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Franz J Kurfess
Cal Poly SLO
San Luis Obispo, California
 

Franz J. Kurfess joined the Computer Science Department of California Polytechnic State University in the summer of 2000, after a short stay with Concordia University in Montreal, Canada, and a longer stay with the New Jersey Institute of Technology. Before that, he spent some time with the University of Ulm, Germany, as a postdoc at the International Computer Science Institute in Berkeley, CA, and at the Technical University in Munich, where he obtained his MS and PhD in Computer Science.

At Cal Poly, he is the coordinator of the human-computer interaction lab, and teaches courses in the areas of artificial intelligence, knowledge-based systems, user-centered design and development, and human-computer interaction. His main areas of research are artificial intelligence and human-computer interaction, with particular interest in the usability and interaction aspects of knowledge-intensive systems. He is currently investigating a framework for the analysis of “interaction spaces,” consisting of the physical space where interaction between humans and computational systems takes place, and a conceptual space delineated between the shared communication channels, symbol systems, vocabularies and languages, and the conceptual model of the domain and the world. So far, humans have been able to accommodate the limitations of computational systems concerning such interactions fairly well. When expanding interaction to situations where robots (or computational systems in general) have to communicate with other robots, it becomes much more critical to have a coherent framework for interaction in place.


     

Machine learning education for artists, musicians, and other creative practitioners
Fiebrink R.  ACM Transactions on Computing Education (TOCE) 19(4): 1-32, 2019. Type: Article

Together with colleagues from computer science, agriculture, food science, biology, and related fields, I am currently working on a framework for teaching artificial intelligence (AI) and machine learning (ML) to students and practitioners with li...

 

Objects with intent: designing everyday things as collaborative partners
Rozendaal M., Boon B., Kaptelinin V.  ACM Transactions on Computer-Human Interaction 26(4): 1-33, 2019. Type: Article

As electronics shrink and become cheaper, everyday objects can be endowed with significant computational capabilities. An observer or user can then perceive aspects of agency and intelligence in such objects. In this paper, the authors explore the...

 

Visus: an interactive system for automatic machine learning model building and curation
Santos A., Castelo S., Felix C., Ono J., Yu B., Hong S., Silva C., Bertini E., Freire J.  HILDA 2019 (Proceedings of the Workshop on Human-In-the-Loop Data Analytics, Amsterdam, the Netherlands,  Jul 5, 2019) 1-7, 2019. Type: Proceedings

Not too long ago, machine learning (ML) methods required significant programming skills in combination with domain expertise and a willingness to manually or programmatically tune the ML system parameters. More sophisticated tools, from front ends...

 

 Guidelines for human-AI interaction
Amershi S., Weld D., Vorvoreanu M., Fourney A., Nushi B., Collisson P., Suh J., Iqbal S., Bennett P., Inkpen K., Teevan J., Kikin-Gil R., Horvitz E.  CHI 2019 (Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK,  May 4-9, 2019) 1-13, 2019. Type: Proceedings

From a human-computer interaction (HCI) perspective, the arrival of virtual assistants (for example, Alexa, Siri, Google Assistant, and Bixby) has led to a shift in interaction mode from visual to speech-based: instead of manipulating objects disp...

 

Long short-term memory fuzzy finite state machine for human activity modelling
Mohmed G., Lotfi A., Pourabdollah A.  PETRA 2019 (Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, Rhodes, Greece,  Jun 5-7, 2019) 561-567, 2019. Type: Proceedings

As sensors become more ubiquitous in smart homes and work environments, the data they provide can offer information about the locations and actions of any occupants. The authors propose using a combination of deep learning and finite state machine...

 
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