Curriculum Vitae
Academic Position
Assistant Professor of Information Systems, 2017-present
DO&IT Department, Robert H. Smith School of Business
University of Maryland, College Park
Education
PhD in Information Systems, 2011 - 2017
NYU Stern School of Business, New York, NY
Committee: Foster Provost (chair), Arun Sundararajan, Daria Dzyabura, Patrick Perry
BS in Mathematical Sciences, 2003 - 2007
Worcester Polytechnic Institute, Worcester, MA
Research Interests
Data science, business analytics, design science, advertising, natural language processing, television, social media, crowdfunding
Research
Publications
Mudambi, M.; Clark, J.; Rhue, L.; Viswanathan, S. (2024). “Fighting Misinformation on Social Media: An Empirical Investigation of the Impact of Prominence Reduction Policies”. Production and Operations Management.
Clark, J., Paiement, J.F., and Provost, F. (2023) ``Who's Watching TV?" Information Systems Research.
Rhue, L., and Clark, J. (2022) “Who Are You and What Are You Selling? Creator-Based and Product-Based Racial Cues in Crowdfunding”. Management Information Systems Quarterly 46.4 (2022): 2229-2260.
Agarwal, R., Bjarnadottir, M., Rhue, L., Dugas, M., Crowley, K., Clark, J., & Gao, G. (2022). Addressing Algorithmic Bias and the Perpetuation of Health Inequities: An AI Bias Aware Framework. Health Policy and Technology, 100702.
Clark, J. and Provost, F. (2019), “Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data”, Data Mining and Knowledge Discovery 33, no 4. (2019).
Martens, D., Provost, F., Clark, J., and Junque De Fortuny, Enric (2016), ``Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics", MIS Quarterly 40, no 4. (2016). Designated European Research Paper of the Year by AIS and CIONET.
Under Review
Rhue, L. and Clark, J. (2024) “Outcome Over Process: Advancing Both Fairness and Performance in Crowdfunding Through ML-Based Promotion” (under review)
Rhue, L., Avery, A., and Clark, J. (2023) “A Field Experiment in Local Personalization for Charitable Crowdfunding” (under review)
Selected Working Papers
Target Variable Engineering. Link to paper.
Labeling in their shoes: Improving Text Annotation with Empathy Priming. Coauthors: Sung Hyun Kwon, Jui Ramaprasad, Il-Horn Hann.
Where Does the Hate Flow? The Impact of Multihoming on User Responses to Content Moderation. Coauthors: Maya Mudambi, Lauren Rhue, Siva Viswanathan.
Credible Signals in AI: How Academic Papers Influence Model Adoption Decisions. Coauthors: Xiru Yang, Peng Huang.
Automating the Mundane: Random Grid Search for Data Preprocessing. Coauthor: Aseem Baji.
The Effect of AI Chatbot on Customer Feedback Collection: Evidence from A Randomized Field Experiment. Coauthors: Wenying Gu, Jui Ramaprasad.
Crowdfunding Community Formation: Fundraiser Race and Gender Homophily. Coathors: Lauren Dahlin and Lauren Rhue. Awarded Best Paper at the 2019 Winter Conference in Business Analytics in Snowbird, Utah.
Bayesian Transfer Multi-Instance Learning for Television Viewership. Coauthors: Jean-Francois Paiement and Foster Provost.
