Founders University Professor at Carnegie Mellon University
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Researcher in Machine Learning, Artificial Intelligence, and Cognitive Neuroscience. I founded the Machine Learning Department at Carnegie Mellon University, and led it as Department Head for its first 10 years. Have done recent research on how AI can improve education, and the impact of AI on the future of work. I consult widely for companies who use AI in their products and internal processes. Check out my webpage http://www.cs.cmu.edu/~tom
Stanford University
PhD, Computer Science, Artificial Intelligence
January 1, 1973 – January 1, 1979
Massachusetts Institute of Technology
Bachelor’s Degree, Electrical Engineering
January 1, 1969 – January 1, 1973
Stanford University
Visiting Scholar at Stanford Digital Economy Lab
July 1, 2024 – Present
Santa Fe Institute
Science Board
January 1, 2019 – Present
Carnegie Mellon University
Interim Dean, School of Computer Science
October 1, 2018 – September 1, 2019
Squirell AI
Chief AI Scientist
September 1, 2018 – February 1, 2020
Laer AI
Member Board of Directors
January 1, 2018 – April 1, 2025
Megagon Labs
Advisor and Research Fellow
January 1, 2015 – Present
Ai2
Science Board
January 1, 2013 – Present
Carnegie Mellon University
Founders University Professor
June 1, 2009 – Present
Carnegie Mellon University
University Professor, Carnegie Mellon University
January 1, 2005 – April 1, 2025
Whizbang! Labs
Chief Scientist
January 1, 2000 – January 1, 2001
Carnegie Mellon University
Founders University Professor
July 1, 1986 – Present
Carnegie Mellon University
Professor, Machine Learning Department
July 1, 1986 – January 1, 2005
Cultural Fit Analysis
The candidate's background is heavily weighted towards academic research, leadership, and advisory roles in AI. While this demonstrates exceptional intellectual prowess and influence in the field, it may not directly align with the day-to-day responsibilities and cultural expectations of a hands-on 'Software Engineer' role in a typical product development environment. The breadth of experience is in high-level strategy and research rather than diverse project implementation.
Soft Skills & Operational Fit
The candidate's extensive leadership and academic roles suggest strong communication, strategic thinking, and collaboration skills. Their long tenure in research and academic settings indicates a high degree of intellectual curiosity and problem-solving ability. However, the provided data does not offer direct insights into operational fit within a typical corporate software engineering team, as most roles are academic, advisory, or high-level scientific leadership.