bigdas@KDD2017: LIVE STREAM
The first ever workshop on Big data analytics-as-a-Service: Architecture, Algorithms, and Applications in Health Informatics is taking place on August 14, 2017 (In conjunction with KDD 2017) in Halifax, Canada. The workshop will consist of a combination of invited presentations, panel discussions, and paper/poster presentations. We allocate significant time for open discussions on best practices and future directions in the research area. This is a new and emerging area for the KDD community and we do hope this workshop will bring together researchers, scientists and interested audiences to explore the open problems, applications, and future directions in this vast progressing domain.
The objective of the bigdas@KDD2017 is to provide a professional forum for health informatics practitioners, machine learning applied researchers, data scientists, data analytics and Business Intelligence individual’s, big data and machine learning-as-a-service providers and customers, amongst data mining researchers working on novel big data machine learning strategies, modeling, and scientific visualization to present their latest research findings, innovations, and developments in turning big data health care analytics into fast, easy-to-use, scalable, and highly available services over the Internet. This workshop is aimed at data science practitioners working at the intersection of big data machine learning, Software as a Service (SaaS) platforms, Internet of Things (IoT), and health informatics. It will highlight current trends and insights for the future of health data analytics, which is bigger and smarter.
We invite submission of papers explaining innovative research studies on all aspects of big data analytics and its application in health informatics. Work-in-progress papers, demos, experimental studies, open-source developments, and visionary papers are also welcome.
[CFP Poster: Download]
Short Bio: Peggy Peissig is a Research Scientist with over 25 years of health care and research informatics experience. She holds the John Melski Endowed Distinguished Scientist Chair for Biomedical Informatics at the Marshfield Clinic where she is Director of the Biomedical Informatics Research Center and Chief Research Informatics Officer for Marshfield Clinic Research Institute. Her primary research interests include electronic health record (EHR) phenotyping, translational and clinical informatics, pharmacogenetics and the application of machine learning to EHR-based phenotyping and adverse drug event detection and surveillance. She has funding from the Federal Drug Administration, National Institute of Health and the Institute for Clinical and Translational Research (University of Wisconsin, USA). She is the co-chair of the American Medical Informatics Association 2017 Joint Summit and also participates in the eMERGE (Electronic Medical Records and Genomics) Network where she co-chairs the electronic health record phenotyping workgroup.
Short Bio: David Page is a Vilas Distinguished Achievement Professor at the University of Wisconsin-Madison. His primary appointment is in the Dept. of Biostatistics and Medical Informatics in the School of Medicine and Public Health, with an appointment in the Dept. of Computer Sciences where he teaches machine learning. His PhD in CS is from the University of Illinois at Urbana- Champaign, and he became involved in biomedical applications of machine learning as a post-doc in what was then the Computing Laboratory at Oxford University. He directs the Cancer Informatics Shared Resource of the Carbone Cancer Center and is a member of the Genome Center of Wisconsin. He previously served on the NIH’s BioData Management and Analysis Study Section and the scientific advisory boards for the Wisconsin Genomics Initiative and the Observational Medical Outcomes Partnership, as well as the editorial boards for Machine Learning and Data Mining and Knowledge Discovery. He
currently is on the National Library of Medicine Study Section (BLIRC) and directs the EHR project within UW-Madison’s BD2K Center for Predictive Computational Phenotyping.
Short Bio: Reza Zadeh is an Adjunct Professor at Stanford University and Founder CEO at Matroid. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. He received his PhD in Computational Mathematics from Stanford University, USA. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Board of Microsoft and Databricks. As part of his research, Reza built the Machine Learning Algorithms behind Twitter’s who-to-follow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark. Through Apache Spark, Reza’s work has been incorporated into industrial and academic cluster computing environments.
Short Bio: Ahmad P. Tafti is a postdoctoral scholar in Biomedical Informatics Research Center at Marshfield Clinic Research Institute. He is an Oracle certified professional, and completed his Ph.D. in Computer Science Department at University of Wisconsin-Milwaukee, USA, where he was working on computational vision, machine learning, and artificial intelligence. Part of his international studies was carried out at Technical University of Vienna and Medical University of Vienna, Austria. He won GE honorable mention award in 2015, and recently he has been selected for the best reviewer award at the Society of Digital Information and Wireless Communications (SDIWC). He was the chair of a special track named “Computer Vision-as-a-Service” at ISVC 2016.
Short Bio: Eric LaRose is a programmer/analyst working for the Biomedical Informatics Research Center at Marshfield Clinic Research Institute. He has experience working in many different programming languages and platforms including several big data and NLP platforms (ElasticSearch DB, Hadoop, cTakes NLP, etc.). His research interests include big data analytics applied to the biomedical field (Machine Learning, Data Management, Natural Language Processing), decision support tools, pharmacogenetics, and health data monitoring. He is a technical reviewer at IEEE 4th International Conference on Artificial Intelligence and Pattern Recognition (AIPR), 2017.
Short Bio: Philippe Cudre-Mauroux is a Full Professor and the Director of the eXascale Infolab at the University of Fribourg in Switzerland. He received his Ph.D. from the Swiss Federal Institute of Technology EPFL, where he won both the Doctorate Award and the EPFL Press Mention in 2007. Before joining the University of Fribourg, he worked on information management infrastructures at IBM Watson Research (NY), Microsoft Research Asia, and MIT. He recently won the Verisign Internet Infrastructures Award, a Swiss National Center in Research award, a Google Faculty Research Award, as well as a 2 million Euro ERC grant. His research interests are in next-generation, Big Data management infrastructures for non-relational data.
Short Bio: Richard Segall is Professor of Computer & Information Technology at Arkansas State University in Jonesboro, AR where he also teaches in the Master of Engineering Management (MEM) Program in the College of Agriculture, Engineering & Technology. Dr. Segall holds a B.S. and a M.S. in Mathematics, and a M.S. in Operations Research from Rensselaer Polytechnic Institute, and PhD in Operations Research from University of Massachusetts at Amherst. He has served on the faculty of Texas Tech University, University of Louisville, University of New Hampshire, University of Massachusetts-Lowell, and West Virginia University. His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling. His research has been funded by National Research Council (NRC), U.S. Air Force (USAF), National Aeronautical and Space Administration (NASA), Arkansas Biosciences Institute (ABI), and Arkansas Science & Technology Authority (ASTA). He has edited two published books and one forthcoming: Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications published by IGI Global in 2011, Research and Applications in Global Supercomputing published by IGI Global in 2015, and Handbook on Big Data Storage and Visualization Techniques to be published by IGI Global in late 2017. He is a member of the Arkansas Center for Plant-Powered-Production (P3), and on the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM) and International Journal of Data Science (IJDS), and served as Local Arrangements Chair of the MidSouth Computational Biology & Bioinformatics Society (MCBIOS) Conference that was hosted at Arkansas State University. Dr. Segall is recipient of Arkansas State University, College of Business Faculty Award for Excellence in Research in 2015.
Short Bio: Angus Roberts is a Senior Research Fellow in Natural Language Processing (NLP) at the University of Sheffield, UK. He leads life science work for GATE, a widely used text engineering toolkit. He is especially interested in NLP infrastructure, and service based text analytics. Much of his research focusses on applications of this to medical record text, and he has led several projects involving electronic health record vendors and public sector health organizations. Before his research career, Angus trained and worked as a hospital biomedical scientist, and worked as a UK health service software developer and development manager.