Research Areas
The Center consists of 30 core computer science faculty active in cutting-edge research, and developing innovative training to educate the data scientists of the future.
Machine learning, probabilistic models, approximate inference and learning techniques, non-likelihood-based model estimation, missing data, time series.
Theoretical computer science.
Data management, data quality, provenance, causality, explanations, data accessibility, and algorithmic bias.
Object-oriented languages (emphasis on implementation), performance measurement and optimization, garbage collection, transactional memory.
Computational ecology and environmental science; machine learning; probabilistic modeling and inference; network models; optimization.dion Content
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.
Computer Vision and Machine Learning
Mohit Iyyer’s main research interest is in designing deep neural networks for traditional natural language processing tasks and new problems that involve understanding creative language. He received his Ph.D. in computer science from University of Maryland College Park in 2017 where he was a member of the Computational Linguistics and Information Processing Lab. Iyyer was a young investigator at the Allen Institute for Artificial Intelligence and joined CICS in September 2018.
Information extraction, knowledge discovery from text, statistical natural language processing, machine learning, graphical models.
Reinforcement learning, decision making, robotics, and AI safety.
Information management; mining, analytics, and exploration of massive data; probabilistic database systems; machine learning; modeling and computer simulation of complex stochastic systems.
Data management, data quality, provenance, causality, explanations, data accessibility, and algorithmic bias.
Programming languages, runtime systems, and operating systems, with a particular focus on systems that transparently improve reliability, security, and performance.
Software engineering and systems research, focusing on software fairness and bias, self-adaptive systems, and distributed systems.
Dr. Clarke’s 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.
Wireless health sensing, ubiquitous computing, wireless communication, embedded systems, energy harvesting, and machine learning for sensor data.
Programming languages, runtime systems, and operating systems, with a particular focus on systems that transparently improve reliability, security, and performance.
Information retrieval, event-based information organization, controversy and misinformation detection.
Information extraction, knowledge discovery from text, statistical natural language processing, machine learning, graphical models.