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I’m Derek Driggs, a researcher in machine learning and optimization.

I am a quantitative researcher, sometimes trading equities and sometimes working on optimal execution algorithms. Before this, I was pursuing a PhD in the Cambridge Centre for Analysis at the University of Cambridge under the supervision of Prof. Carola-Bibiane Schönlieb.

I am grateful to be a Gates Cambridge scholar and a member of the CCIMI.

If you would like to contact me, please use email.

About Me

I am interested in machine learning algorithms for imaging processing. Most of my research is in creating optimization algorithms to solve large-scale machine learning and imaging problems efficiently.

In my most recent projects, I have been developing machine learning algorithms to help clinicians interpret radiographs. I am particularly interested in bridging the gap between academic models and models that can be safely deployed in the clinic.

In March 2020, our research group helped found the AIX-COVNET collaboration, an international group of researchers developing machine learning models to aid clinicians during the COVID-19 pandemic.

News

  • 27 Aug. 2021: Some of my work has been featured in a BBC Tech Tent podcast.
  • 30 July 2021: Some of my work has been featured in MIT Technology Review.
  • 21 Apr. 2021: My paper "Accelerating variance-reduced stochastic gradient methods" won a 2nd place award for the IMA Leslie Fox Prize in Numerical Analysis.
  • 20 Apr. 2021: The Data Skeptic podcast has interviewed me about my work with the AIX-COVNET collaboration on developing machine learning models to aid in the COVID-19 pandemic. This podcast is also available on Spotify.
  • 29 Mar. 2021: I was interviewed for an article in IEEE Spectrum about research standards for machine learning in healthcare.
  • 23 Mar. 2021: VentureBeat has published an article about our review.
  • 17 Mar. 2021: Politico has published an article about our review.
  • 15 Mar. 2021: My paper with AIX-COVNET, "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans," has been published in Nature Machine Intelligence.
  • 3 Mar. 2021: My invited editorial with AIX-COVNET, "Machine learning for COVID-19 diagnosis and prognostication: lessons for amplifying the signal whilst reducing the noise," will appear in Radiology: Artificial Intelligence.
  • 30 Oct. 2020: I will (virtually) be at the Artificial Intelligence in Clinical Imaging conference, presenting my work with AIX-COVNET.
  • July 2020: I will (virtually) be at the SIAM Imaging Sciences conference, presenting my work on stochastic optimization.
  • 15 Sep. 2020: My paper with Dr Matthias Ehrhardt and Prof. Carola-Bibiane Schönlieb, "Accelerating variance-reduced stochastic gradient methods," has been published in Mathematical Programming.
  • Summer 2020: I will be a quantitative research intern at G-Research, improving the numeracy of natural language processing algorithms.
  • Mar. 2020: My paper with Dr Jingwei Liang and Prof. Carola-Bibiane Schönlieb, "On Biased Stochastic Gradient Estimation," is available on ArXiv.
  • 27 Feb. 2020: My paper with Dr Junqi Tang, Dr Jingwei Liang, Prof. Mike Davies, and Prof. Carola-Bibiane Sch$ouml;nlieb, "SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization," is available on ArXiv.
  • Dec. 2019: I will be at the Conference on Decision and Control in Nice, presenting my work with Dr Hamza Fawzi.
  • Aug. 2019: I will be at ICCOPT in Berlin, presenting my work with Dr Matthias Ehrhardt and Prof. Carola-Bibiane Schönlieb.
  • 31 July 2019: My paper with Dr Hamza Fawzi, "AnySOS: An anytime algorithm for semidefinite programming," has been accepted by the Conference on Decision and Control. Find the code on my GitHub page.
  • July 2019: I will be at the Biennial Numerical Analysis Conference in Glasgow, presenting my work with Dr Jingwei Liang, Dr Matthias Ehrhardt, and Prof. Carola Schönlieb on stochastic optimization.
  • 30 Jan. 2019: My paper with Dr Stephen Becker and Dr Jordan Boyd-Graber on Tensor RPCA is now available on arXiv.
  • 1 Jan. 2019: My paper with Dr Stephen Becker and Dr Aleksandr Aravkin on solving low-rank models using parallel architectures has been published in SIAM J. Sci. Comput.
  • 1 Oct. 2017: Started my PhD with Prof. Carola Schönlieb and Dr Hamza Fawzi at the University of Cambridge.
  • 7 May 2017: Graduated from CU Boulder with a BS/MS in Applied Mathematics!