Andreea Deac

PhD Student, Mila / Université de Montréal

About Me

PhD student in Machine Learning at Mila, with Prof Jian Tang. I am broadly interested in how learning can be improved through the use of graph representations, having previously worked on neural algorithmic reasoners for implicit planning and applications to biotechnology, focusing on drug discovery.

Publications

NeurIPS 2021 Spotlight talk

How to transfer algorithmic reasoning knowledge to learn new algorithms?

Xhonneux, L.-P. A. C., Deac, A., Veličković, P., Tang, J.

NeurIPS 2021

Neural message passing for joint paratope-epitope prediction

Del Vecchio, A., Deac, A., Liò, P. and Veličković, P.

Computational Biology Workshop at ICML 2021

Deep Reinforcement Learning Workshop at NeurIPS 2020, Learning Meets Combinatorial Algorithms Workshop at NeurIPS 2020

We use a latent value iteration executor on a state graph derived using self-supervised learning to design an implicit-planner within deep reinforcement learning.

Graph neural induction of value iteration

Deac, A., Bacon, PL. and Tang, J.

Graph Representation Learning and Beyond Workshop at ICML 2020

We propose a GNN-executor aimed at modelling the value iteration (VI) algorithm, across arbitrary environment models, with direct supervision on the intermediate steps of VI.

Structured Multi-View Representations for Drug Combinations

Liu*, S., Deac*, A., Zhu, Z. and Tang, J.

Machine Learning for Molecules Workshop at NeurIPS 2020

We introduce a multi-view framework for drug combinations which leverages information from the drugs’ chemical structure, while also matching the sets of the drugs’ target proteins.

Empowering Graph Representation Learning with Paired Training and Graph Co-Attention

Deac, A., Huang, YH., Veličković, P., Liò, P. and Tang, J.

Pre-print

We use graph co-attention in a paired graph training system for graph classification and regression.

Drug-Drug Adverse Effect Prediction with Graph Co-Attention

Deac, A., Huang, YH., Veličković, P., Liò, P. and Tang, J.

ICML 2019 Workshop on Computational Biology

We propose a neural network architecture able to set state-of-the-art results on the drug-drug interaction (DDI) task—using the type of the side-effect and the molecular structure of the drugs alone—by leveraging a co-attentional mechanism.

Attentive cross-modal paratope prediction

Deac, A., Veličković, P. and Sormanni, P.

Journal of Computational Biology (2019)
ICML 2018 Workshop on Computational Biology (contributed talk)

We use self and cross-modal attention to predict binding probabilities of antibody residues, obtaining state-of-the-art performance as well as new qualitative insights.

Education

Mila / Université de Montréal

Montréal, Canada

PhD in Machine Learning

2019 - Present

Graph representation learning with applications to drug discovery, supervised by Prof Jian Tang.

University of Cambridge

Cambridge, United Kingdom

MEng in Computer Science

2018 - 2019

Murray Edwards College
Honours Pass *with Distinction*

For my dissertation project, I have developed a novel conditional graph-variational autoencoder architecture for targeted drug design.

University of Cambridge

Cambridge, United Kingdom

BA in Computer Science

2015 - 2018

Murray Edwards College

For my dissertation project, I leveraged neural network architectures to analyse which amino acids participate in antibody-antigen interactions.

Experience

DeepMind

London, UK

Research Scientist Intern

March - July 2021

Montréal team

I worked at the intersection of graph representation learning and reinforcement learning with Doina Precup.

Microsoft Research

Cambridge, UK

Research Intern

September - December 2020

Generative Chemistry Team

I analyzed the effectiveness of learning molecular representations conditioned on the information from the target proteins for the task of binding affinity prediction in a low-data scenario.

Google

Zürich, Switzerland

Software Engineering Intern

June - August 2019

Ads Quality Team

My project was focused on developing novel methodologies for keyword scoring.

Mila

Montréal, Canada

Research Intern

June - September 2018

Supervised by Prof Jian Tang

I worked on graph-based neural networks for molecule generation and drug-drug side-effect prediction.

Google

Zürich, Switzerland

Software Engineering Intern

July - September 2017

Google Assistant Team

My project consisted of implementing a new notifications feature end-to-end. My focus was on reminders in particular, using C++ on the back end side and Java/Android for front end.

Google

Stockholm, Sweden

STEP Intern

July - September 2016

Google Hangouts Meet Team

I worked on improving the testing infrastructure of the Android application. The project’s goal was to do UI automation testing, which included working with Java, Python, Android, Espresso, dependency injection and Dagger.

Scholarships and Awards

Google Intern Award for Grace Hopper Celebration

2019

Full travel award for the 2019 Grace Hopper Celebration conference in Orlando, United States

Google Prize for the Best Part III Research Project

2019

Best Computer Science Master's research project at the University of Cambridge for 2018-19

Best presentation prize at Oxbridge Women in Computer Science Conference

2019

Awarded for my work on predicting drug-drug interactions (DDIs)

Rising star prize at Oxbridge Women in Computer Science Conference

2018

Awarded for my work on antibody-antigen interaction prediction

Paula Browne Scholarship from Murray Edwards College

2018

Awarded to up to four undergraduates per academic year