Ashish Knagen
Student Assistants Nature-Inspired Machine Learning (NIMI) Ashish Knagen is working on large language models.
Student Assistants Nature-Inspired Machine Learning (NIMI) Ashish Knagen is working on large language models.
Student Assistants Nature-Inspired Machine Learning (NIMI) Pavan Singavarapu is working on two main projects, both iDoks and E-vita. Pavan is helping in text summarization tasks, and preparation of stories for conversation AI-based systems for elderly people in the E-vita project. [...]
Student Assistants Nature-Inspired Machine Learning (NIMI) Kevin has a bachelor's degree in media informatics and is particularly interested in machine learning, especially natural language processing. As part of the iDoks project, he is actively working on the development of a summary generation [...]
Student Assistant Nature-Inspired Machine Learning (NIMI) Student Research Assistant, working on GeoSpacial data and advanced querying
Student Assistant Nature-Inspired Machine Learning (NIMI) A Master’s student in the computational modeling and simulation in life sciences program at TU Dresden. Previously worked at Siemens. Current research interests lie in the application of knowledge graph embedding and machine learning in the [...]
Student assistant Nature-Inspired Machine Learning (NIMI) Abrar Hyder is a student assistant in the E-vita project and works on building dialogue systems for elderly people. He is heavily involved in data preparation, and system maintenance. [...]
Student Assistant Nature-Inspired Machine Learning (NIMI) Qasid Saleem has been working on several projects, and has a broad range of skills, from reinforcement learning, to knowledge graphs and language models. Qasid finished his master thesis also in the NIMI group on link [...]
Student Assistant Nature-Inspired Machine Learning (NIMI) Prathmesh Dudhe is a master student of TU Dresden and working on the IntelliLung project. He is working on data preparation and exploration of reinforcement learning-based algorithms for supporting improvement of clinician decisions in intensive care [...]