Michael D. Ekstrand, Ph.D

Curriculum Vitae

Dept. of Information Science
Drexel University
3675 Market St.
Philadelphia, PA 19104

Education

Ph.D (2014)
Computer Science, University of Minnesota.
Advisers: John T. Riedl and Joseph A. Konstan
B.S. (2007)
Computer Engineering, Iowa State University.

Employment History

2023–present
Assistant Professor, Dept. of Information Science, Drexel University
2022–2023
Associate Professor, Dept. of Computer Science, Boise State University
Co-director, People and Information Research Team (PIReT)
2016–2022
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, Dept. of Computer Science, University of Minnesota
Summer 2010
Research Intern, Autodesk Research (Toronto)
2007–2008, 2011
Teaching Assistant, Dept. of Computer Science, University of Minnesota
2005–2007
Undergrad RA, Scalable Computing Laboratory, Iowa State University

Students

Ph.D. Graduates

  • Ngozi Ihemelandu (Ph.D. 2024)
  • Amifa Raj (Ph.D. 2023; Applied Scientist at Microsoft)

M.S. Graduates

Undergraduate Student Research

I have supported and mentored the following undergraduate research students: Christine Pinney (BSU, UGRA + REU), Liana Shiroma (Colby Coll., REU 2021), Stephen Randall (U. Pitt, REU 2021), Connor Wood (BSU, REU 2020 + UGRA), Ananda Montoly (Smith Coll., REU 2020), Sandra Ambriz (BSU, HERC + UGRA).

Funding key:

  • UGRA: undergraduate research assistant hired from research funds
  • REU: Research Experience for Undergraduates
  • HERC: Higher Education Research Consortium

Research Funding

External Grants

Internal Grants

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

Publications

Author formatting key: myself, advised student, other student; presenter, §undergraduate student.

Citation counts from Google Scholar (total 4698, h-index 29).

These publications have citations merged in Google Scholar; count is reported on the most most final version, such as the journal expansion of a conference article.

Journal Articles // 9

TORS23
2024

Michael D. Ekstrand, Ben Carterette, and Fernando Diaz. 2024. Distributionally-Informed Recommender System Evaluation. Transactions on Recommender Systems 2(1) (March 7th, 2024), 6:1–27. DOI 10.1145/3613455. arXiv:2309.05892 [cs.IR]. NSF PAR 10461937. Cited 7 times. Cited 4 times.

TORS23-v
2023

Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell, Chloe Bakalar, Craig Boutilier, Amar Ashar, Lex Beattie, Michael Ekstrand, Claire Leibowicz, Connie Moon Sehat, Sara Johansen, Lianne Kerlin, David Vickrey, Spandana Singh, Sanne Vrijenhoek, Amy Zhang, Mckane Andrus, Natali Helberger, Polina Proutskova, Tanushree Mitra, and Nina Vasan. 2023. Building Human Values into Recommender Systems: An Interdisciplinary Synthesis. Transactions on Recommender Systems (November 13th, 2023). DOI 10.1145/3632297. arXiv:2207.10192 [cs.IR]. Cited 35 times. Cited 16 times.

FnT22
2022

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in Information Access Systems. Foundations and Trends® in Information Retrieval 16(1–2) (July 11th, 2022), 1–177. DOI 10.1561/1500000079. arXiv:2105.05779 [cs.IR]. NSF PAR 10347630. Impact factor: 8. Cited 126 times. Cited 65 times.

UMUAI21
2021

Michael D. Ekstrand and Daniel Kluver. 2021. Exploring Author Gender in Book Rating and Recommendation. User Modeling and User-Adapted Interaction 31(3) (February 4th, 2021), 377–420. DOI 10.1007/s11257-020-09284-2. arXiv:1808.07586v2. NSF PAR 10218853. Impact factor: 4.412. Cited 167 times. Cited 81 times (shared with RecSys18).

AJIM20
2020

Michael D. Ekstrand, Katherine Landau Wright, and Maria Soledad Pera. 2020. Enhancing Classroom Instruction with Online News. Aslib Journal of Information Management 72(5) (June 15th, 2020), 725–744. DOI 10.1108/AJIM-11-2019-0309. Impact factor: 1.903. Cited 14 times. Cited 10 times.

JOT16
2016

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. Journal of Object Technology 15(1) (February 1st, 2016), 1:1–31. DOI 10.5381/jot.2016.15.1.a1. Cited 14 times.

TOCHI15
2015

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 1st, 2015). DOI 10.1145/2728171. Impact factor: 1.293. Cited 116 times (shared with L@S14). Cited 28 times.

