Nicholas Duffield (0000-0001-7211-1584)

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Biography

Nick Duffield is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. From 2013 until 2014, he was a Research Professor at DIMACS (the Center for Discrete Mathematics and Theoretical Computer Science) at Rutgers University, New Jersey, USA. From 1995 until 2013, he worked at AT&T Labs-Research, Florham Park, NJ, where he was a Distinguished Member of Technical Staff and an AT&T Fellow. He previously held post-doctoral and faculty positions in Dublin, Ireland, and Heidelberg, Germany. He received a BA in Natural Sciences in 1982, a MMath (Part III Maths) in 1983 from the University of Cambridge, UK, and a PhD in Mathematical Physics from the University of London, U.K., in 1987.

His research focuses on data and network science, particularly applications of probability, statistics, algorithms and machine learning to the acquisition, management and analysis of large datasets in communications networks and beyond. He is a co-inventor of the Smart Sampling technologies that lie at the heart of AT&T’s scalable Traffic Analysis Service. He was Charter Chair of the IETF working group on Packet Sampling and is a member of the Scientific Advisory Board of Talaia Networks. He was an Associate Editor for the IEEE/ACM Transactions on Networking from 2007-2011, and has been an Editor-at-Large since 2014. He is Chief Editor for Big Data at Frontiers in ICT. Dr. Duffield is an IEEE Fellow, and was a co-recipient of the ACM Sigmetrics Test of Time Award in both 2012 and 2013 for work in Network Tomography.

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Texas A&M University: College Station, TX, US

2018-09-01 to present | Director (Texas A&M Institute of Data Science)

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Nicholas Duffield

https://orcid.org/0000-0001-7211-1584

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Biography

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Employment (8)

Texas A&M University: College Station, TX, US

Texas A&M University: College Station, TX, US

Rutgers University New Brunswick: New Brunswick, NJ, US

AT&T Labs: Florham Park, NJ, US

Dublin City University: Dublin, IE

Dublin Institute for Advanced Studies: Dublin, IE

University of Heidelberg: Heidelberg, DE

University College Dubln: Dublin, IE

Education and qualifications (3)

Queen Mary University of London: London, London, GB

University of Cambridge: Cambridge, Cambridgeshire, GB

University of Cambridge: Cambridge, Cambridgeshire, GB

Works (50 of 161)

Cotton Yield Prediction via UAV-Based Cotton Boll Image Segmentation Using YOLO Model and Segment Anything Model (SAM)

Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering

Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data

Towards Invariant Time Series Forecasting in Smart Cities

3D visualization of hurricane storm surge impact on urban infrastructure

Improving Irrigation Management of Cotton with Small Unmanned Aerial Vehicle (UAV) in Texas High Plains

In-Season Cotton Yield Prediction with Scale-Aware Convolutional Neural Network Models and Unmanned Aerial Vehicle RGB Imagery

A reinforcement learning-based routing algorithm for large street networks

Location-Aware Social Network Recommendation via Temporal Graph Networks

6 Hands-On Iii: Building Digital Twins for Precision Livestock Farming: Data Analytics and Big Data Challenges

Multiscale Energy Network Tomography

Revealing the linguistic and geographical disparities of public awareness to Covid-19 outbreak through social media

Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks

Rapid Damage Estimation of Texas Winter Storm Uri from Social Media Using Deep Neural Networks

Bayesian Graph Contrastive Learning

Online Sampling of Temporal Networks

Revealing the Global Linguistic and Geographical Disparities of Public Awareness to Covid-19 Outbreak through Social Media

TEMPORAL NETWORK SAMPLING

A SmartNIC-Accelerated Monitoring Platform for In-band Network Telemetry

Adaptive Shrinkage Estimation for Streaming Graphs

Bayesian Graph Neural Networks with Adaptive Connection Sampling

Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis

Poster: CO2: Collaborative Packet Classification for Network Functions with Overselection

SEMI-IMPLICIT STOCHASTIC RECURRENT NEURAL NETWORKS

A SmartNIC-Accelerated Monitoring Platform for In-band Network Telemetry

Bayesian Graph Neural Networks with Adaptive Connection Sampling

Near-Optimal Disjoint-Path Facility Location Through Set Cover by Pairs

Semi-Implicit Stochastic Recurrent Neural Networks

Micro- and macro-level churn analysis of large-scale mobile games

Editorial

Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction

Semi-Implicit Graph Variational Auto-Encoders

Variational Graph Recurrent Neural Networks

Piecewise Stationary Modeling of Random Processes Over Graphs With an Application to Traffic Prediction

Gap Filling of High‐Resolution Soil Moisture for SMAP/Sentinel‐1: A Two‐Layer Machine Learning‐Based Framework

Variational Graph Recurrent Neural Networks

Semi-Implicit Graph Variational Auto-Encoders

Real-Time Streaming Graph Embedding Through Local Actions

Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework

Graph reconstruction from path correlation data

Sampling for Approximate Bipartite Network Projection

A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games

Sampling for Approximate Bipartite Network Projection

Signature inversion for monotone paths

Stream Aggregation Through Order Sampling

On sampling from massive graph streams

Graphlet decomposition: framework, algorithms, and applications

Efficient Sampling for Better OSN Data Provisioning

On the Tradeoff between Stability and Fit

Efficient Graphlet Counting for Large Networks