My research is driven by a strong desire to better engineer socially integrated
network where a typical user may connect through a smart-phone, socialize
through Facebook, learn from Wikipedia and help bring socio-political change through
Twitter. Such a “social network” is in dire need of better network infrastructure;
a typical user is anxious for help to be able to cope with the information overload;
and scalable computational systems are required to process large amounts of data.
As a network theorist, my contributions towards addressing these challenges have been
three fold: design better wireless access network, process social data and scalable
algorithms that can operate in data center like facility.
Second, unlike human conversations, most interactions on “social networks” are recorded.
This provides us with tremendous opportunity to learn about people and poses challenge
of tackling information overload. We have developed methods and their practical realization
to capture people’s choice from seemingly innocent data like transactions or clicking
on one of many options (movies) on “display” (browsing a store). This can help better
run businesses, provide recommendations and help make collaborative decisions
(see People's Choice
and Celect). To help cope with information overload,
we have developed theory of searching information trail of people, e.g. Tweets on
Twitter. Our search engine Trumor is a practical
incarnation of our theory (see Searching The Source). (Want to learn more?)
Third, data centers and cloud facilities are emerging as canonical solution
to store and process massive amounts of personal and social data. To operate such facilities
while utilizing resources efficiently, we have developed theory of myopic resource scheduling
algorithms to optimize non-myopic performance metric like system throughput or revenue
(see Queue-based myopic policies). The
algorithms for data processing operating in such facilities ought to be distributed and
scalable. Belief propagation (BP) is one such algorithm which has been unexpectedly successful
in practice. To explain the success and recognize limitations of BP, we have developed theoretical
understanding of BP for inference/optimization by establishing connections to a class of
linear programs (see Belief Propagation).
We have also used insights from theory of probability to develop method for evaluating
circuits that can be used in Computer Aided Design. This method speeds up the simulation
necessary for circuit evaluation by 10000 times (e.g. simulation time goes from 2 months
to 2 minutes (see Statistics for Circuits).
A salient feature of our work across this variety of networks is the use of distributed,
iterative or the so-called message-passing procedures: as operational
building blocks in communication networks (e.g. medium access),
for efficient information processing in statistical networks (e.g. belief propagation) and as behavioral model in a social network (e.g. gossip algorithms).