VLDB11
2011

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 1st, 2011), 911–920. Acceptance rate: 18%. Cited 21 times. Cited 9 times.

FnT11
2011

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 1st, 2011), 81–173. DOI 10.1561/1100000009. Cited 1623 times. Cited 643 times.

Peer-Reviewed Conference Papers // 30

ECIR24-m
2024

Ngozi Ihemelandu and Michael D. Ekstrand. 2024. Multiple Testing for IR and Recommendation System Experiments. Short paper in Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24). Lecture Notes in Computer Science 14610:449–457. DOI 10.1007/978-3-031-56063-7_37. NSF PAR 10497108. Acceptance rate: 24.3%.

ECIR24-i
2024

Michael D. Ekstrand, Lex Beattie, Maria Soledad Pera, and Henriette Cramer. 2024. Not Just Algorithms: Strategically Addressing Consumer Impacts in Information Retrieval. In Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24, IR for Good track). Lecture Notes in Computer Science 14611:314–335. DOI 10.1007/978-3-031-56066-8_25. NSF PAR 10497110. Acceptance rate: 35.9%.

ECIR24-g
2024

Amifa Raj and Michael D. Ekstrand. 2024. Towards Optimizing Ranking in Grid-Layout for Provider-side Fairness. In Proceedings of the 46th European Conference on Information Retrieval (ECIR ’24, IR for Good track). Lecture Notes in Computer Science 14612:90–105. DOI 10.1007/978-3-031-56069-9_7. NSF PAR 10497109. Acceptance rate: 35.9%.

WI23
2023

Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Candidate Set Sampling for Evaluating Top-N Recommendation. In Proceedings of the 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT ’23). pp. 88-94. DOI 10.1109/WI-IAT59888.2023.00018. arXiv:2309.11723 [cs.IR]. NSF PAR 10487293. Acceptance rate: 28%.

SIGIR23-q
2023

Amifa Raj, Bhaskar Mitra, Michael D. Ekstrand, and Nick Craswell. 2023. Patterns of Gender-Specializing Query Reformulation. Short paper in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23). DOI 10.1145/3539618.3592034. arXiv:2304.13129. NSF PAR 10423689. Acceptance rate: 25.12%. Cited 1 time. Cited 1 time.

SIGIR23-i
2023

Ngozi Ihemelandu and Michael D. Ekstrand. 2023. Inference at Scale: Significance Testing for Large Search and Recommendation Experiments. Short paper in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23). DOI 10.1145/3539618.3592004. arXiv:2305.02461. NSF PAR 10423691. Acceptance rate: 25.12%. Cited 1 time.

CHIIR23
2023

Christine Pinney, Amifa Raj, Alex Hanna, and Michael D. Ekstrand. 2023. Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval (CHIIR ’23). DOI 10.1145/3576840.3578316. arXiv:2301.04780. NSF PAR 10423693. Acceptance rate: 39.4%. Cited 10 times. Cited 7 times.

SIGIR22
2022

Amifa Raj and Michael D. Ekstrand. 2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22). pp. 726–736. DOI 10.1145/3477495.3532018. NSF PAR 10329880. Acceptance rate: 20%. Cited 35 times. Cited 26 times.

UMAP21
2021

A. K. M. Nuhil Mehdy, Michael D. Ekstrand, Bart Knijnenburg, and Hoda Mehrpouyan. 2021. Privacy as a Planned Behavior: Effects of Situational Factors on Privacy Perceptions and Plans. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21). ACM. DOI 10.1145/3450613.3456829. arXiv:2104.11847 [cs.SI]. NSF PAR 10223377. Acceptance rate: 23%. Cited 18 times. Cited 11 times.

WWW21
2021

Ömer Kırnap, Fernando Diaz, Asia J. Biega, Michael D. Ekstrand, Ben Carterette, and Emine Yılmaz. 2021. Estimation of Fair Ranking Metrics with Incomplete Judgments. In Proceedings of The Web Conference 2021 (TheWebConf 2021). ACM. DOI 10.1145/3442381.3450080. arXiv:2108.05152. NSF PAR 10237411. Acceptance rate: 21%. Cited 36 times. Cited 32 times.

CIKM20-lk
2020

Michael D. Ekstrand. 2020. LensKit for Python: Next-Generation Software for Recommender Systems Experiments. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20, Resource track). ACM, pp. 2999–3006. DOI 10.1145/3340531.3412778. arXiv:1809.03125 [cs.IR]. NSF PAR 10199450. No acceptance rate reported. Cited 73 times. Cited 53 times.

