My current interests involve disruptive mobile health technologies for precision behavioral medicine to support healthy behavior. I also lead scientific and engineering research on information foraging theory, sensemaking and, more generally, the phenomena of human-information interaction.
Zhang, Paik, 7 Pirolli (2014). Reinforcment learning and counterfactual reasoning explain adaptive behavior in a changing environment. Cognitive Science 2014. [APPLIED MODELING AWARD]
Pirolli & Kairam (2013). A knowledge-tracing model of learning from a social tagging system. UMUAI. [JAMES CHEN AWARD]
Canini, Suh, Pirolli (2011). Finding credible information sources in social networks based on content and socisl structure. SocialCom 2011 [BEST PAPER AWARD]
Pirolli & Fu (2003). SNIF-ACT: A model of information foraging on the World Wide Web. User Modeling 2003. [BEST PAPER AWARD]
Best Paper Awards
INTELLIGENT MOBILE HEALTH
Pirolli, P. (2016). A computational cognitive model of self-efficacy and daily adherence in mHealth. Translational behavioral medicine, 1-13.
Du, H., Venkatakrishnan, A., Youngblood, G. M., Ram, A., & Pirolli, P. (2016). A Group-Based Mobile Application to Increase Adherence in Exercise and Nutrition Programs: A Factorial Design Feasibility Study. JMIR Mhealth Uhealth, 4(1),
Konrad, A., Bellotti, V., Crenshaw, N., Tucker, S., Nelson, L., Du, H., Pirolli,P., & Whittaker, S. (2015). Finding the Adaptive Sweet Spot: Balancing Compliance and Achievement in Automated Stress Reduction. Paper presented at the SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), Seoul, Korea.
Du, Youngblood, & Pirolli. (2014). Efficacy of a smartphone system to support groups in behavior change programs. Wireless Health 2014.
COGNITIVE MODELS OF FORAGING AND SENSEMAKING
Paik, Zhang, & Pirolli (2014). Counterfactual reasoning as a key for explaining adaptive behavior in a changing environment. BICA 2014.
Lebiere et al. (2013). A functional model of sensemaking in a neuroscience architecture. Computational Intelligence and Neuroscience.
Paik, Pirolli, Dong, Lebiere, & Thomson (2013). An ACT-R model of sensemaking in geospatial intelligence tasks. BRIMS 2013
Fu & Pirolli (2007). SNIF-ACT: A cognitive model of user navigation on the World Wide Web
SOCIAL COMPUTATIONAL SYSTEMS
Vydiswaran, Zhai, Roth, & Pirolli (2014). Overcoming bias to learn about controversial topics. Journal of the American Society for Information Science and Technology
Mangel, Satterthwaite, Pirolli, Suh, & Zhang (2013). Invasion biology and the success of social collaboration networks, with application to Wikipedia. Israel Journal of Ecology and Evolution.
Peng, Zhang, Pirolli, & Hogg. (2012) Thermodynamic principles in social collaborations Collective Intelligence.
Liao, Q. V., Pirolli, P., & Fu, W. (2012). An ACT-R model of credibility judgment of micro-blogging Web Pages. Proceedings of the International Conference on Cognitive Modeling (ICCM 2012) (pp. 103-108). Berlin, Germany: Universitätsverlag der TU Berlin.
Chen, J., & Pirolli, P. (2012). Why You Are More Engaged: Factors Influencing Twitter Engagement in Occupy Wall Street. International AAAI Conference on Weblogs and Social Media, ICWSM 2012
Vydiswaran, Zhai, Roth, & Pirolli (2012). Unbiased learning of controversial topics. ASIST 2012.
Shneiderman, Preece, & Pirolli. Realizing the value of social media requires innovative computing research. CACM
Suh, Hong, Pirolli, & Chi (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter. SocialCom 2010.
Fu & Pirolli (2013). Establishing the micro-to-macro link in cognitive engineering: Multi-level models of socio-computer engineering. Handbook of Cognitive Engineering
Pirolli (2007). Cognitive models of human-information interaction. Handbook of Applied Cognition (2nd Ed)
Pirolli (2007). Ch 1 of Information Foraging Theory (uncorrected proof)
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