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Jeremy Jancsary

  • Researcher
  • Austrian Research Institute for Artificial Intelligence
  • 15PublicationsNumber of items in Jeremy's My Publications folder on Mendeley.
  • 11Followers

Recent publications

  • Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Art

    • Jancsary J
    • Nowozin S
    • Rother C
  • Regression Tree Fields - An Efficient, Non-parametric Approach to Image Labeling Problems

    • Jancsary J
    • Nowozin S
    • Sharp T
    • et al.

Professional experience


Austrian Research Institute for Artificial Intelligence

March 2006 - Present

Research Intern

Microsoft Research Cambridge

April 2011 - July 2011(3 months)

Embedded Software Engineer

RISE (a spin-off of Vienna University of Technology)

February 2004 - February 2006(2 years)

Teaching Assistant

TU Wien

October 2003 - January 2004(3 months)


Ph.D. Candidate in Electrical Engineering

TU Wien

March 2008 - July 2012(4 years)

M.Sc. in Software Engineering & Internet Computing

TU Wien

October 2005 - February 2008(2 years)

B.Sc. in Information Engineering

TU Wien

October 2001 - August 2005(4 years)


I am currently a researcher at the Language Technology (LT) group of the Austrian Research Institute for Artificial Intelligence (OFAI). Previous work experience includes a position as an embedded software engineer at INSO and a spin-off thereof, RISE, where I contributed to firmware development for the Austrian electronic healthcare card reader (e-Card). Prior to that, I was a teaching assistant (Tutor) at the Compilers and Languages group of Vienna University of Technology. I received my bachelor's degree in Software & Information Engineering from Vienna University of Technology in 2005 and completed a master's program in Software Engineering & Internet Computing in February of 2008. I am now enrolled in a PhD program. My advisors are Harald Trost and Gerald Matz. The overarching goal of my research is to develop methodological advances that solve real-world problems in information processing and data analysis. A lot of these problems are popping up in natural language processing these days, so it is an ideal playground. As one of our principal means of communication, language is also a fascinating topic in itself. Turns out many tasks in natural language processing (and a variety of other areas, such as digital communications and protein design) can be described very accurately using graphical models. While extremely versatile and powerful conceptually, most computations in graphical models are NP-hard. For this reason, approximation algorithms are indispensable. Early algorithms in this field work empirically well, but are not yet completely understood and often have serious drawbacks such as non-convergence. So I currently try to improve my understanding of these issues.

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