The goal of this research is to study an integrated theory of learning, knowledge representation and reasoning and evaluate it on large scale knowledge intensive inferences in the natural language domain. Recent studies, within the Learning to Reason fram
This research seeks to develop an integrated view - theoretical understanding, algorithms development and experimental evaluation - for learning coherent concepts. These are learning scenarios that are common in cognitive learning - where multiple learner
Recent advances in Natural Language Processing, in particular the ability to use unstructured data to answer natural language questions, are very exciting from an educational perspective. They offer the promise of systems that can automatically respond to students' questions, thus supporting not only a guided but also an open ended, exploration based, approach to learning.
The goal of this project is to apply research in Computer Science -- particularly Natural Language Processing -- and the Learning Sciences, to developing an intelligent tutor that can provide the right kind of environment for students, one that facilitates rather than inhibits inquiry through a known knowledge space and provides a jumping-off space for trying to find or generate new knowledge.
The testbed domain in this project involves high school and undergraduate level students studying concepts in BioInformatics.
The ability to speak and understand language is probably the most intricate skill that people possess. It is certainly our most uniquely human ability. This project investigates how such an important skill is acquired and continues to develop throughout o
A significant amount of the software written today interacts with naturally occurring (sensor) data such as text, speech, images and video, streams of financial data, and biological sequences, and needs to reason with respect to concepts that are complex and often difficult to define explicitly in terms of the raw data observed. In this project, we explore a novel software engineering paradigm that allows a programmer seamless incorporation of trainable variables into the program and, consequently, the ability to reason using high-level concepts without the need to explicitly define them in terms of all the variables they might depend on, or the functional dependencies among them; these may be determined in a data-driven way, via learning operators whose details are abstracted away from the programmer.
The project studies a machine learning centered approach to data-intensive and computing-intensive processing for intelligent context-sensitive human-machine interfaces. The future of intelligent human-machine interaction is in the ability to perform co
Dash Optimization has generously provided the XPress-MP Optimization Suite which has served as an advanced optimization tool for the research in our group.
