Andy Schlaikjer is a Ph.D. student in the Language Technologies Institute at Carnegie Mellon University, advised by Prof. Eric Nyberg. He is working to simplify application of advanced information retrieval models to complex datasets, such as relational databases and graphs. His research interests include information retrieval, ranking and recommendation algorithms, question answering and applied machine learning.

Starting July 2011, Andy will be an intern with the Relevance team at Twitter, working on large-scale topic modeling and ranking problems.

From October 2010 to June 2011, Andy collaborated with CERT to design and develop prototype risk assessment tools for the U.S. Secret Service. In early 2011, he joined the iLab to advance his thesis research and investigate new information retrieval tasks.

From January–August 2010, Andy interned at the T.J. Watson Research Center with the DeepQA team—developers of the IBM Watson question answering system. His work there, mentored by Dr. Chris Welty, focused on development of a large-scale relation learning system for DARPA's Machine Reading program.

In Spring and Fall of 2007, under the direction of Prof. Luis von Ahn, Andy investigated ways in which an open-domain speech transcription task might support audio-based CAPTCHA or reCAPTCHA applications. To facilitate this work he conducted a survey of users with varying degrees of visual impairment and collected their transcriptions of short audio clips containing speech.

During the AQUAINT program, Andy was a member of the JAVELIN question answering project at the LTI, investigating the use of shallow semantic representations of text for relational question answering applications. He also contributed work to some of the LTI's other large research projects involving information extraction and question answering.

Prior to entering the LTI Ph.D. program, Andy received his B.S. in Intelligent Systems from Columbia University in 2002 and then joined Columbia's NLP Group as a research assistant. There, he worked on the AQUAINT project team, conducting research in question answering with emphasis on definitional question answering and text summarization.