Resume

For more details, you can download the pdf version of my resume here (updated December 2023).

Education 🎓

University of Oxford 2018 - 2022

PhD Machine Learning

Supervised by Yee Whye Teh and Arnaud Doucet

Stanford University 2014 - 2016

MS Computational Mathematics

GPA: 4.02

Imperial College London 2010 - 2014

BSc Theoretical Physics

Rank: 1/206 students, Grade: 87.2%

Experience 👨‍💼

Google DeepMind Jan 2023 -

Research Scientist

Research on neural fields and machine learning for science

Google DeepMind Mar 2021 - July 2021

Research Scientist Intern

Research with Danilo Rezende on generative models of neural networks

Apple Nov 2019 - June 2020

Part Time Research Intern

Part time research on neural rendering during PhD with collaborators at Apple

Apple June 2019 - Aug 2019

Research Intern

Research on Neural Rendering with Qi Shan

Schlumberger STIC June 2016 - July 2018

Machine Learning Scientist

Created, implemented and deployed machine learning algorithms to solve problems in time series, vision and geology, improving state of the art for several tasks • Research on deep generative models with a focus on learning interpretable representations

Gurobi Optimization June 2015 - Aug 2015

Software Engineering Intern

Researched, formulated and solved integer optimization models for a wide area of industry applications including energy, telecom and medicine

DTU Compute June 2013 - Sep 2013

Research Intern

Research on sparse dynamics for PDEs with Allan Engsig-Karup

Awards 🌟

• Google DeepMind Scholarship 2018
  PhD funding, 150,000 USD
• Schlumberger Out of the Ordinary Award 2018
  Award for extraordinary technical achievements
• Digital Forum Innovation Award 2017
  Schlumberger award for most innovative project among 300+ submissions
• Schlumberger AI Leader 2016
  Elected as leader of the 1000+ AI community within Schlumberger
• Governor's Prize 2014
  Ranked 1st of 206 students in Physics at Imperial College London

Teaching 👨‍🏫

• Teaching Assistant, SB2.1, Statistical Inference Oxford, 2020
  
• Teaching Assistant, SB2.2, Statistical Machine Learning Oxford, 2019
  
• Teaching Assistant, CME 102, Ordinary Differential Equations Stanford, 2016
  

Skills 💻

• Programming
  • Experienced: Python, C++, Matlab
  • Familiar: Javascript, Scala (Spark)
• Frameworks
  • Deep Learning: Pytorch, Jax, Haiku, Keras
  • Visualization: d3, plotly
• Languages
  • Fluent: Danish, English, French
  • Intermediate: German

Projects 🌱

• Visualizations
  Created d3 based interactive visualizations of mathematical concepts, data and generative art, which can be found on my Observable profile
• Open source
  Open sourced code for several deep learning papers with ★1000+ on my Github profile

Academic Services 📚

• Co-organizer
  ICLR 2023 Workshop - Neural Fields across Fields: Methods and Applications of Implicit Neural Representations
• Reviewer
  ICLR 2023, AISTATS 2022, ICLR 2022, ICLR 2021 (Outstanding reviewer), NeurIPS 2020 (Outstanding reviewer), ICML 2020 (Top reviewer), NeurIPS 2019 (Top reviewer)

Invited Talks 🏛️

• Compression with neural fields 2023
  VQEG
• The Curse of Discretization and Learning Distributions of Functions 2021
  ML Collective
• Representational Limitations of Invertible Models 2020
  ICML 2020, INNF+ Workshop
• Combining Physics and Machine Learning with Neural ODEs 2019
  Abingdon, UK
• Deep Learning for Prognostics and Health Management Tutorial 2017
  Prognostics and Health Management Conference, Tampa Bay, FL
• Deep Learning Applications Panel 2017
  Prognostics and Health Management Conference, Tampa Bay, FL