University of Washington

Many sys­tems involve the allo­ca­tion of rewards for achieve­ments, and these rewards pro­duce a set of incen­tives that in turn guide behav­ior. Such effects are vis­i­ble in many domains from every­day life, and they are increas­ingly form­ing a...

Random matrix theory studies the asymptotics of the spectral distributions of families of random matrices, as the sizes of the matrices tend to infinity. Derivation such asymptotics frequently requires analyzing the spectral properties of random...

The positive Grassmannian is a remarkable subset of the real Grassmannian which has recently arisen in diverse contexts such as integrable systems, scattering amplitudes, and free probability. I will provide an elementary introduction to the positive...

Ran­dom matri­ces now play a role in many areas of the­o­ret­i­cal, applied, and com­pu­ta­tional math­e­mat­ics. There­fore, it is desir­able to have tools for study­ing ran­dom matri­ces that are flex­i­ble, easy to use, and pow­er­ful. Over the...

The Eulerian polynomials, which were introduced by Euler over 200 years ago, are pervasive throughout discrete mathematics and arise in a variety of surprising ways. Over the years generalizations and q-analogs possessing fundamental properties of...

The online advertising industry is a rich environment for novel mathematical problems at the intersection of technology and theory. After introducing myself, AppNexus, and how I came to have my role there after having studied differential geometry...

Flow and cut prob­lems on graphs are among the most fun­da­men­tal and exten­sively stud­ied prob­lems in Oper­a­tions Research and Opti­miza­tion, play­ing a foun­da­tional role in both the the­ory and prac­tice of algo­rithm design. While the clas...

The Birnbaum Lecture in Probability will be delivered by Balint Virag (University of Toronto) in 2015.Northwest Probability Seminars are mini-conferences held at the University of Washington and/or Microsoft Research and organized in collaboration...

In many appli­ca­tions one wishes to min­i­mize an objec­tive func­tion that is not con­vex and is not dif­fer­en­tiable at its min­i­miz­ers. We dis­cuss two algo­rithms for min­i­miza­tion of non­smooth, non­con­vex func­tions. Gra­di­ent Sam­pling...