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Staff Scientist @ Coupang | Ex-Amazon, Ex-Microsoft, Ex-UW Lecturer l | Hobbyist Woodworker/Maker
Full Resume: http://tiny.cc/hany I am a senior applied machine learning scientist at Amazon and before that at Microsoft. I led projects and teams in the fields of modeling, recommendation systems, contextual suggestions, ranking, at- tribution, computer vision, natural language processing, language models, data mining, metrics, and modeling user behavioral patterns specifically intention. I also enjoy teaching and worked as a machine learning instructor at the University of Washington continuing education program teaching Introduc- tory and Advanced Machine learning courses. At Amazon Photos I own the facial recognition and clustering space. Before that I led the efforts of developing automation pipelines to to facilitate machine learning models deployment to production and the marketing attribution space. At Microsoft I worked in the conversation and language understanding group as a part of Cortana and substrate intelligence MSAI. Prior to my work in Cortana, I was in the Time & Location Intelligence team which is a part of Outlook. I have a PhD in computer science majoring in information retrieval and web science; with a masters degree in computer vision & artificial intelligence. My doctoral research is centered in the field of information retrieval and social media mining, focusing on the concept of user intention in sharing resources and its relation to information dissemination, preservation, and discovery. I enjoy working on real-world problems, developing cutting edge methodologies on large scale data sets and taking them to production in real-life scenarios, then extending the outcome of such methodologies to patenting or publishing.
Old Dominion University
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2010 – January 1, 2015
Universitat Autònoma de Barcelona
Master's degree, Computer Vision and Artificial Intelligence
January 1, 2008 – January 1, 2009
Alexandria University
Bachelor of Engineering (BEng), Computer Systems Engineering
January 1, 2003 – January 1, 2008
Old Dominion University
Doctor of Philosophy - PhD, Computer Science
N/A – Present
Coupang
Staff Machine Learning Engineer
March 1, 2025 – Present
Seattle, Washington, United States
Amazon
Senior Applied Scientist
April 1, 2020 – October 1, 2024
Seattle, Washington, United States
University of Washington
Lecturer - Advanced Machine Learning
August 1, 2018 – January 1, 2020
University of Washington
Lecturer - Introduction to Machine Learning
August 1, 2018 – January 1, 2020
Magalix
Consultant Head of Machine Learning
July 1, 2017 – August 1, 2018
Greater Seattle Area
Microsoft
Applied Machine Learning Scientist II
July 1, 2017 – April 1, 2020
Microsoft
Data & Applied Scientist
July 1, 2015 – July 1, 2017
National University of Singapore
Invited Researcher
February 1, 2014 – May 1, 2014
Singapore
Old Dominion University
Instructor
January 1, 2012 – May 1, 2015
Microsoft
Engineering Intern
June 1, 2011 – September 1, 2011
Software Development Intern
June 1, 2010 – September 1, 2010
Old Dominion University
Graduate Research Assistant
January 1, 2010 – May 1, 2015
Old Dominion University
Teaching Assistant
January 1, 2010 – December 1, 2013
Microsoft
Research Intern
September 1, 2009 – December 1, 2009
Cairo
Cultural Fit Analysis
The candidate's diverse experience across major tech companies (Amazon, Microsoft, Google, Coupang) and academic institutions (University of Washington, Old Dominion University) demonstrates adaptability and exposure to various organizational cultures. Their involvement in teaching and research, alongside industry roles, suggests a blend of practical application and continuous learning, which aligns well with innovation-driven environments. The progression through senior roles indicates ambition and a drive for impact.
Soft Skills & Operational Fit
The candidate's resume highlights leadership roles (Staff ML Engineer, Senior Applied Scientist, Consultant Head of ML, Lecturer) and mentions 'Communication' as a skill, suggesting strong soft skills. Experience in leading projects and teams, as well as teaching, indicates an ability to articulate complex ideas and guide others. The breadth of experience across different companies and roles suggests adaptability and a strong operational fit for senior ML engineering roles.