Awards and Honors
Allen J. Krowe Award for Teaching Excellence. UMD Smith School of Business
UMD Smith School of Business · College Park, MD · 2024
Distinguished Teaching Award
UMD Smith School of Business · College Park, MD · 2018, 2023, 2024
ISS Social Justice Best Paper Award
INFORMS ISS · Phoenix, AZ · 2023
Best Paper Award
Winter Conference in Business Analytics · Snowbird, Utah · 2019
Paper-a-thon Winner
First-ever ICIS Paper-a-thon · Seoul, South Korea · 2017
European Research Paper of the Year
AIS, CIONET · 2017
Presentations
Conference Presentations
“Target Variable Engineering”
CIST · Seattle, WA · October, 2024
“Target Variable Engineering”
INFORMS Data Science Workshop · Seattle, WA · October, 2024
“Randomized Grid Search Experiments for Data Pre-Processing”
SCECR · Lisbon, Portugal · June, 2024
“Target Variable Engineering”
SCECR · Bogota, Colombia · June, 2023
“Target Variable Engineering”
Workshop on Information Technologies and Systems · Copenhagen, Denmark · December, 2022
“Automatically Promoting Projects? A Study of the Fairness-Economic Tradeoffs in Reducing Bias through AI/ML on a Digital Platform”
INFORMS Annual Meeting · Indianapolis, Indiana · October, 2022
“Unsupervised Dimensionality Reduction vs. Supervised Regularization for Classification from Sparse Data”
INFORMS Annual Meeting · Phoenix, AZ · November, 2018“Who’s Watching TV?”
INFORMS Annual Meeting · Phoenix, AZ · November, 2018“The Raging Crowd: Temporal Dynamics of Uproar in Online Communities”
International Conference on Information Systems · Seoul, South Korea · December, 2018``Bayesian Transfer Multi-Instance Logistic Regression"
Workshop on Information Technologies and Systems · Seoul, South Korea · December, 2017``Big Data is Small Data"
INFORMS Annual Meeting · Houston, TX · October, 2017``Recruiting Members of Underrepresented Groups: What Women-in-Tech Meetups do Differently"
INFORMS Annual Meeting · Houston, TX · October, 2017``Using Dimensionality Reduction for Binary Classification on Massive, Sparse Data: Design Science Experiments and Guidelines"
Conference on Information Systems and Technology · Houston, TX · October, 2017``Who's Watching TV?" (Poster)
Conference on Information Systems & Technology · Nashville, TN · November, 2016``The Role of Dimensionality Reduction in Binary Classification for Social Data"
INFORMS Annual Meeting · Nashville, TN · November, 2016``Who Gets Started on Kickstarter? Demographic Variation in Crowdfunding Success Rates"
INFORMS Annual Meeting · Nashville, TN · November, 2016``Who's Watching TV?" (Poster)
Workshop on Information in Networks · New York NY · October, 2015``Predictive Modeling with Fine-Grained Behavior Data Using Dimensionality Reduction"
Winter Conference in Business Intelligence · Snowbird, Utah · February, 2013
Invited Talks
“Labeling in their shoes: Improving Text Annotation with Empathy Priming”
Heinz College · Carnegie Mellon University · Pittburgh, PA · April 2025
“Labeling in their shoes: Improving Text Annotation with Empathy Priming”
Stern School of Business · New York Univeris · New York, NY · March 2025
“Labeling in their shoes: Improving Text Annotation with Empathy Priming”
Goizueta Business School · Emory University · Atlanta, GA · January 2025
“Target Variable Engineering”
ESCP Seminar · Paris, France · April, 2023
“Altruistically Misinformed: Fighting Misinformation on Social Media with Prominence Reduction Strategies”
WAITS (Washington Area IT Symposium) · George Mason University · Fairfax, VA · October 2021
“Who’s Watching TV?”
UConn School of Business · University of Connecticut · Storrs, CT · February 2017
“Who’s Watching TV?”
Carlson School of Management · University of Minnesota · Minneapolis, MN · January 2017
“Who’s Watching TV?”
Fox School of Business · Temple University · Philadelphia, PA · January 2017
“Who’s Watching TV?”
Warwick Business School · University of Warwick · Coventry, UK · January 2017
“Who’s Watching TV?”
Bauer College of Business · University of Houston · Houston, TX · January 2017
“Who’s Watching TV?”
Stevens Institute of Technology School of Business · Hoboken, NJ · January 2017
“Who’s Watching TV?”
Hong Kong University · Hong Kong · January 2017
“Who’s Watching TV?”
University of Illinois Chicago · Chicago, IL · January 2017
“Who’s Watching TV?”
Carey School of Business · Arizona State University · Tempe, AZ · January 2017
“Who’s Watching TV?”
