I have multiple open positions for post-docs, research assistants, and interns in my team. Please email me if you are interested. You might also want to see this advert for more details.
(July 24, 2017) I will be giving a talk at ERATO in Tokyo on Aug. 3, 2017
(July 1, 2017) I gave a talk at ATR in Kyoto on July 10, 2017
(June 25, 2017) Salma El Aloui (from École Polytechnique) and Zuozhu Liu (from Singapore University of Technology and Design) join as interns.
(June 19, 2017) Prof. Havard Rue from KAUST visiting from Jun 19-25.
I am an area chair for NIPS 2017, a reviewer for AAAI, ICML and UAI 2017, and an action-editor for JMLR.
New paper accepted in the IEEE conference on Security and Privacy, 2017. Currently most smartphones allow users to set their privacy preferences only during the installation of an app (e.g. answers to questions like "Facebook would like to access your location: allow/deny?"). Such static policies may force users to overshare sensitive data because it does not take the context into account (e.g. sharing location might be ok when we are using the app, but not ok when the app is running in the background). In this paper, we propose a machine learning framework to automatically make such decisions on a user's behalf. Our method uses context to achieve an accuracy of around 80% which is a 50% improvement over the static-policies currently used in Android and iOS.
New paper accepted in the conference Building-Simulation 2017. In this paper, we show that Gaussian Process (GP) regression can reliably predict the energy consumption of a building design. Traditionally, designers rely on simulators to assess energy efficiency of their designs, but this could be painfully slow. We show that a GP based emulator gives accurate results within seconds. This work is in collaboration with Dr. Parag Rastogi and Prof. Marilyn Anderson from EPFL.
"My main goal is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings."
My current focus is to understand the role of uncertainty in learning and to develop fast algorithms for uncertainty estimation.
We are also working on the following application projects: