Profile image of Ye Zhu

Dr Ye Zhu

STAFF PROFILE

Position

Senior Lecturer, Computer Science

Faculty

Faculty of Sci Eng & Built Env

Department

School of Info Technology

Campus

Melbourne Burwood Campus

Qualifications

Doctor of Philosophy, Monash University, 2017

Contact

Biography

Dr Ye Zhu is a senior lecturer of computer science at the School of Information Technology at Deakin University. He is also the Data to Intelligence (D2i) Research Centre HDR coordinator and Master of Data Science Course CPL Officer. He received a PhD degree in Artificial Intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017. Dr Zhu joined Deakin University as a post-doc research fellow in complex system data analytics in July 2017 and then became a lecturer in Feb 2019. Dr Zhu is an IEEE senior member and ACM member.

His research works focus on clustering analysis, anomaly detection, similarity learning and their applications for pattern recognition. He has secured 5 research grants of around AUD$150,000 in total for interdisciplinary and industrial research. Dr Zhu has published over 30 papers in top-tier conferences and journals, including SIGKDD, PAKDD, AAAI, AIJ, ISJ, PRJ, JAIR and MLJ. He has served as Program Chair and Program Committee for many top international conferences, such as SIGKDD, AAAI and IJCAI. He obtained both an Early Career Researcher Award and a Teaching and Learning Award at the School of IT recently.

Read more on Ye's profile

Research interests

Clustering analysis
Anomaly detection
Similarity learning
Unsupervised learning

Units taught

SIT718: Real World Analytics

SIT741: Statistical Data Analysis

SIT742: Modern Data Science

Knowledge areas

Data Mining, Machine Learning, Pattern Recognition

Conferences

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

International Joint Conference on Artificial Intelligence

AAAI Conference on Artificial Intelligence

Pacific-Asia Conference on Knowledge Discovery and Data Mining

Research groups

Data Analytics Research Lab (DARL)

https://blogs.deakin.edu.au/darl

Data to Intelligence Research Centre (D2I)

https://data2intelligence.deakin.edu.au/

Awards

Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017.

School of Information Technology Research Award from Deakin University in 2020.

School of Information Technology Teaching and Learning Award from Deakin University in 2021.

Publications

Filter by

2022

Weighted dynamic time warping for traffic flow clustering

M Li, Y Zhu, T Zhao, M Angelova

(2022), Vol. 472, pp. 266-279, Neurocomputing, Amsterdam, The Netherlands, C1

journal article

Point-Set Kernel Clustering

K Ting, Y Zhu

(2022), IEEE Transactions on Knowledge and Data Engineering, C1

journal article

A feature selection-based method for DDoS attack flow classification

Lu Zhou, Ye Zhu, Tianrui Zong, Yong Xiang

(2022), Vol. 132, pp. 67-79, Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, C1

journal article

A novel feature-based framework enabling multi-type DDoS attacks detection

L Zhou, Y Zhu, Y Xiang, T Zong

(2022), World Wide Web, C1

journal article

Clustering-enhanced stock price prediction using deep learning

M Li, Y Zhu, Y Shen, M Angelova

(2022), World Wide Web, C1

journal article

A Comprehensive Feature Importance Evaluation for DDoS Attacks Detection

Lu Zhou, Ye Zhu, Yong Xiang

(2022), pp. 353-367, Advanced Data Mining and Applications. ADMA 2022, Sydney, Australia, E1

conference

Identification of Stock Market Manipulation with Deep Learning

Jillian Tallboys, Ye Zhu, Sutharshan Rajasegarar

(2022), Vol. 13087, Advanced Data Mining and Applications, Sydney, Australia, E1

conference
2021

Anomaly detection of aircraft lead-acid battery

W Zhao, Y Zhang, Y Zhu, P Xu

(2021), Vol. 37, pp. 1186-1197, Quality and reliability engineering international, Chichester, Eng., C1

journal article

Adversarial decision strategies in multiple network phased oscillators: the Blue-Green-Red Kuramoto-Sakaguchi model

