Fernando Diaz

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Core Industry Member
Fernando Diaz
Adjunct Professor, McGill University, Microsoft Research
Fernando Diaz

Fernando Diaz is Assistant Research Director at Microsoft Research. His expertise is in the formal study of the research of small fragments of information in large data sets. His interests include distributed approaches to web-based documentary research, interactive and faceted research, exploration of temporal models from news and queries, multilingual information research and graph-based methods.  

His primary research interest is information retrieval, the formal study of searching large collections of data for small bits of information. The most familiar instance of information retrieval is web search where users search a collection of webpages for one or a few relevant webpages. Information retrieval, however, goes beyond web search and includes topics such as cross-lingual retrieval, personalization, desktop search, and interactive retrieval. Fernando’s research experience includes distributed information retrieval approaches to web search, interactive and faceted retrieval, mining of temporal patterns from news and query logs, cross-lingual information retrieval, graph-based retrieval methods, and exploiting information from multiple corpora. In his dissertation work, he studied the relationship between document clustering and document scoring for retrieval using methods from machine learning and statistics. As a result, he developed an algorithm for system self-assessment and self-tuning which significantly improves the performance of retrieval algorithms across a variety of corpora.



Multi-FR: A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation.
Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz and Xue Liu
arXiv: Information Retrieval
“I Can’t Reply with That”: Characterizing Problematic Email Reply Suggestions
Ronald E Robertson, Alexandra Olteanu, Fernando Diaz, Milad Shokouhi and Peter Bailey
CHI 2021


Estimation of Fair Ranking Metrics with Incomplete Judgments
Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette and Emine Yilmaz
WWW 2021


Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification
Jaime Arguello, Adam Ferguson, Emery Fine, Bhaskar Mitra, Hamed Zamani and Fernando Diaz


Evaluating Stochastic Rankings with Expected Exposure
Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega and Ben Carterette
When Are Search Completion Suggestions Problematic
Alexandra Olteanu, Fernando Diaz and Gabriella Kazai
Proceedings of the ACM on Human-Computer Interaction


Analyzing and Learning from User Interactions for Search Clarification
Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell and Susan T. Dumais
Operationalizing the Legal Principle of Data Minimization for Personalization
Asia J. Biega, Peter Potash, Hal Daumé, Fernando Diaz and Michèle Finck
On the Social and Technical Challenges of Web Search Autosuggestion Moderation.
Timothy J. Hazen, Alexandra Olteanu, Gabriella Kazai, Fernando Diaz and Michael Golebiewski
arXiv preprint arXiv:2007.05039


Overview of the TREC 2019 Fair Ranking Track.
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand and Sebastian Kohlmeier
arXiv preprint arXiv:2003.11650


Fairness and discrimination in recommendation and retrieval
Michael D Ekstrand, Robin Burke and Fernando Diaz


Session details: Session 3A: Recommendations 1
SIGIR 2019
Fairness and Discrimination in Retrieval and Recommendation
Michael D. Ekstrand, Robin Burke and Fernando Diaz
SIGIR 2019
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.
Alexandra Olteanu, Carlos Castillo, Fernando Diaz and Emre Kıcıman
Frontiers in Big Data
Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks
Bhaskar Mitra, Corby Rosset, David Hawking, Nick Craswell, Fernando Diaz and Emine Yilmaz
arXiv preprint arXiv:1907.03693

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