Jacob Goldberger

Faculty of Engineering, Bar-Ilan University
My research deals with developing and analyzing novel efficient algorithms for learning and inference, and applying these algorithms in challenging real world domains. My research interests are mainly related to statistical machine learning and more specifically to the field of graphical models. What is perhaps the most distinctive about the graphical model approach is its naturalness in formulating probabilistic models of complex phenomena, while maintaining control over the computational cost associated with these models. As often pointed out, the same machine learning models and algorithms can be applied in many different research areas. In my research I concentrate on developing and analyzing those algorithms in the context of classical machine learning tasks (clustering, dimensionality reduction etc.) and applying them to a large variety of applications such as computer vision, signal processing, medical image processing and natural language processing.

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Idan Szpektor

Yahoo! Research, Haifa

I am a senior research scientist in Yahoo! Research, Haifa. I received my Ph.D from the Computer Science department of Bar-Ilan University in 2010. My research interests include natural language processing and data mining/analysis, mainly of Web data.

My main line of research is around search, both general search engines such as Yahoo Search and complementing community question answering (CQA) sites such as Yahoo Answers. I am involved in several projects, including search over CQA collections, question recommendation, automatic answering, question and answer quality, churn prediction, automatic question generation and asker and answerer behavior analyses.

In addition, I am interested in various aspects of NLP, with two main focus points: (a) adapting NLP capabilities for user generated content; and (b) Semantics, including textual inference and word representation.

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Chris Biemann

Computer Science Department, TU Darmstadt, Germany
I am assistant professor and head of the Language Technology group at TU Darmstadt in Germany. I received my Ph.D. from the University of Leipzig, and subsequently spent three years in industrial research at Powerset and Microsoft Bing in San Francisco, California. My research is targeted towards self-learning structure from natural language text, specifically regarding semantic representations. Using big-data techniques, we have built a scalable, language-independent framework for distributional semantics and word sense induction in collaboration with the IBM Watson DeepQA team. My current research interests include the unsupervised induction of natural language relations and frames, as well as the connection of graph-based distributional semantic models and formal ontologies. 

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