Pushmeet Kohli is a principal research scientist in Microsoft Research and 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.
As of May 2015, I am part of the Microsoft Research Redmond lab, acting as the Machine Learning advisor to the Chief Research Officer, Rick Rashid.
My research concerns the development of intelligent machines – to “teach” computers to (1) understand the behavior and intent of humans, and (2) to correctly interpret (“Perceive” or “See”) objects and scenes depicted in color/depth images or videos. I work in the areas of Computer Vision (RGB and 3D – KINECT), Machine Learning, Discrete Optimization, Behavioural Game Theory, and Human-Computer Interaction.
My current research interests include 3D Reconstruction and Rendering, Probabilistic Programming, Interpretable and Verifiable Knowledge Representations from Deep Models. In terms of applications, I am interested in Conversation agents for Task completion, Machine learning systems for Healthcare and 3D rendering and interaction for augmented and virtual reality.
- 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
In past life, I have also dabbled a bit in model based checking of non-deterministic software systems. Some of my work can be found in Spec Explorer.
My Curriculum Vitae can be found here.