Lorenz Kuhn

MSc Computer Science Student

ETH Zurich

I am currently a MSc Computer Science Student at ETH Zurich, where I focus on machine learning. At ETH and at IBM Research, I undertook research on information retrieval and text classification for medical applications. Recently, I interned at BCG Gamma as a data scientist where I contributed to the development of a text classification algorithm for financial transactions. Before that, I worked at QantEv, an InsureTech start up which emerged from the Entrepreneur First program. There, I designed and implemented optimal transport methods and contributed to the development of a web application.

Education

MSc in Computer Science, 2020

ETH Zurich
Exchange Programme, 2017

Imperial College London
BSc in Computer Science, 2017

ETH Zurich

Publications

Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks

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

Ivan Girardi, Pengfei Ji, An-phi Nguyen, Nora Hollenstein, Adam Ivankay, Lorenz Kuhn, Chiara Marchiori, Ce Zhang

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Implicit Negative Feedback in Clinical Information Retrieval

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

Lorenz Kuhn, Carsten Eickhoff

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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.