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 & Semantic Web communities. She is president of the User Modeling community UM Inc, which serves as a steering committee for the ACM Conference Series “User Modeling, Adaptation and Personalization” (UMAP) sponsored by SIGCHI and SIGWEB. She is also a member of 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 of and heading 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 conceived the vision of an user-centric experimental lab for computer science researchers at the VU University Amsterdam. She headed the team that made it possible in 2010 to open VU INTERTAIN Lab – the first of its kind in an academic environment. Throughout her carrier, Lora was a principal investigator of a large number of research projects, she organized conferences, workshops, and tutorials to bring together methods and tools from human computation, linked (open) data, data science & human-computer interaction with the goal of building hybrid human-AI systems for augmenting both machine and human intelligence for understanding text, images, and videos with humans-in-the-loop and machines-in-the-loop. Her research projects focussing on semantic search, recommendation systems, personalized access to online multimedia collections have a major impact and established her as a recognized leader in human computation techniques for specific domains, such as digital humanities, cultural heritage, and interactive TV. The focal points in her current research are:
- understanding ambiguity with humans in the loop: teaching machines to deal with ambiguity in text, image & video processing;
- augmenting intelligence: improving interpretation abilities of text, images, and videos with humans-in-the-loop and machines-in-the-loop;
- hybrid human-AI systems: harnessing the power of the crowd and AI to improve recommendation systems, semantic search, access to online multimedia collections in domains like 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.
List of notable research projects, where Lora Aroyo was a principal investigator (PI):
- 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