Zarb School of Business · Hofstra University · Uniondale, NY · December 2016
“Who’s Watching TV?”
Smith School of Business · University of Maryland · College Park, MD · December 2016
Teaching Experience
Instructor, "Data Mining and Predictive Analytics"
UMD Smith School MS in Information Systems: Spring 2018, Spring 2022, Spring 2023, Spring 2024, Spring 20
UMD Smith School MS in Business Analytics: Spring 2020, Spring 2021
UMD Smith School MBA: Fall 2018Instructor, "Data Visualization and Web Analytics"
UMD Smith School MS in Business Analytics: Fall 2020, Spring 2021Instructor, "Machine Learning in Business Research"
UMD Smith School PhD: Fall 2018, Fall 2020, Fall 2022, Fall 2024Instructor, "Google Analytics Online Challenge"
UMD Smith School MS in Business Analytics: Fall 2018Instructor, "Data Mining for Business Analytics"
NYU Stern undergraduate course, Spring 2015 semester.Teaching Fellow, "Data Mining for Business Analytics"
NYU Stern MS in Business Analytics, Summer 2016.
NYU Stern MBA course, Spring 2012, Fall 2013, Spring 2014
NYU MS in Data Science, Fall 2013
Academic Activities & Service
Organizing Committee
International Conference on Information Systems (ICIS)
WISE · Nashville, TN · 2025
CIST · Atlanta, GA · 2025
Workshop on AI and Analytics for Social Good · College Park, MD · 2025
SCECR · Lisbon, Portugal · 2024
Associate Editor
International Conference on Information Systems (ICIS)
”AI and Intelligent Automation” Track · Hyderabad, India · 2020
”AI and Intelligent Automation” Track · Munich, Germany · 2019
”Data Science and Predictive Analytics” Track · San Francisco, USA · 2018
Program Committee Member
Conference on Information Systems and Technology
Indiana, IN · 2022
Anaheim, CA · 2021
Washington, DC · 2020
Phoenix, AZ · 2018
Houston, TX · 2017
Session Chair
INFORMS Annual Meeting
Phoenix, AZ · 2018
”Studying Social Issues with Social Media” · Houston, TX · 2017
Reviewer
Journals
Management of Information Systems Quarterly (MISQ), Information Systems Research (ISR), Management Science, ACM Transactions on Cyber-Physical Systems (TCPS), Machine Learning Journal, Journal of Information and Management
Conferences and Workshops
International Conference on Information Systems (ICIS), Conference on Information Systems and Technology (CIST)
Professional Memberships
AIS, INFORMS
Committees
All-University Graduate Commission
New York University · 2013-2014
Research and Teaching Grants
University of Maryland Faculty Student Research Award. $10,000. 2021-2022.
University of Maryland Teaching Innovation Grant. $14,750. 2020-2021.
AT&T Research Unrestricted Gift. $20,000. 2019-2020.
Professional Experience
AT&T Research, Florham Park, NJ
Intern, June-August 2013
Research internship developing unsupervised machine learning models to predict which member of a household is watching TVImplemented non-negative matrix factorization of digital cable channel viewership, decomposing each household's viewership into component parts, each associated with distinct demographics. G
Generated more intuitive targeting by associating resultant factors with aggregate demographic segments.
Developing deep-learning neural network to further improve targeting.
IMPAQT, Pittsburgh, PA
Senior Decision Support Analyst, 2009-2011
Decision Support Analyst, 2007-2009
Performed statistical R&D to improve effectiveness of clients' online advertising at a search engine marketing agency. Led projects and interfaced with clients.Developed algorithms to optimize paid search campaigns' ROI through automated bid and budget management.
Provided data-driven support for Fortune 1000 clients in retail, consumer packaged goods, financial services, telecommunications, and automotive industries.
Completed ad-hoc statistical projects including estimating the relationship between paid search and television ads, the effect of search engine optimization in the context of other advertising efforts, and the impact of paid search ads on organic site traffic.
Programming Skills
Python
MatLab
Lua/Torch
SAS
SQL
Java