Mila > Team > Fernando Diaz

Fernando Diaz

Affiliate Member
Adjunct Professor, Associate Professor, McGill University, Carnegie Mellon University, Google, Canada CIFAR AI Chair

Fernando Diaz is an Associate Professor at the Carnegie Mellon University’s School of Computer Science. Fernando is also a research scientist at Google Pittsburgh and Adjunct Professor at McGill School of Computer Science. 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.



Exposing Query Identification for Search Transparency.
Ruohan Li, Jianxiang Li, Bhaskar Mitra, Fernando Diaz and Asia J. Biega
arXiv: Information Retrieval


Overview of the TREC 2020 Fair Ranking Track.
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sergey Feldman and Sebastian Kohlmeier
arXiv preprint arXiv:2108.05135


Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice.
Divya Shanmugam, Samira Shabanian, Fernando Diaz, Michèle Finck and Asia Biega
arXiv preprint arXiv:2107.08096
Making Sense of Metrics in the Music Industries
Nancy Baym, Rachel Bergmann, Raj Bhargava, Fernando Diaz, Tarleton Gillespie, David Hesmondhalgh, Elena Maris and Christopher J. Persaud
International Journal of Communication
ACM SIGIR 2021 Chairs' Welcome
Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones and Tetsuya Sakai
SIGIR 2021


The Benchmark Lottery
Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler and Oriol Vinyals
(venue unknown)


Fairness and Discrimination in Information Access Systems
Michael D. Ekstrand, Anubrata Das, Robin Burke and Fernando Diaz
arXiv preprint arXiv:2105.05779
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 preprint arXiv:2105.02951
“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


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


On Evaluating Session-Based Recommendation with Implicit Feedback.


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. (arXiv:2003.11650v1 [cs.IR])
Asia J. Biega, Fernando Diaz, Michael D. Ekstrand and Sebastian Kohlmeier
arXiv Computer Science


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


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


On the Evaluation of Common-Sense Reasoning in Natural Language Understanding
Jackie Chi Kit Cheung, Paul Trichelair, Ali Emami, Adam Trischler, Kaheer Suleman and Fernando Diaz
Critiquing and Correcting Trends in Machine Learning NeurIPS 2018 Workshop


On the Evaluation of Common-Sense Reasoning in Natural Language Understanding.
Paul Trichelair, Ali Emami, Jackie Chi Kit Cheung, Adam Trischler, Kaheer Suleman and Fernando Diaz
arXiv: Learning

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