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team:daniel_nyga [2017/06/16 08:28] – [About] nyga | team:daniel_nyga [2017/08/30 09:07] – [Publications] nyga | ||
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=====Dr.rer.nat. Daniel Nyga====== | =====Dr.rer.nat. Daniel Nyga====== | ||
| {{: | | {{: | ||
- | |::: ||Research Staff\\ \\ || | + | |::: ||Postdoctoral Researcher\\ \\ || |
|:::|Room: |1.77| | |:::|Room: |1.77| | ||
- | |:::|Tel: |--49 -421 218 64008| | + | |:::|Tel: |--49 -421 218 64010| |
|:::|Fax: |--49 -421 218 64047| | |:::|Fax: |--49 -421 218 64047| | ||
|:::|Mail: |< | |:::|Mail: |< | ||
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====Dissertation==== | ====Dissertation==== | ||
- | [[http:// | + | [[http:// |
has been one of the ultimate long-standing goals in both Artificial | has been one of the ultimate long-standing goals in both Artificial | ||
Intelligence and Robotics research. In near-future applications, | Intelligence and Robotics research. In near-future applications, | ||
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vagueness and ambiguity in natural language and infer missing | vagueness and ambiguity in natural language and infer missing | ||
information pieces that are required to render an instruction | information pieces that are required to render an instruction | ||
- | executable by a robot. To this end, \prac formulates the problem of | + | executable by a robot. To this end, PRAC formulates the problem of |
instruction interpretation as a reasoning problem in first-order | instruction interpretation as a reasoning problem in first-order | ||
probabilistic knowledge bases. In particular, the system uses Markov | probabilistic knowledge bases. In particular, the system uses Markov | ||
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====Projects==== | ====Projects==== | ||
- | Daniel Nyga's research interests revolve around topics on Artificial Intelligence and Data Science in general, as well as Machine Learning, Data Mining and Pattern Recognition techniques. In particular, he is interested in probabilistic graphical and relational knowledge representation, | + | Daniel Nyga's research interests revolve around topics on Artificial Intelligence and Data Science in general, as well as Machine Learning, Data Mining and Pattern Recognition techniques. In particular, he is interested in probabilistic graphical and relational knowledge representation, |
He was involved in the European FP7 research projects [[http:// | He was involved in the European FP7 research projects [[http:// | ||
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He is the lead developer in the projects [[http:// | He is the lead developer in the projects [[http:// | ||
- | His GitHub profile can be found [[http:// | + | His GitHub profile can be found [[http:// |
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* Data Mining/ | * Data Mining/ | ||
* Automated Learning/ | * Automated Learning/ | ||
- | * Natural-Language Understanding | + | * Natural-language understanding |
====Teaching==== | ====Teaching==== | ||
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* To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master' | * To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master' | ||
* Web-enabled Learning of Models for Word Sense Disambiguation (Bachelor Thesis, Stephan Epping) | * Web-enabled Learning of Models for Word Sense Disambiguation (Bachelor Thesis, Stephan Epping) | ||
- | * Grounding Words to Objects: A Joint Model for Co-reference and Entity Resolution Using Markov Logic Networks for Robot Instruction Processing (Diploma Thesis, Florian Meyer) | + | * Grounding Words to Objects: A Joint Model for Co-reference and Entity Resolution Using Markov Logic Networks for Robot Instruction Processing (Diploma Thesis, Florian Meyer) |
- | | + | |
====== Publications ====== | ====== Publications ====== | ||
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< | < | ||
</ | </ | ||
+ | |||
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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