Who We Are
Ron Daniel is the Director of Elsevier Labs, an R&D group which concentrates on smart content and on the future of scholarly communications. Educated as an electrical engineer, Ron has done extensive work on metadata standards such as the Dublin Core, RDF, and PRISM. Before joining Elsevier in 2010, he worked at a startup that was acquired for its automatic classification technology, and consulted on taxonomy and information management issues for nine years. Ron received his Ph.D. in Electrical Engineering from Oklahoma State University, and was a postdoctoral researcher at Cambridge University and at Los Alamos National Laboratory. Ron is bemused by the way technology reincarnates itself, specifically in the way that parallel implementations of neural networks for machine vision are currently in vogue, just as they were more than 20 years ago when he was working on them in grad school.
Jessica Cox received her Ph.D. in Biomedical Science with an emphasis in environmental health and nutrition from The Ohio State University. She completed a postdoctoral fellowship at Columbia University, where she researched associations between arsenic metabolism and nutritional biomarkers in vulnerable populations within the United States and Bangladesh. Currently, her research interests lie in research integrity and reproducibility in science. Additionally, Jessica continues to study how researchers can use data analytics, statistics, and programming to transform findings into interesting stories to share with peers and stakeholders.
Helena Deus received her PhD in Bioinformatics from Universidade Nova de Lisboa where she focused on Linked Data and Semantic Web applications for Health Care and Life Sciences, with an emphasis on Cancer Research. Helena specializes in data integration and data wrangling techniques including sparse data management, query parallelization, data reuse and data mining for the facilitation of medical knowledge insights. Helena is passionate about breaking the cancer research silos to allow researchers to derive more value from their data and publications. Prior to joining Elsevier, Helena's roles included directing a knowledge engineering and data science team at Foundation Medicine and leading projects and strategy for Health Care and Life Sciences at the Digital Enterprise Research Institute, National University of Ireland at Galway (DERI/NUIG). Helena has published over 30 peer reviewed papers and was one of the winners of the Big Data Track in the 2013 Semantic Web Challenge and of the Linked Data Cup with her work on linking data from The Cancer Genome Atlas.
Paul Groth holds a Ph.D. in Computer Science from the University of Southampton (2007) and has done research at the University of Southern California and the VU University Amsterdam. His research focuses on dealing with large amounts of diverse contextualized knowledge with a particular focus on the web and science applications. This includes research in data provenance, data science, data integration and knowledge sharing. Paul was co-chair of the W3C Provenance Working Group that created a standard for provenance interchange. He is co-author of Provenance: an Introduction to PROV; The Semantic Web Primer: 3rd Edition as well as numerous academic articles. Paul's personal website is pgroth.com and he blogs about his research and technology on ThinkLinks.
Corey Harper spent nearly 15 years building digital libraries, administering library systems, and managing library metadata. He has held metadata librarian positions at both New York University and the University of Oregon where his research focused on linked data, digital repositories, and library discovery. His current research interests include natural language processing, machine learning, predictive analytics, and data visualization with applications toward issues around research communications. In addition, he is involved in both the Digital Public Library of America (DPLA) and code4lib communities. Corey has an MBA from NYU's Stern School of Business and an MSLS from the University of North Carolina.
Curt Kohler has been a member of Elsevier Labs since 2000. Over the years he has investigated large scale service-oriented architectures, cloud computing, various search technologies, and many NoSQL databases. Most recently he has been investigating issues in Big Data processing using the Apache Spark platform. He is one of the founders of the Cincinnati Apache Spark Meetup group.
Mike Lauruhn is a librarian working in research areas including Linked Data, taxonomies and ontologies, mark-up and annotation, research data lifecycles, and other issues affecting research communications. He is currently a member of the Dublin Core Metadata Initiative's Governing Board. Before joining Labs in 2010, he held consulting and technical positions helping large companies and organizations define and implement taxonomies and metadata schemas. Mike's earlier work experience includes cataloging for the California Newspaper Project at the Center for Bibliographic Studies and Research at the University of California, Riverside.
Darin McBeath has been an architect with Elsevier and is now a researcher within Labs. During his tenure with Labs, he has investigated search, XQuery, NoSQL, Big Data and Cloud Computing. His most recent research focuses on Apache Spark. Darin has participated in both W3C and industry working groups and is the recipient of several patents. He has also created open source projects around XQuery and Apache Spark.
Sujit Pal's interests are in natural language processing and machine learning and how they apply to search. Prior to joining Labs, he was the Director of search research & development for Healthline Networks where he helped build and extend Healthline's semantic search and data applications. Sujit writes about technology on his blog, Salmon Run.
Tony Scerri has worked in and alongside various development teams within Elsevier and has been a member of its Enterprise Architecture team. As a member of of the Labs group, he likes to dabble in all areas of technology, but his main interests are search and natural language processing, analytics and data visualization, and AI related topics.