Eric Nalisnick
Assistant Professor
Informatics Institute
University of Amsterdam
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I am interested in building safe and robust intelligent systems with a human-centered design. To accomplish this, my research develops novel machine learning techniques, which are often rooted in probabilistic modeling and computational statistics. Questions I am particularly interested in are: how can we incorporate a human's prior knowledge?, how can we detect when the system is failing?, and how to combine human and machine decision making? Some applications of importance to me are: healthcare, moderation of online content, and sign language processing.
Affiliations: Amsterdam Machine Learning Lab / UvA-Bosch Delta Lab / SignLab Amsterdam
Selected Publications
Selected Talks
Research Group
- Rajeev Verma (with Voker Fischer)
- Alexander Timans
(with Kaspar Sakmann and Christoph-Nikolas Straehle) - Metod Jazbec (with Dan Zhang)
- Mona Schirmer (with Dan Zhang)
- Dharmesh Tailor
- Putra Manggala (with Holger Hoos)
- Saba Amiri (with Adam Belloum and Sander Klous)
- Urja Khurana (at Vrije Univ., with Antske Fokkens)
- Mrinank Sharma
(at Oxford, with Tom Rainforth and Yee Whye Teh)
- None at this time.
Courses Taught
- Human-in-the-Loop Machine Learning (Graduate, 2023)
- Machine Learning I (Graduate, 2023)
- Leren: Introduction to Machine Learning (Undergraduate, 2020 - 2022)
- Module on Bayesian Deep Learning, Deep Learning II (Graduate, 2022 - 2023)
Venues
- Workshop on Bayesian Deep Learning
- Seminar on Anomaly Detection for Scientific Discovery
- Workshop on Tractable Probabilistic Modeling
- Entropy: Probabilistic Methods for Deep Learning
Funding
- Veni, NWO Talent Programme: Continual Learning under Human Guidance (2022 - 2026)