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 profileResearch 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
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
K Ting, Y Zhu
(2022), IEEE Transactions on Knowledge and Data Engineering, C1
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
A novel feature-based framework enabling multi-type DDoS attacks detection
L Zhou, Y Zhu, Y Xiang, T Zong
(2022), World Wide Web, C1
Clustering-enhanced stock price prediction using deep learning
M Li, Y Zhu, Y Shen, M Angelova
(2022), World Wide Web, C1
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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