Students

One of the great parts of my job is working with students on research and software development. This page collects information for and about those students.

If you are interested in doing research with me, particularly as one of my advisees, see my information for prospective students.

Current Openings

Students interested in doing graduate work me and the People and Information Research Team (PIReT) should apply to our M.S. in Computer Science or Ph.D in Computing program; see my Information for Prospective Students for more detail.

I am also interested in involving Boise State undergraduate students in our research. Sometimes I have funds myself for paid research opportunities; the university also has several programs that support undergraduate students involved in faculty research, and I am happy to discuss the possibilities of doing an independent study or an opportunity where you get course credit for research.

Current Students

Amifa Raj (Ph.D)

Papers: SIGIRec22
2022
Amifa Raj and Michael D. Ekstrand. 2022. Fire Dragon and Unicorn Princess: Gender Stereotypes and Children’s Products in Search Engine Responses. In SIGIR eCom ’22. DOI 10.48550/arXiv.2206.13747. arXiv:2206.13747 [cs.IR]. Cited 1 time.; SIGIR22
2022
Amifa Raj and Michael D. Ekstrand. 2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). pp. 726–736. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 6 times. Cited 3 times.; RSLBR21
2021
Lawrence Spear, Ashlee Milton, Garrett Allen, Amifa Raj, Michael Green, Michael D. Ekstrand, and Maria Soledad Pera. 2021. Baby Shark to Barracuda: Analyzing Children’s Music Listening Behavior. In RecSys 2021 Late-Breaking Results (RecSys ’21). DOI 10.1145/3460231.3478856. NSF PAR 10316668. Cited 1 time. Cited 1 time.; KidRec21
2021
Amifa Raj, Ashlee Milton, and Michael D. Ekstrand. 2021. Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kids’ Products in Search and Recommendations. In Proceedings of the 5th International and Interdisciplinary Workshop on Children & Recommender Systems (KidRec ’21), at IDC 2021. DOI 10.48550/arXiv.2105.09296. arXiv:2105.09296. NSF PAR 10335669. Cited 3 times. Cited 5 times.; FAccTRec20
2020
Amifa Raj, Connor Wood, Ananda Montoly, and Michael D. Ekstrand. 2020. Comparing Fair Ranking Metrics. Presented at the 3rd FAccTrec Workshop on Responsible Recommendation (peer-reviewed but not archived). DOI 10.48550/arXiv.2009.01311. arXiv:2009.01311 [cs.IR]. Cited 15 times. Cited 16 times.

Ngozi Ihemelandu (Ph.D)

Papers: RSPE21-inf
2021
Ngozi Ihemelandu and Michael D. Ekstrand. 2021. Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse. In Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2021 (RecSys ’21). DOI 10.48550/arXiv.2109.06424. arXiv:2109.06424 [cs.IR]. Cited 2 times. Cited 1 time.

Srabanti Guha (MS)

Graduated Students

Carlos Segura Cerna (MS 2020)

Project: Recommendation Server for LensKit

Mucun Tian (MS 2019)

Thesis: Estimating Error and Bias of Offline Recommender System Evaluation Results
Papers: CHIIR20
2020
Mucun Tian and Michael D. Ekstrand. 2020. Estimating Error and Bias in Offline Evaluation Results. Short paper in Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR ’20). ACM, 5 pp. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 5 times. Cited 7 times.; Reveal18-mc
2018
Mucun Tian and Michael D. Ekstrand. 2018. Monte Carlo Estimates of Evaluation Metric Error and Bias. Computer Science Faculty Publications and Presentations 148. Boise State University. Presented at the REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems, a workshop at RecSys 2018. DOI 10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452. Cited 1 time. Cited 1 time.; FAT18-ck
2018
Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:172–186. Acceptance rate: 24%. Cited 144 times. Cited 154 times.

Sushma Channamsetty (MS 2016 @ TXST)

Thesis: Recommender Response to User Profile Diversity and Popularity Bias
Papers: FLAIRS17-rr
2017
Sushma Channamsetty and Michael D. Ekstrand. 2017. Recommender Response to Diversity and Popularity Bias in User Profiles. Short paper in Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (Recommender Systems track). AAAI, pp. 657–660. No acceptance rate reported. Cited 11 times. Cited 12 times.

Mohammed Imran R Kazi (MS 2016 @ TXST)

Thesis: Exploring Potentially Discriminatory Biases in Book Recommendation
Papers: RecSys18
2018
Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM, pp. 242–250. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Cited 112 times. Citations reported under UMUAI21^*.

Vaibhav Mahant (MS 2016 @ TXST)

Thesis: Improving Top-N Evaluation of Recommender Systems
Papers: FLAIRS17-s
2017
Michael D. Ekstrand and Vaibhav Mahant. 2017. Sturgeon and the Cool Kids: Problems with Random Decoys for Top-N Recommender Evaluation. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (Recommender Systems track). AAAI, pp. 639–644. No acceptance rate reported. Cited 8 times. Cited 13 times.

Shuvabrata Saha (MS 2016 @ TXST)

Co-advised with Dr. Apan Qasem.

Thesis: A Multi-Objective Autotuning Framework For The Java Virtual Machine