CGIL Seminar W2020- Dr. Dan Tulpan

Date and Time


ANNU 141 


Dr. Dan Tulpan, an Assistant Professor in the Centre for Genetic Improvement of livestock will present a CGIL Seminar on Friday February 14thThe seminar will begin at 1:30 PM in room 141 of the Animal Science & Nutrition building. The title of the presentation is: Classification of A1|A2 Beta Casein Genotypes from Milk MIR Spectra – a Machine Learning Perspective”.

If you wish to attend this seminar remotely, please view the instructions for connecting to Fuze or join the meeting online by clicking here. It is recommended that you check for updates to the Fuze client before joining each seminar. Please connect at least five minutes before the meeting if you plan to do so. For those with the Fuze client installed, which is the preferred method of connection, the meeting ID is 316-25-830. Please mute your microphone and turn off your camera for the presentation itself. After the presentation, you can unmute the microphone, and optionally turn on the camera, if you wish to speak to the room.

Speaker Biography:

Dr. Dan Tulpan is currently an Assistant Professor in the Department of Animal Biosciences at University of Guelph in the area of computational biology. He obtained a PhD from the University of British Columbia, and has over 15 years of experience in the areas of computational biology, bioinformatics, mathematical modelling, machine learning, bio data visualization and computer vision. Dr. Tulpan started his academic research career at the University of Guelph in 2018, after working as a Research Officer at the Digital Technologies Research Centre, National Research Council (Moncton, New Brunswick). At NRC, he headed the Atlantic Bioinformatics Laboratory. Dr. Tulpan’s research program at the University of Guelph is centered on advanced computing and information technology to provide solutions to challenges in animal agriculture, including automatic animal identification, tracking and phenotype acquisition, bioinformatics data analysis and applications of machine learning and artificial intelligence in breeding and other relevant areas for livestock research.

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