Not available at this time

Not available at this time

Not available at this time

Not available at this time

Weekly seminar requiring students to read an NLP paper and discuss and review it from a variety of perspectives. Some weeks will feature invited speakers instead of paper reviews.

This seminar will explore the relationships between simulation modeling and causal modeling, focusing on the potential of these two research fields to enrich each other. From a simulation point of view, causal models could provide methods for enabling new kinds of tractable probabilistic reasoning about models, for learning models directly from data, and for learning high-level abstractions of a detailed mechanistic model.

This seminar will explore the relationships between simulation modeling and causal modeling, focusing on the potential of these two research fields to enrich each other. From a simulation point of view, causal models could provide methods for enabling new kinds of tractable probabilistic reasoning about models, for learning models directly from data, and for learning high-level abstractions of a detailed mechanistic model.

The deployment of foundation models in resource-constrained mobile and wearable devices presents unique challenges and opportunities, particularly in health and wearable sensing applications. This advanced seminar explores the cutting-edge intersection of large AI models, embedded systems, and digital health, with a focus on real-world applications in wearable and mobile biosignal sensing platforms.

The deployment of foundation models in resource-constrained mobile and wearable devices presents unique challenges and opportunities, particularly in health and wearable sensing applications. This advanced seminar explores the cutting-edge intersection of large AI models, embedded systems, and digital health, with a focus on real-world applications in wearable and mobile biosignal sensing platforms.

Artificial Intelligence will radically change education. Through machine learning, data mining, analytics, robotics, and user models, AI will replace false learning boundaries (e.g., learning places, time, level of study); personalize learning; make learning instantly available to everyone; connect learners with partners; provide multi-media; and augment human learning ability. This seminar examines recent work in explanatory and tutoring systems, presents theories about digital teaching and learning, and describes how to deliver personalized teaching in online systems.