• 2014 to present: Texas A&M University, Department of Electrical and Computer Engineering, Full Professor (with tenure)
• 2018 to present: Director, Texas A&M Institute of Data Science

Other Current Affiliations and Professional Service

• 2015 to present: Professor by Courtesy, Texas A&M Computer Science and Engineering
• 2015-2019: Member of Board of Directors, ACM Sigmetrics
• 2014 to present: Chief Editor for Big Data Networks, Frontiers in ICT
• 2014 to present: Editor-at-Large, IEEE/ACM Transactions on Networking

Publications

• Publication list,: Google Scholar; DBLP; MathSciNet; USPTO
• H-Index: 62 (Google Scholar, > 15,000 citations),  Erdös number: 2

Awards

• IET Fellow, 2016; AT&T Fellow, 2007; IEEE Fellow, 2005.
• ACM SIGMETRICS Test of Time, 2013 and 2012

Teaching

• Fall 2018 ECEN 758 Data Mining and Analysis
• Spring 2015, 2016, Fall 2017, ECEN 689 Data Mining and Analysis
• Fall 2014, Fall 2015: ECEN 689 Data Science for Communications Networks

Current Research Projects and Collaborators

• Streaming Algorithms for Big Graph Sampling
  • Nesreen Ahmed (Intel Research Labs)
  • Liangzhen Xia (Texas A&M MS Student, as advisor)
• Rough Paths in Data Analytics
  • Ni Hao (University of Oxford, Oxford-Man Institute of Quantitative Finance)
  • Weijung Xu (University of Warwick, Mathematics Institute)
• Big Data in Transportation
  • Byron Chigoy (Texas Transportation Institute, Research Specialist)
  • Shawn Turner ( Texas Transportation Institute, Division Head for Mobility)
• Data Science in Hydrology
  • Dhruva Kathuria (Texas A&M Biological and Agricultural Engineering), PhD student)
  • Hanzi Mao (Texas A&M Computer Science and Engineering, PhD student, as advisor)
  • Binayak Mohanty (Texas A&M Biological and Agricultural Engineering)
• Measurement in Software Defined Networking
  • Bin Liu (Tsinghua University)
  • Hao Wu (Tsinghua University)
  • Yunhong Xu (Texas A&M ECE PhD Student, as advisor)
  • Minlan Yu (University of Southern California)
• Network Resilience through Data Analytics (multiple projects)
  • Mahmood Ettehad (Texas A&M Math Phd Student, as advisor)
  • Guofei Gu (Texas A&M Computer Science and Engineering)
  • Kyriakos Manouskis (Applied Communication Sciences)
  • K.K. Ramakrishnan (UC Riverside)
• Read more about current opportunities

Recent Research Funding

• EAGER: Real-Time: Learning-Mediated Control for Traffic Shaping, National Science Foundation, Award 1839816, 10/1/2018-9/30/2020, PI N. Duffield, Co-PI’s, K. Narayanan, S. Shakkottai, A. Talebpour,  Total $300,000 / Duffield $75,000
• Advanced Traffic Analysis For Network Virtualization, 07/20/2018 to 08/31/2018, PI N. Duffield, $61,743
• EAGER: III: Adaptive Sampling of Massive Graph Streams, National Science Foundation, Award 1848596 , 09/01/2018 to 08/21/2020, PI N. Duffield, Total $200,488 / Duffield $200,488
• NeTS: Small: Collaborative Research: Distributed Approximate Packet Classification, National Science Foundation, Award 1618030, 9/1/2016-8/31/2019. PIs Nick Duffield (Texas A&M) & Minlan Yu (Yale). Total $350,996 / Duffield $198,346.
• DEDUCE: Distributed Enclave Defense Using Configurable Edges. DARPA $11,500,000, 7/1/2015-6/30/2018, Applied Communication Sciences. Texas A&M Subaward, PI: Nick Duffield. Total $11,500,000 / Duffield $389,872
• Traffic Measurement from High-level Names in Software Defined Networking, Google Faculty Research Award, 2015,  $79,992, PIs N.G. Duffield and Minlan Yu (Yale).
• Approximation Methods for Massive Graph Analytics, Intel Corporation, 2016, PI N.G. Duffield, $30,000
• Understanding Multi-Scale Hydrology: Fusion of BIG DATA from Ground Networks and Space-Based Satellites, Texas A&M Big Data Seed Grant, January 2016, PI: Binayak Mohanty (Texas A&M Biological and Agricultural Engineering); co-PI: N. Duffield. Total $50,000./ Duffield $25,000
• Improving Understanding of 
Travel Behavior and Transportation Systems through Big Data Analytics, Texas A&M Big Data Seed Grant, January 2016, PI: Shawn Turner (Texas A&M Transportation Institute) co-PI: N. Duffield. Total $50,000 / Duffield $25,000
• Boosting Attack Identification through Correlated System & Network Monitoring. Texas A&M Cybersecurity Seed Grant, January 2016,. PI: N. Duffield, co-PI: Guofei Gu (Texas A&M Computer Science and Engineering). Total  $50,000 / Duffield $25,000

