Angeline Aguinaldo

I develop formal and computational methods for safe, generalizable decision-making in embodied AI systems. My work focuses on how robots can transfer task strategies across domains and embodiments, and how to specify their behavior so it stays interpretable when conditions change.

Angeline Aguinaldo

About

I am a senior research scientist at the Johns Hopkins University Applied Physics Laboratory, where I work on agentic reasoning, planning, and safe autonomous behavior for AI-controlled systems. I recently completed my Ph.D. in Computer Science at the University of Maryland, College Park, advised by William Regli. My dissertation, Sequential, Hierarchical, and Analogical Plan Transfer in Robotics, received the Larry S. Davis Doctoral Dissertation Award.

My research sits at the intersection of AI planning, knowledge representation, and category theory. I have built formal frameworks for how robots can reuse and adapt task strategies across different domains and embodiments, a problem foundation-model-driven robot agents now face empirically at scale. At JHU APL, I have led applied research on safety-critical autonomous systems, real-time geospatial computer vision for humanitarian disaster response, and LLM-based agent architectures.

News

Research

My work is organized around three threads:

Cross-domain plan transfer.

How can a robot reuse learned task plans across different task domains, levels of abstraction, and embodiments without retraining from scratch? My dissertation developed three categories of plan transfer (sequential, hierarchical, and analogical) and formal methods to validate each, with case studies ranging from a Blocksworld-to-Kitchenworld simulation transfer to Baxter & UR5 machine-tending hardware demonstrations (hierarchical composition) in collaboration with Siemens.

Safe and interpretable autonomous behavior.

How do we specify what autonomous systems should and should not do, in ways that are interpretable, formally analyzable, and robust to changes in the system or environment? At JHU APL I have built formal representations of behavior for safety-critical autonomous systems, most recently in a NASA Technical Memorandum (NASA/TM-20250010771, 2025) with NASA Langley applying category-theoretic systems ontology to aviation autonomy integration.

Applied category theory for AI and robotics.

Category theory is a useful mathematical language for compositionality, knowledge representation, and principled plan composition in AI systems. My work contributes practical methods (categorical representation languages for planning, functorial data migrations for analogical transfer, symmetric monoidal categories for composition) and open-source tools within the AlgebraicJulia ecosystem.

Experience

  • 2017 – now Johns Hopkins University Applied Physics Laboratory, Senior Research Scientist
  • 2018 – 2025 University of Maryland, College Park, Ph.D., Computer Science
  • 2022 – 2023 Topos Institute, Research Associate
  • 2012 – 2017 Drexel University, B.S. & M.S., Engineering

Awards & Honors

  • 2025 Larry S. Davis Doctoral Dissertation Award · UMD Department of Computer Science
  • 2023 Best Paper Award · AAAI Fall Symposium on Unifying Representations for Robot Application Development
  • 2022 Special Achievement Award · Johns Hopkins University Applied Physics Laboratory
  • 2020 Special Achievement Award · Johns Hopkins University Applied Physics Laboratory
  • 2019 Special Achievement Award · Johns Hopkins University Applied Physics Laboratory

Dissertation

Aguinaldo, A. Sequential, Hierarchical, and Analogical Plan Transfer in Robotics. Ph.D. Dissertation, University of Maryland, College Park, 2025. Advisor: William Regli.

Full text (DRUM) Defense slides

Publications

Peer-Reviewed Articles

  1. Aguinaldo, A., Patterson, E., Regli, W. Automating Transfer of Robot Task Plans Using Functorial Data Migrations. IEEE Transactions on Automation Science and Engineering, 2025. paper
  2. Aguinaldo, A., Patterson, E., Fairbanks, J., Regli, W., Ruiz, J. A Categorical Representation Language and Computational System for Knowledge-Based Planning. AAAI Fall Symposium on Unifying Representations for Robot Application Development (URRAD), 2023.Best Paper paper slides
  3. Player, R. A., Aguinaldo, A., Merritt, B. B., et al. The META Tool Optimizes Metagenomic Analyses Across Sequencing Platforms and Classifiers. Frontiers in Bioinformatics, 2:969247, 2023. paper
  4. Aguinaldo, A., Bunker, J., Pollard, B., Shukla, A., Canedo, A., Quiros, G., Regli, W. RoboCat: A Category Theoretic Framework for Robotic Interoperability Using Goal-Oriented Programming. IEEE Transactions on Automation Science and Engineering, 2021. Demonstrated on Baxter & UR5 robotic arms in a machine-tending scenario (Siemens collaboration). paper
  5. Winter, M., Mankowski, W., Wait, E., De La Hoz, E. C., Aguinaldo, A., Cohen, A. R. Separating Touching Cells Using Pixel Replicated Elliptical Shape Models. IEEE Transactions on Medical Imaging, 2019. paper
  6. Caino, M., Seo, J., Aguinaldo, A., et al. A neuronal network of mitochondrial dynamics regulates metastasis. Nature Communications, 2016. paper

