Teaching Philosophy
My philosophy of teaching is informed by my understanding of how learning occurs, and consequently how I think I can intervene in the student learning and growth process. At the root of my reasoning is a simple observation that does not - surprisingly - come from my background in cognitive science but from personal experience: there are contents that students won't understand, learn and memorize on their own on a sunny Sunday afternoon while recovering from their Saturday evening! Signal processing, advanced algorithmics and software engineering, mathematics and artificial intelligence, or more generally any dense and formal theoretical material is what students need a professor for. It has always been my policy to prioritize the transmission of these contents for which the student autonomy is not sufficient.
On the reverse, there are skills and abstract notions that are hardly transmissible in a traditional academic way: creativity, aesthetic qualities, originality, etc. These are often best delivered through iterative work on projects with individual and individualized critic and feedback. Therefore, all knowledge and skills cannot be taught the same way and it is important to teach in a methodical, dry but clear manner what needs to be presented that way and to have a more dramatic, metaphorical way of transmitting less palpable knowledge or skills when appropriate. Making use of different forms of media, formats and a range of styles is crucial to supplement the traditional classroom lecture format. Surely, teaching at the School for Interactive Art and Technology implies taking advantage of the variety of technologies that are now available for supporting knowledge and know-how transmission and acquisition. We did not wait for COVID-19 to innovate!
More prosaically, teaching involves having appropriate goals for students. One key factor here is to differentiate students according to courses and levels. One can not teach a first-year freshman with the same methods as a doctoral candidate. In the first case, while university students are all adults, the main goal behind the mere transmission of solid, stable knowledge is to teach them what it means to learn in an academic context. In the latter case, this autonomy being (hopefully) already acquired, the focus has to be on pushing the research and innovation skills of the student to their limits. Also, as the student matures (e.g. at the Ph.D. level) the role of the professor progressively shades into the one of a mentor.
These conceptualizations of learning and teaching are the main ingredients for implementing my philosophy of teaching by defining goals for students' development at the intellectual and practical levels. To know whether this is taking place is hard to gauge and takes a certain faith in the process, but the success of our alumni is a testament, at the very least, of their talent ;)
Courses
- Advanced Generative Art and Computational Creativity (Kadenze)
Simon Fraser University
This course proposes a deepened survey of current practices in generative arts and computational creativity with an emphasis on the formal paradigms and algorithms used for generation. See the Kadenze course page for more details. - Artificial Intelligence in Computational Art and Design (IAT-813)
Simon Fraser University
This class covers a modern approach of artificial intelligence geared towards application in creative and entertainment computing (as opposed to military applications, for example). - Foundations of Computational Art and Design (IAT-800)
Simon Fraser University
Most of SIAT graduate students will eventually be involved in interdisciplinary projects in which the ability to read or write programs will be a strong asset, if not a necessity. In order to develop students’ computer literacy, this course introduces the basics of data structure and algorithmics (the language of programming), object-oriented programming and JAVA/Processing software design skills. - Generative Art and Computational Creativity (Kadenze)
Simon Fraser University
This course proposes an in-depth introduction and overview of the history and practice of generative arts and computational creativity with an emphasis on the formal paradigms and algorithms used for generation. See course page for more details. - Metacreation (IAT-811)
Simon Fraser University
This graduate course covers a wide range of techniques from Agent and Multi-agent Systems , Machine Learning and Artificial Life that can be applied to creative/generative applications in art and entertainment (see the course Web page for more details). - MUME Tutorial (Full day)
IJCAI, 2015
This full day tutorial aims at introducing the field of musical metacreation (MUME), also known as generative music, and computational creativity. We present and discuss its current developments, promises, and challenges, with a particular focus on IJCAI-relevant aspects of the field.
Link - MUME Tutorial (Three hours)
NIME, 2014
This three hour tutorial aims at introducing the field of musical metacreation (MUME) and its current developments, promises, and challenges, with a particular focus on NIME-relevant aspects of the field.
Link - Performance and Technology (IAT-881)
Simon Fraser University
Co-taught with Thecla Schiphorst with the assistance of Greg Corness and Henry Daniel (SCA/SFU) , Robert Gardiner (UBC), Arne Eigenfeldt (SCA/SFU), Jammie Griffiths, Robyn Oppenheimer (U of Washington), Jonathan Aitken (Ryerson University) and Chris Ziegler. This graduate course brings together performers from the school for the comtemporary arts and graduate students of SIAT in a project-based exploration of the use and development of new technologies for performative practices. This course is given in SIAT's Black Box. - Sound Design (IAT-340)
Simon Fraser University
This undergraduate course covers the basic conceptual and technical knowledge required for successful sound design. Laboratories cover ProTools and MAX/MSP (see the course Web page 2008 or 2009 for more details).
