Machine Learning and Data Mining

Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Our research is centered on the following areas: the analysis and modeling of large datasets for finding patterns and extracting knowledge; integration of heterogeneous datasets for understanding correlation and causality; study of dynamic and semantically rich network datasets; development of scalable methods; applications to biology and social networks. A significant part of our research is directed at the analysis of structure and semantics of dynamic, heterogeneous, composite graphs/networks.

Affilated Labs: 
Dynamic Networks: Analysis and Modeling (Dynamo) Lab, Four Eyes Lab, Network Science Lab, NLP Lab, SysML Lab

Faculty

As a systems researcher, I design and build easy-to-use, scalable, and deployable systems that solve real-world problems at the intersection of networking, cybersecurity, and machine learning.

Höllerer directs a research group on Imaging, Interaction, and Innovative Interfaces, with a particular focus on the design, implementation, and evaluation of novel user interfaces that have the potential to augment or challenge established paradigms.

Dr. Yekaterina Kharitonova's research is in Computer Vision and Machine Learning. Specifically, her work focuses on multimedia processing and understanding, image correspondence, and effective image alignment through fitting geometric models.

My research is focused on modeling, analysis, simulation and software, applied to multiscale, networked systems in biology, materials and social networks.   My research group has been developing advanced algorithms for discrete stochastic simulation of systems where the fate of a few key molecules can make a big difference to important outcomes. 

Much of Professor Singh’s research is around data-centric modeling of systems and he focuses on the development of new methods that can be applied to real-world applications.

Computer vision, human-computer interaction, augmented reality, computational photography, pattern recognition, artificial intelligence, multimodal interaction, and mobile computing.

I study the theoretical foundation and practical algorithms for Artificial Intelligence. To build intelligent machines that can tackle challenging reasoning problems under uncertainty, I have pursued answers via studies of Machine Learning, Natural Language Processing, and Interdisciplinary Data Science. More specifically, I am interested in designing scalable inference and learning algorithms to analyze massive datasets with complex structures.

Machine learning

The primary goal of my lab is to develop fundamental concepts and new principles of data mining, design intelligent algorithms and build scalable systems.