AI faculty by area

Investigating the social implications of AI and developing methods to further positive impact or mitigate negative impact of AI.

Developing algorithms for basic problems in AI. Such algorithms form the basis for solving problems across many high level tasks and applications.

Using methodologies from AI and machine learning to solve problems in health, bioinformatics, scientific research, human-computer interaction, and many other fields.

Developing methods for processing, analyzing, and understanding human speech, music, and other audio signals.

Developing AI methodologies for solving new problems by incorporating aspects from solutions of past problems.

The study of cognitive phenomena using mathematical and computational models.

Applying methodologies from AI to understand central questions in biology and to aid in diagnosing and analyzing health problems.

Developing methods to enable computers to perceive and understand the visual world.

Protecting sensitive data by performing computation in a hardware-based, trusted execution environment.

Developing methods and systems for extracting information from large-scale data.

Developing and applying techniques that automatically learn using large neural network models.

Understanding the ethical and societal consequences of AI, and investigating how to avoid or mitigate negative outcomes.

Developing methods to capture and manipulate knowledge in symbolic, logical, probablistic, or other forms to facilitate inference and algorithmic solutions in AI.

Investigating and developing techniques that learn automatically from data.

Developing methods for understanding and generating written and spoken human language.

Developing techniques for finding optimal solutions to complex mathematical problems that can be used in AI algorithms and applications.

Developing algorithms and systems that act in their environment, learn from their interaction, and optimize their long term utility.

Developing AI methods that use probabilistic relations among data or system components for their reasoning.

Studying, designing, and building robotic systems that interact with the environment.

Investigating challenges and opportunities of AI in security and privacy.

Intelligence in computational, storage, edge, IoT, and cyberphysical systems and development of trusted systems in support of AI.

Developing computational models and analysis for machine learning algorithms to explain when and why they are successful.

Understanding the weaknesses in AI systems and protecting them against potential security and privacy risks.