Michael D. Ekstrand, Ph.D

Curriculum Vitae

People and Information Research Team (PIReT)
Dept. of Computer Science
Boise State University
1910 University Drive
Boise, ID 83725-2055


https://md.ekstrandom.net
+1 (208) 426-5761

Education

Ph.D (2014)
Computer Science, University of Minnesota. Thesis: Towards Recommender Engineering: Tools and Experiments for Identifying Recommender Differences.
Advisers: John T. Riedl and Joseph A. Konstan
B.S. (2007)
Computer Engineering (With Distinction), Iowa State University.

Employment History

2016–present
Assistant Professor, Dept. of Computer Science, Boise State University
Co-founder, People and Information Research Team (PIReT)
2014–2016
Assistant Professor, Dept. of Computer Science, Texas State University
2008–2014
Graduate Research Assistant, GroupLens Research, Dept. of Computer Science, University of Minnesota
Su 2012, F 2013
Instructor, University of Minnesota
Summer 2010
Research Intern, Autodesk Research, Toronto, CA
2007–2008, S 2011
Teaching Assistant, University of Minnesota
2005–2007
Undergraduate Research Assistant, Scalable Computing Laboratory, Ames Lab, Iowa State University

Teaching History

Boise State University

TermCourseTitleStudents
F18CS 410/510Databases40
Su18CS 310-HUIntro to Databases6
S18CS 410/510Databases22
F17CS 533Intro to Data Science22
S17CS 597Recommender Systems13
F16CS 410/510Databases28

Texas State University

TermCourseTitleStudents
S16CS 3320Internet Software Development48
S16CS 5369Q/4379QRecommender Systems26
F15CS 4332Intro to Database Systems39
S15CS 5369Q/4379QRecommender Systems28
S15CS 4350Unix Systems Programming32
F14CS 4332Intro to Database Systems50

Coursera

I co-created the Recommender Systems specialization on Coursera, along with its two previous single-class versions, with Joseph A. Konstan.

University of Minnesota

TermCourseTitleRole
F13CSCI 5980-1Intro to Recommender SystemsInst
S12CSCI 1902Structure of Computer Programming IIInst
S11CSCI 5125Collaborative and Social Computing (TA)TA
Su08CSCI 1902Structure of Computer Programming IITA
S08CSCI 1902Structure of Computer Programming IITA
F07CSCI 1902Structure of Computer Programming IITA

Ph.D Students

  • Amifa Raj (started Fall 2018)

M.S. Students

Teaching Professional Development

  • Boise State University Center for Teaching and Learning Course Design Institute, a one-week intensive session in Summer 2017.
  • CTL workshops on service learning, mastery-based grading, and other topics.
  • Texas State University’s Program for Excellence in Teaching and Learning (2014–2015).
  • Preparing Future Faculty at the University of Minnesota.

Publications

Author formatting key: myself, advised student, other Boise State student. Citation counts from Microsoft Academic via Microsoft Cognitive Services.

Book Chapters

Daniel Kluver, Michael D. Ekstrand, and Joseph A. Konstan. 2018. Rating-Based Collaborative Filtering: Algorithms and Evaluation. In Social Information Access. Peter Brusilovsky and Daqing He, eds. Springer-Verlag, Lecture Notes in Computer Science vol. 10100, pp. 344–390. ISBN 978-3-319-90091-9. DOI10.1007/978-3-319-90092-6_10. Cited 8 times.

Journal Publications

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 2016), 1:1–31. DOI10.5381/jot.2016.15.5.a1. Cited 1 times.

Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2015. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. Transactions on Computer-Human Interaction 22(2) (April 2015). DOI10.1145/2728171. Cited 13 times.

Justin J. Levandoski, Michael D. Ekstrand, Michael J. Ludwig, Ahmad Eldawy, Mohamed F. Mokbel, and John T. Riedl. 2011. RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures. Proceedings of the VLDB Endowment 4(11) (August 2011), 911–920. Acceptance rate: 18%. Cited 7 times.

Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Foundations and Trends® in Human-Computer Interaction 4(2) (February 2011), 81–173. DOI10.1561/1100000009. Cited 366 times.

Conference Publications

These papers have been published in peer-reviewed conference proceedings.

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. DOI10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%. Cited 1 times.

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 2 times.

Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018). PMLR, Proceedings of Machine Learning Research 81:35–47. Acceptance rate: 24%. Cited 2 times.

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. AAAI, pp. 639–644.

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. AAAI, pp. 657–660. Cited 1 times.

Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI10.1145/2792838.2800195. Acceptance rate: 21%. Cited 32 times.

Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI10.1145/2645710.2645737. Acceptance rate: 23%. Cited 54 times.

Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2014. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. In Proceedings of the First ACM Conference on Learning @ Scale (L@S ’14). ACM. DOI10.1145/2556325.2566244. Acceptance rate: 37%. Cited 25 times.

Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, and John Riedl. 2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13). ACM. DOI10.1145/2507157.2507188. Acceptance rate: 24%. Cited 26 times.

