Short Bio

I am a final year PhD student in Sebastian Riedel's Machine Reading group at University College London. My studies are generously supported by a Microsoft Research PhD Scholarship.

I am interested in representation learning for natural language processing and automated knowledge base construction and inference. My research concerns the intersection between deep learning and first-order logic reasoning, as well as natural language inference. I worked on regularizing representations by first-order logic rules and recently became interested in neural-symbolic approaches to theorem proving and neural program induction.

I was fortunate to intern at Google DeepMind in summer 2015 under the supervision of Edward Grefenstette. In the past, I contributed to the declarative, functional machine learning language and library Wolfe.

In 2012 I received my diploma 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, where I developed software for named entity recognition of chemicals, mutations, proteins and diseases in biomedical publications.

I am co-organizer of the 1st NIPS 2016 Workshop on Neural Abstract Machines & Program Induction (NAMPI), the 5th NAACL 2016 Workshop on Automated Knowledge Base Construction (AKBC), as well as scientific advisor for the London deep learning startup Bloomsbury AI.


26/03/2017 I am invited to speak about deep learning and automated proving at the 2nd Conference on Artificial Intelligence and Theorem Proving in Obergurgl, Austria.
10/12/2016 I am co-organizing the 1st Workshop on Neural Abstract Machines & Program Induction (NAMPI) at NIPS 2016 in Barcelona, Spain.
16/11/2016 Talk at Imperial College London.


30/07/2016 Papers on Lifted Rule Injection for Relation Embeddings and Stance Detection with Bidirectional Conditional Encoding got accepted at EMNLP 2016!
17/06/2016 I co-organized the 5th Workshop on Automated Knowledge Base Construction (AKBC) at NAACL 2016 in San Diego, California.
10/06/2016 Talk at the University of Cambridge Natural Language and Information Processing Seminar Series.
18/04/2016 Application note on SETH got accepted at Bioinformatics.
13/04/2016 Guest lecture on deep learning for natural language processing at General Assembly's data science course in London.
04/04/2016 Papers on Learning Knowledge Base Inference with Neural Theorem Provers and Regularizing Relation Representations by First-order Implications got accepted at AKBC 2016!



Neural Theorem Provers

AKBC 2016

End-to-end differentiable counterparts of discrete theorem provers that learn representations of symbols and rules.

Reasoning about Entailment with Neural Attention

ICLR 2016

Long short-term memory recurrent neural networks with word-by-word attention for recognizing textual entailment.

Injecting Logical Background Knowledge into Embeddings

AKBC 2016, NAACL 2015, SP14, StarAI 2014

Differentiable logical formulae for regularizing vector representations of relations and entity-pairs. Declarative, Functional Machine Learning

StarAI 2014

Scala domain-specific language for probabilistic and differentiable programming in a declarative and functional way.


SemEval 2013, BioCreative 2013, Bioinformatics 2012

Probabilistic graphical models for bio-molecular event-extraction and named entity recognition of chemicals, mutations, proteins and diseases.


tim [dot] rocktaeschel [at] gmail [dot] com

G.6 5th Floor, One Euston Square, 40 Melton Street, London NW1 2FD, United Kingdom