doi.bio/john_m_jumper

John Michael Jumper

John Michael Jumper is an American senior research scientist at DeepMind Technologies, London, UK, working on state-of-the-art methods in artificial intelligence to tackle scientific problems. He is best known for his work on AlphaFold, an AI model that predicts protein structures from their amino acid sequences with high accuracy.

Early Life and Education

Jumper received his Bachelor of Science in Physics and Mathematics at Vanderbilt University, USA, in 2007, graduating summa cum laude. He then studied at the University of Cambridge on a Marshall Scholarship, researching adaptive time-step methods for quantum Monte Carlo. He holds an MPhil in Theoretical Condensed Matter Physics from Cambridge and a PhD in Theoretical Chemistry from the University of Chicago, which he completed in 2017. His PhD thesis focused on "New methods using rigorous machine learning for coarse-grained protein folding and dynamics", achieving state-of-the-art accuracy and efficiency.

Career

From 2008 to 2011, Jumper worked as a scientific associate at D. E. Shaw Research, where he performed basic science research using molecular dynamics computer simulations and studied supercooled liquids.

In 2018, Jumper joined DeepMind as a senior research scientist, where he continues to work today. At DeepMind, he collaborates with Demis Hassabis and leads a research group that has produced groundbreaking innovations in the field of AI and protein structure prediction.

Notable Achievements and Awards

Jumper is the recipient of numerous awards for his contributions to science and technology. Here is a list of his notable achievements:

Publications

Jumper has numerous publications in prestigious scientific journals, including:

- Nature ("Protein structure predictions to atomic accuracy with AlphaFold", "AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models", and other articles related to AlphaFold)

John Michael Jumper

John Michael Jumper is an American senior research scientist at DeepMind Technologies, London, UK, working on the application of artificial intelligence to scientific problems. He is best known for his work on AlphaFold, an AI model to predict protein structures from their amino acid sequence with high accuracy.

Education

Jumper received a Bachelor of Science degree in Physics and Mathematics from Vanderbilt University, USA, in 2007. He then studied physics at the University of Cambridge on a Marshall Scholarship, researching adaptive time-step methods for quantum Monte Carlo. He holds a PhD in Theoretical Chemistry from the University of Chicago, which he was awarded in 2017 for his research on using machine learning to simulate protein folding and dynamics.

Career

From 2008 to 2011, Jumper worked as a scientific associate at D. E. Shaw Research, where he performed basic science research using molecular dynamics computer simulation. He developed a novel clustering algorithm to extract key dynamical states from noisy observables in molecular simulations and studied the glass transition of supercooled liquids.

Since 2018, Jumper has worked as a senior research scientist at DeepMind, where he and his colleagues created AlphaFold. This deep learning algorithm performs predictions of protein structure, winning the Critical Assessment of Structure Prediction (CASP) competition in 2020.

Awards and Honours

Notable Works

- Atomic-Level Characterization of the Structural Dynamics of Proteins (2010)

John Michael Jumper

John Michael Jumper is an American senior research scientist at DeepMind Technologies, London, UK, working on artificial intelligence to solve scientific problems. He is best known for his work on AlphaFold, an AI model that predicts protein structures from their amino acid sequences with high accuracy.

Education

Jumper received his Bachelor of Science in Physics and Mathematics from Vanderbilt University, USA, in 2007. He then studied at the University of Cambridge on a Marshall Scholarship, researching adaptive time-step methods for quantum Monte Carlo. He also holds a Master of Science in Theoretical Chemistry and an MPhil in Theoretical Condensed Matter Physics from the University of Cambridge.

In 2011, Jumper began his PhD program at the University of Chicago, where he was awarded a PhD in Theoretical Chemistry in 2017 for his research on using machine learning to simulate protein folding and dynamics. His thesis, "New methods using rigorous machine learning for coarse-grained protein folding and dynamics", was supervised by Tobin Sosnick and Karl Freed.

Career

From 2008 to 2011, Jumper worked as a Scientific Associate, performing basic science research using molecular dynamics computer simulations and studying supercooled liquids. He then joined D. E. Shaw Research, where he worked for three years on molecular dynamics simulations of proteins and supercooled liquids.

Since 2018, Jumper has been a senior research scientist at DeepMind, a research lab acquired by Alphabet Inc., Google's parent company. At DeepMind, he has focused on developing state-of-the-art methods in AI to tackle complex scientific problems.

Awards and Recognition

Jumper has received numerous awards for his contributions to the field of AI and biology. In 2021, he was recognised by the scientific journal Nature as one of the ten "people who mattered" in science. In the same year, he won the BBVA Foundation Frontiers of Knowledge Award in the "Biology and Biomedicine" category.

In 2022, Jumper received the Wiley Prize in Biomedical Sciences, and in 2023, he was awarded the Breakthrough Prize in Life Sciences, the Canada Gairdner International Award, and the Albert Lasker Award for Basic Medical Research, all for his work on AlphaFold.

Publications

Jumper has numerous publications in prestigious scientific journals, including:

He has also authored or co-authored several articles on AlphaFold, which have been cited more than 4000 times.