World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
12422
World Ranking
6363
National Ranking
200

Overview

Dinh Phung is affiliated with Monash University in Australia and specializes in computer science with extensive work in artificial intelligence and related subfields. Their research contributions span over 316 publications, covering topics primarily focused on artificial intelligence, computer vision and pattern recognition, information systems, signal processing, and software engineering.

The scientist's recent papers include:

  • "VulRepair: a T5-based automated software vulnerability repair" (2022), published in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
  • "Bridging Global Context Interactions for High-Fidelity Image Completion" (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "Exploiting Domain-Specific Features to Enhance Domain Generalization" (2021), appearing on arXiv (Cornell University)
  • "Universal Graph Transformer Self-Attention Networks" (2022), published in Companion Proceedings of the Web Conference 2022
  • "AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities" (2023), featured in Empirical Software Engineering

Key co-authors who have frequently collaborated with Dinh Phung include:

  • Trung Le
  • Jianfei Cai
  • He Zhao
  • Van Nguyen
  • Gholamreza Haffari

The primary venues where this researcher has published include:

  • arXiv (Cornell University) with 99 publications
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Companion Proceedings of the Web Conference 2022
  • ACM Transactions on Software Engineering and Methodology
  • Proceedings of the AAAI Conference on Artificial Intelligence

Dinh Phung's work extensively covers topics such as:

  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Adversarial Robustness in Machine Learning
  • Advanced Neural Network Applications
  • Anomaly Detection Techniques and Applications
  • Multimodal Machine Learning Applications
  • Natural Language Processing Techniques

The scientist has also contributed to book publications with Springer Science+Business Media, including titles such as "Advances in Knowledge Discovery and Data Mining" published in 2022.

Best Publications

  • Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

    Wei Luo;Dinh Phung;Truyen Tran;Sunil Gupta

  • Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

    Ba-Ngu Vo;Ba-Tuong Vo;Dinh Phung

  • A novel embedding model for knowledge base completion based on convolutional neural network

    Dai Quoc Nguyen;Tu Dinh Nguyen;Dat Quoc Nguyen;Dinh Q. Phung

  • Activity recognition and abnormality detection with the switching hidden semi-Markov model

    T.V. Duong;H.H. Bui;D.Q. Phung;S. Venkatesh

  • Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model

    N.T. Nguyen;D.Q. Phung;S. Venkatesh;H. Bui

  • Predicting healthcare trajectories from medical records: A deep learning approach.

    Trang Pham;Truyen Tran;Dinh Q. Phung;Svetha Venkatesh

  • DeepCare: A Deep Dynamic Memory Model forźPredictive Medicine

    Trang Pham;Truyen Tran;Dinh Phung;Svetha Venkatesh

  • Affective and Content Analysis of Online Depression Communities

    Thin Nguyen;Dinh Phung;Bo Dao;Svetha Venkatesh

  • A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

    Dai Quoc Nguyen;Thanh Vu;Tu Dinh Nguyen;Dat Quoc Nguyen

  • An effective spatial-temporal attention based neural network for traffic flow prediction

    Loan N.N. Do;Hai L. Vu;Bao Q. Vo;Zhiyuan Liu

  • MGAN: Training Generative Adversarial Nets with Multiple Generators

    Quan Hoang;Tu Dinh Nguyen;Trung Le;Dinh Phung

  • Dual discriminator generative adversarial nets

    Tu Dinh Nguyen;Trung Le;Hung Vu;Dinh Q. Phung

  • Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring

    Hung Nguyen;Sarah J. Maclagan;Tu Dinh Nguyen;Thin Nguyen

  • Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

    Truyen Tran;Tu Dinh Nguyen;Dinh Phung;Svetha Venkatesh

  • Column networks for collective classification

    Trang Pham;Truyen Tran;Dinh Q. Phung;Svetha Venkatesh

  • DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

    Trang Pham;Truyen Tran;Dinh Phung;Svetha Venkatesh

  • Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

    Truyen Tran;Truyen Tran;Wei Luo;Dinh Phung;Richard Harvey;Richard Harvey

  • Efficient duration and hierarchical modeling for human activity recognition

    Thi Duong;Dinh Phung;Hung Bui;Svetha Venkatesh

  • Hierarchical hidden Markov models with general state hierarchy

    Hung H. Bui;Dinh Q. Phung;Svetha Venkatesh

  • Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

    Sunil Gupta;Truyen Tran;Truyen Tran;Wei Luo;Dinh Phung

  • Topic Modelling Meets Deep Neural Networks: A Survey

    He Zhao;Dinh Q. Phung;Viet Huynh;Yuan Jin

Frequent Co-Authors

Svetha Venkatesh
Svetha Venkatesh Deakin University
Ognjen Arandjelovic
Ognjen Arandjelovic University of St Andrews
Ba-Ngu Vo
Ba-Ngu Vo Curtin University
Ba-Tuong Vo
Ba-Tuong Vo Curtin University
Helen Christensen
Helen Christensen University of New South Wales
Chitra Dorai
Chitra Dorai IBM (United States)
Wray Buntine
Wray Buntine VinUniversity
John Yearwood
John Yearwood Deakin University
Terry Caelli
Terry Caelli Deakin University
Hang Li
Hang Li ByteDance

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