Publications of Andreas Holzinger > Scholar, DBLP, ORCID

Andreas Holzinger build a solid track record in AI/Machine Learning (see definition) for health informatics always with a strong focus on research-based teaching. Andreas has been working on integrated machine learning, which is manifested in Holzinger’s HCI-KDD approach. This is based on a synergetic combination of two different fields to understand intelligence, enabling context-adaptive systems: Human–Computer Interaction (HCI), rooted in cognitive science, particularly dealing with human intelligence, and Knowledge Discovery/Data Mining (KDD), rooted in computer science, particularly dealing with artificial intelligence. This  approach is the basis for Human-Centered AI in general and Explainability and Causability [1] in particular. Particularly, Andreas has pioneered the interactive machine learning approach with a human-in-the-loop. Holzinger proved his concept with his glass-box approach, which becomes now important due to raising legal, social and privacy issues in the European Union, particularly for the health informatics domain. It is important to make decisions transparent, retraceable and human understandable and to explain why a machine decsion has been made.

Subject: Computer Science > Artificial Intelligence (102001)
Technical Area: Machine Learning (102019)
Application Area: Health Informatics (102020)
Keywords: Human-Centered AI (HC-AI), Explainable Artificial Intelligence (explainable AI, ex-AI), interactive Machine Learning (iML)

Publication metrics as of 18.04.2019 14:00 CET:

Google Scholar citations: 10,859
Google Scholar h-Index: 48
Google Schloar i10-Index: 223
Scopus h-Index = 33
Scopus citations = 5032
Scopus authored papers = 313
DBLP Peer-reviewed conference papers = 173
DBLP Peer-reviewed journal papers = 69
DBLP Edited books and Journal issues = 43
DBLP Peer-reviewed book chapters = 23
ArXiV contributions: 12

1) 3-pages-CV-Andreas-HOLZINGER (pdf, 323 kB)

2a) Publications-last-five-years-Andreas-Holzinger (pdf, 163 kB)

2b) Selected 10 original journal publications of the last 5 years (pdf, 9 kB)

2c) Selected 5 original contributions with comments (pdf, 75 kB)

3) 5-pages research statement (pdf, 170 kB)

4) 5-pages teaching statement (pdf, 607 kB)

5) 9-minutes Youtube Video Research Statement

[1] The notion of Causability is differentiated from Explainability in that Causability is a property of a human (natural intelligence), while explainability is a property of a technologial system (artificial intelligence).

Wichtig: Causability ist eine Eigenschaft einer Person (natürliche Intelligenz), während die Erklärbarkeit eine Eigenschaft eines technischen Systems darstellt (künstliche Intelligenz).