Nesreen K. Ahmed
  • Senior Research Scientist, Intel Research Labs
  • Email: nesreen.k.ahmed [AT]
  • 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 machine learning and data mining and spans the theory and algorithms of large-scale graph mining, statistical machine learning, 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 honored to be selected by UC Berkeley among the rising stars in computer science and engineering in 2014. In addition, I co-founded the open source network repository project, the first data repository with interactive visual graph analytics to help researchers find, explore, and understand graph data in real-time over the web.

My Erdos Number is 3 --> (Nesreen Ahmed -> Nick Duffield -> Noga Alon -> Paul Erdos).

  • Feb 2018

    Serving as PC Chair of the 2018 IEEE Big Data Conference, Industry and Government Track.

  • Feb 2018

    Our paper on Higher-Order Network Embeddings is accepted at WWW 2018

  • Dec 2017

    Invited talk on "Representation Learning for Large Attributed Graphs" at the Women in Machine Learning Workshop - co-located with NIPS 2017

  • Nov 2017

    Invited book chapter on "Role Discovery" at the CRC Press book on "Social Media Analytics: Advances and Applications"

  • Oct 2017

    Our paper on local motif counting is accepted at IEEE Big Data

  • August 2017

    Our work on triangle counting and truss decomposition selected as finalists in the IEEE/Amazon/DARPA Graph Challenge! - co-located with IEEE HPEC

  • August 2017

    Our paper on Stream Aggregation Through Order Sampling is accepted as a full paper at CIKM 2017

  • July 2017

    Our paper On Sampling from Massive Graph Streams is accepted at VLDB 2017, Slides are here!

  • Feb 2017

    Our paper on High-order Network Models accepted at AAAI 2017 workshop on Plan, Activiy and Intent Recognition

  • Jan 2017

    Invited to the PC Commitee of SIGKDD 2017, CIKM 2017, MLG 2017, MOD 2017, ADMA 2017, GHC 2017, WWW 2018

  • Dec 2016

    Our paper on "Estimation of Local Subgraph Counts" accepted at the IEEE Big Data 2016

  • June 2016

    Invited talk at the MMDS workshop on algorithms for modern massive data sets at UC Berkeley (Slides)

  • Jan 2016

    Parallel Parameterized Graphlet Decomposition (PGD) Library - link

  • Nov 2015

    ICDM 2015 Best paper candidate

    paper - code - slides