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KLAVDIIA

MSc, PhD student at DKFZ

Klavdiia Naumova

Hello I am an ELLIS PhD student in the Intelligent Medical Systems Lab (IMSY) at the German Cancer Research Center (DKFZ). My research focuses on developing reliable and fair foundation models for surgical applications. Before joining DKFZ, I worked as an AI Research Engineer at the EPFL Laboratory for Intelligent Global Health & Humanitarian Response Technologies, developing interpretable and trustworthy deep learning methods to support healthcare in resource-limited settings.

Education

German Cancer Research Center (DKFZ) & Heidelberg University
PhD student in Computer Science
2024-present

PhD Thesis

Bias-aware Surgical Foundation Models

Supervisor: Prof. Lena Maier-Hein (DKFZ)

École Polytechnique Fédérale de Lausanne (EPFL)
MSc in Life Sciences Engineering
2021-2023

Master Thesis

inDISCO: INterpretable DIStributed COllaborative learning for biomedical images (iGH/MLO)

Supervisers: Prof. Mary-Anne Hartley (EPFL) and Prof. Martin Jaggi (EPFL)
External advisor: Dr. Sai Praneeth Karimireddy (USC)

Moscow State University (MSU)
Specialist (BSc+MSc) in Fundamental and Applied Chemistry
2015-2021

Final qualifying paper

Drug-templated synthesis as method of obtaining high-capacity silica containers with controlled structure (Surface Phenomena in Polymer Systems Lab, Russian Academy of Science (IPCE RAS))

Superviser: Dr. Olga V. Dement'eva (IPCE RAS)
External advisor: Dr. Maria V. Poteshnova (MSU)

Research

My research interests lie in the field of artificial intelligence for medicine with the main focus on AI reliability and fairness. Currently, I am developing foundation models which can be adapted for various surgical applications such as providing intraoperative assistance, reducing post-operative complications, and improving surgical education. I am also particularly interested in studying biases and fairness of AI for surgery.

In my previous work, I developed an approach based on prototypical part learning to allow visually-interpretable and privacy-preserving bias identification in federated learning for biomedical images. I encourage you to check our latest paper for more details.

Additionally, I review submissions for JAMA journals and care about sustainable AI initiatives.

Publications

  1. MyThisYourThat for Interpretable Identification of Systematic Bias in Federated Learning for Biomedical Images.
    Klavdiia Naumova, Arnout Devos, Sai Praneeth Karimireddy, Martin Jaggi, Mary-Anne Hartley
    npj Digital Medicine, 7, 238 (2024)

    [paper]

  2. inDISCO: INterpretable DIStributed COllaborative learning for images.
    Klavdiia Naumova, Mary-Anne Hartley, Arnout Devos, Sai Praneeth Karimireddy, Martin Jaggi
    Submitted to the ICML 2023 Workshop on Federated Learning

    [paper]

Earlier publications can be found at my Google scholar page.

Talks

Distributed Learning: enabling AI in low resource environments
at the Workshop SMART-AI: Leveraging AI for the design and implementation of Health Decision Support Systems
EPFL, April 29-30, 2024

[LinkedIn post]

Posters

ELLIS PhD Symposium
August 2024

[poster]

EPFL Engineering Industry Day
March 2023

[poster]

Internships

Quantitative biology Intern
Nanolive SA
July-September 2022

Teaching

Personal

I enjoy jogging and dancing. I travel by train where possible and strive to acquire other eco-friendly practices in my daily life. Also, I am a huge fan of books on popular science and feminism. If you are passionate about them too, I'd be delighted to share a list of my favorite pieces to provide you with some captivating reading ideas