Small Data are the myriad of digital traces we each generate everyday. Unfortunately, that data is often unavailable to us in a form that we can make sense of or act upon. Imagine a special kind of app running in the cloud that privately and securely turns your small data into big insights.
We've designed our infrastructure and apps to be modular and reusable so that they can be remixed and repurposed for new services and platform architectures, from research to commercial products. And everything down to the front-end UIs of our small data apps are Open Source. Fork away.
We're not just building tools and services for theoretical use cases and "what if?" scenarios. Through our collaborations with researchers spanning healthcare, behavioral economics, human computer interaction and policy we collaborate to iteratively develop and evolve our services to address issues and problems in the real-world.
We're going beyond recommendation engines to services that contextually parse your small data to provide the right insights at the right time, to help you make (hopefully) the right plans and decisions. And as for the data that drives these insights, we take the utmost care and precaution with regards to its security while advancing architectural frameworks that maximize transparency and individual control of small data usage.
The Feinstein Institute for Medical Research, Cornell Tech, and Sage Bionetworks announced today the launch of a pioneering study to examine the use of a smartphone application to identify and understand impulsivity in daily life. The team intends to develop additional apps using study data with the goal of providing support to those looking to change impulsive behaviors and better their ability to resist unhealthy temptations.
Deborah Estrin, Professor of Computer Science at Cornell Tech and Professor of Public Health at Weill Cornell, has been named the 2017 IEEE Internet Award Recipient. Sponsored by the Nokia Corporation, the award is given for exceptional contributions to the advancement of Internet technology. Specifically, Estrin was selected for “formative contributions and thought leadership in Internet routing and in mobile sensing techniques and applications, from environmental monitoring to personal and community health.” IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.
The Jacobs Technion-Cornell Institute at Cornell Tech and AOL today announced a research technology called “Immersive Recommendations,” a concept where a user opts in to a tool that translates personal digital traces from one platform into content recommendations in another. The new technique was developed by Cornell Tech researchers to address “the cold-start problem” — how to engage users with relevant content when they first start using a platform. As an example, could a service like Netflix suggest better movies for a first-time user, if it tapped into their Twitter data? Could Meetup.com use your Medium posts to tailor events for you? Researchers will present the the new paper at the 25th International World Wide Web Conference in Montreal, Canada.
We hear a lot about how big data, smart devices, and all the '-omics' (for example, genomics, proteomics, metabolomics, and so forth) are going to transform medicine—and they will. But there is another force that is going to change the way we think about and practice health, and that is our small data—small data derived from our individual digital traces.
We leave a trail of digital data breadcrumbs as we go about our days. With access and good apps, we could make sense of this "small data" to help get a clearer picture of our personal health. Deborah Estrin, networked sensing pioneer, Professor of Computer Science at the new Cornell Tech campus in New York City and co-founder of the non-profit startup, Open mHealth, explains at TEDMED 2013.
|An mHealth App for Self-Management of Chronic Lower Back Pain (Limbr): Pilot Study. [PDF] SELTER, A., TSANGOURI, C., ALI, S., FREED, D., VATCHINSKY, A., KIZER, J., SAHUGUET, A., VOJTA, D., VAD, V., POLLAK, J., ESTRIN, D. JMIR Mhealth Uhealth 2018 September 2017|
|Unbiased Offline Recommender Evaluation for Missing-Not-At-Random Implicit Feedback. [PDF] YANG, L., CUI, Y., XUAN, Y., WANG, C., BELONGIE, S., ESTRIN, D. In Twelfth ACM Conference on Recommender Systems (RecSys ’18) October 2018|
|Exploring Recommendations Under User-Controlled Data Filtering. [PDF] WEN, H., YANG, L., SOBOLEV, M., ESTRIN, D. In Twelfth ACM Conference on Recommender Systems (RecSys ’18) October 2018|
|Understanding User Interactions with Podcast Recommendations Delivered Via Voice. [PDF] YANG, L., SOBOLEV, M., TSANGOURI, C., ESTRIN, D. In Twelfth ACM Conference on Recommender Systems (RecSys ’18) October 2018|
|Open mHealth: Common Data Schemas and API for Mobile Health Data. [PDF] CARINI, S., FARRUGIA, E., HADDAD, D., ESTRIN, D., KUMAR, S., SIM, I.|
