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Kevin Murphy

Kevin Murphy

Principal Scientist / Google DeepMind

CountryUnited States
EducationPhD, University of California, Berkeley
Known ForMachine Learning, Bayesian Statistics, Computer Vision, Artificial Intelligence
Facebook
LinkedIn2.7K
Artificial IntelligenceMachine LearningRobotics
Twitter Followers
64.6K
Years at Google
13+ years
Refereed Papers
50+
Textbook Published
1 (2012)
Search More

About Kevin Murphy

Kevin Murphy is a Principal Scientist at Google DeepMind, specializing in Artificial Intelligence, Machine Learning, and Bayesian modeling. He is a highly respected figure in the field, known for his comprehensive book series on probabilistic machine learning, including the seminal "Machine Learning: A Probabilistic Perspective" (2012). His career includes significant research roles at Google and an Associate Professor position at the University of British Columbia.

Education

University of California, Berkeley
University of California, Berkeley

PhD

1996 - 2002
Field of Study

PhD - Computer Science

University of Pennsylvania
University of Pennsylvania

Master of Engineering (MEng)

1992 - 1994
Field of Study

Master of Engineering (MEng) - Computer Science

University of Cambridge
University of Cambridge

Bachelor of Arts (B.A.)

1989 - 1992
Field of Study

Bachelor of Arts (B.A.) - Computer Science

Work Experience

Google DeepMind
Google DeepMind

Principal Scientist

May 2024 - Present
Position

Principal Scientist

Google Brain
Google Brain

Senior Staff Research Scientist

May 2018 - May 2024
Position

Senior Staff Research Scientist

Google Research
Google Research

Staff Research Scientist

Jul 2011 - May 2017
Responsibilities

AI, Machine Learning, Computer Vision. More details at https://www.cs.ubc.ca/~murphyk/

University of British Columbia
University of British Columbia

Associate Professor

Sep 2004 - May 2012
Responsibilities

Taught several classes in the computer science and statistics departments. Advised 6 PhD students and 10 MSc students. Published over 50 refereed papers. Wrote the best-selling textbook "Machine learning: a probabilistic perspective" (MIT Press 2012).

MIT AI Lab
MIT AI Lab

Postdoctoral researcher

2002 - 2004
Responsibilities

I worked on probabilistic models for computer vision and mobile robotics.

Technical Expertise

Deep expertise across artificial intelligence, probabilistic modeling, and statistical computing, leveraging over two decades of research and development experience.

Core AI/ML

Machine Learning

Artificial Intelligence

Bayesian statistics

Pattern Recognition

Statistical Modeling

Related Domains

Computer Vision

Data Mining

Text mining

Computational Biology

Statistics

Computer Science

Programming & Tools

Python

C++

Matlab

Algorithms

Statistical Computing

Key Expertise

Expertise

Machine Learning

Expertise

Bayesian Statistics

Expertise

Computer Vision

Expertise

Artificial Intelligence

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