Nava Tintarev is an Assistant Professor and Delft Technology Fellow in the Web Information Systems group, Faculty of Electrical Engineering, Mathematics and Computer Science at TU Delft. She studies how to best present and adapt the presentation of complex data (using both natural language generation, and visualizations) in artificial advice giving systems.

The ENSURE (ExplaiNing SeqUences in REcommendations) project looks at ways of improving the transparency and decision support for recommender systems (like Amazon and Spotify), in recommendation scenarios that contain both surprising recommendations and trade-offs. She is also leading a smaller project looking at issues relating to critical thinking in higher education: SuSPECT: Scaffolding Student PErspectives for Critical Thinking. She is currently interested in contributing to projects and grant applications tackling issues regarding ethics in big data, algorithmic transparency, fake news, and filter bubbles.

Nava was previously an assistant professor at Bournemouth University (UK), a research fellow at Aberdeen University (UK), and a research engineer for Telefonica Research (Spain).

Relevant keywords: explanations, natural language generation, human-computer interaction, personalization (recommender systems), and visualization.


Track record: I have published in peer-reviewed venues on the topic of personalized information presentation in different application areas, including Recommender Systems, Scrutable Autonomous Systems (SAsSy), nature conservation (MinkApp), and Augmentative and Alternative Communication (AAC) ("How was school today...?"). In the last 2 years I have received grant awards as PI or Co-I totalling over €200K. I have been a member of the ACM since January 2014.

Academic service: I am a senior PC member for the following conferences: I have also reviewed for other leading conferences and journals, as well as served in organizational roles for international conferences and workshops. I examine PhD dissertations and review grant proposals (e.g., for the EPSRC).

Students: I am on the outlook for enthusiastic PhD and Masters students to work on topics relating to the transparency of intelligent systems, and usability/interface issues (e.g. diversity, novelty) in recommender systems.

News

5th of April. Our submission ``Sequences of Diverse Song Recommendations: An exploratory study in a commercial system'' was accepted as an extended abstract and poster to UMAP'17.

5th of April. Excited to announce the Delft Recsys Meetup: Michael Ekstrand and Juliian Urbano will be presenting their research.

22nd of March. 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'17), accepted to the ACM Recommender Systems Conference and first call for papers is out.

21st of March. Looking forward to Recsys.NL hosted by XITE Networks, where I'll be giving a talk titled ``Explain yourself! Arguing with Recommender Systems.''. Join us and sign up to the Meetup here.

23rd of Feb. I'm hiring! 2 year postdoc on explanations in recommender systems at TU Delft.

23rd of Feb. Opinion piece for DIGIT on Ethics in Data Analytics: Balancing Insight and Privacy.

11th of Feb. Joining the Web Information Systems group, Faculty of Electrical Engineering, Mathematics and Computer Science TU Delft as an assistant professor on a personal fellowship.

24th of November: I am delighted to act as track chair for the Intelligent User Interfaces track at UMAP'17, and as senior PC member for Recsys'17.

12th of November: Chairing a panel on ``Data Analytics: Balancing Insight, Privacy & Trust'' at the Big Data Conference, Dynamic Earth, Edinburgh, 8th of December, 2016

2nd of November: Our introduction to the special issue on Human Interaction With Artificial Advice Givers in ACM TiiS has been accepted. Articles will be available here!

31st of October: Our paper, Effects of Individual Differences in Working Memory on Plan Presentational Choices has been accepted for publication in Frontiers in Psychology, section Human-Media Interaction (IF: 2.463).