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
CAREER: Self-Directed Human-LLM Coordination for Language Learning and Information Seeking
Principal Investigator(s): Ge Gao
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Health Informatics > Human-Computer Interaction > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Principal Investigator(s): Ge Gao
Funder: National Science Foundation
Research Areas: Accessibility and Inclusive Design > Health Informatics > Human-Computer Interaction > Information Justice, Human Rights, and Technology Ethics > Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
This project uses AI-powered digital tutors to help individuals with limited majority-language proficiency improve their language skills for real-world information seeking. By enabling users to design personalized tutoring systems, the study advances language learning, AI literacy, and human-computer interaction.
Human-Like Coaching for Home PT Exercises
Principal Investigator(s): Galina Madjaroff Reitz
Funder: Maryland Industrial Partnerships UMD Funded
Research Areas: Health Informatics > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Researchers are developing an AI-powered physical therapy coach that uses real-time motion tracking and personalized feedback to improve exercise adherence and outcomes. By simulating human-like interaction and emotional engagement, the project aims to make home-based rehabilitation more effective and accessible.
Principal Investigator(s): Galina Madjaroff Reitz
Funder: Maryland Industrial Partnerships UMD Funded
Research Areas: Health Informatics > Human-Computer Interaction > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Researchers are developing an AI-powered physical therapy coach that uses real-time motion tracking and personalized feedback to improve exercise adherence and outcomes. By simulating human-like interaction and emotional engagement, the project aims to make home-based rehabilitation more effective and accessible.
An AI-Enhanced Colleague for Teachers: Developing and Studying an Innovative Platform for Efficient, Inclusive Middle-Grade Mathematics Lesson Planning
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
This project supports middle school math teachers by developing an AI-powered lesson planning tool that enhances efficiency, quality, and inclusivity. Integrating generative AI with research-based practices, it offers personalized guidance for creating effective lessons. The project also examines impacts on teacher stress, instructional effectiveness, and student learning outcomes.
Principal Investigator(s):
Funder: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval > Youth Experience, Learning, and Digital Practices
This project supports middle school math teachers by developing an AI-powered lesson planning tool that enhances efficiency, quality, and inclusivity. Integrating generative AI with research-based practices, it offers personalized guidance for creating effective lessons. The project also examines impacts on teacher stress, instructional effectiveness, and student learning outcomes.
Faculty
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Recent News
Chandler Colman ‘28 (left) and Samir Nazar ‘28 work on their final projects for “Knitting = Algorithms + Coding," one of 10 Maker Movement Approach to Computing classes offered this fall. The one-credit courses are designed to make it easier for students of all majors to learn about computing. (Photos by Dylan Singleton)
Maryland Today: Courses Bring Comfort to Learning Computing
INFO faculty bridge creativity and computing, teaching hands-on AI, coding, and design through the Maker Movement at Maryland
Photo licensed by Adobe Stock via InfiniteFlow
Building Trust in AI, One Block at a Time
A unique partnership is closing the digital divide by creating community-led AI literacy programs for underserved youth and families.
Assistant Professor Stephanie Valencia² (center) shows the Spoken app, a commercial AAC tool to help users of speech-generating devices. Photo by Craig Taylor.