I am a researcher with a strong theoretical basis in artificial intelligence. Specifically, reinforcement learning, combinatorial search, multiagent route assignment, game theory, flow and convex optimization, and multiagent modeling and simulation. I view myself as part of the AI community where my work is highly cited. I strive to further the impact of my applicable expertise for solving real-life problems while simultaneously continuing to make theoretical advances that justify the proposed solutions.
Artificial Intelligence
Autonomous Traffic Management
Network Flow Optimization
Game Theory
Multiagent Systems
Reinforcement Learning
Heuristic Search
Graph-Based Pathfinding
Dr. Guni Sharon is an assistant professor in the department of computer science and engineering (CSE) at Texas A&M University. He received his doctoral, master’s and bachelor’s degrees in information systems engineering from Ben-Gurion University. Dr. Sharon has strong research record in artificial intelligence. Specifically, reinforcement learning, combinatorial search, multiagent route assignment, game theory, flow and convex optimization, and multiagent modeling and simulation. He was recognized on his contributions to these disciplines by receiving the Outstanding Paper Award from the Association for the Advancement of Artificial Intelligence (AAAI) and the Prominent Paper Award from the journal of Artificial Intelligence (AIJ). Dr. Sharon gained vast knowledge and experience in utilizing his theoretical foundations towards traffic management and traffic optimization application. Nonetheless, he sees himself as part of the AI community where his work is highly cited. Dr. Sharon strives to further the impact of his applicable expertise for solving real-life problems while simultaneously continuing to make theoretical advances that justify the proposed solutions. As an educator, Dr. Sharon is committed to training engineers who are highly capable of applying advanced, theoretical AI approaches to real-life applications while instilling them with the responsibility of shaping the technology to be safe and sustainable. Dr. Sharon's curriculum vitae is avilable here.
Texas A&M University, Computer Science and Engineering. Develop and provide academic courses at the graduate and undergraduate levels. Guide, lead and mentor students during classes and research projects. Create, innovate and implement career-enhancement programs and activities. Serve and support functional activities of departmental committees.
University of Texas at Austin, Computer Science. Member in TxDOT 6838 Project titled “Bringing smart transport to Texans: ensuring the benefits of a connected and autonomous transport system in Texas”. Part of a research collaboration between UT-Austin and Toyota, InfoTechnology Center Co., Ltd. Coordinating and leading a bi-weekly project meeting on traffic management including 2 faculty members, 3 post-docs and 2 Ph.D. students.
Ben-Gurion University, Information Systems Engineering; Dean award for outstanding Ph.D student. Thesis title “Novel Search Techniques for Path Finding in Complex Environment”. Awarded the "Darom" Graduate Research Scholarship.
Ben-Gurion University. Led discussions in a class of up to 30 students. Prepared course material including laboratory experiments, lectures, exams, homework, and practice problems. TA for the following courses: Introduction to Operation Systems, Operations Research, Introduction to A.I., Automata and computability Theory.
Ben-Gurion University, Information Systems Engineering; graduation with Honors. Thesis title “Optimal Multiagent Pathfinding”. Awarded the Harbor Foundation Graduate Research Scholarship.
Ben-Gurion University, Information Systems Engineering; graduation with Honors. Winner of the South African Zionist Federation Scholarship.
Israeli Defense Force; Head of the operations department of an artillery brigade. supervised two soldiers.
Department of Computer Science and Engineering, H. R. Bright Building, 3112 TAMU, 710 Ross St, College Station, TX 77843
guni (at) tamu (dot) edu
+(1) 979 845 5498