Ninh Pham's homepage

I am a senior lecturer in the School of Computer Science, University of Auckland. Prior to joining UOA, I worked in Copenhagen for 7 years at University of Copenhagen (DIKU) and IT University of Copenhagen (ITU). My main research interests are in designing and analyzing randomized algorithms for big data analytics.

Contact: ninh dot pham at auckland dot ac dot nz

I was a postdoctoral researcher at DIKU, University of Copenhagen, working with Stephen Alstrup in the DABAI project, aiming at designing efficient algorithms for machine learning and using big data for digital learning support.

I was also a postdoctoral researcher in the Algorithms Group, ITU, involving in the SSS project, investigating efficient algorithms for high-dimensional similarity search on big data.

I received my PhD at ITU under the supervision of Rasmus Pagh in 2014. My PhD project, part of the MaDaMS project, focused on efficient randomized algorithms for big data analytics.

I was the recipient of the best paper awards in WWW Conference 2014 and ECML-PKDD 2020. In 2022, has recognized me as the 2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining (Rising Star) for my outstanding and vibrant contributions to this field between 2012 and 2021.


  1. Simple Yet Efficient Algorithms for Maximum Inner Product Search via Extreme Order Statistics

  2. Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search

  3. L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space

  4. Hybrid LSH: Faster Near Neighbors Reporting in High-dimensional Space

  5. Scalability and Total Recall with Fast CoveringLSH

  6. I/O-Efficient Similarity Join

  7. Efficient Estimation for High Similarities using Odd Sketches

  8. Fast and Scalable Polynomial Kernels via Explicit Feature Maps

  9. A Near-linear Time Approximation Algorithm for Angle-based Outlier Detection in High-dimensional Data

  10. Online Discovery of Top-k Similar Motifs in Time Series Data

  11. Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases

  12. HOT aSAX: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery


On the Power of Randomization in Big Data Analytics


  • Thesis supervision

  • Past students

  • University of Auckland

    • CS 225: Discrete Structures in Mathematics and Computer Science, 2020

    • CS 320: Applied Algorithmics, 2019, 2021 - 2022

    • CS 717: Fundamentals of Algorithmics, 2022

    • CS 752: Big Data Management, 2019 - 2022

    • CS 753: Algorithms for Massive Data, 2019, 2020

  • University of Copenhagen:

    • Large-scale Data Analytics, Spring 2017

    • Project Course on “Authorship verification using textual features”, Fall 2017

  • IT University of Copenhagen:

    • Algorithm Design II, 2014, 2015


  • PC: WWW'15 (Poster Track), WWW'16 (Poster Track), ECAI'20, IJCAI'20, WWW'20 (Poster Track), IJCAI'21, IJCAI'22-24 (PC board)

  • External reviewer: PKDD'13, ESA'15, STOC'18

  • Journal reviewer: IEEE Transactions on Big Data (TBD), IEEE Transactions on Knowledge and Data Engineering (TKDE)


  • Best Paper Awards: WWW'14, ECML-PKDD'20

  • 2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining (Rising Star) by

  • Student Travel Award KDD'13, KDD'12