The goal of this panel session is to make KDD researchers aware of the academic journal outlets where they can publish full length extensions of their research programs that are difficult to do with conference papers. For example, ACM TKDD, Big Data and DMKD have published several extensions of work presented at KDD conferences. There are two benefits of journal publications. Big Data papers see tens of thousands of downloads on papers within months of being published by a very wide global audience. Authors have been pleased with the speed and quality of reviews of their work and subsequent download activity of their research. The value added by journal editors involved working with authors to curate their work so that it is more easily accessible to wider audiences.
Vasant Dhar is a Professor at the Stern School of Business and the Center for Data Science at NYU, and Editor-in-Chief of the Big Data journal. He is also the founder of SCT Capital Management, a machine learning based systematic investment entity in New York City. Dhar’s research and practice addresses the following question: when should we trust machines with decisions? His answers are based on 25 years of experience with building autonomous machine-learning-based predictive systems in domains including finance, healthcare, and primary education. Dhar has written over 100 research articles in academic journals and writes regularly about current issues related to Artificial Intelligence in the media that includes the Financial Times, Wall Street Journal, Forbes, and Wired.
Jie (Jery) Tang is a (tenured) associate professor with the Department of Computer Science and Technology at Tsinghua University, and was also visiting scholar at Cornell University, Hong Kong University of Science and Technology, and Southampton University. His interests include social network analysis, data mining, and machine learning. He has published more than 200 journal/conference papers and holds 20 patents. His papers have been cited by more than 8,600 times. He served as PC Co-Chair of CIKM’16, WSDM’15, ASONAM’15, SocInfo’12, KDD-CUP/Poster/Workshop/Local/Publication Co-Chair of KDD’11-15, and Acting Editor-in- Chief of ACM TKDD, Associate Editors of IEEE TKDE/TBD and ACM TIST. He leads the project AMiner.org for academic social network analysis and mining, which has attracted more than 8 million independent IP accesses from 220 countries/regions in the world. He was honored with the UK Royal Society-Newton Advanced Fellowship Award, CCF Young Scientist Award, and NSFC Excellent Young Scholar.
Johannes Fürnkranz is a full Professor for Knowledge Engineering at TU Darmstadt, Germany. His main research interests are machine learning and data mining, in particular inductive rule learning, learning of intrpretable models, multi-label classification and preference learning, and their applications in game playing, web mining, and scientific data mining. Since 2015, he serves as the editor-in-chief of Data Mining and Knowledge Discovery, the most traditional and renown journal in this area. He is also a long-time action editor for Machine Learning, and current or past editorial board member of several other well-known journals. He is also a regular PC or senior PC member of premier conferences in the areas of machine learning, data mining, information retrieval, and artificial intelligence, and was nominated “best reviewer” at two Machine Learning conferences, “outstanding PC member” at the AAAI conference, and “outstanding editor” of the Machine Learning journal. In 2006, he co-chaired the 6th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases in Berlin, the largest international joint conference in the areas of machine learning and data mining. In 2010, he served as the cochair of the 27th International Conference on Machine Learning (Haifa, Israel), the most renown and traditional conference in this area. He also co-chaired the 16th International Conference on Discovery Science (Singapore 2013), and is currently chairing the 40th German Conference on Artificial Intelligence (Dortmund 2017).