Dr Ninh Dang Pham

PhD at IT University of Copenhagen


I am a lecturer at the School of Computer Science, University of Auckland since Dec 2018. Prior to joining UOA, I worked in Copenhagen for 7 years at University of Copenhagen (DIKU) and IT University of Copenhagen (ITU).

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.

My personal homepage: https://sites.google.com/site/phamninh/

Research | Current

Design and analyze efficient and practical randomized algorithms for large-scale machine learning and data mining tasks.

Selected publications:

Full publications: DBLP, Google scholar


Teaching | Current

Thesis supervision

University of Auckland:

  • Big Data Management (COMPSCI 752), Semester 1, 2019, 2020, 2021
  • Algorithms for Massive Data (COMPSCI 753), Semester 2, 2019, 2020
  • Applied Algorithmics (COMPSCI 320), Semester 2, 2019, 2021
  • Discrete structures in Mathematics and Computer Science (COMPSCI 225 - NEFU), Semester 2, 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, Fall 2014 - 2015

Postgraduate supervision

I am looking for students to solve computational challenges in machine learning and data mining problems.

Current Ph.D. students:

  • Jingrui Zhang (2020): Scalable and Interpretable Anomaly Detection (co-supervisor: Gill Dobbie)
  • Zhengjie Shi (2021): Efficient Algorithms for Federated Learning (co-supervisor: Kate Lee)

Past students




  • Best Paper Awards: WWW 2014, ECML-PKDD 2020
  • Student Travel Awards: KDD 2012 - 2013


Coordinator (Deputy) of the Master of Data Science programme.

Areas of expertise

Randomized Algorithms; Hashing; Data Stream; Machine Learning; Data Mining; Big Data

Committees/Professional groups/Services

Program Committee: WWW 2015 (Poster track), WWW 2016 (Poster track), WWW 2020 (Poster track), ECAI 2020, IJCAI 2020, IJCAI 2021, IJCAI 2022 - 2024 (PC board)

Journal Reviewer: TKDE

External Conference Reviewer: ECML-PKDD 2013, ESA 2015, STOC 2018


Contact details

Primary office location

Level 5, Room 565
New Zealand

Web links