a number of applications ranging from software personal assistants to intelligent tutoring systems and security applications.
In human-aware planning systems, when the system recommends a plan (e.g., a route from A to B) to a human user, it is often the case that the user might not understand why the recommended plan is good, for example, compared to an alternative plan in the user’s mind. In such a scenario, there is a need for the system to explain its plan to the user, providing them with the necessary information to understand properties of the plan (e.g., optimality, feasibility, etc.).
SMART GRID AND SMART HOMES (archived 2021)
EDGE COMPUTING (archived 2021)
INCREMENTAL SEARCH (archived 2019)
RISK-SENSITIVE PLANNING (archived 2017)
Communication-Aware Distributed Constraint Optimization Problems: Foundations, Models, and Algorithms.
Binational Science Foundation (2019 – 2022).
Anytime Reasoning and Analysis for Kill-Web Negotiation and Instantiation Across Domains (ARAKNID).
Defense Advanced Research Projects Agency (2019 – 2022).
Integrated Computational and Cognitive Workflows for Improved Security and Usability.
RI: Small: Collaborative Research: Preference Elicitation and Device Scheduling for Smart Homes.
National Science Foundation (2018 – 2021).
Mission-oriented Adaptive Placement of Task and Data (MAP).
Defense Advanced Research Projects Agency (2017 – 2021).
CAREER: Decentralized Constraint-based Optimization for Multi-Agent Planning and Coordination.
National Science Foundation (2016 – 2021).
BSF: 2014012: Robust Solutions for Distributed Constraint Optimization Problems.
National Science Foundation (2015 – 2019).
iCREDITS: Interdisciplinary Center of Research Excellence in Design of Intelligent Technologies for Smartgrids.
National Science Foundation (2014 – 2019).
- Early Career Spotlight Talk, International Joint Conference on Artificial Intelligence (IJCAI) (2018)
- CAREER Award, National Science Foundation (2016)
- AI’s 10 to Watch, IEEE Intelligent Systems (2015)
- Outstanding Research Assistant Award, Computer Science Department, University of Southern California (2009)
- Best Student Paper Award Nomination for Caching Schemes for DCOP Search Algorithms, International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2009)