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

michaelekstrand@boisestate.edu
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-director, People and Information Research Team (PIReT)
2014–2016: Assistant Professor, Dept. of Computer Science, Texas State University
2008–2014: Graduate Research Assistant, GroupLens Research, 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: Undergrad Research Assistant, Scalable Computing Laboratory, Iowa State University

Teaching History

Boise State University

Term	Course	Title	Students
F18	CS 410/510	Databases	40
Su18	CS 310-HU	Intro to Databases	6
S18	CS 410/510	Databases	22
F17	CS 533	Intro to Data Science	22
S17	CS 597	Recommender Systems	13
F16	CS 410/510	Databases	28

Texas State University

Term	Course	Title	Students
S16	CS 3320	Internet Software Development	48
S16	CS 5369Q/4379Q	Recommender Systems	26
F15	CS 4332	Intro to Database Systems	39
S15	CS 5369Q/4379Q	Recommender Systems	28
S15	CS 4350	Unix Systems Programming	32
F14	CS 4332	Intro to Database Systems	50

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

Term	Course	Title	Role
F13	CSCI 5980-1	Intro to Recommender Systems	Inst
S12	CSCI 1902	Structure of Computer Programming II	Inst
S11	CSCI 5125	Collaborative and Social Computing (TA)	TA
Su08	CSCI 1902	Structure of Computer Programming II	TA
S08	CSCI 1902	Structure of Computer Programming II	TA
F07	CSCI 1902	Structure of Computer Programming II	TA

Ph.D Students

Amifa Raj (started Fall 2018)

M.S. Students

Adam Keener (M.S. expected 2020)
Mucun Tian (M.S. expected 2019)
Vaibhav Mahant (M.S. 2016, Texas State University; thesis: Improving Top-N Evaluation of Recommender Systems)
Sushma Channamsetty (M.S. 2016, Texas State University; thesis: Recommender Response to User Profile Diversity and Popularity Bias)
Mohammed Imran R Kazi (M.S. 2016, Texas State University; thesis: Exploring Potentially Discriminatory Biases in Book Recommendation)
Shuvabrata Saha (M.S. 2016, Texas State University; co-advised with Dr. Apan Qasem; thesis: A Multi-objective Autotuning Framework For The Java Virtual Machine)

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. 10.1007/978-3-319-90092-6_10. Cited 10 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. 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) (April 2015). 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 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. 10.1561/1100000009. Cited 379 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. 10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR]. Acceptance rate: 17.5%.Cited 3 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 4 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 3 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 3 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. 10.1145/2792838.2800195. Acceptance rate: 21%.Cited 34 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. 10.1145/2645710.2645737. Acceptance rate: 23%.Cited 57 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. 10.1145/2556325.2566244. 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. 10.1145/2507157.2507188. Acceptance rate: 24%.Cited 27 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. 10.1145/2365952.2365974. Acceptance rate: 20%.Cited 19 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. 10.1145/2365952.2366002. 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. 10.1145/2247596.2247608. 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. 10.1145/2043932.2043958. 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. 10.1145/2047196.2047220. Acceptance rate: 25%.Cited 26 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. 10.1145/1864708.1864740. Acceptance rate: 19%.Cited 56 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.. 10.1145/1641309.1641317. 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. 10.18122/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. 10.18122/cs_facpubs/147/boisestate. arXiv:1809.03125 [cs.IR]. Cited 1 times.

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. 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. 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. 10.1145/2959100.2959179. 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.

Research Funding

External Grants

2018–2023: NSF award CHS 17-51278, $482,081: CAREER: User-Based Simulation Methods for Quantifying Sources of Error and Bias in Recommender Systems (PI)

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

December 7, 2018: ‘User, Agent, Subject, Spy’ seminar at Clemson University
November 9, 2018: ‘User, Agent, Subject, Spy’ seminar at Carnegie Mellon University Human-Computer Interaction Institute
November 16, 2017: ‘Making Information Systems Good for People’ at Whitman College (Walla Walla, WA)
June 26, 2017: ‘Recommending for People’ seminar at RecSysNL at TU Delft
June 20, 2017: ‘Recommending for People’ seminar at Jheronimus Academy of Data Science
June 19, 2017: ‘Recommending for People’ seminar at UCL Mons
June 15, 2017: ‘Responsible Recommendation’ at the Brussels Big Data and Ethics Meetup, the inaugural event of the DigitYser Big Data community
November 21, 2016: ‘Recommending for People’ colloquium at the University at Albany Dept. of Computer Science
October 27, 2016: ‘Introduction to Recommender Systems’ at the Clearwater Developer Conference
September 20, 2015: ‘Challenges in Scaling Recommender Systems Research’ at the Workshop on Large-Scale Recommender Systems at RecSys ’15 in Vienna, Austria
September 19, 2015: ‘Levelling Up your Academic Career’ at the Doctoral Symposium at RecSys ’15 in Vienna, Austria
2012: ‘Flexible Recommender Experiments with LensKit’ at the RecSys Challenge Workshop at RecSys ’12 in Dublin, Ireland
2012: ‘The MovieLens Data Set’ (invited talk) at the RecSys Challenge Workshop at RecSys ’12 in Dublin, Ireland

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:

LensKit, a toolkit for building, researching, and studying recommender systems, used in many published papers. http://lenskit.org
Grapht, a dependency injection framework for Java with novel configuration and static analysis capabilities. http://grapht.grouplens.org
Goanna (now defunct), a graphical tool for visualizing InfiniBand networks and compute clusters. Written while at the Scalable Computing Laboratory.

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