Michael D. Ekstrand, Ph.D

Curriculum Vitae

Education

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

Summer 2018
CS 310-HU (Databases Hatchery)
Spring 2018
CS 410/510 (Databases)
Fall 2017
CS 533 (Introduction to Data Science); 22 students
Spring 2017
CS 597 (Recommender Systems); 13 students
Fall 2016
CS 410/CS 510 (Databases); 28 students

Texas State University

Spring 2016
CS 3320 (Internet Software Development); 48 students
CS 5369Q/4379Q (Recommender Systems); 26 students
Fall 2015
CS 4332 (Introduction to Database Systems); 39 students
Spring 2015
CS 5369Q/4379Q (Recommender Systems); 28 students
CS 4350 (Unix Systems Programming); 32 students
Fall 2014
CS 4332 (Introduction to Database Systems); 50 students

In addition, I have supervised several independent study students.

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

Fall 2013
CSCI 5980-1 (Introduction to Recommender Systems), co-taught with Joseph A. Konstan; also offered as a MOOC on Coursera.
Summer 2012
CSCI 1902 (Structure of Computer Programming II)
Spring 2011
CSCI 5125 (Collaborative and Social Computing), as teaching assistant
2007–2008
CSCI 1902 (Structure of Computer Programming II), as teaching assistant (3 terms)

M.S. Students Supervised

Publications

Author formatting key: myself, advised student, other Boise State student. Citation counts from Microsoft Academic via Microsoft Cognitive Services. With this data, I have an h-index of 13 and i10-index of 13.

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, ed. Springer-Verlag. ISBN 978-3-319-90091-9.

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), pp 1:1–31. DOI 10.5381/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, Article 10 (April 2015), 23 pages. DOI 10.1145/2728171. 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 6 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), pp 81–173. DOI 10.1561/1100000009. Cited 677 times.

Refereed Conference Publications

These are full papers 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. To appear in Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). DOI 10.1145/3240323.3240373. Acceptance rate: 17.5%.

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 Conference on Fairness, Accountability and Transparency. PMLR 81:172–186. Acceptance rate: 24%.

Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the Conference on Fairness, Accountability and Transparency. PMLR 81:35–47. Acceptance rate: 24%.

Michael D. Ekstrand and Vaibhav Mahant. 2017. Sturgeon and the Cool Kids: Problems with Top-N Recommender Evaluation. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference.

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 Ninth ACM Conference on Recommender Systems (RecSys ’15). ACM. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 26 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 Eighth ACM Conference on Recommender Systems (RecSys ’14). ACM. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 75 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 (ACM L@S ’14). ACM. DOI 10.1145/2556325.2566244. Acceptance rate: 37%. Cited 23 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 Seventh ACM Conference on Recommender Systems (RecSys ’13). ACM. DOI 10.1145/2507157.2507188. Acceptance rate: 24%. Cited 24 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. DOI 10.1145/2365952.2365974. Acceptance rate: 20%. Cited 16 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, 86–96. DOI 10.1145/2247596.2247608. Acceptance rate: 23%. Cited 9 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, 133–140. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 141 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, 195–204. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 24 times.

Michael D. Ekstrand, Praveen Kannan, James A. Stemper, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys ’10). ACM, 159–166. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 73 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. DOI 10.1145/1641309.1641317. Acceptance rate: 36% (Selected as Best Paper). Cited 23 times.

Short Papers

These are short research papers published in conference proceedings. They are also peer-reviewed.

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.

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, 233–236. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 24 times.

Workshops, Seminars, Posters, Etc.

Maria Soledad Pera, Katherine Wright, 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) at IDC 2018. DOI 10.18122/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.

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. DOI 10.1145/2959100.2959179. Acceptance rate: 36% (Past, Present, and Future track). Cited 7 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.

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

Other Publications

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. Manifesto from Dagstuhl Perspectives Workshop 17442: Towards Performance Modeling and Performance Prediction across IR/RecSys/NLP. To appear in Dagstuhl Manifestos, Schloss Dagstuhl–Leibniz-Zentrum für Informatik.

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, pp. 91–101.

Michael D. Ekstrand and Amit Sharma. 2017. The FATREC Workshop on Responsible Recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems.

Michael D. Ekstrand. 2014. Towards Recommender Engineering: Tools and Experiments in Recommender Differences. Ph.D Thesis, University of Minnesota. Cited 2 times.

Martijn Willemsen, Dirk Bollen, and Michael Ekstrand. 2011. UCERSTI 2: Second Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces. Workshop at the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, 395–396. DOI 10.1145/2043932.2044020. Cited 3 times.

Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: a modular recommender framework. Demo presented at the Fifth ACM Conference on Recommender Systems (RecSys ’11). ACM, 349–350. DOI 10.1145/2043932.2044001. Cited 16 times.

Research Funding

NSF 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

Professional Service

  • PR & Publicity Co-chair, 2nd Confernece on Fairness, Accountability, and Transparency (ACM FAT* 2018)
  • General co-chair, ACM RecSys 2018
  • Participant in Dagstuhl Perspectives Workshop Towards Cross-Domain Performance Modeling and Prediction: IR/RecSys/NLP
  • Organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017 & 2018
  • Co-organizer, FairUMAP workshop at UMAP 2018
  • Track co-chair, 2018 Conference on Fairness, Accountability, and Transparency Systems track
  • Distinguished Reviewer, ACM Transactions on Interactive Intelligent Systems (2017–present)
  • Steering committee member, ACM Conference on Recommender Systems, 2017–present
  • Steering committee member, Conference on Fairness, Accountability, and Transparency, 2017–present
  • Program committee, ACM RecSys (2014–2017) and poster session (2016)
  • Program committee, FLAIRS Special Track on Recommender Systems (2015, 2016, 2017)
  • Publicity co-chair, ACM RecSys 2016
  • Program committee, ACM WWW Track on Behavior Analysis and Personalization (2016, 2017)
  • 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)
    • Workshops at ACM RecSys: IntRS (2016–2017), LSRS (2016), KidRec (2017)
    • FATML 2017
    • 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 (Fall 2017)
  • Boise State CS Dept. Curriculum Committee (2017–2018)
  • 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

  • December 2017 — Boise Public Library panel on preparing for a career in computer science
  • Judge, 2015 Travis Elementary School Science Fair