Guni SharonPiStar is a research laboratory directed by Dr. Guni Sharon and housed within the Computer Science and Engineering (CSCE) department at Texas A&M University. Established with a steadfast commitment to pioneering advancements in artificial intelligence (AI), PiStar's mission is twofold: to deepen theoretical insights into AI paradigms and to drive the practical applicability of AI solutions to real-world challenges.

At PiStar, a diverse team of researchers, including faculty, graduate students, and collaborators, converge to explore the frontier of AI. The lab's research endeavors encompass a broad spectrum of AI domains, with a primary focus on theoretical developments and their translation into practical solutions.

Core research areas at PiStar include:

  • Reinforcement Learning: Investigating algorithms and methodologies to enable autonomous agents to learn and adapt through interactions with environments, paving the way for robust decision-making in dynamic settings.
  • Combinatorial Search: Delving into the complexities of search algorithms and optimization techniques to tackle combinatorial problems efficiently, with applications spanning from logistics to scheduling.
  • Intelligent Transportation Systems: Harnessing AI techniques to optimize transportation networks, enhance traffic management, and improve overall system efficiency and safety.
  • Multiagent Pathfinding: Developing algorithms and strategies to facilitate the coordination and navigation of multiple agents in complex environments, such as robotics and gaming.
  • Algorithmic Game Theory: Exploring the intersection of algorithms and game theory to analyze strategic interactions in various scenarios, including auctions, resource allocation, and mechanism design.
  • Flow and Convex Optimization: Advancing methods for optimizing flow networks and convex problems, with implications for diverse domains like supply chain management, telecommunications, and energy systems.
  • Multiagent Modeling and Simulation: Constructing models and simulation environments to study the emergent behavior of complex systems involving multiple interacting agents, informing decision-making and policy design.

Through rigorous inquiry and innovation, PiStar strives to not only push the boundaries of AI research but also to catalyze the adoption of AI solutions for addressing pressing societal and industrial challenges. By bridging the gap between theory and practice, PiStar envisions a future where AI not only demonstrates remarkable intelligence but also delivers tangible benefits to individuals and communities worldwide.