The Springer proceedings for IPAW and the joint poster and demo session are now available at https://link.springer.com/book/10.1007/978-3-030-80960-7. The TaPP proceedings are available on the USENIX page for TaPP: https://www.usenix.org/conference/tapp2021.
We are glad to announce that Hazeline Asuncion will be TaPP’s keynote speaker. Hazeline Asuncion is an Associate Professor at the University of Washington Bothell. Her research focuses on traceability of data that may be found in different file types, locations, and owner groups. In the domain of software engineering, software traceability aids in various development tasks, such as system comprehension, system debugging, and communication between various stakeholders. In the domain of eScience, tracing how a dataset arrived at its current state, referred to as data provenance, is necessary in assessing a dataset’s integrity and in supporting repeatability of analyses or experiments. She has published over 30 peer-reviewed papers spanning these two topics. Her work has been funded by the National Science Foundation, including NSF REUs and an NSF Career. She received her Ph.D., M.S., and B.S., in Information and Computer Science from the University of California, Irvine.
The proceedings for IPAW have been published by Springer since 2008. You can find them here: https://link.springer.com/conference/ipaw
Due to the uncertainty of the COVID-19 pandemic, we have made the decition to conduct Provenance Week 2021 as a full virtual event.
We are glad to announce that Paolo Missier will be IPAW’s keynote speaker. Paolo Missier is Full Professor in Large-scale Information Management with the School of Computing, Newcastle University, UK, and a Fellow of the Alan Turing Institute for Data Science and AI. He joined academia in 2011, after a prior career as a Research Scientist at Bell Communications Research, USA (1994-2001), and as a Research Fellow at the University of Manchester, School of Computer Science (2004-2011) where he also obtained his PhD in 2008. Most of his research contributions have been in the area of scientific workflows for e-science and on data provenance, including contributions to the W3C PROV Working group (2011-2013). He has recently launched new research efforts in two new areas: the study of fairness in Machine Learning, and applications of (Scalable) Data Science to enable preventive, predictive, and personalised healthcare. In Newcastle, Paolo leads the School’s post-graduate module on scalable technologies for Big Data Analytics.
The first version of our call for papers is online. Check it out!