Draft:Shawndra Hill

• Comment: In accordance with Wikipedia's Conflict of interest guideline, I disclose that I have a conflict of interest regarding the subject of this article. Anthonyc7599 (talk) 21:04, 12 February 2026 (UTC)

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Shawndra Hill is an American data scientist and researcher. She is a Principal Scientist and Manager in the Core Data Science team at Meta and a Senior Lecturer in the Marketing Division at Columbia Business School. Hill is known for her research in data mining, machine learning, generative AI, and their applications in business and social contexts, particularly in marketing and advertising. She is believed to be the first African American woman to earn a Ph.D. in Information Systems from New York University's Stern School of Business.

Early life and education

Hill earned a Bachelor of Science in mathematics from Spelman College in 1995 and a Bachelor of Electrical Engineering from the Georgia Institute of Technology in the same year. She later attended New York University's Stern School of Business, where she received her Master of Philosophy in Information Systems in 2003 and her Ph.D. in Information Systems in 2007 under the supervision of Foster Provost. She is believed to be the first African American woman to graduate from NYU Stern's Information Systems Ph.D. program.

Career

Early career

Hill began her career as a Commissioning Engineer at Siemens Energy & Automation from 1995 to 1999, followed by a role as an Account Manager from 1999 to 2000.

Academic positions

Hill joined the faculty at the Wharton School of the University of Pennsylvania, where she was an Assistant Professor and later an Adjunct Associate Professor in the Operations and Information Management Department from 2007 to 2016. During her time at Wharton, she was also an Annenberg Public Policy Center Distinguished Research Fellow and a Wharton Customer Analytics Initiative Senior Fellow. She was also a member of the Penn Social Media and Health Innovation Lab and the Warren Center for Network and Data Sciences.

From 2015 to 2020, Hill was a Principal Researcher at Microsoft Research in New York City. During her tenure at Microsoft Research, she founded the Microsoft Undergraduate Research Internship Program in Computing, a 12-week summer internship program designed to develop talent and experience for careers in computing research among underrepresented groups in engineering and computer science.

In 2020, Hill joined Meta (formerly Facebook) as a Principal Scientist and Manager in the Core Data Science team, where she currently leads the Networks and Behavioral Modeling team within Central Applied Science. Her team focuses on applying network science and behavioral modeling to business problems. Concurrently, she has been a Senior Lecturer at Columbia Business School since October 2020, where she teaches courses on generative AI and marketing data.

At Meta, Hill organizes an annual workshop entitled "Research Meets Practice: Gen AI in Marketing and Advertising," which brings together researchers and practitioners to explore the latest developments in generative AI applications for marketing.

Hill has also held visiting positions at Addis Ababa University (2009-present), the Indian School of Business, and AT&T Labs Research (2003–2015).

Professional service and leadership

Hill serves on several advisory boards and committees:

• Advisory Board, Data Science Programs at New York University
• Computer Science Advisory Board at the University of the People
• Board on Mathematical Sciences and Analytics at the National Academies of Sciences, Engineering and Medicine
• Working Group on Women of Color in Computing at the National Academies of Sciences, Engineering and Medicine

Hill has served on numerous conference program committees and has chaired conferences, including serving as chair of the Symposium on Statistical Challenges in Electronic Commerce Research (SCECR).

Research

Hill's research focuses on data mining, machine learning, statistical relational learning, and generative AI, with a particular emphasis on their application to business problems in marketing and advertising. Her work explores the value of mining consumer interaction data from online platforms for targeted marketing, advertising, health, and fraud detection. Her research has been funded by the Office of Naval Research, Google, and the National Institutes of Health.

Network-based marketing

Her research on "network-based marketing" demonstrated that consumers are significantly more likely to adopt a new service if they are connected to a prior customer. This work, co-authored with Foster Provost and Chris Volinsky and published in Statistical Science in 2006, showed that "network neighbors"—consumers linked to prior customers—adopt services at a rate three to five times greater than baseline groups. The study provided direct statistical support for the hypothesis that network linkage can directly affect product and service adoption. Hill was awarded the 2009 INFORMS ISS Design Science Award for this work.

Social media and public health

Hill's research also involves the analysis of social media data for public health surveillance and understanding health-related behaviors. She has co-authored studies on the use of social media by U.S. hospitals, the online discussion of drug side effects among breast cancer survivors, and work on linking social media data with medical records. Her work in this area has demonstrated how social media platforms can serve as valuable sources of health information and has opened new avenues for medical hypothesis generation and adverse effect detection. This research was conducted as part of her work with the Penn Social Media and Health Innovation Lab.

Television advertising and digital behavior

Hill's work also extends to the intersection of television advertising and online behavior, where she has studied the impact of TV ads on online search behavior and the phenomenon of "second screening"—the practice of using digital devices while watching television. Her recent research has made contributions to understanding cross-channel advertising effectiveness, including studies on competitive advertising on brand search, moment marketing, and the dynamic effects of TV ad suspension on keyword search. These studies have provided actionable insights for marketers seeking to optimize their advertising strategies across traditional and digital channels. She founded and directed The Social TV Lab (thesocialtvlab.com) to advance research in this area.

Generative AI in marketing and advertising

Hill's research also involves generative AI (GenAI) applications for marketing and advertising. Her recent research explores how multimodal large language models can be used to understand video advertising at scale and optimize marketing performance. In 2026, she co-authored research on MLLM-VADStory, a novel framework using multimodal large language models for video ad storyline understanding, which analyzed 50,000 social media video ads to identify top-performing story arcs and demonstrate that story-based creatives improve video retention. She has also contributed to research on prompt optimization for large language models, addressing the challenge that LLMs are highly sensitive to input prompts.

