Andrej Karpathy

Andrej Karpathy

Computer Scientist, Researcher, Educator

Computer scientist and artificial intelligence researcher

Personal Details
Born	23 October 1986 Bratislava, Slovakia
Nationality	Canadian-American
Career & Education
Occupation	Computer Scientist, Researcher, Educator
Education	University of Toronto; University of British Columbia; Stanford University
Alma Mater	University of Toronto; University of British Columbia; Stanford University
Institution	Stanford University; OpenAI; Tesla
Known For	Deep learning, computer vision, neural networks, artificial intelligence, and autonomous driving
Years Active	2010s–present
Recognition & Links
Website	Official Website

Andrej Karpathy (born 23 October 1986) is a computer scientist and artificial intelligence researcher known for his work in deep learning, computer vision, neural networks, and autonomous driving. He has held research and engineering roles at organizations including OpenAI, Tesla, Inc., and Stanford University.

Karpathy is also known for his educational work in artificial intelligence and machine learning. His lectures, technical articles, and programming resources have been used by students, developers, and researchers studying neural networks and deep learning.

Early life and education

Karpathy was born on 23 October 1986 in Bratislava, in present-day Slovakia. He later moved to Canada and pursued higher education in computer science.

He attended the University of Toronto, where he earned a bachelor's degree in computer science and physics. He subsequently studied at the University of British Columbia, earning a master's degree in computer science.

Karpathy later attended Stanford University, where he completed a PhD in computer science. His doctoral research focused on deep learning and computer vision, particularly methods for teaching neural networks to understand and generate descriptions of visual information.

Stanford University

During his time at Stanford, Karpathy became involved in research involving deep neural networks and computer vision.

He was an instructor for Stanford's CS231n: Convolutional Neural Networks for Visual Recognition course. The course introduced students to the principles and practical implementation of convolutional neural networks for image recognition.

The course materials and lectures became widely used educational resources for people learning modern computer vision and deep learning.

Karpathy's academic work during this period included research into visual recognition, image captioning, recurrent neural networks, and large-scale neural-network training.

OpenAI

Karpathy joined OpenAI as a research scientist during the organization's early years. His work involved deep learning and artificial intelligence research.

At OpenAI, he worked on neural-network methods and contributed to research related to machine learning systems. His work at the organization preceded his later transition into industry.

Tesla

In 2017, Karpathy joined Tesla, Inc. as director of artificial intelligence and Autopilot Vision.

At Tesla, he led teams working on computer vision and neural-network systems for autonomous-driving applications. His responsibilities included developing machine-learning systems capable of interpreting information collected by vehicles.

Tesla's Autopilot technology uses cameras, neural networks, and other vehicle systems to assist with driving tasks. Karpathy's work focused particularly on the computer-vision and machine-learning components of the system.

He became one of the prominent technical leaders associated with Tesla's Autopilot and computer-vision efforts.

Karpathy left Tesla in 2022 after approximately five years with the company.

Return to OpenAI

In 2023, Karpathy returned to OpenAI. His work there included research and development related to artificial intelligence and machine learning.

He later left OpenAI in 2024.

Educational work

Karpathy has produced educational material explaining the principles and implementation of neural networks and deep learning.

His educational resources cover subjects including backpropagation, neural-network architectures, optimization, computer vision, language models, and the implementation of machine-learning systems using programming.

One of his best-known educational projects is micrograd, a small automatic-differentiation engine and neural-network library designed to demonstrate the fundamental mechanisms behind neural-network training.

He has also developed nanoGPT, a compact implementation for training and working with GPT-style language models. The project is intended primarily as an educational and experimental resource for understanding the basic components of modern language-model training.

Research and technical interests

Karpathy's technical interests include:

• Artificial intelligence
• Deep learning
• Machine learning
• Computer vision
• Neural network
• Natural language processing
• Autonomous driving
• Large language models
• Machine-learning education

His work has combined academic research, software engineering, artificial-intelligence development, and technical education.

Public technical work

Karpathy maintains a public collection of technical articles, software projects, and educational material. His writing frequently explains machine-learning concepts through practical programming examples.

His public educational work has contributed to the accessibility of deep-learning concepts for software developers and students who want to understand how neural networks work at an implementation level.

See also

• Artificial intelligence
• Machine learning
• Deep learning
• Computer vision
• Neural network
• OpenAI
• Tesla, Inc.
• Stanford University

External links

• Andrej Karpathy's official website
• Andrej Karpathy on GitHub
• Stanford CS231n
• Stanford University
• OpenAI