Photo of Shawndra Hill

Shawndra Hill

Assistant Professor

Research Interests: "social tv", data mining/knowledge discovery in databases, design science and information systems, dynamic networks, machine learning, network-based marketing, statistical relational learning

Links: CV, Personal Website, The Social TV Lab

Contact Information

Address: 3730 Walnut Street, 567 Jon M. Huntsman Hall, Philadelphia, PA 19104
Email: shawndra@wharton.upenn.edu
Office: (215) 573-5677

Overview

Shawndra Hill is an Assistant Professor in Operations and Information Management at the Wharton School of the University of Pennsylvania. Generally, she studies data mining, machine learning and statistical relational learning and their alignment with business problems. Specifically, she researches the value to companies of mining data on how consumers interact with each other -- for targeted marketing, advertising and fraud detection. Her current research focuses on the interactions between TV content and Social Media. Her past and present industry partners include AT&T Labs Research, ClearForest, and Siemens Energy & Automation. Her recent work appears in IEEE Transactions on Data and Knowledge Engineering, Journal of Computational and Graphical Statistics, SIGKDD Explorations, and Statistical Science. Her research is funded in part by the Office of Naval Research, Google, and the National Institutes of Health (NIH). Shawndra holds a B.S. in Mathematics from Spelman College, a B.E.E. from the Georgia Institute of Technology and a Ph.D. in Information Systems from NYU's Stern School of Business.

