Pushmeet Kohli

Pushmeet Kohli is a computer scientist at Google DeepMind where he heads the "Robust and Reliable AI" and "AI for Science" teams. Before joining DeepMind, he was partner scientist and director of research at Microsoft Research and a post-doctoral fellow at the University of Cambridge. Kohli's research investigates applications of machine learning and computer vision.[1][3] He has also made contributions in game theory, discrete algorithms and psychometrics.[4]

Pushmeet Kohli
Alma materNational Institute of Technology, Warangal
Oxford Brookes University (PhD)
Scientific career
FieldsMachine learning
Artificial intelligence
Computer vision[1]
InstitutionsGoogle
DeepMind
ThesisMinimizing dynamic and higher order energy functions using graph cuts (2007)
Doctoral advisorPhilip Torr[2]
Websiteresearch.google/people/105667

Education

Kohli was educated at National Institute of Technology, Warangal and Oxford Brookes University where his PhD awarded in 2007[5] was supervised by Philip Torr.[2]

Career and research

Current and previous research projects include:

  • AlphaFold[6]
  • Robust and Reliable AI[7]
  • Neural Program Synthesis[8]
  • Probabilistic Programming[9]
  • 3D-scene Reconstruction and Understanding
  • MAP Inference in Higher Order Graphical Models
  • Community based Crowdsourcing of Data for Training AI Models[10]
  • Behavioral analysis and Personality prediction using on Online networks[11][12]
  • Human Pose Estimation using the Kinect[13]
  • Video Editing (Unwrap Mosaics)[14]


Awards and honours

Kohli is the recipient of the British Machine Vision Association and Society for Pattern Recognition (BMVA) Sullivan Prize. His papers have received awards at UAI 2018,[15] Conference on Computer Vision and Pattern Recognition (CVPR) 2015, International World Wide Web Conference (WWW2014),[16] International Symposium on Mixed and Augmented Reality (ISMAR) 2011 and European Conference on Computer Vision (ECCV) 2010.[17]

References

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.