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 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.
- Simple Yet Eﬀicient Algorithms for Maximum Inner Product Search via Extreme Order Statistics
- Ninh Pham
- KDD 2021
- Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search
- Stephan Lorenzen, Ninh Pham
- ECML-PKDD 2020, Best Paper Award, Invited to The Sister Conference Best Paper Track at IJCAI 2021
- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space
- Ninh Pham
- ECML-PKDD 2018
- I/O-Efficient Similarity Join*
- Rasmus Pagh, Ninh Pham, Francesco Silvestri, Morten Stöckel
- ESA 2015 - One of best papers invited to Algorithmica 2017
- Efficient Estimation for High Similarities using Odd Sketches*
- Fast and Scalable Polynomial Kernels via Explicit Feature Maps
- Ninh Pham, Rasmus Pagh
- KDD 2013
- Featured in scikit-learn, one of the most popular libraries in machine learning (Github Top-100 stars)
Teaching | Current
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
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)
- 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
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