I just received my Ph.D. in Computer Science from the University of Maryland, College Park, where I was affiliated with the CLIP Lab. My main source of funding is through my research assistantship at the Army Research Lab’s Content Understanding Branch. I was co-advised by Rachel Rudinger of UMCP and Claire Bonial of the Army Research Lab. I have also received substantial guidance from Frank Ferraro of UMBC.

Research Interests

Overall, I am interested in understanding and improving grounded LLM reasoning using established linguistic concepts. I am currently focused on developing physical common sense in LLMs for robots to understanding natural language instructions during disaster work.

The main questions I am investigating in my current research are:

1. What level of reasoning do Large Language Models have when it comes to object-based functionality?
2. How can we improve smaller Large Language Models for computationally constrained scenarios?
3. How can we develop LLMs that integrate specific technical knowledge with common sense reasoning?

I have expertise in

• developing ontologies for describing object functionality and state of being
• creating annotator pipelines based on expert-in-the-loop feedback
• developing synthetic datasets with LLMs
• Fine-tuning and evaluating LLMs on low-resource scenarios and tasks

Non-research facts about Mollie

1. I am very good at baking.
2. I am a life-long piano student.
3. I love reading science fiction, especially books written by women and people of color (but also Dune)