Recent Short Term Research Visits

• 7/17/2017-7/28/2017, Alan Turing Institute, London, UK
• 7/3/2017-7/14/2017, Computer Science, University of Warwick, UK
• 6/20/2016-7/15/2016, Intel Research, Santa Clara, CA
• 7/1/2015-7/31/2015, Oxford-Man Institute of Quantitative Finance

 Recent Talks

• 3/29/2017: University of Utah, Salt Lake City, UT
• 2/6/2017: Tulane University, New Orleans, LA
• 12/5/2016: Keynote, 2016 BigGraphs Workshop at IEEE BigData’16, Washington, DC
• 11/29/2016: Yale University, New Haven, CT
• 7/13/2016: Intel Research, Santa Clara, CA
• 7/8/2016: University of Southern California
• 1/21/2016: Informatics Institute, University of Florida
• 11/2015: University of Texas at Austin
• 12/6/2014: Keynote, IPCCC, Austin TX
• 9/12/2014: Invited talk, Heilbronn Conference, Bristol, UK
• 8/2014: Sampling for Big Data: A Tutorial (with G. Cormode), ACM SIGKDD 2014
• 2/26/2014: Big Data Lecture Series, University of Illinois at Urbana Champaign
• 2/11/2014: Computer Engineering Eminent Scholar Seminar, Texas A&M University
• 12/10/2013: Facebook, Menlo Park CA
• 12/10/2013: Google, Mountain View CA
• 11/13/2013: Invited Speaker and Panelist, Big Data in the Mathematical Sciences, Warwick University
• 09/11/2013: Keynote Speaker, ITC 25, Shanghai

Recent Service

• Technical Program Committee, WWW 2018
• Technical Program Committee, IEEE Big Data Conference 2017
• Technical Program Committee, IFIP Performance 2017
• Program Committee 2016 BigGraphs Workshop at IEEE BigData’16
• Scientific Programme Committee, 9th Bernoulli/IMS World Congress in Probability and Statistics, Toronto 2016
• Chief Editor for Big Data, Frontiers in ICT, since October 2014
• Editor-at-Large, IEEE/ACM Transactions on Networking, since September 2014
• TPC, International Workshop on Traffic Monitoring Analysis (TMA). Barcelona, April 2015
• TPC, IEEE International Conference on Network Protocols, ICNP 2014
• TPC, Workshop on MAthematical performance Modeling and Analysis, MAMA 2014
• TPC Co-Chair, IFIP Performance 2013,

Previous Positions and Education

• 2013-2014: Rutgers University / DIMACS, NJ: Research Professor
• 1995-2013: AT&T Labs-Research, NJ:  Distinguished Member of Technical Staff (final level) and AT&T Fellow.
• 1986-1995: Post-doc and faculty positions in Dublin, Ireland and Heidelberg, Germany.
• 1983-1986: Queen Mary College, University of London, PhD, Mathematical Physics (1987)
• 1979-1983: Christ’s College, University of Cambridge, BA Natural Sciences (1982) and MMath (Part III Maths, 1983), MA (1986)

Previous Research Areas

• Data Analytics and Mobility Modeling in Cellular Networks
• Machine Learning in Network Management: Exploring the complex causal relationships between measurable network behavior and customer experience
• Optimal Sampling (including Smart Sampling of Flow Records): Developing efficient sampling algorithms that provably optimize trade-off between sample size and estimation variance, even for heavy-tailed distributions typical in the Internet. Structure aware sampling for more accurate range queries. Fair sampling over subpopulations. Versions currently deployed in network measurement infrastructure.
• Architectures for Fine Grained Traffic Measurement: Router level data structures for storage and retrieval of delay measurements. Enhancing performance measurement through temporal locality,
• Graph Sampling: unbiased node sampliing in P2P networks.
• Network Security and Attack Detection: stress testing traffic, machine learning of flow classifiers
• Performance Monitoring. Sampling for temporal loss patterns, now standardized in the IETF.
• Trajectory Sampling. Coordinate packet sampling across routers based on hash of invariant packet fields. Now standardized in the IETF.
• Network Tomography.
  • Traffic Matrices: inferring traffic matrices from link level traffic aggregates. Deployed in network measurement infrastructure.
  • Link Performance: inferring link level loss and delay by correlating end-to-end measurements across a mesh of paths. Support for reporting by transport protocols standardized in the IETF.
• The Hose Model for Virtual Private Networks: service without advance need of a traffic matrix
• Network Measurement Infrastructure: early generation (T3 speed) passive monitors
• Analysis and Measurement of Effective Bandwidths: using
  • Large Deviations and Martingale methods to establish generic bounds on queue tail probabilities, without the need for a detailed traffic model.
  • Admission Control using measured effective bandwidth.
• Statistical Mechanics
  • Quantum dynamical semigroups and mean-field theory
  • Large deviations and group representations
  • Non-equilibrium phase transitions