• Andrew McCallum Professor

    The main goal of my research is to dramatically increase our ability to mine actionable knowledge from unstructured text. I am especially interested in information extraction from the Web, understanding the connections between people and between organizations, expert finding, social network analysis, and mining the scientific literature & community. Toward this end my group develops and employs various methods in statistical machine learning, natural language processing, information retrieval and data mining---tending toward probabilistic approaches and graphical models.


  • Pedram Rooshenas Postdoc, started 2017

    Currently, I'm working on tractable learning and inference algorithms and probabilistic graphical models.

  • Jeffrey Flanigan Postdoc, started 2017

    My research interests lie in the areas of structured prediction, machine learning, and natural language processing. I work to make computers understand and generate natural language better (semantic parsing and generation) and to help people who speak different languages communicate (machine translation).

PhD Students

  • Ari Kobren PhD, started 2012

    Currently, I'm working on interactive data integration--or combining multiple, heterogeneous sources of data into a single, consistent knowledge base with a human in the loop.

  • Luke Vilnis PhD, started 2012

    I am broadly interested in machine learning, natural language processing and knowledge base construction. Areas of interest include common sense knowledge, representation learning, structured prediction, coreference, and optimization.

  • Emma Strubell PhD, started 2012

    I am interested in developing new machine learning techniques to facilitate fast (and accurate) natural language processing of text. I am very grateful to be supported by an IBM Ph.D. Fellowship Award as of fall 2017.

  • Patrick Verga PhD, started 2012

    My research is broadly in the areas of information extraction, natural language processing, and machine learning. I primarily work on extracting and representing entities and their relations for constructing embedded knowledge bases.

  • Nicholas Monath PhD, started 2014

    The focus of my research is on scalable methods for entity resolution, the task of determining the underlying entities referred to by a set of ambiguous mentions. I am interested in two settings of this problem, with and without an existing knowledge base.

  • Craig Greenberg PhD, started 2014

    I'm generally interested in artificial intelligence, machine learning, and reasoning under uncertainty, especially as it applies human language technology. My research focuses on compact representation of uncertainty, as well as its transmission, measurement, and calibration.

  • Rajarshi Das PhD, started 2016 (spring)

    Rajarshi's research interest lies in open domain question answering and in reasoning on large knowledge bases and on anything in the interesection of those two.

  • Trapit Bansal PhD, started 2015

    My current research is on knowledge representation and reasoning. Broadly, I am interested in deep learning and reinforcement learning.

  • Haw-Shiuan Chang PhD, started 2015

    My current interests include knowledge representation, text entailment, relation extraction, graph embedding, and active learning.

  • Dung Thai PhD, started 2016

    My research focus on structured predictions which involve deep learning and probabilistic graphical models. I am also interested in generative models and tasks concern both language model and vision model.

  • Xiang Li (Lorraine) PhD, started 2016

    My research interests involve Natural Language Processing, especially commonsense knowledge reasoning.

  • Sheshera Mysore PhD, started 2018

    Current interests include neural network models to learn representations of common sense knowledge defined as event frames and event chains.

Masters Students