I am a research scientist in deep learning and recently joined Google DeepMind on a quest to artificial general intelligence.

I am passionate about deep learning, in particular recurrent neural networks, statistical language models and time series analysis – passions I acquired during my PhD at New York University under the guidance of neural network guru Yann LeCun. I particularly love to solve real world problems and have focused my studies on several different projects, including epileptic seizure prediction from EEG and the inference of gene regulation networks. In my previous research appointment as a member of technical staff at Bell Labs (2011-2013, working with the amazing Tin Kam Ho, who among other things pioneered Random Decision Forests), I helped deliver an electric load forecasting solution to a power utility, hacked a Kinect-powered mobile robot that mapped the WiFi signal strength (for indoor geo-localization) and coded Simultaneous Localization and Mapping from the pocket (reconstructing a 3D trajectory using only sensors present on a smartphone). Prior to Google DeepMind, I worked as a data scientist and software engineer at Microsoft Bing in London (2013-2014), crunching big data problems, and employing deep learning and natural language processing methods for enhancing search query formulation.

I love backpacking around the world, opera, theatre and improv theatre acting. Before moving to London, I used to be part of New York-based volunteering improv comedy group Cherub Improv.