Crowdsourcing systems, in which numerous tasks are electronically distributed
to numerous "information piece-workers", have emerged as an effective paradigm
for human-powered solving of large scale problems in domains such as image
classification, data entry, optical character recognition, recommendation, and
proofreading. Because these low-paid workers can be unreliable, nearly all
crowdsourcers must devise schemes to increase confidence in their answers,
typically by assigning each task multiple times and combining the answers in
some way such as majority voting.

We consider the problem of static assortment optimization, where the goal is
to find the assortment of size at most $C$ that maximizes revenues. This is a
fundamental decision problem in the area of Operations Management. It has been
shown that this problem is provably hard for most of the important families of
parametric of choice models, except the multinomial logit (MNL) model. In
addition, most of the approximation schemes proposed in the literature are
tailored to a specific parametric structure.

We consider a queueing network in which there are constraints on which queues
may be served simultaneously; such networks may be used to model input-queued
switches and wireless networks. The scheduling policy for such a network
specifies which queues to serve at any point in time. We consider a family of
scheduling policies, related to the maximum-weight policy of Tassiulas and
Ephremides (1992), for single-hop and multihop networks.We specify a fluid
model, and show that fluid-scaled performance processes can be approximated by
fluid model solutions.

Recently there has been considerable interest in the design of efficient
carrier sense multiple access(CSMA) protocol for wireless network. The basic
assumption underlying recent results is availability of perfect carrier sense
information. This allows for design of continuous time algorithm under which
collisions are avoided. The primary purpose of this note is to show how these
results can be extended in the case when carrier sense information may not be
perfect, or equivalently delayed.

We consider the recovery of a nonnegative vector x from measurements y = Ax,
where A is an m-by-n matrix whos entries are in {0, 1}. We establish that when
A corresponds to the adjacency matrix of a bipartite graph with sufficient
expansion, a simple message-passing algorithm produces an estimate \hat{x} of x
satisfying ||x-\hat{x}||_1 \leq O(n/k) ||x-x(k)||_1, where x(k) is the best
k-sparse approximation of x. The algorithm performs O(n (log(n/k))^2 log(k))
computation in total, and the number of measurements required is m = O(k
log(n/k)).

We consider the problem of recovering a function over the space of
permutations (or, the symmetric group) over $n$ elements from a given partial
set of its Fourier series coefficients. This problem naturally arises in
several applications such as ranked elections, multi-object tracking, ranking
systems, etc.

We visit the following problem: For a `generic' model of consumer choice
(namely, distributions over preference lists) and a limited amount of data on
how consumers actually make decisions (such as marginal preference
information), how may one predict revenues from offering a particular
assortment of choices? This is a central problem in operations research and
marketing. We present a framework to answer such questions and design a number
of tractable algorithms from a data and computational standpoint for the same.

Motivated by applications of distributed linear estimation, distributed
control and distributed optimization, we consider the question of designing
linear iterative algorithms for computing the average of numbers in a network.
Specifically, our interest is in designing such an algorithm with the fastest
rate of convergence given the topological constraints of the network. As the
main result of this paper, we design an algorithm with the fastest possible
rate of convergence using a non-reversible Markov chain on the given network
graph.