Scholar
Antoine Wehenkel
Google Scholar ID: LFz-kl0ZkNkC
Apple AI/ML Research
Citations & Impact
Citations
943
H-index
14
i10-index
17
Publications
20
Co-authors
19
list available
Publications
Resume (English only)
Academic Achievements
- Published a pre-print paper titled 'Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference', co-authored with Jens Behrmann, Maria R Cervera, Andrew C Miller, and others.
Research Experience
- Worked as a postdoctoral researcher at Apple from November 2022 to November 2023, advised by Jörn-Henrik Jacobsen (Health AI) and Marco Cuturi (ML Research). Investigated strategies for deriving robust simulation-based inference algorithms to address issues with misspecified simulators, particularly in their application to health technologies.
Education
- Earned an M.Sc. in Computer Engineering from the University of Liège in 2018, spending the final year as an exchange student at École Polytechnique Fédérale de Lausanne (EPFL), where he conducted his master's thesis focusing on estimating parameters of electrical distribution networks. Completed a Ph.D. in Computer Science with an FNRS Research Fellowship under the supervision of Professor Gilles Louppe at the University of Liège, Belgium, in October 2022.
Background
- Machine Learning Research Scientist at Apple, within the Health AI team. Works on developing new deep learning algorithms that integrate formal domain expertise, such as that defined by scientific simulators, with real-world data to advance the design of novel sensing technologies for health. Remains attached to fundamental research and the academic world, while excited about demonstrating the industrial value of scientific probabilistic modelling. Research interests include deep probabilistic modeling, biophysical sensor design, and simulation-based inference.
Miscellany
- Vision is to enhance the interplay between fundamental sciences and machine learning techniques, both to spur scientific discovery and to develop predictive models that can be reliably deployed in the real world.
