Hazhar Rahmani

Hi everybody. Welcome to my webpage.
My name is Hazhar Rahmani. I recently graduated from the University of South Carolina, where I was a PhD student in computer science. I received my M.Sc. degree in computer science from the Sharif University of Science and Technology, Iran, 2012, and B.Sc. degree in computer (software) engineering from the Iran University of Science and Technology, Iran, 2008. My dissertation, which was supervised by Prof.Jason O'Kane, focused on automata-theoretic approaches to planning in robotics. I am now a postdoc at the University of Florida and work in Dr. Jie Fu's lab, where we work on developing theories and algorithms for planning and decision-making in robotics and intelligent systems.

Research Interests:

• Planning in Robotics
• Formal Methods in Robotics
• Computational Geometry
• Machine Learning

Publications

• Journal Articles
  • Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane. Planning to Chronicle: Optimal Policies for Narrative Observation of Unpredictable Events. International Journal of Robotics Research, 2022.
  • Hazhar Rahmani, Jason M. O'Kane. Equivalence notions for state space minimization of combinatorial filters. In IEEE Transactions on Robotics, 2021.
  • Hazhar Rahmani, Jason M. O'Kane. Integer linear programming formulations of the filter partitioning minimization problem. In Journal of Combinatorial Optimization, 2020.
• Refereed Conference Papers
  • Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane. Sensor selection for detecting deviations from a planned itinerary. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021.
  • Diptanil Chaudhuri, Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane. Tractable Planning for Coordinated Story Capture: Sequential Stochastic Decoupling. In Proc. International Symposium on Distributed Autonomous Robotic Systems, 2021.
  • Diptanil Chaudhuri, Rhema Ike, Hazhar Rahmani, Aaron T. Becker, Dylan A. Shell, Jason M. O'Kane. Conditioning Style on Substance: Plans for Narrative Observation. In Proc. IEEE International Conference on Robotics and Automation, 2021.
  • Yulin Zhang, Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane. Accelerating combinatorial filter reduction through constraints. In Proc. IEEE International Conference on Robotics and Automation, 2021.
  • Hazhar Rahmani, Jason M. O'Kane. What to Do When You Can't Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020.
  • Hazhar Rahmani, Dylan A. Shell, Jason M. O'Kane. Planning to Chronicle. In Proc. International Workshop on the Algorithmic Foundations of Robotics (WAFR XIV), 2020.
  • Hazhar Rahmani, Jason M. O'Kane. Optimal temporal logic planning with cascading soft constraints. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019.
  • Hazhar Rahmani, Jason M. O'Kane. On the relationship between bisimulation and combinatorial filter reduction. In Proc. IEEE International Conference on Robotics and Automation, 2018.

Research Projects

• State space reduction of combinatorial filters: Combinatorial filters are formal structures used for filtering, reasoning, and planning in systems that deal with discrete sensory data. This project studies exact and approximate solutions to the NP-hard problem of state space minimization of such kind of filters.
• Planning to chronicle: This project focuses on the idea of deploying robots to observe a stochastic process in order to capture a sequence of events that meets specifications given by the user. This idea can be used in applications where we want to use robots to autonomously make a video or a documentary of events that happen unpredictably in an environment. The project proposes mathematical modeling for that purpose and solves in that context, several optimization problems, such as the problem of minimizing the expected time to record a desired video.
• Sensor selection for detecting deviations from a planned itinerary: This project focuses on the problem of choosing an optimal selection from a set of feasible sensors in the environment to ensure that an agent commits to a pre-disclosed itinerary. This problem is useful in security and surveillance applications, and is also applicable in contexts where one wants to test a hypothesis about the behavior of a robot or a system.
• Temporal logic planning: Temporal logics, which offer high-level, user-friendly languages for specifying complex missions and tasks, have become very useful tools for planning in robotics. This project considers temporal logic planning with soft constraints and in the context of using robots to make chronicle of events that happen unpredictably in stochastic environments.

Recent Services

• Journal Review (3), IEEE Transactions on Robotics (T-RO).
• Journal Review (1), IEEE Robotics and Automation Letters (RA-L).
• Conference Review, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan.
• Conference Review, 39th IEEE International Conference on Robotics and Automation (ICRA 2022), Philadelphia, USA.
• Conference Review, 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR XIV), Oulu, Finland.

Awards

• Outstanding student award, Department of Mathematics and Computer Science, Sharif University of Technology, Iran, 2012.