CIKM20-ee
2020

Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, and Ben Carterette. 2020. Evaluating Stochastic Rankings with Expected Exposure. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM ’20). ACM, pp. 275–284. DOI 10.1145/3340531.3411962. arXiv:2004.13157 [cs.IR]. NSF PAR 10199451. Acceptance rate: 20%. Nominated for Best Long Paper. Cited 156 times. Cited 136 times.

CHIIR20
2020

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, pp. 5. DOI 10.1145/3343413.3378004. arXiv:2001.09455 [cs.IR]. NSF PAR 10146883. Acceptance rate: 47%. Cited 11 times. Cited 9 times.

RecSys18
2018

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%. Citations reported under UMUAI21. Citations reported under UMUAI21.

FAT18-fp
2018

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 87 times. Cited 67 times.

FAT18-ck
2018

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 247 times. Cited 189 times.

FLAIRS17-s
2017

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 15 times. Cited 10 times.

FLAIRS17-rr
2017

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 22 times. Cited 18 times.

RecSys16
2016

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, Past, Present, and Future track). ACM. DOI 10.1145/2959100.2959179. Acceptance rate: 36%. Cited 123 times. Cited 88 times.

RecSys15
2015

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. DOI 10.1145/2792838.2800195. Acceptance rate: 21%. Cited 130 times. Cited 98 times.

RecSys14
2014

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. DOI 10.1145/2645710.2645737. Acceptance rate: 23%. Cited 268 times. Cited 178 times.

L@S14
2014

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 (S ’14). ACM. DOI 10.1145/2556325.2566244. Acceptance rate: 37%. Citations reported under TOCHI15. Cited 75 times.

RecSys13
2013

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. DOI 10.1145/2507157.2507188. Acceptance rate: 24%. Cited 58 times. Cited 41 times.

RecSys12-f
2012

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. DOI 10.1145/2365952.2366002. Acceptance rate: 32%. Cited 85 times. Cited 70 times.

RecSys12-b
2012

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 43 times. Cited 37 times.

EDBT12
2012

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. DOI 10.1145/2247596.2247608. Acceptance rate: 23%. Cited 18 times. Cited 16 times.

RecSys11
2011

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. DOI 10.1145/2043932.2043958. Acceptance rate: 27% (20% for oral presentation, which this received). Cited 238 times. Cited 193 times.

UIST11
2011

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. DOI 10.1145/2047196.2047220. Acceptance rate: 25%. Cited 53 times. Cited 47 times.

RecSys10
2010

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. DOI 10.1145/1864708.1864740. Acceptance rate: 19%. Cited 123 times. Cited 101 times.

WikiSym09
2009

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, pp. 10. DOI 10.1145/1641309.1641317. Acceptance rate: 36%. Selected as Best Paper. Cited 34 times. Cited 27 times.

Book Chapters // 2

RSHB3E
2022

Michael D. Ekstrand, Anubrata Das, Robin Burke, and Fernando Diaz. 2022. Fairness in Recommender Systems. In Recommender Systems Handbook (3rd edition). Francesco Ricci, Lior Roach, and Bracha Shapira, eds. Springer-Verlag. DOI 10.1007/978-1-0716-2197-4_18. ISBN 978-1-0716-2196-7. Cited 27 times. Cited 15 times.

SocAcc
2018

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. DOI 10.1007/978-3-319-90092-6_10. ISBN 978-3-319-90091-9. Cited 139 times. Cited 96 times.

