Where I express personal thoughts in words
Alba Cervera-Lierta is a postdoctoral researcher at the Alán Aspuru-Guzik group at the University of Toronto. She obtained her Ph.D. at the University of Barcelona in 2019. Her background is in particle physics and quantum information theory. She has focused on quantum computation algorithms in the last years, particularly those suited for noisy-intermediate scale quantum computation.
Last month, I had the opportunity to attend the Toronto Machine Learning Summit 2020, organized by the great people at the Toronto Machine Learning Society. I previously attended their MLOps event in the summer, which I also have written an in-depth recap here.
The summit aims to promote and encourage the adoption of successful machine learning initiatives within Canada and abroad. There was a variety of thought-provoking content tailored towards business leaders, practitioners, and researchers. In this long-form post, I would like to dissect content from the talks that I found most useful from attending the conference.
Luis Serrano is a Quantum AI Research Scientist at Zapata Computing. He is the author of the book Grokking Machine Learning and maintains a popular YouTube channel to explain machine learning in pedestrian terms. Luis has previously worked in machine learning at Apple and Google, and at Udacity as the head of content for AI and data science. He has a Ph.D. in mathematics from the University of Michigan, a master's and bachelor's from the University of Waterloo, and worked as a postdoctoral researcher in mathematics at the University of Quebec at Montreal.
Colleen M. Farrelly is a data scientist whose experience spans biotech, healthcare, pharma, marketing, finance, operations, edtech, manufacturing, and disaster logistics. Her research focuses mostly on the intersection of topology/geometry, machine learning, statistics, and quantum computing. She’s passionate about poetry, surfing, Gators football, and socioeconomics.
