# Graduate Seminar

A seminar organized by/for the graduate students.

## Past Events › Graduate Seminar

### Events List Navigation

## Graduate Seminar: Kelly Bodwin and John Palowitch

Kelly Bodwin and John Palowitch UNC-Chapel Hill Object-Set Testing Methods for Association Mining The exploratory mining of hidden, modular subgroups in complex systems is an ever-important task in our increasingly interconnected world. Go-to clustering approaches still involve classical methods like k-means, aggolmerative pairing, and latent-space modeling. These methods usually operate via an objective score of a partition of objects in the system under study: the higher the score, the stronger the "modularity" of the partition. In this talk, we present…

Find out more »## Grad Student Seminar: Yunxiao Liu

Yunxiao Liu UNC-Chapel Hill Semiparametric Inference of Integrated Volatility Functionals Using High-frequency Financial Data With the advent of intraday high-frequency data of financial assets since the late 1990s, the research of financial econometrics has entered into a “big data” era. New theoretical techniques using the theory of continuous time stochastic processes has been extensively developed, and new empirical evidence has been documented. In particular, due to its far-reaching applications in various fields such as risk management and option pricing, the…

Find out more »## Grad Student Seminar: Bryan Davis, Indeed.com

Bryan Gilbert Davis Indeed.com SEM Portfolio Optimization This talk will introduce the basic format and mechanisms of Search Engine Marketing and introduce the business motivations for its utilization. It will then delve into ongoing work at Indeed aimed at optimizing the distribution of budget over different queries, and will highlight the different modeling and engineering components of this work.

Find out more »## Grad Student Seminar: Hyungsuk Tak, SAMSI

Hyungsuk Tak SAMSI Bayesian Estimates of Astronomical Time Delays between Gravitationally Lensed Stochastic Light Curves The gravitational field of a galaxy can act as a lens and deflect the light emitted by a more distant object such as a quasar. Strong gravitational lensing causes multiple images of the same quasar to appear in the sky. Since the light in each gravitationally lensed image traverses a different path length from the quasar to the Earth, fluctuations in the source brightness are…

Find out more »## Grad Seminar: Wanyi Chen, Eric Friedlander

Wanyi Chen Dynamic Decision Making In A Queueing System With Secondary Service Motivated by operational practices in emergency departments, we consider a queueing model where each job is one of two types. Type 1 jobs need only a primary service given by a single server. Type 2 jobs need an additional secondary service. Secondary service is conducted by infinitely many servers. Primary servers cannot serve a new job until secondary service of a job is over. Jobs incur waiting costs…

Find out more »## Grad Student Seminar: Jianyu Liu and Zhengling Qi

Jianyu Liu Joint Skeleton Estimation of Multiple Directed Acyclic Graphs for Heterogeneous Population The directed acyclic graph (DAG) is a powerful tool to model the interactions of high-dimensional variables. While estimating edge directions in a DAG often requires interventional data, one can estimate the skeleton of a DAG (i.e., an undirected graph formed by removing the direction of each edge in a DAG) using observational data. In real data analyses, the samples of the high-dimensional variables may be collected from…

Find out more »## Grad Student Seminar: Tianxiao Sun & Jonathan Williams

Tianxiao Sun Globally convergent Newton-type methods for convex optimization based on smoothness structures We study the smooth structure of convex functions by generalizing a powerful concept so-called self-concordance introduced by Nesterov and Nemirovskii to a broader class of convex functions. The proposed theory provides a mathematical tool to analyze both local and global convergence of Newton-type methods as long as the underlying functionals fall into our generalized self-concordant function class. First, we introduce the class of generalized self-concordant functions, which…

Find out more »## Grad Student Seminar: Siliang Gong & Leo Liu

Siliang Gong (Joint work with Kai Zhang and Yufeng Liu) Penalized linear regression with high-dimensional pairwise screening In variable selection, most existing screening methods focus on marginal e ects and ignore dependence between covariates. To improve the performance of selection, we incorporate pairwise e ects in covariates for screening and penalization. We achieve this by studying the asymptotic distribution of the maximal absolute pairwise sample correlation among independent covariates. The novelty of the theory is in that the convergence is…

Find out more »## Grad Student Seminar: Gabor Pataki, Bryan Davis

Gabor Pataki UNC-Chapel Hill Bad semidefinite programs with short proofs, and elementary linear algebra Semidefinite programs (SDPs) – optimization problems with linear constraints, linear objective, and semidefinite matrix variables – are some of the most useful, versatile, and pervasive optimization problems to emerge in the last 30 years. They find applications in combinatorial optimization, machine learning, and statistics, to name just a few areas. Unfortunately, SDPs often behave pathologically: the optimal values of the primal and dual problems may differ…

Find out more »## Grad Student Seminar: Yifan Cui and Melody Zhu

Yifan Cui Some asymptotic results of survival tree and forest models We develop atheoretical framework and asymptotic results for survival tree and forest models under right censoring. We first investigate the method from the aspect of splitting rules, where the survival curves of the two potential child nodes are calculated and compared. We show that existing approaches lead to a potentially biased estimation of the within-node survival and cause non-optimal selection of the splitting rules. This bias is due to…

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