Publications

Preprint Existing generalization measures that aim to capture a model’s simplicity based on parameter counts or norms fail to …

Preprint Dilated Convolutions have been shown to be highly useful for the task of image segmentation. By introducing gaps into …

Health Text Mining and Information Analysis workshop at EMNLP 2018 We present an operational component of a real-world patient triage …

Medical Information Retrieval Workshop at ACM SIGIR 2016 In this paper, we reflect on ways to improve the quality of bio-medical …

Experience

 
 
 
 
 
March 2021 – August 2021
Toronto

Machine Learning Researcher

Cohere

At Cohere, I:

  • Conducted extensive experiments on efficient transformer archtiectures.
  • Coordinated a team effort to improve the quality of our models which lead to multiple new production models that strongly outperformed our previous generation of models.
 
 
 
 
 
June 2019 – August 2019
Munich

Visiting Data Scientist

BCG Gamma

At BCG Gamma, I:

  • Developed a text classification algorithm based on our analysis of our client’s business needs.
  • Reduced loading times in our prototype from two minutes to a few seconds by introducing cache warming to our Continuous Deployment pipeline.
 
 
 
 
 
March 2019 – June 2019
Paris

Data Science Intern

QantEv

QantEv is an insurance tech start up which emerged from the Entrepreneur First program. During my time at QantEv, I:

  • Developed and implemented Optimal Transport models to predict which health care providers the patients in a given region will use.
  • Implemented state-of-the-art text classification methods to automatically annotate text description of medical services with standardised codes (CPT).
  • Made large contributions to the front and back end of a web app, working with Flask and ReactJS.
 
 
 
 
 
August 2017 – January 2018
Zurich

Research Intern

IBM Research

Contributed to the development of an Artificial Intelligence Medical Recommendation system:

  • Proposed a 10x speedup for existing system.
  • Designed, implemented and tested novel classification algorithms leading to a 25% improvement of recommendation accuracy.
  • Lead project on redesign of data management system.

Projects

In this project, we contributed an implementation of Boosting Black Box Variational Inference to the Pyro probabilistic programming library.

In this project we introduce low cost methods of improving dilated convolutions in an image segmentation application. We achieve comparable results to state-of-the-art segmentation performance while being computationally more efficient than previously proposed methods.

For this project we leverage matrix factorization and neural network methods to build a recommender system for movies.