Lora Aroyo is a Full Professor in Computer Science. Currently, she is a visiting scholar at the Columbia Data Science Institute at Columbia University, New York. She is also Chief of Science for a NY-based startup Tagasauris, which works on hybrid machine learning and human-assisted computing strategies to enrich multimedia (e.g. video, images, and text) with meaningful information about its content, and ultimately improve video search and discovery.
Lora is an active member of the Human Computation, User Modeling and Semantic Web communities. SheShe is an ACM distinguished speaker and the president of the User Modeling community (UM Inc). UM Inc serves as a steering committee for the ACM Conference Series “User Modeling, Adaptation and Personalization” (UMAP) and is part of both SIGCHI and SIGWEB. In her role of UMAP steering committee chair, she also participates in the ACM SIGCHI conferences board. Since 2010 she has actively worked towards shaping the concept of “User-Centric Data Science“, which ultimately led to the forming and also heading of the User-centric Data Science group at the Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands.
As an expert in user-centric data science, Lora initiated the vision of user-centric experimental lab for computer science researchers at the VU University Amsterdam, which led to the realization in 2010 of the VU INTERTAIN Lab – the first of its kind in an academic environment. She led a large number of research projects, organized conferences, workshops, and tutorials bringing together methods and tools from crowdsourcing, human computation, linked (open) data, data gathering and analysis and human-computer interaction for building hybrid human-AI systems for understanding text, images, and videos. In this context, she has led major research projects in semantic search, recommendation systems, personalized access to online multimedia collections, and through these has become a recognized leader in human computation techniques for specific domains, such as digital humanities, cultural heritage, and interactive TV. She has led a number of research projects that focus on:
- the understanding ambiguity and teaching machines to deal with ambiguity by applying techniques from crowdsourcing and human computation, data science, data quality assessment, and especially hybrid human-AI systems for text and video understanding.
- applying Semantic Web technologies for semantic search, recommendation systems, event-driven access to online multimedia collections, and through these has become a recognized leader in digital humanities, cultural heritage, and interactive TV
She is a four times holder of IBM Faculty Award for her work on CrowdTruth: for crowdsourcing ground truth data in the context of adapting the IBM Watson system to the medical domain and applying CrowdTruth for capturing ambiguity for the purpose of understanding misinformation.
Some of the notable research projects:
- ReTV: Reinventing the TV for the Digital Age: Re-purposing and re-using digital content across Smart TVs, Web and mobile applications, social media and other emerging platforms
- Capturing Bias: Diversity-aware Analysis of Bias in News Videos: models for bias- and diversity-aware accuracy measures for reliable and explainable big data analysis of media collections
- CrowdTruth: human-assisted computing, specifically targeting workflows for the creation of ground truth data
- CLARIAH: Common Lab Research Infrastructure for the Arts and Humanities
- DIVE: Event-centric Exploration of Linked Heritage
- Accurator: Annotating Fashion with Nichesourcing
- SealincMedia: Socially-enriched Access to Linked Cultural Media
- ControCurator: discover and understand controversial topics and events
- VISTA-TV: Combining LOD and behavioral information for TV analyses
- PrestoPrime: WAISDA? Crowdsourcing Game for Video Annotation
- NoTube: integration of Web and TV data with the help of semantics
- CHIP: Cultural Heritage Information Personalization