Masooma Ali
About Masooma Ali
Masooma Ali is a Senior Applied Research Scientist with extensive experience in machine learning and data analysis, particularly in pulsar astronomy and audio processing. She holds a Master of Science in Astrophysics from The University of Bonn and has worked in various research roles across Canada.
Work at Thomson Reuters
Masooma Ali currently holds the position of Senior Applied Research Scientist at Thomson Reuters, a role she began in 2024. Prior to this, she served as an Applied Research Scientist focusing on Natural Language Processing (NLP) at the same company from 2022 to 2024. Her work involves applying advanced research techniques to enhance the capabilities of NLP systems, contributing to the development of innovative solutions within the organization.
Education and Expertise
Masooma Ali earned her Master of Science in Astrophysics from The University of Bonn, where she studied from 2009 to 2011. Prior to that, she completed her Bachelor of Science in Physics at Delhi University from 2006 to 2009. Her academic background provides a strong foundation for her expertise in areas such as machine learning, speaker identification, and semantic similarity tasks.
Background
Masooma Ali has a diverse professional background that includes significant research experience in both academic and industry settings. She worked as a Doctoral Researcher at the University of New Brunswick from 2014 to 2019, where she also served as a Graduate Teaching Assistant for three years. Following her doctoral work, she transitioned to industry roles, including positions as a Senior Data Scientist at Sesh and a Postdoctoral Researcher at Perimeter, before joining Thomson Reuters.
Achievements in Machine Learning
Throughout her career, Masooma Ali has developed and deployed machine learning models for various applications, including audio sentiment recognition and speech intelligibility scoring. Her work also includes tasks related to jargon detection and active speaker recognition. She possesses strong coding skills that enable efficient iteration through the research-prototype-deploy cycle for machine learning models.