I am a postdoctoral researcher with Shimon Whiteson in the Whiteson Research Lab at the University of Oxford. Before, I was a PhD student in the Machine Reading group at University College London under the supervision of Sebastian Riedel. I am a recipient of a Google PhD Fellowship in Natural Language Processing and a Microsoft Research PhD Scholarship.
My research focus is on machine learning models that learn reusable abstractions and that generalize from few training examples by incorporating various forms of prior knowledge. My work is at the intersection of deep learning, reinforcement learning, program induction, logic, and natural language processing.
I was fortunate to work as a Research Intern at Google DeepMind in Summer 2015 under the supervision of Edward Grefenstette. In 2012, I received my Diploma (equivalent to M.Sc) in Computer Science from the Humboldt-Universität zu Berlin. Between 2010 and 2012, I worked as a student assistant and in 2013 as research assistant in the Knowledge Management in Bioinformatics group of Ulf Leser.
I am co-organizer of the 7th UAI 2017 International Workshop on Statistical Relational AI (StarAI), the 1st NIPS 2016 Workshop on Neural Abstract Machines & Program Induction (NAMPI), and the 5th NAACL 2016 Workshop on Automated Knowledge Base Construction (AKBC), as well as scientific advisor for the London deep learning startup Bloomsbury AI.
|12/10/2017||I will be talking at the GPU Techonlogy Conference (GTC Europe) in Munich, Germany.|
|27/09/2017||I am a lecturer at the 2nd Internationael Summer School on Data Science (SSDS) and will talk about Deep Learning for Natural Language Processing.|
|04/09/2017||Our paper on End-to-end Differentiable Proving got accepted for oral presentation (1.2% acceptance rate) at NIPS 2017 in Long Beach, CA.|
|29/08/2017||Invited talk about End-to-end Differentiable Proving at Google Research in Mountain View, CA.|
|15/08/2017||I co-organized the 7th Workshop on Statistical Relational AI (StarAI) at UAI 2017 in Sydney, Australia.|
|26/07/2017||Invited talk about End-to-end Differentiable Proving at DeepMind.|
|14/06/2017||Invited talk about End-to-end Differentiable Proving at the South England Natural Language Processing Meetup.|
|07/06/2017||Invited talk about End-to-end Differentiable Proving at the Future of Humanity Institute.|
|12/06/2017||Paper on Adversarial Sets for Regularising Neural Link Predictors got accepted at UAI 2017 in Sydney, Australia!|
|06/06/2017||I got interviewed by Matt Gardner and Waleed Ammar on the Allen Institute for Artificial Intelligence Podcast.|
|01/06/2017||Pre-print of our paper on End-to-end Differentiable Proving is online!|
|22/05/2017||Invited talk about End-to-end Differentiable Proving at the London Machine Learning Meetup.|
|13/05/2017||Paper on Programming with a Differentiable Forth Interpreter got accepted at ICML 2017 in Sydney, Australia!|
|02/05/2017||I joined the Whiteson Research Lab at University of Oxford as postdoctoral researcher.|
Deep recurrent neural networks with attention mechanisms for recognizing textual entailment.
An end-to-end differentiable interpreter to train neural networks from program input-output data.