This project aims to understand and characterize user behavior in online social networks. Specifically, we posit, analyze, and validate different models of formation and evolution of opinions in social networks. We characterize our models both in terms of stability and goodness in terms of an approximation ratio w.r.t. to a social optimum (e.g., Price of Anarchy, Price of Stability etc).
We are working toward a theoretical foundation of developing large-scale human-machine systems that combine the intelligence of human and the computing power of machine to solve the problems that are difficult to solve by either human or machine alone.
Research around information aggregation and prediction, including polls, probability elicitation, and prediction markets.These methods, broadly defined as wisdom of the crowds, are utilized for a range of outcomes: elections, marketing, internal corporate, military intelligence, etc. We demonstrate some serious advances. (1) Combinatorial Prediction Markets: frontend, backened, and unique questions. (2) Experimental Prediction Markets and Polling. (3) Forecasts, Sentiment, and Data Analytics
Labs: New York
We introduced a game-theoretic framework for crowdsourcing systems.
This project examines a range of mobile phone-based banking and payment solutions across countries, understanding the usability of m-banking systems by low-literate clients, the security of financial transactions conducted over low-end phones, as well as the social and economic context and impact of the new channel on low-income households.
Ranking plays a critical role in both Web search and sponsored search. In sponsored search, the problem is even more complex and challenging because we need to consider multiple factors during the ranking process.
We conduct research in the area of algorithms, systems, and services where user behaviour is a key factor – this includes algorithmic aspects, design of systems and services, and understanding of user behaviour. Our research is based on both theoretical and empirical methodologies including algorithm design, applied probability, game theory, systems approach, user studies, measurements and large-scale data mining.
How do households transition from one level of social and economic well-being to another, both within and across generations? What characterises high upward mobility from disadvantaged conditions?
Kelsa+ is a program that offers the low-income service staff in modern offices Internet-connected PCs for free, unrestricted use during their off-duty hours. This project assesses how such a program affects workers' self-esteem, basic digital literacy, English proficiency, and career opportunities.
This project involves investigating ways in which the use of technological solutions to enable various aspects of financial service delivery can result in more cost-effective and scalable operations for providers, and cheaper, better quality finance for the poor.
Trust is the lubricant of both game and business online transactions. Good reputation boosts trust, but it can be compromised by unreliable feedback. We use probabilistic models to accurately learn about people's reputation, and improve trust.
If the idea of constructing family is one theme, then another is the converse: the idea that domestic spaces might be socially and technologically fractionated in ways that people desire. Hence research in this theme is looking at how ‘domestic’ or private settings may be constituted by connections to other places and people and in other cases by partitionings and separations of places and people.
Here research is examining how the ‘idea of family’ can be a sociological topic and a design orientation leading to technical innovation and new user experiences. Various research activities are seeking ways of capturing traces of family activity, assembling and creating new representations of these activities, as well as inventing new ways to interact with and display those traces.
Businesses with five or fewer employees, called micro-enterprises, support many rural and urban households in developing nations. Microsoft Research India is conducting qualitative and quantitative research to explore the overall information and communication behaviors of micro-enterprises. The main research in India has included a photo-intensive qualitative analysis of 49 small and informal businesses in Bangalore (April 2006), a survey of 347 similar businesses in Hyderabad (January 2007),
This is an umbrella project for several related efforts at Microsoft Research Silicon Valley that address various Multi-Armed Bandit (MAB) formulations motivated by web search and ad placement. The MAB problem is a classical paradigm in Machine Learning in which an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies.
Labs: New York
Rural PC kiosks are shared-access computer centers, run either as community centers or as businesses, which seek to deliver services to support socio-economic development of poor rural areas. Our research suggests that there are systemic reasons why it is difficult to sustain development-oriented kiosks, though variations of kiosks can and do endure.
In web search today, a user types a few keywords and gets back links to web pages consisting of unstructured data. This leaves a lot to be desired for when there is structure data stores or the user includes some structural semantics in their query. With our work we aim to allow web results to include information from structured data sources ranging from fully relational databases, to flat tables and XML files to hidden data accessible only via web forms. Additionally, we aim to automaticall
We study problems in the intersection of Computer Science, Game Theory and Microeconomics. We are particularly focused on effects of strategic behavior in electronic markets, as such behavior has significant implications on the economic performance of these markets.