James Hopgood

PROF, Director of Electronics and Electrical Engineering

Accepting PhD Students

PhD projects

<p>PhD Studentship Available: Adaptive time-resolved optical imaging for medical applications. Closing date, 13th June 2022.</p>
<p>This PhD position looks to use advanced optical imaging techniques to develop new methods for accessing optical “fingerprints” of disease, tailored to translatable medical applications. We are seeking an outstanding physics/engineering/computing student with great interest in understanding scanned optical image generation and processing to design and develop novel new adaptive image acquisition algorithms and image processing methods.</p>
<p>For more information, <a href="https://www.findaphd.com/phds/project/adaptive-time-resolved-optical-imaging-for-medical-applications/?p144853">please click here</a></p><p>Also, offering projects in Machine Learning and Statistical Signal Processing, including Probabilistic Graphical Models for Information Fusion. For further information, please see my range of interests below. </p>

If you made any changes in Pure these will be visible here soon.

Personal profile

Positions available


PhD Studentship Available: Adaptive time-resolved optical imaging for medical applications  (Closing date: 13th June 2022)

Optical imaging is rapidly becoming a key tool in medical procedures to aid clinicians. Fluorescence, fluorescence lifetime and Raman provide approaches to interrogate optical “fingerprints” of disease states and provide information on areas such as drug-target engagement, in real time and shorten pathways to diagnosis and treatment. There are significant challenges with current implementations of scanned optical imaging systems which rely on coherent imaging fibre bundles to access a desired sample location, namely imaging speed and signal to noise and data volume. We propose that the use of adaptive optical scanning (spatially, temporally and spectrally), combined with new image processing will increase the signal to noise, speed of acquisition and information content of minimally invasive optical imaging. For more information, please see: https://www.findaphd.com/phds/project/adaptive-time-resolved-optical-imaging-for-medical-applications/?p144853


CURRENT PROJECT: 2020-2023: EPSRC Healthcare Impact Partnership project on "Next-Generation Sensing For Human In Vivo Pharmacology - Accelerating Drug Development In Inflammatory Diseases (EP/S025987/1)."  Signal Processing and Machine Learning for Microendoscopy Imaging: developing signal and image processing and machine learning algorithms for a breakthrough healthcare imaging system, addressing problems in modern drug development. 



CURRENT PROJECT: 2019-2024: Developing algorithms for Probabilistic Graphical Models in Multi-Target Tracking at the School of Engineering at the University of Edinburgh, through the University Defence Research Collaboration (UDRC) phase 3 project jointly funded by EPSRC and UK Ministry of Defence. This work contributes to the UDRC3 under Work Package 2.1, within the Institute for Digital Communications at the University of Edinburgh.

Multi-target tracking (MTT) is an important problem in many defence and civilian applications, from tracking airborne targets, to maritime scenarios, to tracking people in urban environments. Recent work has been concerned with tracking an unknown number of targets from multiple multimodal asynchronous sensors, such as combining detections from multiple-radars and infra-red. Moreover, strong MoD investment in modular hierarchical autonomous sensor systems such as SAPIENT requires the development of inference algorithms for fixed-resource middleware platforms that scale with the variety, velocity, and veracity of the data, especially in cases where the sensor head has pre-processing capability.

However, multi-target tracking algorithms typically assume many hyperparameters that are often unknown but assumed, or heuristically determined, in existing algorithms presented in the literature. These include probability of detections and noise profiles, clutter profiles, sensor positions and orientations, and model dynamics. Recent work in the message-passing framework for MTT has attempted to incorporate joint estimation of these model parameters. However, current approaches still place a restriction on the model parameters in that they are drawn from a small finite discrete set, rather than the continuous-space in which they naturally reside.

This project will investigate message passing algorithms that explicitly deal with mixed continuous and discrete variables. In particular, we will develop relaxed structured decompositions of graphical models in mixed-variable problems, while considering scalable solutions, and consider recent message passing approximations. This includes realistically considering how many of these sensor and model hyperparameters can be estimated in linear time on a reasonable computational platform.

This project was formally advertised here:






James Hopgood is a Senior Lecturer in the Institute for Digital Communications, within the School of Engineering, at the University of Edinburgh, Scotland. He works in the disciplines of Data Science and Machine Learning within the field of Statistical Signal Processing, a branch of Electronic Engineering. 

James's research specialisation include model-based Bayesian signal processing, speech and audio signal processing in adverse acoustic environments, including blind dereverberation and multi-target acoustic source localisation and tracking, single channel signal separation, distant speech recognition, audio-visual fusion, medical imaging, blind image deconvolution, and general statistical signal and image processing.

James received the M.A., M.Eng. degree in Electrical and Information Sciences in 1997 and a Ph.D. in July 2001 in Statistical Signal Processing, part of Information Engineering, both from the University of Cambridge, England. He was then a Post-Doctoral Research Associate for the year after his Ph.D within the same group, at which point he became a Research Fellow at Queens’ College continuing his research in the Signal Processing Laboratory in Cambridge. James joined the University of Edinburgh in April 2004.

