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 30 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.


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.



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 and machine learning. Darin has participated in both W3C and industry working groups and is the recipient of several patents. He has also created the open source projects xqDoc,  spark-xml-utils, and AnnotationQuery.


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. 

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Paul Snyder received his Ph.D. in Computer Science from Drexel university, focusing on nature-inspired computation and self-organization for distributed systems. He joined Elsevier in 2017 to lead the development of H-Graph, a knowledge graph for healthcare concepts and information. He became a member of the Labs group in 2019, and has active research interests in automated knowledge graph construction and natural language processing. Prior to joining Elsevier, he has worked in a wide range of roles, including emerging technology and creating high-performance distributed systems.


Pierre-Yves Vandenbussche received his Ph.D. in computer science from Pierre et Marie Curie Paris University and a B.Sc. in biology. His expertise lies in knowledge representation, knowledge extraction and graph mining. Before joining Elsevier Labs, he developed the Linked Open Vocabulary (LOV) initiative to facilitate semantic data representation on the Web and led the Knowledge Engineering and Discovery team at Fujitsu Labs focusing on accelerating cancer research using knowledge graph mining techniques. Pierre-Yves is the recipient of 5 awards of excellence, author of 8 patents, and 60 scientific publications in top conferences such as ECML, ISWC or BIB.