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team:daniel_nyga [2017/06/16 09:27] – [Dissertation] nygateam:daniel_nyga [2018/07/03 10:52] – [Teaching] nyga
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 =====Dr.rer.nat. Daniel Nyga====== =====Dr.rer.nat. Daniel Nyga======
 | {{:wiki:daniel_nyga.jpg?0x180}} |||| | {{:wiki:daniel_nyga.jpg?0x180}} ||||
-|::: ||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: |<cryptmail>nyga@cs.uni-bremen.de</cryptmail>| |:::|Mail: |<cryptmail>nyga@cs.uni-bremen.de</cryptmail>|
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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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 showcase that PRAC is capable of closing the loop from  showcase that PRAC is capable of closing the loop from 
 natural-language instructions to their execution by a robot. natural-language instructions to their execution by a robot.
 +
 +====Master's Thesis====
 +
 +[[https://ai.uni-bremen.de/_media/team/ma_nyga_small.pdf|{{:team:ma-nyga-cover.png?180 |}}]]//Abstract//-- This thesis investigates boosting algorithms for classifier learning in the presence of imbalanced classes and uneven misclassification costs. In particular, we address the well-known AdaBoost procedure and its extensions for coping with class imbalance, which typically has a negative impact on the classification accuracy regarding the minority class. We give an extensive survey of existing boosting methods for classification and enhancements for tackling the class imbalance problem, including cost-sensitive variants. Regularized boosting methods, which are favourable when dealing with noise and overlapping class distributions, are considered too. We theoretically analyze several strategies for introducing costs and their applicability in the case of imbalance. For one variant (AdaC1) we show that it is instable under certain conditions. We identify drawbacks of an often-cited cost-sensitive boosting algorithm (AdaCost), both theoretically and empirically. We also expose that an algorithm for tackling imbalance without using explicit costs (RareBoost) is a special case of the RealBoost algorithm, a probabilistic variant of AdaBoost. We approve our findings by empirical evaluation on several real-world data sets and academic benchmarks.
  
 ====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, learning and inference methods.+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, learning and inference methods, and in applications thereof in natural-language understanding, knowledge processing and robotics.
  
 He was involved in the European FP7 research projects [[http://www.robohow.org|RoboHow]] and [[http://www.acat-project.eu|ACAT]]. He was involved in the European FP7 research projects [[http://www.robohow.org|RoboHow]] and [[http://www.acat-project.eu|ACAT]].
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 He is the lead developer in the projects [[http://www.pracmln.org|pracmln]] and [[http://www.actioncores.org/|PRAC]]. He is the lead developer in the projects [[http://www.pracmln.org|pracmln]] and [[http://www.actioncores.org/|PRAC]].
  
-His GitHub profile can be found [[http://www.github.com/danielnyga|here]]+His GitHub profile can be found [[http://www.github.com/danielnyga|here]].
  
  
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   * Data Mining/Knowledge Discovery   * Data Mining/Knowledge Discovery
   * Automated Learning/Understanding of WWW information   * Automated Learning/Understanding of WWW information
-  * Natural-Language Understanding+  * Natural-language understanding
  
 ====Teaching==== ====Teaching====
 +  * Foundations of Artificial Intelligence ([[https://ai.uni-bremen.de/teaching/kiss2018|SS2018]]) (Lecture)
 +  * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws17|WS2017/18]]) (Lecturer)  
   * Master Seminar: Data Mining and Data Analytics ([[http://ai.uni-bremen.de/teaching/datamining_ss17|SS2017]])   * Master Seminar: Data Mining and Data Analytics ([[http://ai.uni-bremen.de/teaching/datamining_ss17|SS2017]])
   * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws16|WS2016/17]]) (Lecturer)   * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws16|WS2016/17]]) (Lecturer)
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   * To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master's Thesis, Mareike Picklum)   * To see what no robot has seen before - Recognizing objects based on natural-language descriptions (Master's Thesis, Mareike Picklum)
   * 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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 <author>nyga</author> <author>nyga</author>
 </bibtex> </bibtex>
 +
  
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