Nesreen K. Ahmed
  • Senior Staff Research Scientist, Intel Research Labs
  • Email: nesreen.k.ahmed [AT] intel.com
  • Github: nkahmed
  • Twitter: n_kahmed

For more recent updates and information, please check my Curriculum Vitae.

I'm a senior member of the research staff at Intel Labs. I received my Ph.D. from the Computer Science Department at Purdue University, and my M.S. in statistics and computer science from Purdue University. Before joining Intel Labs, I had the opportunity to work at a number of industrial research labs including Facebook data science, Adobe research Labs, Technicolor research and innovation Labs, Intel data analytics, and the data mining and computer modeling center of excellence in Egypt.

My research lies in the field of large-scale machine learning and spans the theory and algorithms of graphs, statistical machine learning methods, and their applications in social and information networks. I have authored numerous papers and tutorials in top-tier conferences and journals. My research was selected among the best papers of ICDM in 2015, BigMine in 2012, and covered by popular press such as the MIT Technology Review.

I'm very honored to be a recipient of the class of 2014 Rising Stars in Computer Science (selected by top faculty at UC Berkeley, Stanford University, MIT, etc). In 2018-19, I received the Intel Labs recognition award for my significant contribution and impact to Intel's DARPA HIVE proposal and project. In 2013, I co-founded the open source network repository project, the first data repository with interactive visual graph analytics to promote reproducibility and help researchers find and explore graph data in real-time over the web.

Erdos Number 3



  • Nov 2020

    Paper accepted to NeurIPS 2020 workshop: Neural Algorithms for Graph Navigation - Paper link

  • Oct 2020

    Paper accepted to NeurIPS 2020: Adaptive Shrinkage Estimation for Streaming Graphs - Paper link

  • Sep 2020

    Serving as the PC chair of the 2021 IPDPS Workshop on Graphs, Architectures, Programming, and Learning (GrAPL)

  • Aug 2020

    Paper accepted to IEEE TKDE: Role-based Graph Embeddings

  • July 2020

    Paper accepted to ACM TKDD: Heterogenous Graphlets

  • June 2019

    Organizing the 7th BigGraphs workshop at IEEE Big Data 2020 with Mohammed Hasan, Kamesh Madduri, and Shaikh Arifuzzaman

  • May 2020

    Journal paper accepted to DAMI: Deep Graph Similarity Learning: A Survey

  • April 2020

    Paper on Network Science Infrastructure accepted to Gateways 2020

  • March 2020

    Paper accepted to ACM TKDD: On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications

  • Jan 2020

    Invited to the PC Commitee of ICML 2020, IJCAI 2020, SIGKDD 2020, SIGIR 2020, AAAI 2020, NeurIPS 2019, NeurIPS Reproducibility Challenge 2020, ICPP 2020, WWW 2020, SDM 2020



  • Dec 2019

    Paper accepted to SDM 2020: Deep Parametric Model for Discovering Group-cohesive Functional Brain Regions

  • Nov 2019

    Invited Panelist at the Workshop on Irregular Applications: Architectures and Algorithms at SuperComputing 2019

  • Nov 2019

    Paper accepted to NeurIPS workshop on ML for systems: Learning to Vectorize Using Deep Reinforcement Learning - link

  • Oct 2019

    Paper accepted to WSDM 2020: A Structural Graph Representation Learning Framework

  • Oct 2019

    New preprint: Temporal Network Sampling - arXiv link

  • Oct 2019

    Paper accepted to CGO 2020: NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning - arXiv link

  • Sep 2019

    New preprint: Deep Reinforcement Learning in System Optimization - arXiv link

  • Aug 2019

    Paper accepted to CIKM 2019: Deep Graph Similarity Learning for Brain Data Analysis - link

  • Aug 2019

    Invited Keynote at the SIGKDD Workshop on Offline and Online Evaluation of Interactive Systems - [Slides]

  • April 2019

    Paper accepted to ACM TKDD 2019: Attention Models in Graphs: A Survey - link

  • May 2018

    Invited Keynote at the IPDPS 2018 workshop on the intersection of graph algorihms and machine learning GraML - [Slides]