About me

Currently, I am working as a Quant Developer at UBS, specializing in designing and developing big data tools and solutions for Treasury Risk Control’s balance sheet analytics.

I hold an MSc in Computer Science from ETH Zurich, where I worked on data valuation and performative prediction under the guidance of Dr. David Dao and Prof. Dr. Ce Zhang. In my research, I explored how we can value data and create robust strategies when data contributors may exhibit adversarial behavior.

Prior to my graduate studies, I completed my BSc in Computer Science at University of Copenhagen and researched sustainability and resource efficiency of machine learning, supervised by Prof. Dr. Raghav Selvan. Additionally, I have worked as a software developer at Nykredit and had a short stint as an AI / Machine Learning Specialist at the Alexandra Institute.

Publications

• Carbontracker: Tracking and predicting the carbon footprint of training deep learning models
  L. F. W. Anthony, B. Kanding, and R. Selvan
  ICML Workshop on Challenges in Deploying and monitoring Machine Learning Systems, 2020
  Paper Code
• Carbon footprint of selecting and training deep learning models for medical image analysis
  R. Selvan, N. Bhagwat, L. F. W. Anthony, B. Kanding, and E. B. Dam
  International Conference on Medical Image Computing and Computer-Assisted Intervention — MICCAI 2022, 2022
  Paper