Research

Advancing algorithms and theoretical foundations for intelligent data analysis and autonomous decision-making in AI systems.

Ben Marlin

Machine learning, probabilistic models, approximate inference and learning techniques, non-likelihood-based model estimation, missing data, time series.

Andrew McGregor

Theoretical computer science with applications in AI and machine learning algorithms.

Alexandra Meliou

Data management, data quality, provenance, causality, explanations, data accessibility, and algorithmic bias in AI systems.

J. Eliot Moss

Object-oriented languages (emphasis on implementation), performance measurement and optimization, garbage collection, transactional memory.

Dan Sheldon 

Computational ecology and environmental science; machine learning; probabilistic modeling and inference; network models; optimization.

Developing AI systems that perceive and interpret visual information.

Vangelis Kalogerakis

Computer graphics and vision: geometric modeling, 3D deep learning, animation, shape analysis and synthesis, scene modeling, 3D reconstruction

Erik Learned-Miller
Computer vision and machine learning. Deep learning. Probabilistic and statistical methods in vision and image processing. Non-parametric and distribution-free statistics. Information theoretic methods. Unsupervised and semi-supervised learning. Low-shot learning.

Subhransu Maji

Advancements in computer vision and machine learning techniques.

Enhancing AI’s language understanding and generation capabilities.

Mohit Iyyer

Designing deep neural networks for NLP tasks, understanding creative language.

Andrew McCallum

Information extraction, knowledge discovery from text, statistical NLP, machine learning and graphical models.

Creating AI agents for intelligent decision-making in robotics.

Bruno Castro da Silva

Reinforcement learning, decision making, robotics, and AI safety.

Optimizing data management and systems for AI workloads.

Peter Haas

Information management, data analytics, probabilistic databases, machine learning, simulation of complex stochastic systems.

Alexandra Meliou

Data management, data quality, provenance, causality, explanations, data accessibility, and algorithmic bias.

Improving AI application development and reliability.

Emery Berger

Programming languages, runtime systems, and operating systems, with a particular focus on systems that transparently improve reliability, security, and performance.

Yuriy Brun

Software engineering and systems research, focusing on software fairness and bias, self-adaptive systems, and distributed systems.

Lori Clarke

Research is in the area of software engineering, primarily focusing on model checking of concurrent systems and requirements engineering. Recently she has been investigating applying software engineering technologies to detect errors and vulnerabilities in complex, human-intensive processes in domains such as healthcare, scientific workflow, and digital government. She is also involved in several efforts to increase participation of underrepresented groups in computing research.

Deepak Ganesan

Machine learning for sensor data, contributing to AI in health and ubiquitous computing.

Enhancing information access with AI.

James Allan

Information retrieval, event-based organization, controversy and misinformation detection with AI.

Andrew McCallum 

Information extraction, knowledge discovery from text, statistical natural language processing, machine learning, graphical models.

Digital Forensics and AI systems for security.

Brian Levine

Child rescue and digital forensics, networking, network security, and cryptoeconomics