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Kurfess, Franz
Cal Poly SLO
San Luis Obispo, California
 
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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.

 
 
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- 10 of 74 reviews

   
   Attention models in graphs: a survey
Lee J., Rossi R., Kim S., Ahmed N., Koh E.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-25, 2019. Type: Article

Having spent some time on early attempts to bring together neural networks and symbol-oriented knowledge representation, I am intrigued by the more recent work on deep learning and knowledge graphs. While many of the approaches appear conceptually...

Sep 20 2021  
   Inclusive design for a digital world: designing with accessibility in mind
Gilbert R.,  Apress, New York, NY, 2019. 272 pp. Type: Book (978-1-484250-15-0)

Having taught courses in user-centered design (UCD) and human-computer interaction (HCI) for over 20 years, I am familiar with both the history of the field and the methods and tools currently in use. On several occasions, I’ve also had the ...

Sep 7 2021  
   Detection of crop pests and diseases based on deep convolutional neural network and improved algorithm
Wu J., Li B., Wu Z.  ICMLT 2019 (Proceedings of the 2019 4th International Conference on Machine Learning Technologies, Nanchang, China,  Jun 21-23, 2019) 20-27, 2019. Type: Proceedings

Especially in large monoculture-based agricultural settings, an outbreak of pests or diseases can have a major impact on yield or quality of a crop. Advances in image processing based on convolutional neural network (CNN) architecture over the pas...

Sep 29 2020  
   Jupyter Notebook in CS1: an experience report
Zastre M.  WCCCE 2019 (Proceedings of the Western Canadian Conference on Computing Education, Calgary, AB, Canada,  May 3-4, 2019) 1-6, 2019. Type: Proceedings

Having observed the ease with which computer science (CS) students with little to no experience in machine learning can start working with existing code presented through Jupyter Notebooks (JNs), together with a few colleagues I explored their use...

Jun 17 2020  
   A systematic literature review on intelligent user interfaces: preliminary results
Gonçalves T., Kolski C., de Oliveira K., Travassos G., Grislin-Le Strugeon E.  IHM 2019 (Proceedings of the 31st Conference on l’Interaction Homme-Machine, Grenoble, France,  Dec 10-13, 2019) 1-8, 2019. Type: Proceedings

Designers of user interfaces often face a fundamental dilemma: how much of the underlying functionality of the system should they expose to the user? An experienced user may want direct access to most, or all, of the functionality, while casual us...

May 14 2020  
   Image processing techniques for detecting and classification of plant disease: a review
Hungilo G., Emmanuel G., Emanuel A.  IMIP 2019 (Proceedings of the 2019 International Conference on Intelligent Medicine and Image Processing, Bali, Indonesia,  Apr 19-22, 2019) 48-52, 2019. Type: Proceedings

For experienced farmers and biologists, detecting and identifying plant diseases is usually fairly straightforward: they are familiar with the most common problems related to their crops and can use visual inspection, possibly in combination with ...

Mar 9 2020  
  Self-healing UI: mechanically and electrically self-healing materials for sensing and actuation interfaces
Narumi K., Qin F., Liu S., Cheng H., Gu J., Kawahara Y., Islam M., Yao L.  UIST 2019 (Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology, New Orleans, LA,  Oct 20-23, 2019) 293-306, 2019. Type: Proceedings

Compared to living things like animals or plants, today’s electronic devices are quite fragile. Even minor “injuries” render them incapacitated, or limit their functionality. Sometimes they can be repaired, although this requires...

Jan 23 2020  
   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...

Nov 15 2019  
   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...

Oct 30 2019  
   From word to sense embeddings: a survey on vector representations of meaning
Camacho-Collados J., Pilehvar M.  Journal of Artificial Intelligence Research 63(1): 743-788, 2018. Type: Article

One application area where deep learning methods have shown remarkable success is natural language processing (NLP). The speech recognition and language understanding of virtual agents like Alexa and Siri, automatic translation between languages, ...

Oct 2 2019  
 
 
 
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