Biography
I got my PhD in Artificial Intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017. My current research works focus on the fields of data mining and machine learning. Particular topics include clustering analysis, anomaly detection, similarity learning and their applications for pattern recognition and information retrieval. Most research outcomes have been published in top-tier conferences and journals, including SIGKDD, PAKDD, AAAI, Artificial Intelligence Journal, Pattern Recognition Journal and Machine Learning Journal.
Read more on Ye's profileResearch interests
clustering analysis, anomaly detection, metric 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, PAKDD, AAAI
Research groups
Data Analytics Group
https://blogs.deakin.edu.au/darl
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.
Publications
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
Ye Zhu, Kai Ting, Mark Carman, Maia Angelova Turkedjieva
(2021), pp. 1-41, Pattern Recognition, Amsterdam, The Netherlands, C1
S Kusmakar, C Karmakar, Y Zhu, S Shelyag, S Drummond, J Ellis, M Angelova
(2021), Vol. 8, Royal Society Open Science, C1
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), pp. 1-61, 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
- 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
- 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
- 2019: $30,000
Modelling networked combat, adversarial C2 and information operations.
Prof Maia Angelova Turkedjieva, Dr Sergiy Shelyag, Dr Ye Zhu
- 2020: $30,000
Industry and Other Funding
Explore the association rules between the fundamentals and short-term price dynamics
Dr Ye Zhu
- 2021: $10,692
Supervisions
No completed student supervisions to report