Dr Ye Zhu

STAFF PROFILE

Position

Lecturer in Information Technology

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

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, Pattern Recognition Journal and Machine Learning Journal.

Read more on Ye's profile

Research 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

Awards

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

Publications

Filter by

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), pp. 1-15, 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), pp. 1-61, 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

  • 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

Supervisions

No completed student supervisions to report