Machine Learning, AI, Computational Linguistics, and Information Retrieval

Developing methods that allow computers to perform learned tasks autonomously, creating practical solutions for human needs.

Research Projects

Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES)
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
Computational Thinking to Unlock the Japanese American WWII Camp Experience
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Exploring the legacy of WWII Japanese American Incarceration through computational archival science approaches.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.

Recent News

Screenshot of Jeopardy Masters host Ken Jennings

Jeopardy: UMD’s Jordan Boyd-Graber Receives Shout-Out on Jeopardy Masters

INFO affiliate faculty Jordan Boyd-Graber mentioned by Jeopardy contestant during show
SoDa Symposium over a yellow background.

(Video) SoDa Symposium Seed Grant Series: How Can Large Language Models Help Us Identify and Use Constructs That We Can Trust?

Computational linguistics experts speak on a new approach to large language models