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AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Is our machines learning?
Over the last two decades, the machine learning community has made amazing progress. Computers can recognize speech, diagnose cancer, and drive cars. Computers can do things that we could barely imagine years ago. So why aren’t there more products that use machine learning? Why do we need machine learning PhDs to train successful models? Why is building software powered by machine learning so hard? I think about these questions, search for answers, and in the process, end up building software that makes it easier for more people to use machine learning.
University of Washington
Doctor of Philosophy (Ph.D.), Computer Science
January 1, 2005 – January 1, 2012
Stanford University
MS, Computer Science (focus on AI)
January 1, 2003 – January 1, 2005
Carnegie Mellon University
BS, Computer Science and Human Computer Interaction
January 1, 1999 – January 1, 2003
Meta
Unstable Chimera
April 1, 2024 – Present
Seattle, Washington, United States · On-site
My very own startup
Doer of things
September 1, 2023 – April 1, 2024
Seattle, Washington, United States
Apple
Usable ML Hipster
June 1, 2017 – June 1, 2023
Greater Seattle Area
Columbia University in the City of New York
Edumacator
September 1, 2013 – January 1, 2014
New York, NY
Blacksmith
December 1, 2012 – June 1, 2017
Seattle, New York
Microsoft
Research Intern
September 1, 2009 – January 1, 2010
Redmond
University of Washington
Graduate Student
September 1, 2005 – August 1, 2012
Stanford University
Graduate Student
January 1, 2003 – January 1, 2005
Carnegie Mellon University
Undergrad Researcher
January 1, 2000 – January 1, 2002
Introduction to Data Science
September 1, 2013 – Present
This course serves as an introduction to the interdisciplinary and emerging field of data science. Students will learn to combine tools and techniques from statistics, computer science, data visualization and the social sciences to solve problems using data. Central threads include: (1) the data science process from data collection to product, (2) tools for working with both big and small datasets, (3) statistical modeling and machine learning, and (4) real world topics and case studies. The course consists of: (1) core lectures by the instructors, (2) guest lectures from data scientists who are experts in their fields, and (3) a course-long project. Topics and tools will include data wrangling and munging, machine learning algorithms, statistical models, data visualization, data journalism, R, ethics, MapReduce, and data pipelines.
Cultural Fit Analysis
The candidate's diverse experience across academia and industry, including leading innovative teams at major tech companies, suggests a strong cultural fit for roles requiring innovation, leadership, and a blend of research and product development. Their academic background and early research in self-driving cars align well with a Computer Vision role. The entrepreneurial stint, though brief, also indicates a proactive and adaptable mindset. The project descriptions, while limited, show an interest in interdisciplinary problem-solving.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, initiative, and a user-centric approach to technology development. Their experience in teaching and leading teams suggests excellent communication and collaboration skills. The focus on understanding user workflows and building tools to ease their jobs indicates a problem-solving and empathetic operational fit. However, without specific psychometric test results, a detailed assessment of stress handling or team collaboration beyond what's inferred from leadership roles is not possible.