Daniel Kluver, Tien T. Nguyen, Michael Ekstrand, Shilad Sen, and John Riedl. 2012. How Many Bits per Rating?. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12). ACM, pp. 99–106. DOI10.1145/2365952.2365974. Acceptance rate: 20%. Cited 18 times.

Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. Short paper in Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12). ACM, pp. 233–236. DOI10.1145/2365952.2366002. Acceptance rate: 32%. Cited 29 times.

Justin J. Levandoski, Mohamed Sarwat, Mohamed F. Mokbel, and Michael D. Ekstrand. 2012. RecStore: An Extensible And Adaptive Framework for Online Recommender Queries Inside the Database Engine. In Proceedings of the 15th International Conference on Extending Database Technology (EDBT ’12). ACM, pp. 86–96. DOI10.1145/2247596.2247608. Acceptance rate: 23%. Cited 11 times.

Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, and John T. Riedl. 2011. Rethinking The Recommender Research Ecosystem: Reproducibility, Openness, and LensKit. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 133–140. DOI10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 99 times.

Michael Ekstrand, Wei Li, Tovi Grossman, Justin Matejka, and George Fitzmaurice. 2011. Searching for Software Learning Resources Using Application Context. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST ’11). ACM, pp. 195–204. DOI10.1145/2047196.2047220. Acceptance rate: 25%. Cited 25 times.

Michael D. Ekstrand, Praveen Kannan, James A. Stempter, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys ’10). ACM, pp. 159–166. DOI10.1145/1864708.1864740. Acceptance rate: 19%. Cited 54 times.

Michael D. Ekstrand and John T. Riedl. 2009. rv you’re dumb: Identifying Discarded Work in Wiki Article History. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (WikiSym ’09). ACM, 10 pp.. DOI10.1145/1641309.1641317. Acceptance rate: 36% (Selected as Best Paper). Cited 23 times.

Workshops, Seminars, Posters, Etc.

These papers have undergone some form of peer review, and are published in workshops, poster proceedings, and similar venues.

Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18), at RecSys 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. DOI10.18122/cs_facpubs/148/boisestate. NSF PAR 10074452.

Michael D. Ekstrand. 2018. The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. Computer Science Faculty Publications and Presentations 147. Boise State University. Presented at the REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems, a workshop at RecSys 2018. DOI10.18122/cs_facpubs/147/boisestate. arXiv:1809.03125 [cs.IR].

Maria Soledad Pera, Katherine Wright, and Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec ’18), at IDC 2018. DOI10.18122/cs_facpubs/140/boisestate.

Rezvan Joshaghani, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2018. Do Different Groups Have Comparable Privacy Tradeoffs?. At Moving Beyond a ‘One-Size Fits All’ Approach: Exploring Individual Differences in Privacy, a workshop at CHI 2018.

Michael D. Ekstrand and Maria Soledad Pera. 2017. The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users. In RecSys 2017 Poster Proceedings. CEUR, Workshop Proceedings 1905.

Michael D. Ekstrand. 2017. Challenges in Evaluating Recommendations for Children. In Proceedings of the International Workshop on Children & Recommender Systems (KidRec), at RecSys 2017.

Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the Tenth ACM Conference on Recommender Systems (RecSys ’16). ACM. DOI10.1145/2959100.2959179. Acceptance rate: 36% (Past, Present, and Future track). Cited 9 times.

Jennifer D. Ekstrand and Michael D. Ekstrand. 2016. First Do No Harm: Considering and Minimizing Harm in Recommender Systems Designed for Engendering Health. In Proceedings of the Workshop on Recommender Systems for Health at RecSys ’16. Cited 4 times.

Michael D. Ekstrand. 2014. Building Open-Source Tools for Reproducible Research and Education. At Sharing, Re-use, and Circulation of Resources in Cooperative Scientific Work, a workshop at CSCW 2014.

Other Publications and Presentations

These publications are unreviewed reports, preprints, etc.

Katherine Landau Wright, David McNeill, Michael D. Ekstrand, and Maria Soledad Pera. 2019. Supplementing Classroom Texts with Online Resources. At 2019 American Educational Research Association Conference.

Michael D. Ekstrand and Joseph A. Konstan. 2019. Recommender Systems Notation: Proposed Common Notation for Teaching and Research. Computer Science Faculty Publications and Presentations 177. Boise State University. arXiv:1902.01348 [cs.IR]. DOI10.18122/cs_facpubs/177/boisestate.

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 2018. From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences (Dagstuhl Perspectives Workshop 17442). Dagstuhl Manifestos 7(1) (November 2018), 96–139. DOI10.4230/DagMan.7.1.96.

Katherine Landau Wright, Michael D. Ekstrand, and Maria Soledad Pera. 2018. Supplementing Classroom Texts with Online Resources. At 2018 Annual Meeting of the Northwest Rocky Mountain Educational Research Association.

Toshihiro Kamishima, Pierre-Nicolas Schwab, and Michael D. Ekstrand. 2018. 2nd FATREC Workshop: Responsible Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). ACM. DOI10.1145/3240323.3240335.

