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Training Multimodal Generative Models at Microsoft Research
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Harvard University
Doctor of Philosophy (Ph.D.), Engineering Sciences
January 1, 2009 – January 1, 2014
University of Pennsylvania
MSE, Robotics
January 1, 2007 – January 1, 2009
University of Pennsylvania
BSE, Mechanical Engineering, Mathematics
January 1, 2004 – January 1, 2009
The Wharton School
BSE, Finance
January 1, 2004 – January 1, 2009
Microsoft
Principal Machine Learning Engineer - Microsoft Research
June 1, 2019 – Present
Greater Boston
American College of Radiology
Senior Scientist in Machine Learning - Data Science Institute
September 1, 2017 – Present
MGH & BWH Center for Clinical Data Science
Director of Machine Learning
May 1, 2017 – June 1, 2019
Greater Boston
Fitbit
Senior Research Scientist (Machine Learning & Modeling)
October 1, 2015 – April 1, 2017
Greater Boston
Vecna Technologies
Team Lead, Robot Software
January 1, 2015 – October 1, 2015
Vecna Technologies
Research Scientist
July 1, 2014 – October 1, 2015
Harvard University
Teaching Fellow - Parallel Programming & Machine Learning
August 1, 2011 – December 1, 2011
Cambridge, MA
Harvard University
Teaching Fellow - Mechanical Design & Machining
August 1, 2010 – December 1, 2010
Cambridge, MA
Cultural Fit Analysis
The candidate's extensive experience across diverse organizations (Microsoft, American College of Radiology, MGH & BWH, Fitbit, Vecna Technologies) and academic background demonstrates a broad range of interests and adaptability. Their involvement in both research and productization roles suggests a pragmatic approach to problem-solving, which aligns well with a culture that values both innovation and practical application. The focus on health data and robotics also indicates a breadth of domain expertise.
Soft Skills & Operational Fit
The candidate's experience in leading teams, advising organizations on ML policy, and managing development from concept to productization suggests strong leadership, communication, and project management skills. Their diverse roles indicate adaptability and a collaborative approach, which are crucial for operational fit in a senior ML role.