Andrey Tolstikhin
About Andrey Tolstikhin
Andrey Tolstikhin is a Team Lead in the Core Technology, ML Group at Hinge Health, where he has worked since 2021. He holds a Bachelor's degree in Electrical and Computer Engineering from the University of Toronto and a Master's degree from McGill University.
Work at Hinge Health
Andrey Tolstikhin currently serves as Team Lead for the Core Technology, ML Group at Hinge Health, a position he has held since 2021 in Montreal, Quebec, Canada. In this role, he has executed a redesign of the product’s APIs, which has enhanced user experience and contributed to key customer acquisitions. He has also overseen the adaptation of advanced deep learning methods for production use. Additionally, he led the development of a deep learning-based engine for human pose estimation, managing feature development and ensuring timely delivery.
Education and Expertise
Andrey Tolstikhin studied at the University of Toronto, where he earned a Bachelor of Applied Science (B.A.Sc.) in Electrical and Computer Engineering with a Minor in Engineering Business from 2010 to 2014. He furthered his education at McGill University, obtaining a Master’s Degree in Electrical and Computer Engineering from 2014 to 2016. His academic background provides a strong foundation for his expertise in core technology and machine learning.
Previous Work Experience
Before joining Hinge Health, Andrey Tolstikhin worked at wrnch as Team Lead for Core Tech from 2016 to 2021, where he contributed to the development of technology solutions. He also held a position as a Hardware Engineer at Epiphan Systems for three months in 2015. Earlier in his career, he gained experience as an Engineering Intern at BLINQ Networks in 2012 and EcoVu in 2011, both in the Ottawa, Canada Area. Additionally, he worked as a Teaching Assistant at McGill University from 2015 to 2016.
Technical Contributions
At Hinge Health, Andrey Tolstikhin has made significant technical contributions, particularly in the area of machine learning. He led the development of a deep learning-based engine for human pose estimation, which involved managing feature development and ensuring that projects were delivered on time. His work in adapting deep learning methods for production use has further established him as a key player in the integration of advanced technologies within the company.