AT&T Graduate Student Symposium


On November 9th, 2018, AT&T will hold the 2nd annual one-day graduate student symposium for outstanding students working in the areas of Data Science and AI research. The symposium provides an opportunity for selected graduate students to present their research to AT&T Labs. Additionally, AT&T researchers will discuss opportunities for collaboration, offer career advice about working in an industry lab, and describe projects in these active research areas:

Below please find the 2018 call for participation as well as highlights of the 2017 Graduate Student Symposium, including the selected students and their presentation titles.

2018 Call for Participation



2017 Graduate Student Symposium





Students' Presentations

Shannon Gallagher, Carnegie Mellon University (Statistics) Building a Better Agent-Based Model

Lucy Gao, University of Washington (Biostatistics) Are Clusterings of Multiple Data Views Independent?

Xianyi Gao, Rutgers University (Electrical and Computer Engineering) Transforming Speed Sequences into Road Rays on the Map with Elastic Pathing

Fred Hohman, Georgia Tech (Computer Science) Graph Playgrounds: 3D Exploration of Graph Layers via Vertex Cloning

Lingzi Hong, University of Maryland (Information Science) Patterns of Digital Footprints Applied in Government Decision Making

Hanyu Jiang, Stevens Institute of Technology (Computer Engineering) A GPU-Based Parallel Framework for Accelerating Genomic Data Analysis

Xinyue Li, Yale University (Biostatistics) Penalized Multi-Band Learning for Accelerometer Data

Zongru Shao, Stevens Institute of Technology (Computer Engineering) Detecting Alzheimer’s and Aphasia with Text Mining Techniques

Simon Shaolei Du, Carnegie Mellon University (Machine Learning) When is a Convolutional Filter Easy to Learn?

Emily Wall, Georgia Tech (Computer Science) Modeling and Mitigating Cognitive Bias in Mixed-Initiative Visual Analytics

Huaxia Wang, Stevens Institute of Technology (Electrical Engineering) Adversarial Perturbations in Deep Neural Networks: Attack and Defense

Yixin Wang, Columbia University (Statistics) Robust Probabilistic Modeling with Bayesian Data Reweighting

Julia Wrobel, Columbia University (Biostatistics) Registration for Exponential Family Functional Data

Qiong Wu, College of William and Mary (Computer Science) Foundations of Nonparametric Preference Learning from Rank Data

Location

AT&T, 33 Thomas Street, New York, NY 10007
The symposium is held in the AT&T Research Center in downtown Manhattan located at 33 Thomas Street, New York, NY 10007. See map.

Contact Us

The AT&T Graduate Student Symposium is organized by Cheryl Flynn and Jim Klosowski.
Email us.
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