Christophe Quignon erhält ITCO Absolventenpreis
ausführlich
Johannes Wicht (MA AI Präsentation): Entwicklung eines Systems zur Erkennung von Eskalationen in der Assist-Datenbank (in Kooperation mit Siemens)
ausführlich
Matthias Linhardt (MA CiTH Präsentation): Konzeption und exemplarische Ausführung eines
Assistenzsystems für ein Content-Management-System (in Kooperation mit docufy)
ausführlich
Michael Siebers: (Goal-dependent) Conditional Annulling of Actions in Automated Planning
ausführlich
Lukas Berle (BA Präsentation, Projekt EMN-Moves): Matching natural language activity descriptions by using self-expanding ontologies -- An application to support mobility of elderly people
ausführlich
22.10.12
Im Oktober ist das ESF-finanzierte Projekt "Alumnae-Tracking" gestartet.
Ziel des Projekts ist die Analyse von Bildungs- und Berufsverläufen der Absolventen der Informatik im Gendervergleich.
ausführlich
Kognitionswissenschaftler tagen in Bamberg
ausführlich
Head of Cognitive Systems Group
Address:
Professur für Angewandte Informatik insb. Kognitive Systeme
Fakultät Wirtschaftsinformatik und Angewandte Informatik
Otto-Friedrich-Universität Bamberg
D-96045 Bamberg
Office: Room WE5/05.043
An der Weberei 5 (ERBA)
96047 Bamberg
Consultation Hour: by appointment
Phone: ++49-0951-863 2860
Fax: ++49-951-863 2862
E-Mail: Mail to Ute Schmid
Ute Schmid holds a diploma in psychology and a diploma in computer science, both from Technical University Berlin (TUB), Germany. She received her doctoral degree (Dr. rer.nat.) in computer science from TUB in 1994 and her habilitation in computer science in 2002. From 1994 to 2001 she was assistant professor (wissenschaftliche Assistentin) at the AI/Machine Learning group, Department of Computer Science, TUB. Afterwards she worked as lecturer (akademische Rätin) at the Department of Mathematics and Computer Science at University Osnabrück. Since 2004 she holds a professorship of Applied Computer Science/Cognitive Systems at the University of Bamberg. Her research interests are mainly in the domain of high-level learning on structural data, especially inductive programming, knowledge level learning from planning, learning structural prototypes, analogical problem solving and learning. Further research is on various applications of machine learning (e.g., classifier learning from medical data and for facial expressions) and empirical and experimental work on high-level cognitive processes and usability evaluation.