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.
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.
- Amifa Raj (Ph.D)
- Ngozi Ihemelandu (Ph.D)
- Srabanti Guha (MS)
Carlos Segura Cerna (MS 2020)
Mucun Tian (MS 2019)
- Estimating Error and Bias of Offline Recommender System Evaluation Results
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. Acceptance rate: 47%. Cited 5 times.and .
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. Cited 1 time.and .
Sushma Channamsetty (MS 2016 @ TXST)
- Recommender Response to User Profile Diversity and Popularity Bias
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.and .
Mohammed Imran R Kazi (MS 2016 @ TXST)
- Exploring Potentially Discriminatory Biases in Book Recommendation
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. Acceptance rate: 17.5%. Cited 78 times., , , , and .
Vaibhav Mahant (MS 2016 @ TXST)
- Improving Top-N Evaluation of Recommender Systems
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.and .
Shuvabrata Saha (MS 2016 @ TXST)
Co-advised with Dr. Apan Qasem.