Research


  • Heather Griffis, Austin Kilaru, Rachel M. Werner, David Asch, John C. Hershey, Shawndra Hill, Yoonhee P. Ha, Allison Sellers, Charlene Wong (Under Review), Adoption and Utilization of Social Media Across US Hospitals.
  • Umberto Panniello, Shawndra Hill, Ma Liye, Matteo Gorgoglione, Kartik Hosanagar (Work In Progress), Incorporating Profit Margins into Recommender Systems: A Randomized trial of Consumer Trust and Purchase Behavior.
  • Oliver Hinz, Shawndra Hill, Ju-Young Kim (Under Review), The Big Distraction: The Impact of Popular TV on Online Retail Sales.
  • Shawndra Hill, Adrian Benton, Christophe Van den Bulte, When Does Social Network-based Prediction Work? A Large Scale Analysis of Brand and TV Audience Engagement by Twitter Users.
  • Priya Govindan, Jin Xu, Shawndra Hill, Tina Eliassi-Rad, Chris Volinsky, Structural Features Threaten Privacy across Social Graphs
  • Benjamin L. Ranard, Yoonhee P. Ha, Zach F. Meisel, David A. Asch, Shawndra Hill, Lance B. Becker, Anne K. Seymour, Raina M. Merchant (2013), Crowdsourcing--Harnessing the Masses to Advance Health and Medicine, a Systematic Review, Journal of General Internal Medicine
  • Getachew Berhan, Solomon Atnafu, Tsegaye Tadesse, Shawndra Hill (Forthcoming), Drought Prediction System for Improved Climate Change Mitigation.
  • Adrian Benton, Aman Nalavade, Shawndra Hill (Working), Social TV: Real-Time Social Media Response to TV Advertising.  
  • Kobi Abayomi, Shawndra Hill (Under Review), Statistics for Re-Identification in Networks, (Under review).
  • Adrian Benton, Shawndra Hill (Working), The Spoiler Effect?: Designing Social TV Content That Promotes Ongoing WOM.  
  • Shawndra Hill, Raina Merchant, Lyle Ungar (2013), Lessons Learned About Public Health from Online Crowd Surveillance, Big Data
  • AM Chang, Alison Leung, Olivia Saynisch, Heather Griffis, Shawndra Hill, John C. Hershey, Lance B. Becker, David Asch, A Seidman, Raina M. Merchant (2013), Using a Mobile App and Mobile Workforce to Validate Data About Emergency Public Health Resources, Emergency Medicine Journal, in press.
  • Raina M. Merchant, David Asch, John C. Hershey, Heather Griffis, Shawndra Hill, Olivia Saynisch, Alison Leung, Jeremy Asch, Kirk Lozada, Lindsay Nadkarni, Austin Kilaru, Charles Branas, Larry Starr, Fran Shofer, Graham Nichol, Lance B. Becker (2013), A Crowdsourcing Innovation Challenge To Locate and Map Automated External Defibrillators, Circulation: Cardiovascular Quality and Outcomes
  • Jun J Mao, Annie Chung, Adrian Benton, Shawndra Hill, Lyle Ungar, Charles Leonard, Sean Hennessey, John Holmes (2013), Online Discussion of Drug Side Effects and Discontinuation Among Breast Cancer Survivors, Pharmacoepidemiology and Drug Safety  
  • Alison Leung, David Asch, Kirkland Lozada, Olivia Saynisch, Jeremy Asch, Nora Becker, Heather Griffis, Frances Shofer, John C. Hershey, Shawndra Hill, Charles Branas, Graham Nichol, Lance B. Becker, Raina M. Merchant (2013), Where Are Lifesaving Automated External Defibrillators Located and How Hard is it to Find Them in a Large Urban City?, Resuscitation  
  • Shawndra Hill, Adrian Benton (Under Review), Talkographics: Using What Viewers Say Online to Calculate Audience Affinity Networks for Social TV-based Recommendations.
  • Ofir Ben-Assuli, Moshe Leshno, Itamar Shabtai, Shawndra Hill, Assessing the Contribution of EHR Systems to Medical Decision making. Workshop on Information Technologies and Systems, pp. 163-168, December 2012.  
  • Shawndra Hill, Adrian Benton, Jin Xu, Social Media-Based Social TV Recommendation System. Workshop on Information Technologies and Systems, pp. 79-84, December 2012.  
  • Shawndra Hill, Adrian Benton, Social TV: Linking TV Content to Buzz and Sales. International Conference on Information Systems, December 2012.  
  • Justin C. Bosley, Nina Zhao, Shawndra Hill, Fran Shofer, David Asch, Lance B. Becker, Raina M. Merchant (2012), Decoding Twitter: Surveillance and Trends for Cardiac Arrest and Resuscitation Communication, Resuscitation  
  • Adrian Benton, John Holmes, Shawndra Hill, Annie Chung, Lyle H. Ungar (2012), Medpie: An Information Extraction Package for Medical Message Board Posts, Bioinformatics , 28 (5), 743.  
  • Shawndra Hill, Adrian Benton, Lyle Ungar, Annie Chung, Sofus Mackskassey, John Holmes, A Cluster-based Method for Identifying Influence on Twitter. Workshop on Information Technologies and Systems, December 2011.  
  • Getachew Berhan, Shawndra Hill, Tsegaye Tadesse, Solomon Atnafu (2011), Using Satellite Images for Drought Monitoring: A Knowledge Discovery Approach, Journal of Strategic Innovation and Sustainability, 7 (1), 135 - 153.  
  • Adrian Benton, Shawndra Hill, Lyle H. Ungar, Annie Chung, Charles E. Leonard, Cristin Freeman, John Holmes (2011), A System for De-identifying Medical Message Board Text, BMC Bioinformatics, 12, s2.  
  • Shawndra Hill, Noah Ready-Campbell (2011), Expert Stock Picker: The Wisdom of (Experts in) Crowds, International Journal of Electronic Commerce, 15 (3), 73 - 102.  
  • Adrian Benton, Lyle Unger, Shawndra Hill, Sean Hennessy, Jun Mao, Annie Chung, Charles E. Leonard, John H. Holmes (2011), Identifying Potential Adverse Effects Using the Web: A New Approach to Medical Hypothesis Generation, Journal of Biomedical Informatics, 44 (6), 989 - 996.  
  • Shawndra Hill, Jun J. Mao, Lyle H. Ungar, Sean Hennessy, Charles E. Leonard, John Holmes (2010), Natural Supplements for H1N1 Influenza: Retrospective Observational Infodemiology Study of Information and Search Activity on the Internet, Journal of Medical Internet Research, 13 (9), 1 - 11.  
  • Shawndra Hill, Foster Provost, Chris Volinsky, Learning and Inference in Massive Social Networks. International Workshop on Mining and Learning with Graphs, August 2007.
  • Shawndra Hill, D. Agarwal, R. Bell, C. Volinsky (2006), Building an Effective Representation for Dynamic Networks, Journal of Computational & Graphical Statistics, 15 (3), 584 - 608.  
  • Shawndra Hill, F. Provost, C. Volinsky (2006), Network-based Marketing: Identifying Likely Adopters via Consumer Networks, Statistical Science, 21 (2), 256 - 276.    Abstract  Description
  • A. Bernstein, F. Provost, Shawndra Hill (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.    Abstract
  • Shawndra Hill, F. Provost (2003), The Myth of the Double-Blind Review? Author Identification Using Only Citations, SIGKDD Explorations, 5 (2), 179 - 184.    Abstract
  • Abraham Bernstein, Scott Clearwater, Shawndra Hill, Claudia Perlich, Foster Provost, Discovering Knowledge from Relational Data Extracted from Business News. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 2002.

