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Friday, November 12, 2021 2:00 pm - 2:00 pm EST (GMT -05:00)

Dual Seminars • Systems and Networking

Please note: These two talks — the first a seminar, the second a PhD seminar — will be given sequentially online.

Bryant Curto, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Martin Karsten

photo of Griffin Keglevich, Ruslan Nikolaev and Rob Khazzam
Thursday, November 4, 2021

Company co-founded by Cheriton School of Computer Science alums attracts $37-million CAD financing round

A company founded by two University of Waterloo graduates is on track to become a giant in the Canadian business-to-business credit card market.

Float, which was co-founded by Cheriton School of Computer Science alums Griffin Keglevich and Ruslan Nikolaev, recently brought in a whopping $37 million CAD in new investments.

The company’s third co-founder and current CEO, Rob Khazzam, who joined Float in March 2021, brings a wealth of experience in business and finance, having previously worked for Uber and various venture capital advisory firms.

Thursday, November 11, 2021 11:30 am - 11:30 am EST (GMT -05:00)

PhD Seminar • Cryptography, Security, and Privacy (CrySP) • Attacks and Fixes on Distributed Key Generation Protocols

Please note: This PhD seminar will be given online.

Chelsea Komlo, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Ian Goldberg, Douglas Stebila

Monday, November 22, 2021 1:00 pm - 1:00 pm EST (GMT -05:00)

PhD Defence • Human-Computer Interaction • Affective Expressions in Conversational Agents for Learning Environments: Effects of Curiosity, Humour, and Interjections

Please note: This PhD defence will be given online.

Jessy Ceha, PhD Candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Edith Law

photo of Fatema Tuz Zohora
Thursday, October 28, 2021

Computer scientists developing method to identify disease biomarkers with high accuracy

Researchers at the Cheriton School of Computer Science are incorporating a deep learning network into a more accurate method to identify disease biomarkers. The new method achieves up to 98 per cent detection of peptide features in a dataset. That means scientists and medical practitioners have a greater chance of discovering possible diseases through tissue sample analysis.