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.

Erdos Number 3

  • August 2018

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

  • June 2018

    Invited talk at the Dagstuhl Seminar on High Performance Graph Algorithms Schloss Dagstuhl Germany - [Slides]

  • May 2018

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

  • April 2018

    Our paper on "Sampling for Approximate Bipartite Network Projection" is accepted for oral presentation at IJCAI 2018 - [Slides]

  • April 2018

    Our paper on "Learning Role-based Graph Embeddings" is accepted at the Statistical Relational AI Workshop StarAI 2018 co-located with ICML 2018

  • Feb 2018

    Our paper on Higher-order Network Representation Learning is accepted at WWW 2018

  • Feb 2018

    Our paper on Deep Inductive Network Representation Learning is accepted at WWW-BigNet 2018

  • Feb 2018

    Our paper on Continuous-Time Dynamic Network Embeddings is accepted at WWW-BigNet 2018

  • Jan 2018

    Invited to the PC Commitee of AAAI 2019, WWW 2018-2019, SDM 2019, SIGKDD 2018, CIKM 2018, DSAA 2018, EDBT 2018, HiPC 2018, Complex Networks 2018, NetScix 2019, IPDPS-GraMl 2018, SC-IA^3 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"

  • 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 2016

    Parallel Parameterized Graphlet Decomposition (PGD) Library - link