Awards And Honors

  • $12,500 AWS in Education Coursework Grant, 2014, 2014
  • Blacks in Action, Alpha Kappa Alpha Sorority, Inc., 2013
  • $10,100 AWS in Education Coursework Grant, 2013
  • $2500 WCAI Privacy Preserving Social Network Strategies, 2011
  • $25,000 Wharton Junior Faculty Dean’s Research Fund: De-Anonymization in Social Networks, 2011
  • $1500 Seed fund from Wharton Global Initiatives for Identifying Research Problems in Ethiopia, 2010
  • PI on Google and WPP Marketing Award ($80,000): Collective Inference for Online Advertising, 2010
  • Co-PI on Award from NIH ($254,333): Mining Internet Conversations for Evidence of Herbal Association, 2010
  • AIS Volunteer Spotlight, 2010
  • INFORMS ISS Design Science Award, 2009
  • Co-PI on Award from Office of Naval Research Multi University Research Initiative (MURI) on Analysis of Networks Grant 2008 (for Effective Matching in Dynamic Networks work). My portion $400,000. Total $7.5million for 5 years, 2008
  • Finalist INFORMS George B. Dantzig Dissertation Award, 2007
  • Herman E. Kroos Award (given to the NYU Stern PhD graduate who has completed the program with distinction and presented an outstanding doctoral dissertation), 2007

In The News

Knowledge @ Wharton

Courses

Current

  • OPIM410 - Decision Support Systems

    OPIM410401 

    The past few years have seen an explosion in the amount of data collected by businesses and have witnessed enabling technologies such as database systems, client-server computing and artificial intelligence reach industrial strength. These trends have spawned a new breed of systems that can support the extraction of useful information from large quantities of data. Understanding the power and limitations of these emerging technologies can provide managers and information systems professionals new approaches to support the task of solving hard business problems. This course will provide an overview of these techniques (such as genetic algorithms, neural networks, and decision trees) and discuss applications such as fraud detection, customer segmentation, trading, marketing strategies and customer support via cases and real datasets.

    OPIM672401 

  • OPIM672 - Decision Support Systems

    The past few years have seen an explosion in the amount of data collected by businesses and have witnessed enabling technologies such as database systems, client-server computing and artificial intelligence reach industrial strength. These trends have spawned a new breed of systems that can support the extraction of useful information from large quantities of data. Understanding the power and limitations of these emerging technologies can provide managers and information systems professionals new approaches to support the task of solving hard business problems. This course will provide an overview of these techniques (such as genetic algorithms, neural networks, and decision trees) and discuss applications such as fraud detection, customer segmentation, trading, marketing strategies and customer support via cases and real datasets.

    OPIM672001 

Previous

  • OPIM410 - Decision Support Systems

    The past few years have seen an explosion in the amount of data collected by businesses and have witnessed enabling technologies such as database systems, client-server computing and artificial intelligence reach industrial strength. These trends have spawned a new breed of systems that can support the extraction of useful information from large quantities of data. Understanding the power and limitations of these emerging technologies can provide managers and information systems professionals new approaches to support the task of solving hard business problems. This course will provide an overview of these techniques (such as genetic algorithms, neural networks, and decision trees) and discuss applications such as fraud detection, customer segmentation, trading, marketing strategies and customer support via cases and real datasets.

  • OPIM672 - Decision Support Systems

    The past few years have seen an explosion in the amount of data collected by businesses and have witnessed enabling technologies such as database systems, client-server computing and artificial intelligence reach industrial strength. These trends have spawned a new breed of systems that can support the extraction of useful information from large quantities of data. Understanding the power and limitations of these emerging technologies can provide managers and information systems professionals new approaches to support the task of solving hard business problems. This course will provide an overview of these techniques (such as genetic algorithms, neural networks, and decision trees) and discuss applications such as fraud detection, customer segmentation, trading, marketing strategies and customer support via cases and real datasets.

  • OPIM950 - Perspectives on Information Systems

    Provides doctoral students in Operations and Information Management and other related fields with a perspective on modern information system methodologies, technologies, and practices. State-of-the-art research on frameworks for analysis, design, and inplementation of various types of information systems is presented. Students successfully completing the course should have the skills necessary to specify and implement an information system to support a decision process.

  • OPIM960 - Research Seminar in Information Technology - Economic Perspectives

    Explores economic issues related to information technology, with emphasis on research in organizational or strategic settings. The course will follow a seminar format, with dynamically assigned readings and strong student contribution during class sessions (both as participant and, for one class, as moderator.)