Since September 2011, he is Editor-in-Chief for the IET Journal of Signal Processing. James is the Programme Director for the MSc in Signal Processing and Communications at the University of Edinburgh. 

Research Interests

James’s interest in audio-signal processing in adverse acoustic environments covers a variety of research topics including: acoustic source localisation and tracking; blind enhancement of de-reverberation of speech and music; environmental noise reduction for acoustic sensing from remote mobile platforms; acoustic weapons fire and gunshot localisation; intelligent sensing form mobile robots; and ad-hoc acoustic sensor networks.

You can listen to some of James's recent work in his short public lecture talk from Edinurgh's Innovative Learning Week entitled "Separation and Marriage: Signal Processing for Life Science".

Positions available

Other PhD Positions available: Microphone array based acoustic event analysis, Distributed Source Localisation. Please contact me for further information.


Teaches the undergraduate course, Sensor Networks and Data Analysis (formally Signals and Communications 2) and previously tuaght Signals and Communications 3, in the second and third year respectively. James has over 16 years experience of teaching undergraduate and masters students, and is Director of Electronics and Electrical Engineering. James was previously Programme Director for the MSc in Signal Processing and Communications

Also teaches a 20-credit (double) Masters course on "Probability, Estimation Theory, and Random Signals (PETARS)". Extremely comprehensive courses notes are available for all four courses, along with a series of lecture recordings, tutorial questions, and solutions.

I also teach on the UDRC Summer School at Master's level. For more information, watch this video!

James has been using lecture recording technologies for the last seven years for supporting the student experience. His pedagogical approach is to provide visualisation, intuition, and real-world examples for describing complex topics, while providing teaching materials suitable for a range of learning needs.

Research Groups

Post Doctoral Researchers

Current PDRAs

  1. Dr Ali Taimori, working on EPSRC EP/S025987/1 Next-Generation Sensing For Human In Vivo Pharmacology- Accelerating Drug Development In Inflammatory Diseases
  2. Dr Mengwei Sun. Working on Scalable and Dynamic Inference on EP/S000631/1 Signal Procssing in the Information Age

Past PDRAs

  1. Dr Konstantinos Diamantis. Working on super-resolution methods for sonar and ultrasound.
  2. Hamed Azami, now ith Harvard University. Short project on Super-resolution techniques.
  3. Steven Herbert, now with DAMPT, University of Cambridge. Worked on Adaptive Waveform Design.
  4. Ebtihal Yousif, worked on Gel Electrophoresis signal modelling.
  5. Ibrahim Almajai, worked on LOCOBOT project.
  6. Christine Evers, now at University of Southampton, previously EPSRC fellow at Imperial College.
  7. Steven Fortune, worked on Blind Speech Dereverberation.

Research students

Current PhD Students (as First Supervisor or Lead co-supervisor)

  1. Student name: Tarek Haloubi
    Thesis title:
    Machine Learning Techniques for Evaluating Disease and Drug Effectiveness in Fibre-Bundle Endomicroscopy Systems 
    Commenced 1st March, 2021
    GSK/Cross-College EPSRC DTP

  2. Student name: Sofie MacDonald
    Thesis title:
     Distributed Sensor Networks for Scene Analysis in GPS denied environments
     Commenced 1st October, 2019
    Leonardo/EPSRC DTP

  3. Student name: Saleh Hanano
    Provisional thesis title: Machine Learning for Real-time Classification of Liver Disease using Ultrasound & Opto-acoustic Imaging
    Dates: Commenced 1st October, 2017
    Funding: Optical Medical Imaging CDT (Optima)

Completed PhDs (as First Supervisor)

  1. Student name: David Cormack
    Thesis title:
     Novel Methods for Multi-target Tracking with Applications in Sensor Registration and Fusion.
     Commenced 1st October, 2016, submitted March 2020, viva 6th May 2020
    External Examiner:
     Prof Simon Maskell, University of Liverpool
    Internal Examiner:
     Dr Yoann Altmann

  2. Student name: Saurav Sthapit
    Thesis title: Computation offloading for algorithms in absence of the Cloud
    Dates: Commenced 1st January, 2014, submitted January 2018, viva 27th March 2018.
    External Examiners: Prof Andrea Cavallaro, Queen Mary, University of London, and Prof Stephan Weiss, University of Strathclyde.
    Funding: UDRC related PhD (see EPSRC Standard Research: EP/K014277/1)

  3. Student name: Ashley Hughes
    Thesis title: Acoustic Source Localisation and Tracking Using Microphone Arrays
    Dates: Commenced 4th October, 2010, submmitted June 2015, viva 2nd October, 2015.
    External Examiner: Dr Wenwu Wang, University of Surrey
    Funding: 50% doctoral-training grant (DTG), 50% scholarships from Maxwell Advanced Technology Fund and Maxwell Fondation.

