World's Best Scientists 2026 revealed!
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Computer Science
UK
2026

D-Index & Metrics

Computer Science

D-Index
119
Citations
132469
World Ranking
148
National Ranking
6

Research.com Recognitions

  • 2026 - Research.com Computer Science in United Kingdom Leader Award
  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

Pushmeet Kohli is affiliated with DeepMind in the United Kingdom. Their research spans the fields of Computer Science and Biochemistry, Genetics and Molecular Biology, with significant contributions in both areas.

Their subfields of study include Artificial Intelligence, Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, and Computer Vision and Pattern Recognition. These domains reflect a broad interdisciplinary approach linking computational methods to biological and chemical sciences.

Kohli's main research topics focus on Protein Structure and Dynamics, Adversarial Robustness in Machine Learning, Machine Learning in Bioinformatics, Enzyme Structure and Function, Machine Learning in Materials Science, RNA and protein synthesis mechanisms, and Machine Learning and Data Classification. These topics indicate a consistent interest in applying machine learning techniques to complex biological problems.

Significant recent papers authored or co-authored by Kohli include:

  • Highly accurate protein structure prediction with AlphaFold (2021, Nature)
  • Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, Nature)
  • AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021, Nucleic Acids Research)
  • Protein complex prediction with AlphaFold-Multimer (2021, bioRxiv - Cold Spring Harbor Laboratory)
  • Improved protein structure prediction using potentials from deep learning (2020, Nature)

Their work has been published in various venues, most frequently in arXiv (Cornell University) with 32 publications, followed by Nature with 16, Science with 5, bioRxiv (Cold Spring Harbor Laboratory) with 3, and Nature Medicine with 3 papers. This indicates an active engagement with both preprint and peer-reviewed scientific communication channels.

Kohli has collaborated extensively with a number of researchers. Frequent co-authors include Demis Hassabis, John Jumper, Andrew Senior, K Taki, and Alexander Pritzel. These partnerships highlight a network of collaboration in advancing artificial intelligence and biological structure prediction.

Best Publications

  • Highly accurate protein structure prediction with AlphaFold

    John M. Jumper;Richard O. Evans;Alexander Pritzel;Tim Green

  • AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.

    Mihaly Varadi;Stephen Anyango;Mandar Deshpande;Sreenath Nair

  • Indoor segmentation and support inference from RGBD images

    Nathan Silberman;Derek Hoiem;Pushmeet Kohli;Rob Fergus

  • KinectFusion: Real-time dense surface mapping and tracking

    Richard A. Newcombe;Shahram Izadi;Otmar Hilliges;David Molyneaux

  • Protein complex prediction with AlphaFold-Multimer

    Richard Evans;Michael O'Neill;Alexander Pritzel;Natasha Antropova

  • Improved protein structure prediction using potentials from deep learning

    Andrew W. Senior;Richard Evans;John Jumper;James Kirkpatrick

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Highly accurate protein structure prediction for the human proteome

    Kathryn Tunyasuvunakool;Jonas Adler;Zachary Wu;Tim Green

  • KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera

    Shahram Izadi;David Kim;Otmar Hilliges;David Molyneaux

  • Effective gene expression prediction from sequence by integrating long-range interactions.

    Žiga Avsec;Vikram Agarwal;Daniel Visentin;Joseph R. Ledsam

  • Robust higher order potentials for enforcing label consistency

    P. Kohli;L. Ladicky;P. Torr

  • Associative hierarchical CRFs for object class image segmentation

    L'ubor Ladicky;Chris Russell;Pushmeet Kohli;Philip H.S. Torr

  • Deep convolutional inverse graphics network

    Tejas D. Kulkarni;William F. Whitney;Pushmeet Kohli;Joshua B. Tenenbaum

  • Efficient Human Pose Estimation from Single Depth Images

    Jamie Shotton;Ross Girshick;Andrew Fitzgibbon;Toby Sharp

  • Holoportation: Virtual 3D Teleportation in Real-time

    Sergio Orts-Escolano;Christoph Rhemann;Sean Fanello;Wayne Chang

  • Discovering faster matrix multiplication algorithms with reinforcement learning

    Unknown

  • A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories

    Nasrin Mostafazadeh;Nathanael Chambers;Xiaodong He;Devi Parikh

  • Personality and patterns of Facebook usage

    Yoram Bachrach;Michal Kosinski;Thore Graepel;Pushmeet Kohli

  • Accurate, Robust, and Flexible Real-time Hand Tracking

    Toby Sharp;Cem Keskin;Duncan Robertson;Jonathan Taylor

  • Fusion4D: real-time performance capture of challenging scenes

    Mingsong Dou;Sameh Khamis;Yury Degtyarev;Philip Davidson

  • The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

    Jiayuan Mao;Chuang Gan;Pushmeet Kohli;Joshua B. Tenenbaum

  • On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

    Sven Gowal;Krishnamurthy Dvijotham;Robert Stanforth;Rudy Bunel

Frequent Co-Authors

Philip H. S. Torr
Philip H. S. Torr University of Oxford
Jamie Shotton
Jamie Shotton Microsoft (United States)
Shahram Izadi
Shahram Izadi Google (United States)
Carsten Rother
Carsten Rother Heidelberg University
Rishabh Singh
Rishabh Singh Google (United States)
David Kim
David Kim Microsoft (United States)
Yoram Bachrach
Yoram Bachrach DeepMind (United Kingdom)
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Antonio Criminisi
Antonio Criminisi Microsoft (United States)

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