prof brrrr

dicksonb [at] iu [dot] edu

PhD student in computer science at Indiana University with Zoran Tiganj.

Research Interests:

  • Long-context modeling, efficient attention, state-space models, hybrid models.
  • Evaluating multimodal large language model capabilities.

Applied Interests:

  • NLP pipelines for adverse drug event detection and analysis.
  • RAGs, knowledge graphs, ontologies, agents.

Previously:

I did my master’s in computational linguistics at Indiana University with Damir Cavar in the NLP Lab working on time and event reasoning, and applied NLP with knowledge graphs, ontologies, LLMs, and RAGs, and did my bachelor’s at Michigan State University where I studied linguistics, TESOL, Chinese, and Korean, and did a summer language program at Harbin Institute of Technology.

Posters & Publications:

Dickson, B., Mochizuki-Freeman, J., Kabir, MR., & Tiganj, Z. (in press). Time-local Transformer. Computational Brain and Behavior.

[pdf][code][website] Dickson, B., Maini, S. S., Sanders, C., Nosofsky, R., & Tiganj, Z. Comparing Perceptual Judgments in Large Multimodal Models and Humans. Behavior Research Methods 57, 203 (2025). https://doi.org/10.3758/s13428-025-02728-w

[pdf] Davis, A., Dickson, B., Cavar, D., Valdez, D., & Tyers, F. (2025). Advancing Adverse Drug Event Detection in Social Media Through Knowledge Graph and GraphRAG LLM Architectures [Poster presentation]. American Academy of Health Behavior (AAHB), San Diego, CA, United States.

[pdf] Davis, A., Dickson, B., & Kubler, S. (2024). A Two-Stage NLP System for Extracting and Normalizing Adverse Drug Events from Tweets. In Proceedings of the 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 117–120, Bangkok, Thailand. Association for Computational Linguistics.

[pdf] Cavar, D., Tiganj, Z., Mompelat, L. V., & Dickson, B. Computing Ellipsis Constructions: Comparing Classical NLP and LLM Approaches. Society for Computation in Linguistics 7(1), 217–226 (2024). https://doi.org/10.7275/scil.2147

[pdf] Dickson, B., Maini, S. S., & Tiganj, Z. (2024). Comparing LLMs and Cognitive Models of Memory [Poster presentation]. Midwest Speech & Language Days, Ann Arbor, MI, United States.

[pdf] Cavar, D., Aljubailan, A., Mompelat, L., Won, Y., Dickson, B., Fort, M., Davis, A., & Kim, S. (2022). Event sequencing annotation with TIE-ML. In Proceedings of the 18th Joint ACL - ISO Workshop on Interoperable Semantic Annotation within LREC2022 (pp. 33–41). Marseille, France: European Language Resources Association.

[pdf] Cavar, D., Dickson, B., Aljubailan, A., & Kim, S. (2021). Temporal Information and Event Markup Language: TIE-ML Markup Process and Schema Version 1.0. In Proceedings of the 15th International Conference on Advances in Semantic Processing (SEMAPRO 2021), pages 29-36, Barcelona, Spain.

Teaching:

Summer 2024, Generative AI and Symbolic Knowledge Representations: Large Language Models, Knowledge, and Reasoning (ESSLLI 2024, Leuven, Belgium)

Spring 2022, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Assistant Instructor, Graduate Data Mining (Indiana University)

Spring 2020, Adult Communicative Focused English (Michigan State University)

Education:

PhD, Computer Science, Cognitive Science, Indiana University, ~2027

MS, Computer Science, Indiana University, 2025

MS, Computational Linguistics, Indiana University, 2022

BA, Linguistics, Michigan State University, 2020