Bamshad Mobasher, Robin Burke, Michael D. Ekstrand, and Bettina Berendt. 2018. UMAP 2018 Fairness in User Modeling, Adaptation and Personalization (FairUMAP 2018) Chairs’ Welcome & Organization. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation, and Personalization (UMAP ’18). ACM. DOI10.1145/3213586.3226200.

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindén, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52(1) (June 2018), 91–101. DOI10.1145/3274784.3274789. Cited 2 times.

Michael D. Ekstrand and Amit Sharma. 2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17). ACM. DOI10.1145/3109859.3109960. Cited 1 times.

Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D thesis, University of Minnesota. HDL 11299/165307. Cited 3 times.

Martijn Willemsen, Dirk Bollen, and Michael Ekstrand. 2011. UCERSTI 2: Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 395–396. DOI10.1145/2043932.2044020. Cited 4 times.

Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: A Modular Recommender Framework. Demo recorded in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys ’11). ACM, pp. 349-350. DOI10.1145/2043932.2044001. Cited 18 times.

Research Funding

External Grants

Internal Grants

  • 2017: $19K Boise State College of Education Empathy Grant LITERATE: Locating Informational Texts for Engaging Readers And Teaching Equitably (co-PI; with PI Katherine Wright & co-PI Sole Pera)
  • 2014: $8K Texas State University Research Enhancement Program (competitive internal research grant) Temporal Analysis of Recommender Systems (PI)

Invited Talks

Software

I have built several open-source software packages in the course of my research and other work. Open-source software distribution is a key piece of my research dissemination strategy. My more significant development efforts include:

Service

Ongoing Professional Service and Memberships

  • Steering committee, ACM Conference on Recommender Systems (RecSys), 2017–present
  • Steering committee, ACM Conference on Fairness, Accountability, and Transparency (FAT*), 2017–present
  • Distinguished Reviewer, ACM Transactions on Interactive Intelligent Systems (TiiS), 2017–present
  • Member of the ACM (professional member 2014–present)

Professional Service

  • Guest editor, 2020 special issue of User Modeling and User-Adapted Interaction on fairness in user modeling.
  • Co-organizer, TREC 2019 Track on Fairness in Information Retrieval
  • Senior program committee, ACM RecSys 2019
  • Program committee, ACM UMAP 2019
  • PR & Publicity Co-chair, 2nd Confernece on Fairness, Accountability, and Transparency (ACM FAT* 2018)
  • General co-chair, ACM RecSys 2018
  • Program committee, ACM WWW Track on Behavior Analysis and Personalization (2016—2019)
  • Co-organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017 & 2018
  • Co-organizer, FairUMAP workshop at UMAP 2018 & 2019
  • Track co-chair, 2018 Conference on Fairness, Accountability, and Transparency Systems track
  • Participant in Dagstuhl Perspectives Workshop Towards Cross-Domain Performance Modeling and Prediction: IR/RecSys/NLP
  • Program committee, ACM RecSys (2014–2017) and poster session (2016)
  • Program committee, FLAIRS Special Track on Recommender Systems (2015–2017)
  • Publicity co-chair, ACM RecSys 2016
  • External advisor, CrowdRec (EU Framework Programme collaborative research project, 2014–2016)
  • Proceedings co-chair, ACM CHI 2012–2013
  • Demos co-chair, ACM RecSys 2012
  • Reviewer for numerous conferences and journals, including:
    • ACM conferences CHI (2015, 2013, 2016, 2017), CSCW (2015, 2017), IUI (2017), SAC Recommender Systems track (2013, 2017), UIST (2012, 2016, 2017), WikiSym (2012), UMAP (2018)
    • Workshops at ACM RecSys: IntRS (2016–2017), LSRS (2016), KidRec (2017–2018), HealthRecSys (2018)
    • FATML 2017
    • Communications of the ACM
    • International Journal of Artificial Intelligence Tools (2016)
    • JMLR Open Source (2016)
    • IBM Journal of Research and Development (2016)
    • ICWSM 2012
    • ACM journals TIST, TOIS, TWEB, TKDD, and TIIS
    • IEEE journals TDSC, TKDE
    • Interacting with Computers
    • International Conference on Service-Oriented Computing (2016)
    • PLOS ONE (2016)
    • User Modeling and User-Adapted Interaction
    • Information Retrieval Journal
    • ACM Computing Surveys (2014, 2015)
    • User Modeling
    • Artificial Intelligence Review (Springer)
    • Hindawi journals Advances in Multimedia, Advances in Artificial Intelligence

Department and University Service

  • Boise State Ph.D in Computing Steering Committee (2017–present)
  • Boise State CS Dept. Curriculum Committee (2017–present)
  • Boise State CS Dept. Graduate Recruiting Committee (2017)
  • Texas State CS Dept. Undergraduate Committee (2014–2016)
  • Texas State CS Dept. Written Comp Exam Grading (2014–2016)
  • UMN CS Graduate Student Association secretary (2009–2010)

Community Service

  • February 2019 — testified before Idaho State House Judiciary Committee on H.B. 118, regulating pretrial risk assessment algorithms.
  • December 2017 — Boise Public Library panel on preparing for a career in computer science
  • Judge, 2015 — Travis Elementary School Science Fair