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 for details and current openings.

My Drexel University students are part of the INERTIA Laboratory (Impact, Novation, Effectiveness, and Responsibility of Technology for Information Access), and you can read more at the lab website.

Current Ph.D. Students

Samira Vaez Barenji

Papers: FairUMAP25
2025
Samira Vaez Barenji, Sushobhan Parajuli, and Michael D. Ekstrand. 2025. User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation. To appear in Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (Adjunct ’25), Workshop on Fairness in User Modeling, Adaptation and Personalization, Jun 16, 2025. DOI 10.1145/3708319.3733710. arXiv:2505.04518.

Sushobhan Parajuli

Papers: FairUMAP25
2025
Samira Vaez Barenji, Sushobhan Parajuli, and Michael D. Ekstrand. 2025. User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation. To appear in Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (Adjunct ’25), Workshop on Fairness in User Modeling, Adaptation and Personalization, Jun 16, 2025. DOI 10.1145/3708319.3733710. arXiv:2505.04518.

Past Ph.D. Students

Ngozi Ihemelandu (Ph.D. 2024, Boise State)

Dissertation: Best Practices for Offline Evaluation for Top-N Recommendation: Candidate Set Sampling and Statistical Inference
Papers: ECIR24m
2024
Ngozi Ihemelandu and Michael D. Ekstrand. 2024. Multiple Testing for IR and Recommendation System Experiments. Short paper in Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24), Mar 24–28, 2024. Lecture Notes in Computer Science 14610:449–457. DOI 10.1007/978-3-031-56063-7_37. NSF PAR 10497108. Acceptance rate: 24.3%. Cited 3 times.; WI23
2023
Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Candidate Set Sampling for Evaluating Top-N Recommendation. In Proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’23), Oct 26–29, 2023. pp. 88-94. DOI 10.1109/WI-IAT59888.2023.00018. arXiv:2309.11723 [cs.IR]. NSF PAR 10487293. Acceptance rate: 28%. Cited 6 times. Cited 1 time.; SIGIR23i
2023
Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Inference at Scale: Significance Testing for Large Search and Recommendation Experiments. Short paper in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), Jul 23, 2023. pp. 2087–2091. DOI 10.1145/3539618.3592004. arXiv:2305.02461. NSF PAR 10423691. Acceptance rate: 25.1%. Cited 4 times. Cited 1 time.; 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), Sep 25, 2021. arXiv:2109.06424 [cs.IR]. Cited 8 times. Cited 6 times.

Amifa Raj (Ph.D. 2023, Boise State)

Dissertation: Fair Layouts in Information Access Systems: Provider-Side Group Fairness in Ranking Beyond Ranked Lists
Papers: ECIR24g
2024
Amifa Raj and Michael D. Ekstrand. 2024. Towards Optimizing Ranking in Grid-Layout for Provider-side Fairness. In Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24, IR for Good track), Mar 24–28, 2024. Lecture Notes in Computer Science 14612:90–105. DOI 10.1007/978-3-031-56069-9_7. NSF PAR 10497109. Acceptance rate: 35.9%. Cited 2 times. Cited 2 times.; FAccTRec23
2023
Amifa Raj and Michael D. Ekstrand. 2023. Towards Measuring Fairness in Grid Layout in Recommender Systems. Presented at the 6th FAccTrec Workshop on Responsible Recommendation at RecSys 2023 (peer-reviewed but not archived). arXiv:2309.10271 [cs.IR]. Cited 1 time.; SIGIR23q
2023
Amifa Raj, Bhaskar Mitra, Michael D. Ekstrand, and Nick Craswell. 2023. Patterns of Gender-Specializing Query Reformulation. Short paper in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), Jul 23, 2023. pp. 2241–2245. DOI 10.1145/3539618.3592034. arXiv:2304.13129. NSF PAR 10423689. Acceptance rate: 25.1%. Cited 4 times. Cited 1 time.; CHIIR23
2023
Christine Pinney, Amifa Raj, Alex Hanna, and Michael D. Ekstrand. 2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (CHIIR ’23), Mar 19, 2023. pp. 269–279. DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 27 times. Cited 13 times.; 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, Jul 15, 2022. 9 pp. DOI 10.48550/arXiv.2206.13747. arXiv:2206.13747 [cs.IR]. Cited 14 times. Cited 6 times.; 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), Jul 11, 2022. pp. 726–736. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 73 times. Cited 51 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), Sep 26, 2021. pp. 639–644. DOI 10.1145/3460231.3478856. NSF PAR 10316668. Cited 12 times. Cited 5 times.; 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, Jun 27, 2021. arXiv:2105.09296. NSF PAR 10335669. Cited 10 times. Cited 6 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 at RecSys 2020 (peer-reviewed but not archived). arXiv:2009.01311 [cs.IR]. Cited 37 times. Cited 29 times.

M.S. Students

Srabanti Guha (MS 2023, Boise State)

Project: Explaining Misallocated Exposure across Multiple Rankings

Carlos Segura Cerna (MS 2020, Boise State)

Project: Recommendation Server for LensKit

Mucun Tian (MS 2019, Boise State)

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), Mar 14, 2020. ACM, 5 pp. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 14 times. Cited 10 times.; Reveal18mc
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 at RecSys 2018. DOI 10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452. Cited 1 time. Cited 1 time.; FAT18ck
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), Feb 23, 2018. PMLR, Proceedings of Machine Learning Research 81:172–186. Acceptance rate: 24%. Cited 312 times. Cited 223 times.

Sushma Channamsetty (MS 2016, TXST)

Thesis: Recommender Response to User Profile Diversity and Popularity Bias
Papers: FLAIRS17rr
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), May 29, 2017. AAAI, pp. 657–660. No acceptance rate reported. Cited 21 times. Cited 20 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), Oct 3, 2018. ACM, pp. 242–250. DOI 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Citations reported under UMUAI21^◊. Citations reported under UMUAI21^◊.

Vaibhav Mahant (MS 2016, TXST)

Thesis: Improving Top-N Evaluation of Recommender Systems
Papers: FLAIRS17s
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), May 29, 2017. AAAI, pp. 639–644. No acceptance rate reported. Cited 25 times. Cited 11 times.

Shuvabrata Saha (MS 2016, TXST)

Co-advised with Dr. Apan Qasem.

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