At Columbia Business School, Hill teaches "GenAI Meets Marketing Data," an experiential course that explores how generative AI reshapes marketing data, including the creation of labels for users and ads, synthetic content generation, measurement of consumer responses, and transformation of advertising workflows. The course is designed for MBA and EMBA students interested in marketing, consulting, product management, and data-driven strategy.

Patents

Hill holds at least one U.S. patent related to her research in content analytics and advertising:

• US 10,579,685 B2 - "Content Event Insights" (March 3, 2020). Inventors: Shawndra Benita Hill, Michael Barto, David Gordon Burtch. Assignee: Microsoft Technology Licensing, LLC. This patent describes methods for analyzing the effects of content events (such as TV programming) on correlated search queries, enabling more accurate measurement of how content influences consumer search behavior across different time periods, devices, and geographic regions.

Impact and service

Hill is actively involved in efforts to increase diversity and inclusion in computing and data science. Beyond founding the Microsoft Undergraduate Research Internship Program, she is a member of the Working Group on Women of Color in Computing at the National Academies of Sciences, Engineering and Medicine. She has maintained a visiting professorship at Addis Ababa University in Ethiopia since 2009, contributing to the development of data science education in Africa.

Awards and recognition

Hill has received numerous awards and recognitions for her research and teaching contributions:

• ISS Practical Impacts Award, INFORMS Information Systems Society (2020) - Winner of the inaugural award, recognizing distinguished information systems academics who have demonstrated outstanding leadership and sustained impact on the industry
• INFORMS ISS Design Science Award (2009) - Winner for Social Network-Based Marketing System
• George B. Dantzig Dissertation Award Finalist, INFORMS (2007)
• ECRI Institute Health Devices Achievement Award (finalist)
• Recognized for Outstanding Ph.D. Student Teaching, NYU Stern School of Business
• 3rd Place, KDD Cup (2003) [1]

As of 2024, her work has been cited over 4,600 times according to Google Scholar, with an h-index of 32.

Selected publications

Hill has authored or co-authored over 40 research publications. Her works include:

Foundational works

• Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 21(2), 256–276. (921 citations)
• Bernstein, A., Provost, F., & Hill, S. (2005). Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. IEEE Transactions on Knowledge and Data Engineering, 17(4), 503–518. (344 citations)
• Hill, S., & Provost, F. (2003). The myth of the double-blind review? Author identification using only citations. ACM SIGKDD Explorations Newsletter, 5(2), 179–184. (165 citations)
• Hill, S., & Ready-Campbell, N. (2011). Expert stock picker: the wisdom of (experts in) crowds. International Journal of Electronic Commerce, 15(3), 73–102. (141 citations)

Social media and health

• Ranard, B. L., Ha, Y. P., Meisel, Z. F., Asch, D. A., Hill, S. S., Becker, L. B., ... & Merchant, R. M. (2014). Crowdsourcing—harnessing the masses to advance health and medicine, a systematic review. Journal of General Internal Medicine, 29(1), 187–203. (407 citations)
• Griffis, H. M., Kilaru, A. S., Werner, R. M., Asch, D. A., Hershey, J. C., Hill, S., ... & Merchant, R. M. (2014). Use of social media across US hospitals: descriptive analysis of adoption and utilization. Journal of Medical Internet Research, 16(11), e264. (273 citations)
• Benton, A., Ungar, L., Hill, S., Hennessy, S., Mao, J., Chung, A., ... & Holmes, J. H. (2011). Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of Biomedical Informatics, 44(6), 989–996. (228 citations)
• Padrez, K. A., Ungar, L., Schwartz, H. A., Smith, R. J., Hill, S., Antanavicius, T., ... & Merchant, R. M. (2016). Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department. BMJ Quality & Safety, 25(6), 414–423. (97 citations)

Marketing and advertising research

• Liu, J., Toubia, O., & Hill, S. (2021). Content-based model of web search behavior: An application to TV show search. Management Science, 67(10), 6378–6398. (40 citations)
• Simonov, A., & Hill, S. (2021). Competitive advertising on brand search: Traffic stealing and click quality. Marketing Science, 40(5), 923–945. (35 citations)
• Liu, J., & Hill, S. (2021). Frontiers: Moment marketing: Measuring dynamics in cross-channel ad effectiveness. Marketing Science, 40(1), 13–22.
• Hinz, O., Hill, S., & Sharma, A. (2022). Second screening—The influence of concurrent TV consumption on online shopping behavior. Information Systems Research, 33(3), 809–823.
• Swaminathan, V., Schwartz, A., Hill, S., & Menezes, R. (2022). The language of brands in social media: Using topic modeling on social media conversations to drive brand strategy. Journal of Interactive Marketing. (44 citations)
• Hill, S., Benton, A., & Pasinello, U. (2019). Talkographics: Measuring TV and brand audience demographics and interests from user-generated content. International Journal of Electronic Commerce, 23(3), 364–404.

Generative AI and large language models

• Yang, J., Zhang, P., & Hill, S. (2026). MLLM-VADStory: Domain knowledge-driven multimodal LLMs for video ad storyline insights. arXiv preprint arXiv:2601.07850.
• Wu, Y., Verma, S., Yuan, Y., & Hill, S. (2025). LLM prompt duel optimizer: Efficient label-free prompt optimization. arXiv preprint arXiv:2510.13907.

Data science for development

• Beshah, T., & Hill, S. (2010). Mining road traffic accident data to improve safety: Role of road-related factors on accident severity in Ethiopia. AAAI Spring Symposium: Artificial Intelligence for Development, 24, 1173–1181. (221 citations)