Nino Scherrer
<firstname>.<lastname>@gmail.com
I am a visiting research scientist at FAR AI, working with Claudia Shi (Columbia) on a causal perspective for reward learning from human feedback. Previously, I was a research intern at the Vector Institute (hosted by Prof. Animesh Garg) and at Mila (hosted by Prof. Yoshua Bengio). During this research visits, I have been fortunate to work closely with Nan Rosemary Ke, Anirudh Goyal and Prof. Stefan Bauer.
My research interest centers around the causal perspective on machine learning to build robust and adaptive models that can handle various sorts of distribution shifts. I did my
masters and bachelors in computer science at ETH Zürich and collaborated with Mila and MPI for Intelligent Systems, Tübingen during my master thesis on active neural causal discovery.
Previously, I did a diploma (vocational education) in computer science and worked multiple years as a systems engineer at SFS AG.
Info: I'm currently on the job market for PhD positions and research scientist positions.
Fields of Interest
Causal Inference X Machine Learning
Representation Learning
Meta Learning
Reinforcement Learning
AI for Healthcare
AI for Sciences
Research
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Learning Neural Causal Models with Active Interventions
Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick Schwab, Bernhard Schölkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer & Nan Rosemary Ke,
Preprint (arXiv) & NeurIPS WHY-21 Workshop, 2021,
[Paper] [Blog]
Causal Discovery
Experimental Design
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Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning
Christian Federau, Soren Christensen,
Nino Scherrer, Johanna M. Ospel, Victor Schulze-Zachau, Noemi Schmidt, Hannes-Christian Breit, Julian Macclaren, Maarten Lansberg & Sebastian Kozerke,
Radiology: Artificial Intelligence, 2020,
[Paper]
Medical Image Analysis
Semantic Segmentation
Talks
- December 2022: "On the Synergies of Causality and DeepLearning", Neuroscience in ML Seminar, ETH Zurich
- April 2022: "Learning Neural Causal Models with Active Interventions", XAI seminar series, Imperial College London
- October 2021: "Learning Neural Causal Models with Active Interventions", Causality Reading Group, TU Darmstadt