  4. Student name: Xionghu Zhong
    Thesis title: A Bayesian framework for multiple acoustic source tracking
    Dates: Commenced October 2006, submission May 2010, viva 13th October, 2010.
    External Examiner: Dr Wenwu Wang, University of Surrey
    Funding: University of Edinburgh Chinese Scholarship.

  5. Student name: Christine Evers
    Thesis title: Blind Dereverberation of Speech From Moving and Stationary Speakers Using Sequential Monte Carlo Methods
    Dates: Commenced October 2006, submitted March 2010, viva 10th June, 2010
    External Examiner: Prof Simon Godsill, University of Cambridge
    Funding: ERP Prize Scholarship.

  6. Student name: Sharad Nagappa
    Submitted thesis title: Time-Varying Frequency Analysis of Bat Echolocation Signals using Monte Carlo Methods
    Dates: Commenced October 2005, submitted December 2009, viva 6th April, 2010.
    External Examiner: Prof Mark Plumbley, Queen Mary University
    Funding: BIAS grant.

  7. Student name: Yan Yan
    Thesis title: Statistical signal processing for echo signals from ultrasound linear and nonlinear scatterers
    Dates: Commenced October 2005, submitted, September 2009, viva 10th December, 2009.
    External Examiner: Prof Paul White, University of Southampton
    Funding: 50% by BIAS grant, 50% by Institute for Digital Communications funds.

  8. Student name: Tom Bishop
    Thesis title: Blind Image Deconvolution: Nonstationary Bayesian approaches to restoring blurred photos
    Dates: Commenced October 2004, submitted November 2008, viva March 2009.
    External Examiner: Prof Nick Kingsbury, University of Cambridge
    Funding: EPSRC DTA

Notable External PhD Examinations

  1. Institution: University of Newcastle
    Department: School of Engineering
    Student Name: Yang Sun
    Date: September 2019
    Thesis title: "Deep Neural Networks for Monaural Source Separation"
    Supervisor: Dr Mohsen Naqvi

  2. Institution: University of Surrey
    Department: Centre for Vision, Speech and Signal Processing Faculty of Engineering and Physical Sciences
    Student Name: Atiyeh Alinaghi
    Date: September 2016
    Thesis title: "Blind Convolutive Stereo Speech Separation and Dereverberation"
    Supervisor: Drs Philip JB Jackson and Wenwu Wang

  3. Institution: University of Cambridge
    Department: Signal Processing and Communications Laboratory, Department of Engineering
    Student name: ZHANG, Xiao
    Date: October July, 2016
    Thesis title: Probablistic Models & Filters for Financial Time Series.
    Supervisor: Prof. Simon Godsill

  4.  Institution: Imperical College London
    Department: Communications and Signal Processing Group, Electrical & Electronic Engineering Department
    Student name: WANG, Yu
    Date: October 2015
    Thesis title: Speech Enhancement in the Modulation Domain
    Supervisor: Dr Mike Brooks

  5. Institution: University of Cambridge
    Department: Signal Processing and Communications Laboratory, Department of Engineering
    Student name: Maurice Fallon
    Date: Septemer 2008
    Thesis title: Acoustic Source Tracking using Sequential Monte Carlo
    Supervisor: Prof. Simon Godsill

  6. Institution: Imperical College London
    Department: Communications and Signal Processing Group, Electrical & Electronic Engineering Department
    Student name: Nikolay Dian Gaubitch
    Date: December 2006
    Thesis title: Blind identification of acoustic systems and enhancement of reverberant speech
    Supervisor: Dr. Patrick Naylor
  7. Institution: University of Cambridge
    Department: Signal Processing and Communications Laboratory, Department of Engineering
    Student name: Zaifei Liu
    Date: October 2006
    Thesis title: Monte Carlo Methods for Bayesian Inference in Digital Communications
    Supervisor: Prof. Arnaud Doucet

 Internal PhD Examinations

External examiner for over 20 candidates at the University of Edinburgh.

Administrative Roles

  • 2019 -- Director of Electronics and Electrical Engineering, School of Engineering
  • (2012-2019) Programme Director for the MSc in Signal Processing and Communications.
  • (2018-2010) Academic Champion for Tutors and Demonstrators in the School of Engineering.
  • (2010-2019) Administrator for School of Engineering Scholarships for "Straight A" Students applying for Electrical and Electronic Engineering at Edinburgh.

Education/Academic qualification

Doctor of Philosophy (PhD), Nonstationary Signal Processing with Application to Reverberation Cancellation in Acoustic Environments, University of Cambridge

Award Date: 1 Jan 2001

Master of Arts, University of Cambridge

Award Date: 1 Jan 2000

Master of Engineering, Electrical and Information Sciences (EIST), University of Cambridge

Award Date: 1 Jan 1997


Dive into the research topics where James Hopgood is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
  • 1 Similar Profiles

Collaborations and top research areas from the last five years

Recent external collaboration on country/territory level. Dive into details by clicking on the dots or