Invited Talks

  • Mar. 2024: Keynote at IR4U2 workshop at ECIR 2024
  • Feb. 2024: Seminar at University of Colorado at Boulder
  • Oct. 2023: Virtual seminar at the University of Glasgow
  • May 2023: Invited talk at ICA post-conference panel
  • Mar. 2023: Seminar at the University of Texas at Austin HCI group
  • Jan. 2023: Seminar at the University of Washington RAISE group
  • Nov. 2022: Keynote at IBIS2022 (Information-Based Inductive Systems and Machine Learning) workshop (Tsukuba, Japan)
  • Nov. 2022: Seminar at Waseda University (Japan)
  • Oct. 2022: Keynote at EvalRS workshop on rounded evaluation of recommender systems at CIKM 2022
  • Sep. 2022: Guest lecture on IR fairness and test collections for University of Maine IR course
  • Mar. 2022: ‘You Might Also Think This Is Unfair’ at University of Michigan School of Information (online)
  • Nov. 2021: ‘Information Systems for Human Flourishing’ at Vector Institute, Toronto, Canada (online)
  • Oct. 2020: Guest lecture on recommender systems and fairness for Carnegie Mellon University Human-AI Interaction course
  • Apr. 2020: Guest lecture on recommender systems and fairness for Emory University recommender systems course
  • Mar. 2020: ‘User, Agent, Subject, Spy’ seminar at Boise State University Ph.D in Computing Colloquium
  • Oct. 2019: ‘Online Recommendation: What? Where? Why? How?’ session at the Idaho Library Association 2019 Conference
  • Aug. 2019: ‘User, Agent, Subject, Spy’ seminar at Microsoft Research Montréal
  • Jul. 2019: ‘User, Agent, Subject, Spy’ seminar at Criteo AI Labs, Paris, France
  • May 2019: ‘Recommendations, Decisions, Feedback Loops, and Maybe Saving the Planet’ at the CRA CCC Visioning Workshop on Economics and Fairness.
  • Dec. 2018: ‘User, Agent, Subject, Spy’ seminar at Clemson University
  • Nov. 2018: ‘User, Agent, Subject, Spy’ seminar at Carnegie Mellon University Human-Computer Interaction Institute
  • Nov. 2018: Guest lecture on recommender systems for Carnegie Mellon University Human-AI Interaction course
  • Nov. 2017: ‘Making Information Systems Good for People’ at Whitman College (Walla Walla, WA)
  • Jun. 2017: ‘Recommending for People’ seminar at RecSysNL at TU Delft
  • Jun. 2017: ‘Recommending for People’ seminar at Jheronimus Academy of Data Science
  • Jun. 2017: ‘Recommending for People’ seminar at UCL Mons
  • Jun. 2017: ‘Responsible Recommendation’ at the Brussels Big Data and Ethics Meetup, the inaugural event of the DigitYser Big Data community
  • Nov. 2016: ‘Recommending for People’ colloquium at the University at Albany Dept. of Computer Science
  • Oct. 2016: ‘Introduction to Recommender Systems’ at the Clearwater Developer Conference
  • Sep. 2015: ‘Challenges in Scaling Recommender Systems Research’ at the Workshop on Large-Scale Recommender Systems at RecSys ’15 in Vienna, Austria
  • Sep. 2015: ‘Levelling Up your Academic Career’ at the Doctoral Symposium at RecSys ’15 in Vienna, Austria
  • Sep. 2012: ‘Flexible Recommender Experiments with LensKit’ at the RecSys Challenge Workshop at RecSys ’12 in Dublin, Ireland
  • Sep. 2012: ‘The MovieLens Data Set’ (invited talk) at the RecSys Challenge Workshop at RecSys ’12 in Dublin, Ireland

Teaching

Drexel University

  • DSCI 641 (Recommender Systems for Data Science)
  • INFO 659 (Intro to Data Analytics)

Boise State University

  • CS 410/510 (Databases)
  • CS 533 (Intro to Data Science)
  • CS 538 (Recommender Systems)
  • CS 697 (Special Topics: Equity and Discrimination in Computing Systems)

Texas State University

  • CS 4332 (Intro to Database Systems)
  • CS 3320 (Internet Software Development)
  • CS 5369Q/4379Q (Recommender Systems)
  • CS 4350 (Unix Systems Programming)

Coursera

I co-created the Recommender Systems specialization on Coursera, along with its two previous single-class versions, with Joseph A. Konstan. This course has reached over 95,000 learners across its 3 iterations.

University of Minnesota

  • Instructor for CS 5980-1 (Intro to Recommender Systems)
  • Summer instructor for CS 1902 (Structure of Computer Programming II)
  • TA for CSCI 5125 (Collaborative and Social Computing) and CSCI 1902

Teaching Professional Development

  • Boise State University teaching portfolio faculty learning community.
  • Boise State University Ten for Teaching program.
  • 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.

Service

Ongoing Professional Service, Memberships, and Honors

  • Associate editor, ACM Transactions on Recommender Systems (2024–)
  • Editorial board, Foundations and Trends in Information Retrieval (2023–)
  • Co-chair, FAccT Network, 2019–
  • Steering committee, ACM Conference on Recommender Systems (RecSys), 2017–
  • Senior Member of the Association for Computing Machinery (since 2019)
  • Distinguished Reviewer, ACM Transactions on Interactive Intelligent Systems (TiiS) (2017–present)

