Claudio Fanconi

Claudio Fanconi

Ph.D. Student in Machine Learning, University of Cambridge


Hello! I am first year Ph.D. student in Machine Learning and AI for Medicine at the University of Cambridge advised by Mihaela van der Schaar.

Previously, I graduated with a Bachelor's and Master's from ETH Zürich in Information Technology and Electrical Engineering with a focus on Machine Learning (ML). I conducted my Master's thesis under the supervision of Tina Hernandez-Boussard at Stanford University, researching predictive uncertainty and natural language processing to identify patients at risk of acute care use.

My current research topics are:
(i) the application of ML in high-stakes environments such as medicine
(ii) using machine learning to understand human decision-making
(iii) the alignment of large language models (LLMs).

Reach out to me with anything you want to talk about!

Additionally, please check out the Fanconi Cancer Foundation and their initiatives against Fanconi Anemia.


Updates

  • (2024/12) 📝 Preprint together with Kasia (Paper): Develops a method for few-shot steerable alignment of rewards and policy models with neural processes.
  • (2024/09) 📝 Co-first-author paper accepted at NeurIPS (Paper): Demonstrates LLMs generating optimization algorithms for their preference learning.
  • (2024/07) 📝 Published in JAMIA (Paper): Demonstrates BERT models can effectively identify depression concerns in cancer patients' portal messages.
  • (2024/04) 🎉 I have officially started a PhD in Machine Learning at the University of Cambridge under the supervision of Mihaela van der Schaar.
  • (2024/01) 📝 Published in JMIR Medical Informatics (Paper): Develops machine learning models to predict depression risk in cancer patients starting treatment.
  • (2023/09) 📝 Co-first-author paper accepted at EMNLP (Paper): Introduces prototype-based methods for enhanced interpretability in NLP.
  • (2023/06) 📝 First-author paper published in The Lancet eBioMedicine (Paper): Demonstrates the added value of uncertainty estimation to predict the risk of ACU for cancer patients.
  • (2023/03) 🎉 I have joined Sony AI as a junior research scientist (internship), working on computer vision algorithms for robotic perception systems.
  • (2023/03) 📝 First-author paper accepted at AMIA Informatics Summit (Paper): Clinical notes can predict acute care use in oncology patients nearly as well as structured data.
  • (2023/02) 📝 Published in Journal of Nephrology (Paper): Systematically reviews how artificial intelligence has been deployed to predict, diagnose, and treat chronic kidney disease.
  • (2022/10) 🎉 Graduated with an MSc in Information Technology and Electrical Engineering from ETH Zürich, after conducting my Master's thesis at Stanford University.
  • (2021/06) 📝 Co-first-author paper accepted at an ICML Workshop (Paper): Critiques prototype-based networks, highlighting mismatches in latent-input space similarity.

Education

University of Cambridge

Ph.D. Student in Machine Learning
University of Cambridge Apr. 2024 - Present

Stanford University

Visiting Student Researcher
Stanford University Mar. 2022 - Oct. 2022

ETH Zürich

M.Sc. Information Technology and Electrical Engineering
ETH Zürich Sept. 2020 - Oct. 2022

Chinese University of Hong Kong

Exchange Semester
Chinese University of Hong Kong Sept. 2018 - Dec. 2018

ETH Zürich

B.Sc. Information Technology and Electrical Engineering
ETH Zürich Sept. 2016 - Aug. 2019


Professional Experience

Sony AI

Junior Research Scientist
Sony AI Mar. 2023 - Dec. 2023

McKinsey & Company

Management Consulting Intern
McKinsey & Company Jun. 2020 - Aug. 2020

IBM

Machine Learning Research Intern
IBM Oct. 2019 - Mar. 2020