Profile

2019-12-08

Profile

Dr. Maximilian Schich

The University of Texas at Dallas

Maximilian Schich is a multi-disciplinary scholar who contributes towards a systematic science of cultural interaction. He is an Associate Professor in Arts & Technology at the University of Texas at Dallas and a founding member of The Edith O’Donnell Insitute of Art History. His specific aim is to reveal and understand cultural interaction and complex dynamics as they emerge from large amounts of local activity on a historical scale.

Multidisciplinary expertise

Maximilian's multidisciplinary expertise and research practice is rooted in a threefold formation, including a substantial postdoc-phase in Complex Network Science at Northeastern University, and what is now called Computational Social Science at ETH Zurich (2008–2012). Preceding this, he aquired over a decade of work experience as a “database pathologist” dealing with large knowledge graphs, i.e. large networked databases in art research (1996–2006). Concurrently, he earned a PhD in Art History at Humboldt-University in Berlin and Bibliotheca Hertziana, Max-Planck-Institute for Art History in Rome (2001–2007), building on an M.A. in Art History, with minors in Classical Archaeology and Psychology from Ludwig-Maximilians-University in Munich (1995–2001). Taken together, his formation summarizes to a threefold track-record, combining “traditional” art research, with “applied” and “pure” multidisciplinary science, feeding into the emerging field of cultural data analytics.

Research mission & practice

Maximilian's research mission and practice are in line with his multidisciplinary formation, using a variety of modes in terms of methods, products, and styles of collaboration, while being coherently aligned and consistently centered around deepening our understanding of networks and complexity in art and cultural history. Throughout his career he has designed and performed several research projects that have led to significant results, recognized as notable by scholars in multidisciplinary science and in art research. He has engaged in independent research since early in his career, acquiring PhD and postdoc funding to support his own research designs, which were both conducted monographically and in close co-authorship, predominantly with physicists, computer scientists, and information designers – practices that he continues to pursue in parallel. Following the PhD monograph (Verlag Biering & Brinkmann), he has co-authored peer-reviewed journal articles and conference proceedings with physicists and computer scientists (in Science Magazine, Physical Review Letters, EPJ Data Science, Transportation Research Part B, KDD-MLG, etc.). He has also written articles and chapters targeting traditional audiences in art history, archaeology, and social science (Journal for Digital Art History, BullCom, Skira, Prestel, Springer VS, etc.). His contributions in information design and visualization (Nature video, O’Reilly, etc.) are recognized by leading practitioners in the field (FlowingData, Visualcomplexity, SciMaps.org, etc.).

Current work

Maximilian's next major product will be a monograph titled Cultural Interaction, which caters to a broader audience and outlines a systematic science of art and culture, based on two decades of research and cultural data analysis. The first chapter is published as a prelude https://doi.org/10.11588/artdok.00006347.

Maximilian's further ongoing collaborative research projects bring together physicists, computer scientists, and art historians, using quantification and machine learning to understand family resemblance in large art collections, and physics methods of time-series analysis to understand fundamental patterns of periodicity and canon.

Teaching

Maximilian's teaching focuses on multidisciplinary topics including cultural data analytics, data visualization, ecologies of complex networks, data-driven art history, visual sample and remix, understanding urban ecologies, and highly multidisciplinary courses, where students apply a broad variety of methods and skills from the sciences, arts, humanities, and technology.