Our faculty are engaged in research projects ranging from language documentation and morphological analysis to semantic analysis to Biomedical Informatics. We are also currently working on an autonomous conversational agent in a junior high through college classroom setting. Featured below are some of the projects we are most proud of, both past and present.
Ongoing
Jan 28th
THYME
Temporal History of Your Medical Events
Our goal is automatically extracting the timeline of a disease and its treatment from patient records. This benefits individual patients and their doctors by providing quick, accurate summaries of a patient’s history covering several years. Moreover, aggregating together timelines for large numbers of patients can also aid in analyzing the effectiveness of alternative treatments and the development of new treatments, benefitting all patients.
Problem
Ever increasing amounts of electronic clinical data and medical subspecialization hinder the ability of doctors and patients to stay on top of all aspects of a patient’s medical history.
Solution
Natural Language Processing can automatically process thousands of patient records in seconds. This allows automatic identification of salient diseases, signs, symptoms, and treatments, while preserving the timeline of the patient’s medical history.
Techniques Used
Ongoing
Jan 28th
iSAT
Institute for Student-AI Teaming
Our goal is to use Artificial Intelligence to transform classrooms into more effective, engaging and equitable learning environments.
Problem
Students learn most effectively in collaborative situations where they can investigate and articulate questions about new topics. Break-out groups facilitate an environment where this is possible, however, one teacher can’t engage with several breakout groups simultaneously.
Solution
We are developing new approaches to how machines process human language, gestures and emotions so that we can place an effective AI Partner in each break-out group. It will support the group learning process and provide feedback to the teacher by listening to, analyzing and facilitating problem solving.
Techniques Used
Ongoing
Jan 28th
Universal NLP
NLP is making immense contributions to the English and Chinese speaking worlds. Automating teaching to give children access to education and automatic machine translation increasing access to healthcare are just two examples. For the rest of the world to benefit from NLP, it needs to function in their languages too.
Problem
The majority of the world's 7000 languages have limited data available for Natural Language Processing.
Solution
When we don’t have enough data to use classical NLP, there are approaches that can make up for this lack.
Techniques Used
- Transfer Learning
- Pre-training
- Multi-task Training
- Meta Learning