Seminar W2024- Dr. Ghader Manafiazar

Date and Time

Location

Online via Microsoft Teams

Details

 
This week we are happy to have Dr. Ghader Manafiazar, Assistant Professor in the Department of Animal Science and Aquaculture at Dalhousie University, along with his students Hamza Jawad and Olufemi Osonowo, presenting for us on Friday, March 8th, 2024. The seminar will begin at 1:30 PM EDT/EST on the virtual platform Microsoft Teams. The presentation title is: “Utilizing metabolomics and genomic information to improve feed efficiency and disease resilience in sheep”.
 
Speaker Biography: Dr. Ghader Manafiazar, is an Assistant Professor in the Department of Animal Science and Aquaculture at Dalhousie University (https://www.linkedin.com/in/manafiazar/). Dr. Manafiazar's research focuses on improving livestock production systems with a specific emphasis on feed efficiency, greenhouse gas (GHG) emissions reduction, and the development of resilient traits. He employs cutting-edge techniques, including genetics/genomics, nutrition, and machine learning, to enhance the cost-effectiveness and profitability of the livestock industry. He has also been working on the use of machine learning algorithms to classify beef cattle for feed efficiency and stayability. Importantly, Dr. Manafiazar's team pioneered the introduction of 'on-farm' methane measurement systems to Canada in 2014, making him one of the first individuals in Canada to non-invasively measure methane emissions from dairy cows in the country. In addition to academic experience, Dr. Manafiazar works closely with industry as a Research and Development Associate with a beef genetics service provider and co-founder of iClassifier, an AI-powered solution for efficient monitoring of animals and improved farming outcomes.
 
*Recordings of previous CGIL seminars are available at: https://www.youtube.com/channel/UCAQ_5WCTMRQ6Gs35yROqGIQ/featured
 
________________________________________________________________________________
Microsoft Teams meeting
Join on your computer, mobile app or room device

Click here to join the meeting

Meeting ID: 271 174 183 05
Passcode: mSDD7W

________________________________________________________________________________

Share this page

Events Archive