Dr Jingjing Deng
Lecturer
Computer Science
Telephone: (01792) 602019
Room: Office - 223
Second Floor
Computational Foundry
Bay Campus

Areas of Expertise

  • Computer Vision
  • Data Mining
  • Machine Learning
  • Artificial Intelligence

Publications

  1. & (2017). Nested Shallow CNN-Cascade for Face Detection in the Wild. Presented at 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017),, 165-172. Washington, DC, USA: IEEE International Conference on Automatic Face Gesture Recognition. doi:10.1109/FG.2017.29
  2. & A bag of words approach to subject specific 3D human pose interaction classification with random decision forests. Graphical Models 76(3), 162-171.
  3. & Fixing the root node: Efficient tracking and detection of 3D human pose through local solutions. Image and Vision Computing 52, 73-87.
  4. & From pose to activity: Surveying datasets and introducing CONVERSE. Computer Vision and Image Understanding 144, 73-105.
  5. & (2015). 3D interactive coronary artery segmentation using random forests and Markov random field optimization. Presented at 2014 IEEE International Conference on Image Processing (ICIP),, 942-946. Paris, France: doi:10.1109/ICIP.2014.7025189

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Teaching

  • CS-150 Concepts of Computer Science 1

    This module along with CS-155 gives an overview of some of the main principles underlying computers and computing from both a theoretical and an applied point of view. Following a brief history of computers and software an introduction to the representation of data and the basic components of a computer will be given. Students will be introduced to the principles of programming at assembly language level. The module is accessible and relevant to students of all disciplines who wish to learn about, or reinforce their understanding of, computers and computer science.

  • CSCM35 Big Data and Data Mining

    This module introduces students to the fundamental topics of data mining, including data pre-processing techniques, applied probability and statistics, data mining algorithms (incl. associate rule, classification, clustering, outlier detection and probabilistic graphical model), and big data frameworks.

  • CSCM38 Advanced Topics: Artificial Intelligence and Cyber Security

    This module introduces students to the state-of-the-art methods and research topics of artificial intelligence, cyber security, including quantum computing, data science, deep learning and reinforcement learning. The inspiration behind these approaches will be discussed, along with their relative merits for application in cyber security.

  • CSDM03 Computational Thinking skills for Digital Social Scientists.

    This module will discuss some of the most widely used and artificial intelligence and machine learning, regression & clustering techniques and their applications to big data social science questions. The students will gain and understanding of both strengths and weaknesses of learning and practical know-how in applying those theories to real world problems. Topics include big social data concepts, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.

  • CSLM35 Big Data and Data Mining

    This module introduces students to the fundamental topics of data mining, including data pre-processing techniques, applied probability and statistics, data mining algorithms (incl. associate rule, classification, clustering, outlier detection and probabilistic graphical model), and big data frameworks.

Supervision

  • Electroencephalography Sequential Data Word Embedding in Decision Deadlocks (current)

    Student name:
    MRes
    Other supervisor: Prof Xianghua Xie
  • Deep Learning on Irregular Domains (current)

    Student name:
    PhD
    Other supervisor: Prof Xianghua Xie
  • Visual Text Understanding (current)

    Student name:
    PhD
    Other supervisor: Prof Xianghua Xie