She is a double major student working on her degrees in Computer Science and Psychology. She is a member of the Honors College, the Vice President of the Computer Science Education club, a member of the Retriever Robotics Club, a CWIT affiliate, and the Curriculum Development Coordinator for the Creative Coders Program. She is also a Research Assistant for CS Matters in Maryland and a Research Assistant for the Multi-Agent Planning and Learning (MAPLE) lab. Previously, she was the Secretary of QUMBC. Her future ambitions are to continue researching Artificial Intelligence or a related field and to continue with outreach in computer science education.
Stephanie's research will explore a recently developed method of hierarchical planning: a hierarchy of abstract Markov decision processes (AMDPs). AMDPs seek to address the immensely challenging problems that decision-making agents face when operating in large environments to solve complex tasks. A hierarchy of AMDPs provides a framework for decomposing such problems into distinct, related subtasks. Her previous tasks for research in this area were to design an AMDP hierarchy for a house-building domain and to collaborate with her lab members to research and write a paper combining the R-MAX algorithm with AMDPs. Currently, she is working with her lab members to research and write a paper on constructing and reasoning over composite objects.
Read more about her research here…