Pushmeet Kohli is a principal research manager for Microsoft Research. Formerly, he was the technical advisor to Rick Rashid, the Chief Research Officer of Microsoft. He is also 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 3D Reconstruction and Rendering, Probabilistic Programming, Interpretable and Verifiable Knowledge Representations from Deep Models. He is also interested in Conversation agents for Task completion, Machine learning systems for Healthcare and 3D rendering and interaction for augmented and virtual reality.
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 papers have appeared in Computer Vision (ICCV, CVPR, ECCV, PAMI, IJCV, CVIU, BMVC, DAGM), Machine Learning, Robotics and AI (NIPS, ICML, AISTATS, AAAI, AAMAS, UAI, ISMAR), Computer Graphics (SIGGRAPH, Eurographics), and HCI (CHI, UIST) conferences. They have won awards in ICVGIP 2006, 2010, ECCV 2010, ISMAR 2011, TVX 2014, CHI 2014, WWW 2014 and CVPR 2015. His research has also been the subject of a number of articles in popular media outlets such as Forbes, Wired, BBC, New Scientist and MIT Technology Review. Pushmeet is a part of the Association for Computing Machinery’s (ACM) Distinguished Speaker Program.
- Structured Representations for Visual Knowledge and Commonsense
- Low-level vision problems: Image Segmentation, Dense Stereo, Optical Flow
- Object Recognition and Segmentation
- Human Pose Estimation from KINECT
- Localization and Reconstruction using KINECT
- Verifiable and Interpetable Models
- Probablistic Programming
- MAP Inference in Discrete Models (Discrete Optimization)
- Structured Learning
- Learning of Interactive Systems
- Behavioral game theory research using social networks such as Facebook
- Finding Optimal Coalitions in Cooperative Games
- Reconstructing Coalitional Games
- Computing Optimal Coalition Structures
- Personalizing Search
- Psycho-metric profiles for capturing user intent
Curriculum Vitae can be found here.