Octavian Eugen Ganea

E-mail: octavian.ganea at
Office: ETH Zürich, CAB F 42.1

Resume: PDF, Linkedin

I am a doctoral student in the Data Analytics Lab at ETH Zürich, working with prof. Thomas Hofmann.

Research interests:

I am broadly interested in representation learning for text, graphs or images through statistical or geometric models that could be devised and understood in a mathematically principled manner. In particular, I have recently explored finding and learning latent hierarchical structures in data via hyperbolic geometry.

Interested in: Representation Learning, Hierarchical Structures, Natural Language Processing, Neural Networks and Deep Learning, Word and Entity Embeddings, Probabilistic Graphical Models, Structured Prediction


Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Octavian-Eugen Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn.

Hyperbolic Neural Networks
Octavian-Eugen Ganea*, Gary Bécigneul*, Thomas Hofmann.
Full paper @ NIPS 2018: Conference on Neural Information Processing Systems. Spotlight (top 4% of all submitted papers).
[Paper] [Video] [Poster] [BLOG] [Data + Code]

Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann.
Full paper @ ICML 2018: International Conference on Machine Learning. Oral talk.
[PDF] [Slides] [Poster] [Data + Code]

End-to-end Neural Entity Linking
Nikolaos Kolitsas*, Octavian-Eugen Ganea*, Thomas Hofmann.
Full paper @ CoNLL 2018: Conference on Natural Language Learning. Poster.
[PDF] [Data + Code]

Poincaré GloVe: Hyperbolic Word Embeddings
Alexandru Tifrea*, Gary Bécigneul*, Octavian-Eugen Ganea*
Full paper @ ICLR'19: International Conference on Learning Representations.
[PDF] [Data + Code]

Riemannian Adaptive Optimization Methods
Gary Bécigneul, Octavian-Eugen Ganea
Full paper @ ICLR'19: International Conference on Learning Representations.

Deep Joint Entity Disambiguation with Local Neural Attention
Octavian-Eugen Ganea, Thomas Hofmann.
Full paper @ EMNLP 2017: Conference on Empirical Methods in Natural Language Processing. Poster
[PDF] [Slides] [Poster] [Data + Code]

Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
Octavian-Eugen Ganea, Marina Ganea, Aurelien Lucchi, Carsten Eickhoff, Thomas Hofmann.
Full paper @ WWW 2016: International World Wide Web Conference. Oral talk.
[PDF] [Slides] [Poster] [Data + Code] [Online system (Gerbil - D2KB)] [Comparison with existing systems (Jan 2018)]

Learning and Evaluating Sparse Interpretable Sentence Embeddings
Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko, Thomas Hofmann.
EMNLP'18 Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. 2018. .

Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
Till Haug, Octavian-Eugen Ganea, Paulina Grnarova .
Short paper @ ECIR 2018: European Conference on Information Retrieval .
[PDF] [Slides]

Web2Text: Deep Structured Boilerplate Removal
Thijs Vogels, Octavian-Eugen Ganea, Carsten Eickhoff .
Long paper @ ECIR 2018: European Conference on Information Retrieval .
[PDF] [Slides] [Source Code]

Community service

Reviewer at ACL 2018 and EMNLP 2018.


Check out the currently available student projects in our group.
Supervised Theses/Projects:
Alexandru Tifrea: Hyperbolic Word Embeddings,
MSc thesis (jointly with Gary Becigneul), ETH, April - October 2018
Kolitsas Nikolaos: End-to-end Neural Entity Linking,
MSc thesis, ETH, October 2017 - April 2018
Junlin Yao: Detecting Medication and Adverse Drug Events from Electronic Health Records,
MSc thesis (jointly with Carsten Eickhoff), ETH, September 2017 - March 2018
Igor Petrovski: Hyperbolic Sentence Embeddings,
MSc thesis (jointly with Gary Becigneul), ETH, October 2017 - April 2018
Valentin Trivonov: Sparse and Interpretable Embeddings,
MSc thesis (jointly supervised with Anna Potapenko), ETH, December 2017 - May 2018
Andreas Hess: Reinforcement Learning for Question Answering with Semi-structured Tables,
MSc thesis, ETH, December 2016 - May 2017
Yifan Su: Deep Structured Prediction for Joint Entity Linking and Coreference Resolution,
MSc thesis (jointly supervised with Aurelien Lucchi), ETH, July - December 2016
Till Haug: Convolutional and Recursive Neural Networks for Question Answering on Semi-structured Tables ,
BSc thesis (jointly supervised with Paulina Grnarova), ETH, August 2016 - January 2017
Severin Bahman: Memory Networks for Entity Linking,
Research project (jointly supervised with Aurelien Lucchi), ETH, March - July 2016
Andreas Georgiadis: Learning Sentence and Entity Representations for Question Answering,
Research project (jointly supervised with Aurelien Lucchi), ETH, March - July 2016
Thijs Vogels: Structured Prediction for Web Page Content Extraction,
Research project (jointly supervised with Carsten Eickhoff), ETH, Sep 2015 - May 2016
Monteiro Freire Ribeiro João Pedro: Unsupervised Knowledge Base Fact Prediction using Matrix Word Embeddings,
BSc thesis (jointly supervised with Paulina Grnarova), ETH, Nov 2015 - May 2016
Teaching assistant:
Deep Learning: Fall 2017, 2018
Computational Intelligence Lab: Spring 2015, 2016, 2017, 2018
Information retrieval: Fall 2014, 2015, 2016
Advanced Algorithms (EPFL): Fall 2011
Numerical Methods (UPB): Fall 2008,2009


The mathematics of the expectation maximization algorithm
Learning in pairwise conditional random fields