Distinguished ACM Speaker:
Based in United Kingdom
Pushmeet Kohli is a research scientist in the Machine Learning and Perception group at Microsoft Research Cambridge, and an associate of the Psychometric Centre and Trinity Hall, University of Cambridge.
Pushmeet’s research revolves around Intelligent Systems and Computational Sciences, and he publishes in the fields of Machine Learning, Computer Vision, Information Retrieval, and Game Theory. His current research interests include “human behaviour analysis” and the “prediction of user preferences”. Pushmeet is interested in designing autonomous and intelligent computer vision, bargaining and trading systems which learn by observing and interacting with users on social media sites such as Facebook. He is also investigating the use of new sensors such as KINECT for the problems of human pose estimation, scene understanding and robotics.
Pushmeet has won a number of awards and prizes for his research. His PhD thesis, titled "Minimizing Dynamic and Higher Order Energy Functions using Graph Cuts", was the winner of the British Machine Vision Association’s “Sullivan Doctoral Thesis Award”, and was a runner-up for the British Computer Society's “Distinguished Dissertation Award”. Pushmeet’s was also one of the two United Kingdom nominees for the ERCIM Cor-Bayern award in 2010. Pushmeet’s papers have appeared in SIGGRAPH, NIPS, ICCV, AAAI, CVPR, PAMI, IJCV, CVIU, ICML, AISTATS, AAMAS, UAI, ECCV, and ICVGIP and have won best paper awards in ICVGIP 2006, 2010 and ECCV 2010. His research has also been the subject of a number of articles in popular media outlets such as Forbes, The Economic Times, New Scientist and MIT Technology Review.
- Developing Machines that See:
The last couple of decades have seen computing devices become an increasingly important part of our lives. In this talk, I will discuss some of the work my colleagues and I have been doing at Microsoft Research Cambridge to make such machi...
- Discrete Optimization for Computer Vision:
Many problems in computer vision and machine learning require inferring the most probable values of certain hidden or unobserved variables. This inference problem can be formulated in terms of minimizing a function of discrete variables. The sca...
- Teaching Machines to Understand Users:
Since the time computers were first invented, scientists have been trying to make them intelligent. Artificial intelligence has become even more important today given that humans are increasingly becoming dependent on computing devices in their ...