Claudio Fanconi
Ph.D. Student in Machine Learning, University of Cambridge
Hello! I am second-year PhD student in Machine Learning and Artificial Intelligence 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.
In my PhD I am researching how AI can be used to enhance decision-making. More specifically, I am investigating at these areas:
(i) Understanding and quantifying reasoning in LLMs (e.g. through inverse reinforcement learning)
(ii) Analysing frameworks for collaboration between multiple LLMs and experts
(iii) Developing methodologies for personalisation to enhance the effectiveness of 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
- (2026/03) 📝 First-author paper accepted (poster) at the ICLR Workshop on LLM Reasoning (Paper): Researches adversarial inverse RL to elicit and quantify reasoning in LLMs.
- (2026/03) 📝 Paper with Paulius accepted (spotlight) at the ICLR Workshop on Recursive Self-Improvement (Paper): Investigates tiny autoregressive recursive networks.
- (2025/09) 📝 First-author paper accepted (poster) at NeurIPS 2025 (Paper): Researches cost-efficient cascaded-LLM Systems for decision-making.
- (2025/06) 📝 Co-first-author paper accepted (oral, top 10%) at the ICML 2025 Workshop on AI Alignment 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 (poster) at NeurIPS 2024 (Paper): Demonstrates LLMs generating optimisation 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.
Education
Ph.D. Student in Machine Learning
University of Cambridge
Apr. 2024 - Present
Visiting Student Researcher
Stanford
University
Mar. 2022 - Oct. 2022
M.Sc. Information Technology and Electrical
Engineering
ETH
Zürich Sept. 2020 - Oct. 2022
Exchange Semester
Chinese University of Hong Kong
Sept. 2018 - Dec. 2018
B.Sc. Information Technology and Electrical
Engineering
ETH
Zürich Sept. 2016 - Aug. 2019
Professional Experience
Research Scientist Intern
Sony AI
Mar. 2023 - Dec. 2023
Management Consulting Intern
McKinsey &
Company Jun. 2020 - Aug. 2020
Machine Learning Engineer Intern
IBM
Oct. 2019 - Mar. 2020