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Sunberg, Zachary

Assistant Professor

Positions

Research Areas research areas

Research

research overview

  • Zachary Sunberg's research focuses on decision making under uncertainty to enable safe and efficient autonomous vehicle operation. Specifically, his primary current focus is on problems where uncertainty is the most difficult aspect of the problem, primarily focusing on the partially observable Markov decision process (POMDP) and partially observable stochastic game (POSG) formalisms. His most significant contributions are in the area of online POMDP planning.

keywords

  • markov decision processes, partially observable markov decision processes, MDP, POMDP, human robot interaction, self-driving cars, urban air mobility, advanced air mobility, unmanned aerial vehicles, incomplete information games

Publications

selected publications

Teaching

courses taught

  • ASEN 3128 - Aircraft Dynamics
    Primary Instructor - Fall 2022
    Develops the fundamental concepts of aircraft dynamics. Covers flight mechanics, performance, dynamics and control of aircraft and how they impact aircraft design.
  • ASEN 3728 - Aircraft Dynamics
    Primary Instructor - Spring 2024
    Develops the fundamental concepts of aircraft dynamics. Covers flight mechanics, performance, dynamics and control of aircraft and how they impact aircraft design.
  • ASEN 4018 - Senior Projects 1: Design Synthesis
    Primary Instructor - Fall 2020
    Focuses on the synthesis of technical knowledge, project management, design process, leadership, and communications within a team environment. Students progress through the design process beginning with requirements development, then preliminary design and culminating with critical design. Offered fall only.
  • ASEN 4028 - Senior Projects 2: Design Practicum
    Primary Instructor - Spring 2021
    Focuses on the fabrication, integration, verification and validation of designs produced in ASEN 4018. Students work within the same teams from ASEN 4018. Offered spring only.
  • ASEN 5264 - Decision Making under Uncertainty
    Primary Instructor - Spring 2023 / Spring 2024
    Covers algorithms for optimal sequential decision making in the presence of uncertainty. Mathematical formalisms include the Markov decision process (MDP), partially observable Markov decision process (POMDP), and Games. Solution techniques include exact dynamic programming, Monte Carlo tree search, deep reinforcement learning, and alpha vector value approximation for POMDPs. Assignments require programming in a high level language (Julia as of 2023). Previously offered as a special topics course. Recommended prerequisite: fluency in a high-level programming language, willingness to learn another language if required for homework assignments and basic understanding of probability. Same as CSCI 5264.
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