Rising Stars 2020:

Mahsa Ghasemi

PhD Candidate

University of Texas at Austin

Areas of Interest

  • Artificial Intelligence
  • Control, Intelligent Systems, and Robotics


Efficient Data Processing and Trustworthy Decision Making through Structured Task Representation


The recent breakthroughs in the design of efficient data processing techniques indicate the possibility of integrating potentially large-scale and heterogeneous data into decision making. Nevertheless, such efficiency is frequently put at odds with the interpretability and the trustworthiness of the decision making process. Therefore, it is challenging to design autonomous agents that can efficiently process data and still provide performance guarantees. In this talk, I focus on simultaneous perception and planning for autonomous agents operating with uncertain dynamics and in partially known environments. In this setting, I argue that a pivotal factor in overcoming the said challenge is through structured task representations. In particular, I present our recent results on utilizing temporal logic task specifications as a bridge between perception and planning. We exploit the rich structure of temporal logic specifications to develop task-oriented active perception strategies. Furthermore, by taking the dynamics uncertainties and the evolving perception uncertainties into account, we establish high-probability performance guarantees that hold at runtime.


Mahsa Ghasemi is pursuing a Ph.D. in Electrical and Computer Engineering Department at the University of Texas at Austin under the supervision of Prof. Ufuk Topcu. She received her M.S.E. degree in Mechanical Engineering from the University of Texas at Austin, and her B.Sc. degree in Mechanical Engineering from Sharif University of Technology. Her research objective is to develop theory and algorithms for task-oriented knowledge acquisition and decision-making. In particular, she focuses on 1) active identification and gathering of actionable information from large-scale, multi-modal, and noisy data, 2) fusion of a priori and real-time information while providing quantifiable measures of uncertainty in knowledge, and 3) planning and learning based on partial and evolving knowledge, in real-time and with theoretical performance guarantees.

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