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, there are two basic steps:

If you are curious about the kinds of skills you need or will learn in the course of working on research with our group, see my student research skills list.

If you are e-mailing me about our graduate program, please see my e-mail guidance.

For information on applying to graduate school, not just at Boise State, see resources for grad school applications.

Recommended Classes

Most of my students take several of the following classes:

  • CS 533 (Introduction to Data Science, every fall)
  • CS 534 (Machine Learning, every spring)
  • CS 538 (Recommender Systems, odd springs)
  • CS 537 (Introduction to Information Retrieval, odd falls)
  • CS 697 (Advanced Topics in Information Retrieval, even springs)
  • CS 569 (Human Computer Interaction, odd springs)
  • Math 561 (Probability and Statistics)
  • Math 562 (Computational Statistics)

I strongly recommend students interested in working with me take the first information retrieval or recommender systems class they can, and take the CS 533/534 sequence their first year.

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. Feel free to e-mail me, but I am not directly involved in admissions and rarely commit to a student until after the admissions process is complete. I also usually don’t know how many funding lines I will have available until the spring, so it’s difficult for me to predict far in advance whether or not I will be able to take a new student in any particular upcoming year.

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)
  • Ngozi Ihemelandu (Ph.D)
  • Srabanti Guha (MS)
  • Adam Keener (MS)

Graduated Students

Carlos Segura Cerna (MS 2020)
Recommendation Server for LensKit
Mucun Tian (MS 2019)
Estimating Error and Bias of Offline Recommender System Evaluation Results

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.


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.

Sushma Channamsetty (MS 2016 @ TXST)
Recommender Response to User Profile Diversity and Popularity Bias

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.

Mohammed Imran R Kazi (MS 2016 @ TXST)
Exploring Potentially Discriminatory Biases in Book Recommendation

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 73 times.

Vaibhav Mahant (MS 2016 @ TXST)
Improving Top-N Evaluation of Recommender Systems

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 5 times.

Shuvabrata Saha (MS 2016 @ TXST)

Co-advised with Dr. Apan Qasem.

A Multi-Objective Autotuning Framework For The Java Virtual Machine