In IoT and Crowds, there is a focus on problems at the intersection of networking and big data. Our goal is to harness the power of the crowd for solving problems in a variety of domains. We have the following projects active in this space.

Active Projects


Mobile Urban Sensing, Informatics and Control (MUSIC)

Urban informatics problems such as pollution and traffic monitoring at real time require efficient systems for data acquisition, sensor control and actuation. We aim to build a full-fledged platform for using mobile devices as edge nodes to control and power futuristic IoT applications.

People: Shiva R. Iyer, Lakshminarayanan Subramanian, Fatima Zarinni.


Crowdsourced Facial Expression Mapping Using a 3D Avatar

Facial expression mapping is the process of attributing signal values to a particular set of muscle activations in the face. This paper proposes the development of a broad lexicon of quantifiable, reproducible facial expressions with known signal values using an expressive 3D model and crowdsourced labeling data. Traditionally, coding muscle movements in the face is a time-consuming manual process performed by specialists. Identifying the communicative content of an expression generally requires generating large sets of posed photographs, with identifying labels chosen from a circumscribed list. Consequently, the widely accepted collection of configurations with known meanings is limited to six basic expressions of emotion. Our approach defines mappings from parameterized facial expressions displayed by a 3D avatar to their semantic representations. By collecting large, free-response label sets from naïve raters and using natural language processing techniques, we converge on a semantic centroid, or single label quickly and with low overhead.

People: Lakshminarayan Subramanian.


Privacy through Contextual Integrity

Designing programmable privacy logic frameworks that correspond to social, ethical, and legal norms has been a fundamentally hard problem. Contextual integrity (CI) (Nissenbaum, 2010) offers a model for conceptualizing privacy that is able to bridge technical design with ethical, legal, and policy approaches. While CI is capable of capturing the various components of contextual privacy in theory, it is challenging to discover and formally express these norms in operational terms.

People: Yan Shvartzshnaider, Helen Nissembaum, Lakshminarayanan Subramanian, Prateek Mittal, Thomas Wies.


Dengue Outbreak Prediction

Thousands of lives are lost every year in developing countries for failing to detect epidemics early because of the lack of real-time disease surveillance data. In this project, we use data from a large-scale deployment of a telephone triage service as a basis for dengue forecasting in Pakistan. Our system uses statistical analysis of dengue-related phone calls to accurately forecast suspected dengue cases 2 to 3 weeks ahead of time at a sub-city level. The system has been operational at scale in Pakistan for the past 3 years and has received more than 300,000 phone calls. The predictions from the system are widely disseminated to public health officials and form a critical part of active government strategies for dengue containment. This work is the first to demonstrate, with significant empirical evidence, that an accurate, location-specific disease forecasting system can be built using analysis of call volume data from a public health hotline.

People: Talal Ahmad, Umar Saif, Lakshminarayanan Subramanian.


Crowdsourced Learning

The conventional education ecosystem in developing regions is plagued by the lack of good quality textbooks and educational resources, lack of skilled teachers and high variability across student skill and motivational levels. This paper makes the case for establishing a crowdsourced learning ecosystem that leverages the collective intelligence of educators around the world to design a collaborative platform [Arias et al. 2000] to easily share, search, organize, rate and present educational materials for teachers and students around the world. In particular, we make two important contributions: (a) Modeling learning outcomes in crowdsourced learning: We propose a mathematical framework that enables systematic modeling and comparison amongst different education paradigms. Our framework provides a means to quantify student learning under a given paradigm based on critical factors such as the student skill (or ability), quality of the reading material (or the teacher), etc. (b) Crowdsourced Learning Platform: We describe the design of YeSua, an initial prototype of our crowdsourced learning platform that uses an inquiry-based framework for generating annotated lesson plans for different subjects.

People: Ashwin Venkataraman, Shiva R. Iyer, Lakshminarayanan Subramanian, Srikanth Jagabathula.

Past Projects


Personalized Health Monitoring

Type-1 Diabetes is a chronic illness affecting an estimated 3-million americans. Attentive regulation of blood glucose is needed to avoid dangerous and potentially deadly side effects. Our research aims to offload some of this management work to mobile applications and web-based analytical tools; making use of a combination of contextual computing, continuous glucose monitoring (CGM) technology, and machine learning.

People: Sam F Royston, Lakshminarayanan Subramanian.