Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
Project
Deep Learning for Machine Reading Comprehension
The goal of this project is to teach a computer to read and answer general questions pertaining to a document. We recently released a large scale MRC dataset, MS MARCO. We developed a ReasoNet model…
Group
Perception and Interaction Group
The Perception and Interaction group studies the interface between the virtual world and the physical world of people and objects. We aim to enrich this interface through sensing technologies, smart signal processing, new modes of…
Group
Reinforcement Learning
The Reinforcement Learning research group works on theoretical foundations, algorithms, and systems for autonomous decision making. Our main research areas include exploration-exploitation trade-offs, off-policy learning, and generalization for contextual bandits, Markov decision processes, and contextual…
Project
Situated Interaction
The situated interaction research effort aims to enable computers to reason more deeply about their surroundings, and engage in fluid interaction with humans in physically situated settings. When people interact with each other, they engage…
Project
Foundations of Optimization
Optimization methods are the engine of machine learning algorithms. Examples abound, such as training neural networks with stochastic gradient descent, segmenting images with submodular optimization, or efficiently searching a game tree with bandit algorithms. We…
Project
Biomedical Natural Language Processing
The biomedical sciences are beginning to undergo a major transformation. Precision medicine has the potential to make treatments much more effective by better understanding patients, biological mechanisms, and therapeutic effects. However, current approaches only reach…
Video
The Malmo Collaborative AI Challenge
A long-term goal of artificial intelligence research is to develop artificial assistants (AI agents) that can collaborate with and empower their users. This goal raises important questions, such as how AI agents may understand a…
Video
Counterfactual Multi-Agent Policy Gradients
Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. In this talk, I overview some of the key challenges in developing reinforcement learning…