Mathew Zuparic, Maia Angelova Turkedjieva, Ye Zhu, Alexander Kalloniatis

(2021), Vol. 95, pp. 1-15, Communications in Nonlinear Science and Numerical Simulation, Amsterdam, the Netherlands, C1

journal article

CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

Y Zhu, K Ting, M Carman, M Angelova

(2021), Vol. 117, Pattern Recognition, C1

journal article

A machine learning model for multi-night actigraphic detection of chronic insomnia: development and validation of a pre-screening tool

S Kusmakar, C Karmakar, Y Zhu, S Shelyag, S Drummond, J Ellis, M Angelova Turkedjieva

(2021), Vol. 8, pp. 1-17, Royal Society open science, London, Eng., C1

journal article

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

Ye Zhu, Kai Ting

(2021), Vol. 71, pp. 667-695, Journal of Artificial Intelligence Research, Palo Alto, Calif., C1

journal article

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

Ye Zhu, Kai Ting, Yuan Jin, Maia Angelova

(2021), Vol. 103, pp. 1-16, Information Systems, Amsterdam, The Netherlands, C1

journal article

Systematic evaluation of abnormal detection methods on gas well sensor data

Xichen Tang, Jinlong Wang, Ye Zhu, Robin Doss, Xin Han

(2021), pp. 1-6, ISCC 2021 : Proceedings of the 2021 IEEE Symposium on Computers and Communications, Athens, Greece, E1

conference
2020

Cloud-assisted privacy-conscious large-scale Markowitz portfolio

Y Zhang, J Jiang, Y Xiang, Y Zhu, L Wan, X Xie

(2020), Vol. 527, pp. 548-559, Information sciences, Amsterdam, The Netherlands, C1

journal article

Density estimates on the unit simplex and calculation of the mode of a sample

M Angelova Turkedjieva, Gleb Beliakov, Sergiy Shelyag, Ye Zhu

(2020), Vol. 35, pp. 850-868, International journal of intelligent systems, London, Eng., C1

journal article

Machine learning enabled team performance analysis in the dynamical environment of soccer

S Kusmakar, S Shelyag, Y Zhu, D Dwyer, P Gastin, M Angelova Turkedjieva

(2020), Vol. 8, pp. 90266-90279, IEEE Access, Piscataway, N.J., C1

journal article

Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data

M Angelova Turkedjieva, Chandan Karmakar, Ye Zhu, Sean Drummond, Jason Ellis

(2020), Vol. 8, pp. 74413-74422, IEEE Access, Piscataway, N.J., C1

journal article

A technical survey on statistical modelling and design methods for crowdsourcing quality control

Yuan Jin, Mark Carman, Ye Zhu, Yong Xiang

(2020), Vol. 287, pp. 1-35, Artificial intelligence, Amsterdam, The Netherlands, C1

journal article
2019

Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms

K Ting, Y Zhu, M Carman, Y Zhu, T Washio, Z Zhou

(2019), Vol. 108, pp. 331-376, Machine learning, New York, N.Y., C1

journal article

Data-driven natural gas spot price forecasting with least squares regression boosting algorithm

M Su, Z Zhang, Y Zhu, D Zha

(2019), Vol. 12, pp. 1094-1094, Energies, Basel, Switzerland, C1

journal article

Commutative fragile zero-watermarking and encryption for image integrity protection

M Li, D Xiao, Y Zhu, Y Zhang, L Sun

(2019), Vol. 78, pp. 22727-22742, Multimedia tools and applications, New York, N.Y., C1

journal article

Data driven natural gas spot price prediction models using machine learning methods

Moting Su, Zongyi Zhang, Ye Zhu, Donglan Zha, Wenying Wen

(2019), Vol. 12, pp. 1-17, Energies, Basel, Switzerland, C1

journal article

Density-based clustering using approximate natural neighbours

Maia Angelova Turkedjieva, Gleb Beliakov, Ye Zhu

(2019), Vol. 85, pp. 1-10, Applied soft computing, Amsterdam, The Netherlands, C1

journal article

Nearest-neighbour-induced isolation similarity and its impact on density-based clustering