|Characterizing User Skills from Application Usage Traces with Hierarchical Attention Recurrent Networks. [PDF] YANG, L., FANG, C., JIN, H., HOFFMAN, M.D., ESTRIN, D. ACM Transactions on Intelligent Systems and Technology (TIST)|
|Small Data: Applications and Architecture. [PDF] HSIEH, C.K., ALQUADDOOMI, F., OKEKE, F., POLLAK, JP, GUNASEKARA, L., ESTRIN, D. Proceedings of the Fourth International Conference on Big Data, Small Data, Linked Data and Open Data, April 2018.|
|Towards A Framework for Mobile Behavior Change Research. OKEKE, F., SOBOLEV, M., ESTRIN, D. In Technology, Mind, and Society: APAScience, Washington DC, USA, April 2018|
|Ranking Subreddits by Classifier Indistinguishability in the Reddit Corpus. [PDF] ALQUADDOOMI, F., ESTRIN, D. Proceedings of the Tenth International Conference on Information, Process, and Knowledge Management, March 2018.|
|OpenRec: A Modular Framework for Extensible Recommendation Algorithms. [PDF] YANG, L., BAGDASARYAN, E., GRUENSTEIN, J., HSIEH, C., ESTRIN, D. Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM), 2018. openrec.ai|
|Yum-me: A Personalized Nutrient-based Meal Recommender System. [PDF] YANG, L., HSIEH, C., YANG, H., POLLAK, JP, DELL, N., Belongie, S., COLE, C., ESTRIN, D., ACM Transactions on Information Systems (TOIS), 2017|
|Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces. [PDF] YANG, L., FANG, C., JIN, H., HOFFMAN, M.D., ESTRIN, D. In Proceedings of 26th International World Wide Web Conference (WWW), Perth, Australia, April 2017.|
|Collaborative Metric Learning. [PDF] HSIEH, C.H., YANG, L., CUI, Y., LIN, T.Y., BELONGIE, S., ESTRIN, D., In Proceedings of 26th International World Wide Web Conference (WWW), Perth, Australia, April 2017.|
|The Pace of Technologic Change: Implications for Digital Health Behavior Intervention Research. [PDF] PATRICK, K., HEKLER, E.B, ESTRIN, D., MOHR, D.C., RIPER, H., CRANE, D., GODINO, J., RILEY, W.T., American Journal of Preventive Medicine. 2016 Nov; 51(5):816-824.|
|Internet Scale Research Studies using SDL-RX. [PDF] KIZER, J., SAHUGUET, A., LAKIN, N., CARROLL, M., POLLAK, JP AND ESTRIN, D. Presented at the Data For Good Exchange, September, 2016.|
|Your Activities of Daily Living (YADL): An Image-based Survey Technique for Patients with Arthritis. [PDF] YANG, L., FREED, D., WU, A., WU, J., POLLAK, JP. AND ESTRIN, D. In Proceedings of the 10th International Conference on Pervasive Computing Technologies for Healthcare, Cancun, Mexico, May 2016.|
|Leveraging Multi-Modal Sensing for Mobile Health: a Case Review in Chronic Pain. [PDF] AUNG, M. S. H., ALQUADDOOMI, F., HSIEH, A., RABBI, M., YANG, L., POLLAK, J.P., ESTRIN, D. and CHOUDHURY, T. IEEE Journal of Selected Topics in Signal Processing. 2016 Aug; 10(5): 962-974.|
|Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces. [PDF] HSIEH, C.K., YANG, L., WEI, H., NAAMAN, M. AND ESTRIN, D. In Proceedings of the 25th International World Wide Web Conference (WWW), Montréal, Quèbec, Canada, April 2016.|
|GroupLink: Group Event Recommendations Using Personal Digital Traces. [PDF] Wei, H., Hsieh, C., Yang, L., Estrin, D., In the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW’16), 2016|
|Reassembling Our Digital Selves. [PDF] ESTRIN, D. AND JUELS, A. 2016 Winter. Daedalus, 145, 1 , 43-53 (doi: 10.1162/DAED_a_00364).|
|Smartphone Data in Rheumatoid Arthritis - What Do Rheumatologists Want?[PDF] SAY, P.R., STEIN, D., ANCKER J.S., HSIEH A, POLLAK JP. AND ESTRIN, D. In Proceedings of the AMIA Annual Symposium, San Francisco, CA, November 2015.|
|PlateClick: Bootstrapping Food Preferences Through an Adaptive Visual Interface. [PDF] YANG, L., CUI, Y., ZHANG, F., POLLAK, JP., BELONGIE, S. AND ESTRIN, D. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), Melbourne, Australia, October 2015|
|Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis. [PDF] YANG, L., HSIEH, C. AND ESTRIN, D., In Proceedings of Data Mining Workshop (ICDMW), IEEE International Conference. 2015|
|Pushcart: Supporting and Scaling Nutritionist-Client Relationships. [PDF] BAUM, A., CARROLL, M., ESTRIN, D., GUNASEKARA, L. AND POLLAK, JP. In Proceedings of CSCW 2015: Workshop on Moving Beyond e-Health and the Quantified Self, Vancouver, Canada, March, 2015, CSCW 2015.|
|small data, where n=me. [PDF] ESTRIN, D. 2014. CACM, Viewpoint Column, Communications of the ACM, 57, 4, 32-34.|
|The Email Analysis Framework: Aiding the analysis of personal natural language texts. [PDF] ALQUADDOOMI, F., KETCHAM, C. and ESTRIN, D. In Workshop on Linking The Quantified Self (LinkQS), Santiago, Chile, 2014|
|Lifestreams: A Modular Sense-making Toolset for Identifying Important Patterns from Everyday Life. [PDF] HSIEH, C.K., TANGMURARUNKIT, H., ALQUADDOOMI, F., JENKINS, J., KANG, J., KETCHAM, C., LONGSTAFF, B., SELSKY, J., SWENDEMAN, D., ESTRIN, D. AND RAMANATHAN, N. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys), Rome Italy, November 2013.|