Andreea Deac

PhD Student, Mila / Université de Montréal

About Me

PhD student in Machine Learning at Mila, with Prof Jian Tang. Interested in graph neural networks and applications to biotechnology (especially drug discovery and drug interactions).

Publications

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

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

Submitted

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

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