Mathieu Ravaut

Mathieu Ravaut

Machine Learning Scientist | PhD Candidate

Nanyang Technological University

I am currently a third-year PhD Candidate in Natural Language Processing at Nanyang Technological University in Singapore, in NTU-NLP group. I am advised by Dr. Shafiq Joty and Dr. Aixin Sun. Through my SINGA scholarship, I am also attached at ASTAR-I2R Human Language and Technology (HLT) Lab, where I am supervised by Dr. Nancy Chen. As of Spring 2023 I am doing an internship at Huawei Noah Ark Lab in Singapore.

My PhD focus is on abstractive text summarization. Specifically, I am interested in how to build better summarization systems by going beyond the standard framework of MLE with token-level cross-entropy loss and teacher forcing. For instance, I have explored in depth how to learn summarization at the entire summary level (see my papers SummaReranker, SummaFusion, and SummScore). I am also interested in controllable summarization, unsupervised summarization and few-shot summarization.

I am extremely passionate about machine learning, convinced that we are going through an AI revolution, and have been in this area since 2016. Before PhD, I obtained a MSc in Applied Computing (MScAC) from University of Toronto Department of Computer Science and a MEng from Ecole Centrale Paris (a leading French Engineering School now known as CentraleSupelec and part of the newly formed Paris Saclay University). I also worked full-time as Machine Learning Research Scientist at Layer 6 AI, TD Bank’s AI lab in Toronto, where my research was featured in Nature and I interned at Thales and ASTAR-I2R Visual Computing Lab in Singapore. In my spare time, I like participating in machine learning competitions like those hosted by Kaggle.

Research Community Service:
Reviewer (journals): IEEE/ACM TASLP (2021-)
Reviewer (conferences): SIGDIAL 2023, CoNLL 2023, AACL 2023

  • Machine Learning
  • Natural Language Processing
  • Recommender Systems
  • Machine Learning for Healthcare
  • PhD in Computer Science, 2021-2024

    Nanyang Technological University (Singapore)

  • MSc in Applied Computing, 2017-2018

    University of Toronto (Canada)

  • Master of Engineering, 2015-2018

    Ecole Centrale Paris (France)

  • Bachelor of Engineering, 2014-2015

    Ecole Centrale Paris (France)

  • Prepa MPSI/MP*, 2011-2014

    Lycée Montaigne (France)


Research Intern
Huawei Noah Ark
January 2023 – July 2023 Singapore
Research on conversational recommender systems, in the Search & Recommendation Team. Supervised by Dr. Hao Zhang.
Teaching Assistant
Nanyang Technological University
January 2021 – November 2022 Singapore
TA for various 1st-year CS courses: Introduction to computational thinking and programming, Data Structures and Algorithms ; as well as Grad-level Deep Learning for NLP.
Machine Learning Research Scientist
Layer 6 AI
May 2018 – July 2020 Toronto, Canada
  • Research on machine learning applied to healthcare, resulting in publications in Nature, JAMA and BMJ journals.
  • Insurance claim fraud detection with NLP.
  • Member of ACM RecSys 2020 2nd-place team.
Teaching Assistant
University of Toronto
January 2018 – April 2018 Toronto, Canada
TA for 1st-year course Introduction to Stasticial Thinking and Programming.
Research Intern
February 2017 – July 2017 Singapore
Research in computer vision, in the Visual Computing Lab. Supervised by Dr. Vijay Chandrasekhar.
Research Intern
Thales Solutions Asia
August 2016 – February 2017 Singapore
Research in computer vision applied to smart cities, in Thales Research & Technology. Supervised by Dr. Antoine Fagette.

Journal Publications

(2022). Predicting Hospitalisations Related to Ambulatory Care Sensitive Conditions with Machine Learning for Population Health Planning: Derivation and Validation Cohort Study. In BMJ.

Cite Source Document

(2021). Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes. In JAMA.

Cite Source Document

(2021). Predicting Adverse Outcomes Due to Diabetes Complications with Machine Learning Using Administrative Health Data. In Nature.

Cite Source Document


Singapore Data Science Consortium Phd Fellowship 2023
10k SGD award to support my PhD research.
Singapore International Graduate Award (SINGA)
Tuition-fees waiver and monthly stipend to support my PhD research.
MITACS Accelerate Program
30k CAD award to support my MSc internship research at Layer 6 AI.
Singapore International Pre-Graduate Award (SIPGA)
Monthly stipend to support my graduate internship at A-STAR I2R.


The AutoCast Competition - Forecasting the Future with AI (warm-up phase) (Solo)
Ranked 6th/109 (username=matravox).
CommonLit Readability Prize (Solo)
Ranked 29th/3633, top 1% & Silver Medal (username=Ravox).
Jane Street Market Prediction (Solo)
Ranked 291st/4245, top 7% & Bronze Medal (username=Ravox).
Riiid Answer Correctness Prediction (Solo)
Ranked 84th/3395, top 3% & Silver Medal (username=Ravox).
Mechanisms of Action (MoA) Prediction (Solo)
Ranked 40th/4373, top 1% & Silver Medal (username=Ravox). See my solution write-up here.
MIND News Recommendation Competition (Solo)
Ranked 4th/30+ (3rd prize) (username=Ravox).
RecSys Challenge 2020 (Team)
Ranked 2nd/30+ (username=learner). See our solution write-up here.
Two Sigma Using News to Predict Stock Movements (Solo)
Ranked 232nd/2927, top 8% & Bronze Medal (username=LeComteDeBronze).
Google Cloud and YouTube-8M Video Understanding Challenge (Team)
Ranked 22nd/655, top 4% & Silver Medal (username=DL2.0). See our approach explained here.