Taming Big Data: From management and curation to storage and analysis.

Cuso Winter School, February 1-5, 2016, Champéry, Switzerland


Professor Renée J. Miller (University of Toronto, Canada)

Renée J. Miller received BS degrees in Mathematics and in Cognitive Science from the Massachusetts Institute of Technology. She received her MS and PhD degrees in Computer Science from the University of Wisconsin in Madison, WI. She received the Presidential Early Career Award for Scientists and Engineers (PECASE) the highest honor bestowed by the United States government on outstanding scientists and engineers beginning their careers. She received the National Science Foundation Early Career Award for her work on data integration. She is a Fellow of the ACM, a former President of the VLDB Endowment, and the Program Chair for ACM SIGMOD 2011 in Athens, Greece. She and her co-authors (Fagin, Kolaitis and Popa) received the (10 Year) ICDT Test-of-Time Award for their influential 2003 paper establishing the foundations of data exchange. Her research interests are in the efficient, effective use of large volumes of complex, heterogeneous data. This interest spans data integration, data exchange, data curation and data sharing. She is a Professor and the Bell Canada Chair of Information Systems at the University of Toronto. In 2011, she was elected to the Fellowship of the Royal Society of Canada (FRSC), Canada’s national academy.

Professor Yannis Velegrakis (University of Trento, Italy)

Yannis Velegrakis is a faculty member of the Department of Information Engineering and Computer Science at the University of Trento and the director of the Data Management Group. He is also the coordinator of the Data and Knowledge Management Research Program at the University of Trento. His research area of expertise includes Social Data Analysis, Knowledge Discovery, Big Data Management & Analytics, Information Integration & Data Exchange, and Keyword Searching. He holds a PhD degree from the University of Toronto and a MSc. and BSc. degree from the University of Crete, all in Computer Science. Before joining the University of Trento, he was a researcher at the AT&T Research Labs. He has spent time as a visitor at the IBM Almaden Research Center, the Center of Advanced Studies of the IBM Toronto Lab, and the University of California, Santa-Cruz. He has served in program committees of many national and international conferences and has been a reviewer for numerous international journals. In 2013, he served as the general chair of VLDB.

Professor Emmanuel Müller (University of Potsdam, Germany)

Prof. Dr. Emmanuel Müller is head of the Knowledge Discovery and Data Mining Research Group. Data Mining, as part of many scientific and industrial applications, does not end with the execution of algorithms. With data mining algorithms, resulting in discovery of unknown, novel, and unexpected patterns, one should aim at assisting humans in their daily decision making. On the one side, we investigate efficient algorithms, which scale with size and complexity of the data. And on the other side, our algorithms generate verifiable knowledge for human users. The group’s research goals are such scalable and verifiable data mining methods for large and complex data. These include algorithms for the selection of relevant attributes in high dimensional data, correlation analysis in multivariate data streams, and homophile structures in attributed graphs. Furthermore, the group develops data mining algorithms for multi-scale sensor data and interactive exploration of heterogeneous information systems in cooperation with the GFZ German Research Centre for Geosciences.