Xiaoyu Qin, Kai Ting, Ye Zhu, Vincent Lee

(2019), Vol. 33, pp. 4755-4762, Proceedings of the Combined Conferences : 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Hawaii, E1

conference
2018

Isolation-based anomaly detection using nearest-neighbor ensembles

T Bandaragoda, K Ting, D Albrecht, F Liu, Y Zhu, J Wells

(2018), Vol. 34, pp. 968-998, Computational intelligence, Chichester, Eng., C1

journal article

Local contrast as an effective means to robust clustering against varying densities

B Chen, K Ting, T Washio, Y Zhu

(2018), Vol. 107, pp. 1621-1645, Machine learning, New York, N.Y., C1

journal article

Grouping points by shared subspaces for effective subspace clustering

Y Zhu, K Ting, M Carman

(2018), Vol. 83, pp. 230-244, Pattern recognition, Amsterdam, The Netherlands, C1

journal article

User activity pattern analysis in telecare data

M Angelova, J Ellman, H Gibson, P Oman, S Rajasegarar, Y Zhu

(2018), Vol. 6, pp. 33306-33317, IEEE access, Piscataway, N.J., C1

journal article

Leveraging label category relationships in multi-class crowdsourcing

Y Jin, L Du, Y Zhu, M Carman

(2018), Vol. 10938, pp. 128-140, PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference, Melbourne, Victoria, E1

conference

A distance scaling method to improve density-based clustering

Y Zhu, K Ting, M Angelova

(2018), Vol. 10939, pp. 389-400, PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference, Melbourne, Victoria, E1

conference

Distinguishing question subjectivity from difficulty for improved crowdsourcing

Y Jin, Mark Carman, Ye Zhu, Wray Buntine

(2018), Vol. 95, pp. 192-207, ACML 2018 : Proceedings of the 10th Asian Conference on Machine Learning Research, Beijing, China, E1

conference
2016

Density-ratio based clustering for discovering clusters with varying densities

Y Zhu, K Ting, M Carman

(2016), Vol. 60, pp. 983-997, Pattern recognition, Amsterdam, The Netherlands, C1-1

journal article

Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure

K Ting, Y Zhu, M Carman, Y Zhu, Z Zhou

(2016), pp. 1205-1214, KDD 2016 : Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, E1-1

conference
2015

Bivariate probability-based anomaly detection

H Lou, Y Zhu

(2015), pp. 1-6, BESC 2014 : International Conference on Behavior, Economic and Social Computing (BESC), Shanghai, China, E1-1

conference

Funded Projects at Deakin

Other Public Sector Funding

Multiple networks in dynamical combat modelling and critical phenomena.

Prof Maia Angelova Turkedjieva, Dr Ye Zhu

Department of Defence

  • 2018: $12,778

Intelligent Sensor processing for Enhancing Defence Decision Support.

Prof Maia Angelova Turkedjieva, Dr Ye Zhu, A/Prof Tim Wilkin, Dr Dan Dwyer, Dr Alex Kalloniatsis, Prof Paul Gastin

Defence Science Institute

  • 2019: $30,000
  • 2018: $20,000

Adversarial decision making networks and directed fires with non-combatant populations.

Prof Maia Angelova Turkedjieva, Dr Ye Zhu, Dr Sergiy Shelyag

DSTO Grant - Research - Defence Science & Technology Organisation

  • 2019: $30,000

Modelling networked combat, adversarial C2 and information operations.

Prof Maia Angelova Turkedjieva, Dr Sergiy Shelyag, Dr Ye Zhu

Department of Defence

  • 2020: $30,000

Industry and Other Funding

Explore the association rules between the fundamentals and short-term price dynamics

Dr Ye Zhu

MITA Capital Management LLC

  • 2021: $21,384

Supervisions

No completed student supervisions to report