Honors:

• Best Paper Awards: WWW'14, ECML-PKDD'20 (Data Mining)

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

Current Project: 

• PI: “Federated Nearest Neighbour Search: Theory and Practice” 2023 – 2026 (Marsden Fast-Start 2022) (AI: Assoc. Prof. F. Silvestri)

Ph.D. students: 

• Jingrui Zhang (2020): Scalable and Interpretable Anomaly Detection (co-supervisor: Gillian Dobbie)

• Linghan Zeng (2023): Nearest Neighbor Search under Privacy Constraints (co-supervisor: Francesco Silvestri, Sebastian Link)

• Junyan Zhong (2024): Robust Graph-Based Recommendation Systems (co-supervisor: Jingfeng Zhang, Andre Nies)

• Yingtao Zheng (2024): Efficient Clustering Algorithms on GPUs (co-supervisor: Andre Nies)

Publications:

1. On Finding Hubs in High Dimensions with Sampling

   • Huiwen Dong, Linghan Zeng, Zhiwen Zhao, Francesco Silvestri, Ninh Pham

   • AAAI 2025

2. Scalable DBSCAN with Random Projections

   • HaoChuan Xu, Ninh Pham

   • NeurIPS 2024, Arxiv, Code, 5-min talk

   • This work is an extension of Xu's (honours) bachelor thesis

3. A Semi-supervised Feature Selection for Anomaly Detection 

   • Jingrui Zhang, Gill Dobbie, Ninh Pham

   • Weakly Supervised and Cautious Learning (WSCL) Workshop at ECAI 2024

4. On Deploying Mobile Deep Learning to Segment COVID-19 RT-PCR Test Tube Images

   • Ting Xiang, Richard Dean, Jiawei Zhao, and Ninh Pham

   • PSIVT 2023, Data science showcase 2023

5. A Transductive Forest for Anomaly Detection with Few Labels

   • Jingrui Zhang, Ninh Pham, Gillian Dobbie

   • ECML-PKDD 2023, Python Code

6. Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search

   • Ninh Pham, Tao Liu

   • NeurIPS 2022 (Spotlight - Top 5%), Arxiv, Online talk at MIT, Code, 5-min teaser at NeurIPS

   • This work is an extension of Liu's master thesis, Data Science Showcase 2023

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

   • Ninh Pham

   • KDD 2021, Supplementary, Arxiv, Slide, C++ Code

8. Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search

   • Stephan Lorenzen, Ninh Pham

   • ECML-PKDD 2020, Best Data Mining Paper Award, Slide, C++ Code, Datasets

   • IJCAI 2021 Sister Conference Best Paper Track (invited extended abstract) 

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

   • Ninh Pham

   • ECML-PKDD 2018, Slide, Supplementary, Python Code, C++ Code, Datasets

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

    • Ninh Pham

    • EDBT 2017 (short paper), Python Code

11. Scalability and Total Recall with Fast CoveringLSH 

    • Ninh Pham, Rasmus Pagh

    • CIKM 2016, Slide, C++ Code, Matlab Code

12. I/O-Efficient Similarity Join 

    • Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel 

    • ESA 2015

    • Full version appears in Algorithmica 2017 (invited paper)

13. Efficient Estimation for High Similarities using Odd Sketches 

    • Michael Mitzenmacher, Rasmus Pagh, Ninh Pham 

    • WWW 2014, Best Paper Award, Selected as one of Notable Books and Articles in Computing of 2014, Slide, Matlab Code 

    • Media: CPH Post, Videnskab, Berlingske

14. Fast and Scalable Polynomial Kernels via Explicit Feature Maps 

    • Ninh Pham, Rasmus Pagh 

    • KDD 2013 (Oral), Slide, Matlab Code (featured in scikit-learn)

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

    • Ninh Pham, Rasmus Pagh 

    • KDD 2012, Slide, C++ Code

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

    • Hoang Thanh Lam, Toon Calders, Ninh Pham 

    • SDM 2011, C++ Code

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

    • Ninh D. Pham, Quang Loc Le, Tran Khanh Dang 

    • APWeb 2010

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

    • Ninh D. Pham, Quang Loc Le, Tran Khanh Dang 

    • ACIIDS 2010

Others:

On the Power of Randomization in Big Data Analytics 

18. • PhD Thesis 2014, Slide

Teaching:

• Thesis supervision

• Past master/bachelor students

• University of Auckland

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

  • CS 320: Applied Algorithmics, 2019, 2021 - 2024

  • CS 717: Fundamentals of Algorithmics, 2022 - 2024

  • CS 752: Big Data Management, 2019 - 2024

  • 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

Services:

• PC: WWW 15-16, 20 (Poster Track), ECAI 20, IJCAI 20-24, ECML-PKDD 23-24, NeurIPS 24, ICLR 25, ICML 25

• External reviewer: PKDD'13, ESA'15, STOC'18, KDD'21, PODS'24

• Journal reviewer: TKDE, TIST