Scholar
Michael Wand
Google Scholar ID: gZD4LcoAAAAJ
The Swiss AI Lab IDSIA, USI & SUPSI / MeDiTech, SUPSI
Citations & Impact
Citations
1,155
H-index
17
i10-index
23
Publications
20
Co-authors
18
list available
Contact
No contact links provided.
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Board member of the European Neural Network Society (ENNS); Involved in projects such as Prototouch, INPUT, AIDD, and AiChemist, which deal with haptic devices, upper limb prostheses control, and high-end chemical and pharmaceutical challenges.
Research Experience
- Affiliated as a researcher with the newly founded SUPSI institute for Digital Technologies for Personalized Healthcare (MeDiTech) since July 2021; Senior Researcher at IDSIA since January 2019; Researcher at IDSIA since June 2014.
Education
- PhD (Dr.-Ing.) in January 2014 from Cognitive Systems Lab, Karlsruhe Institute of Technology, with a thesis titled 'Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling'; PhD studies in Silent Speech Interfaces based on Electromyography (EMG) from 2008-2014; Diploma degree (roughly equivalent to Master's) in mathematics from Karlsruhe University (now KIT), 2001-2007; Visiting student researcher at Carnegie Mellon University in 2006; Exchange student at Queen's University, Kingston, Ontario, Canada, 2004-2005.
Background
- Michael Wand is a Senior Researcher at the Dalle Molle Institute for Artificial Intelligence, focusing on applying state-of-the-art Machine Learning to real-world problems. His research interests include Silent Speech recognition, lipreading, and the application of machine learning to challenging, real-life data.
Miscellany
- Interested in all kinds of applications of machine learning to analysis of challenging, real-life data; Was a Marie Curie fellow in the Prototouch project, working on machine learning for diverse kinds of data related to haptics and haptic/tactile devices; Since 2021, managing IDSIA's research within the AIDD and AiChemist projects, aiming towards a 'One Chemistry' model that can predict outcomes of chemical reactions, derive properties of compounds, and assist in molecule generation and synthesis.
