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| team:daniel_nyga [2017/10/16 11:46] – [Teaching] nyga | team:daniel_nyga [2018/11/08 19:38] – [About] nyga |
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| ====About==== | ====About==== |
| Daniel Nyga is a postdoctoral researcher at the Institute for Artificial Intelligence (IAI), University of Bremen. Before he joined the IAI Bremen, he studied computer science at the Technical University of Munich, where he received a Bachelor's degree in 2008 and a Master's degree in computer science in 2010. In 2011, he started his PhD supervised by Prof. Michael Beetz at the //Intelligent Autonomous Systems// group at TUM, which he has finished at the Institute for Artificial Intelligence Bremen with his thesis on the [[http://nbn-resolving.de/urn:nbn:de:gbv:46-00105882-13|Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning]] (see below). | |
| | Daniel Nyga is a postdoctoral researcher at the Institute for Artificial Intelligence (IAI), University of Bremen. He holds a Bachelor and Master's degree in computer science from the Technical University of Munich (TUM), with a major in Artificial Intelligence and Machine Learning, as well as a doctor's degree (summa cum laude) in computational science from the University of Bremen for his thesis on the [[http://nbn-resolving.de/urn:nbn:de:gbv:46-00105882-13|Interpretation of Natural-language Robot Instructions: Probabilistic Knowledge Representation, Learning, and Reasoning]] (see below). He was a visiting scholar in the Bio-intelligence Laboratory headed by Prof. Byoung-Tak Zhang at Seoul National University (SNU), South Korea, and in the Robust Robotics Group headed by Prof. Nicholas Roy at the Computer Science and AI Laboratory of MIT, USA. |
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| ====Dissertation==== | ====Dissertation==== |
| 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. |
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| | ====Master's Thesis==== |
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| | [[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. |
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| ====Projects==== | ====Projects==== |
| 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]], [[http://www.actioncores.org/|PRAC]] and [[http://www.pyrap.org|pyrap]]. |
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| 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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| ====Teaching==== | ====Teaching==== |
| | * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws18|WS2018/19]]) (Lecturer) |
| | * Foundations of Artificial Intelligence ([[https://ai.uni-bremen.de/teaching/le-ki1_ss18|SS2018]]) (Lecturer) |
| * AI: Knowledge Acquisition and Representation ([[https://ai.uni-bremen.de/teaching/le-ki2-ws17|WS2017/18]]) (Lecturer) | * 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]]) |
| <author>nyga</author> | <author>nyga</author> |
| </bibtex> | </bibtex> |
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