
Many problems in data analytics can be formulated as estimating a lowrank matrix from
noisy data. This task is particularly challenging in the face of uncanonical data corruption
and growing size…


Lowrank structures are common in modern data analysis, and they play essential roles in various applications. It is challenging to recover lowrank structures reliably and efﬁciently from corrupted…


The fifteen years following World War II saw the development of a wide range of theories of decision making and resource allocation. Notable fields arising from slim foundations included: sequential…


Abstract: New online platforms are profoundly altering our social and economic interactions by empowering new marketplaces and new collective behavior at increasingly larger scales. Many of these…




Elicitation is the study of mechanisms which incentivize the truthful reporting of private
information from selfminded agents. In this talk I will present a general theory of elicitation
grounded in…


The Promotion Optimization Problem (POP) is a challenging problem for supermarkets. The retailer needs to decide which items to promote, what is the price discount and finally, when to schedule the…


Motivated by today’s cloud computing capabilities in large server farms, we present a queueing
model where jobs are split into a number of pieces which are then randomly routed to…


Principal components analysis (PCA) is a wellknown technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets…


Selective Inference is the problem of testing hypotheses that are chosen or suggested by the
data. Inference after variable selection in highdimensional linear regression is a common
example of…


In this talk, we study two different diffusion models on the random graphs. In the first part, we
consider first passage percolation. We analyze the impact of the edge weights on distances
in…


The information revolution is spawning systems that require very frequent decisions and provide high volumes of data concerning past outcomes. Fueling the design of algorithms used in such systems is…


Continuous optimization is a key component of modern data analysis. Recently, the demands
of extremely largescale applications have shifted the focus from high cost, high accuracy
methods to low…




Within the broad field of personalized medicine, there has been a recent surge of clinical interest in the idea of responseguided dosing. Roughly speaking, the goal is to develop dosing strategies…


We discuss optimization problems at Amazon Logistics. Amazon is the biggest online retailer, fulfilling millions of orders a day. This leads to many classical OR problems. In addition, many of these…