Program Committee and Editorial Service

  • ACM CIKM main program (PC 2024), resource track (PC 2020–2021)
  • ACM RecSys main program (SPC 2019–2021, 2023–2024; PC 2014–2017), Reproducibility (PC 2021, 2023), LBR (PC 2019–2020), Posters (PC 2016–2017)
  • ACM FAccT (AC 2018, 2023–2024; PC 2019–2021)
  • ACM SIGIR main program (AC 2024; PC 2020–2021, 2023), Perspectives (PC 2021), short papers (PC 2021), resource track (PC 2021)
  • ECIR main program (PC 2024), short papers (PC 2024), IR for Good (PC 2024), tutorials (PC 2024)
  • Best paper committee, ACM SIGIR 2023
  • SIGIR Asia-Pacific (SPC 2023)
  • Best paper committee, TheWebConf 2023
  • Track chair, UMAP 2023 (Responsibility, Compliance, and Ethics)
  • Program co-chair, 16th ACM Conference on Recommender Systems (RecSys 2022)
  • Guest editor, 2021 special issue of User Modeling and User-Adapted Interaction (UMUAI) on fairness in user modeling.
  • TheWebConf User Modeling, Behavior, & Personalization (SPC 2021; PC 2016, 2018–2020), Behavior Analysis and Recommendation (PC 2016)
  • Track Chair, UMAP 2021
  • ACM WSDM (PC 2020–2021)
  • Ethics reviewer, NeurIPS 2021
  • UMAP (PC 2018–2020)
  • CHI Posters (PC 2019)
  • FLAIRS Special Track on Recommender Systems (PC 2015–2017)
  • ACM SAC Recommender Systems (PC 2013, 2016)
  • NeurIPS
  • Additional conference reviews for CHI (2012, 2015–2017, 2019–2020), CSCW (2014, 2017, 2019–2020), FAccT (2020), ICSOC (2016), IUI (2016), and UIST (2012, 2016–2017, 2020).
  • Journal reviews for Advances in AI, Artificial Intelligence Review, CACM, CSUR, IBM Journal of Research and Development, INRT, Information Retrieval Journal, Interacting with Computers, International Journal of Artificial Intelligence Tools, JMLR Open Source, JRC, Journal of Librarianship & Information Science, PLOS ONE, PeerJ Computer Science, TDS, TDSC, TIST, TKDE, TOCHI, TOIS, TORS, TSC, TWEB, TiiS, and UMUAI.
  • Reviewer for numerous workshops at RecSys, UMAP, and elsewhere.

Other Professional Service

  • Executive committee, ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2020–2023
  • Steering committee, ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2017–2023 (inaugural member)
  • Co-author and signatory, FAccT Statement on AI Harms and Policy (2023); covered by VentureBeat and The Hill (op-ed)
  • Co-organizer, CRAFT panel “Theories of Change in Responsible AI” at FAccT 2023
  • Ph.D. symposium mentor, CIKM 2023
  • Co-organizer, SimuRec Workshop on Simulation and Synthetic Data for Recommender Systems at RecSys 2021
  • Sponsorship co-chair, ACM FAccT 2021–2022
  • Doctoral symposium co-chair, RecSys 2020
  • Organized and moderated panel at RecSys 2019 on responsible recommendation
  • Co-organizer, TREC Track on Fairness in Information Retrieval (2019–2022)
  • PR & Publicity co-chair, 2nd Conference on Fairness, Accountability, and Transparency (ACM FAT* 2019)
  • General co-chair, ACM RecSys 2018
  • Publications working group, FAccT steering committee (2017)
  • Co-organizer, FATREC Workshop on Responsible Recommendation at RecSys 2017, 2018, 2020, 2021
  • Co-organizer, Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval (FACTS-IR) at SIGIR 2019
  • Co-organizer, FairUMAP workshop at UMAP 2018–2020
  • 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 (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

Department and University Service

  • Drexel IS 2023-2024 Faculty Search Committee
  • Drexel IS Ph.D. committee (2023-2024)
  • Boise State 2020–2021 CS Faculty Search Committee
  • Boise State COEN SAGE Scholars Program Mentor (2019–2021)
  • Boise State College of Engineering Curriculum Committee (2019–2022)
  • Boise State Ph.D. in Computing Steering Committee (2017–2022)
  • Boise State CS Dept. Curriculum Committee (2017–2022)
  • 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 and Civic Service

  • January 2023 — joined amicus brief before SCOTUS on Gonzalez v. Google.
  • July 2020 — taught continuing education session for Idaho Council for Libraries.
  • October 2019 — presented at Idaho Library Association Annual Conference.
  • February 2019 — addressed Idaho State House Judiciary Committee on H.B. 118, regulating pretrial risk assessment algorithms; through subsequent engagement, I contributed language that is in the final enacted legislation.
  • December 2017 — Boise Public Library panel on preparing for a career in computer science.
  • 2015 — Judge for Travis Elementary School Science Fair.