Publications

We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to …

In this paper, we reflect on ways to improve the quality of bio-medical information retrieval by drawing implicit negative feedback …

Experience

 
 
 
 
 
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 pipline.
 
 
 
 
 
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

Efficient Smoothing of Dilated Convolutions for Image Segmentation

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.

Collaborative Filtering: Stacking Collaborative Filtering and Neural Networks for Improved Recommendations

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

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