Technical Reports

  1. Mahlum, M., Jarvis, S., Niu, N., Aguinaldo, A., Hicks, A., Levitt, I. Formal Structures in Systems Ontology towards Air Traffic Management Architectures. NASA Technical Memorandum NASA/TM-20250010771, November 2025. paper

Workshop Papers & Preprints

  1. Aguinaldo, A., Regli, W. Modeling traceability, change information, and synthesis in autonomous system design using symmetric delta lenses. ICRA Compositional Robotics Workshop, 2022. paper
  2. Aguinaldo, A., Regli, W. Encoding Compositionality in Classical Planning Solutions. IJCAI Workshop on Generalization in Planning, 2021. paper
  3. Aguinaldo, A., Regli, W. A Graphical Model-Based Representation for Classical AI Plans using Category Theory. ICAPS Workshop on Explainable AI Planning, 2021. paper
  4. Aguinaldo, A., Chiang, P., Gain, A., Patil, A., Pearson, K., Feizi, S. Compressing GANs using Knowledge Distillation. arXiv preprint, 2019. paper

Books

  1. Co-author. Relational Thinking: From Abstractions to Applications. Digital textbook, Topos Institute, 2024. read online

Selected Talks

  • 2025 Analogical Plan Transfer in Robotics using Functorial Data Migrations. AMS Joint Mathematics Meetings, Applied Category Theory Special Session. slides
  • 2024 "What's the plan?", asked my robot. JuliaCon. video slides
  • 2023 A Category Theoretic Approach to Planning in a Complex World. Microsoft Future Leaders in Robotics and AI Seminar Series. video
  • 2022 Category theory for automated planning and program compilation in robotics. Topos Institute Berkeley Seminar. video
  • 2022 Contextual affordances in context-aware autonomous systems. NIST Compositional Structures for Systems Engineering and Design Workshop.
  • 2022 Modeling traceability, change information, and synthesis in autonomous system design using symmetric delta lenses. ICRA Compositional Robotics Workshop.
  • 2022 Applications of category theory to automated planning and program compilation in robotics. Xerox PARC Design Seminar.

Full list of talks on the CV.

Writing

Software

AlgebraicJulia: open-source contributor to the ecosystem of Julia packages for applied category theory, including Catlab.jl and AlgebraicRewriting.jl, used for categorical databases, scientific computing, and structured knowledge representation.

string-diagram-viewer: interactive tool for visualizing string diagrams from symmetric monoidal categories, developed during the dissertation work on sequential plan composition.

Additional code on GitHub.

Service & Community

  • 2026Co-organizer, Applied Category Theory Special Session, AMS Joint Mathematics Meetings.
  • 2024 – 26Seminar Series Chair, Agentic AI and Human-AI Collaboration, Johns Hopkins University Applied Physics Laboratory.
  • 2023Local Organizer, Applied Category Theory (ACT) Conference.
  • 2023Co-organizer, ICRA Compositional Robotics: Mathematics and Tools Workshop.
  • 2022, 2023Co-organizer, The Adjoint School.
  • 2021Student, The Adjoint School.
  • ongoingReviewer, International Conference on Automated Planning and Scheduling (ICAPS).
  • ongoingReviewer, Mathematical Structures in Computer Science

In the Media

  • 2025 UMD Computer Science · Larry S. Davis Doctoral Dissertation Award recipient.
  • 2023 UMD Computer Science · Best Paper Award at AAAI Fall Symposium.
  • 2020 JHU APL · HADR program named a 2020 Fast Company World Changing Ideas Finalist in the AI & Data category.
  • 2019 JHU APL · APL Shaping an Intelligent Approach to Disaster Response and Relief.