The University of Southampton

Telephone:
+442380597678
Email:
jsh2@ecs.soton.ac.uk

 

Personal homepage
http://github.com/jonhare
http://twitter.com/jon_hare
http://arxiv.org/a/hare_j_4.html
https://scholar.google.co.uk/citations?user=UFeON5oAAAAJ
  • Associate Professor of Computer Science
  • Director of Programmes (Computer Science)

I am an Associate Professor in the School of Electronics & Computer Science. I hold a BEng degree in Aerospace Engineering and PhD in Computer Science, both from the University of Southampton. My main research interests are centred around learnt representations of data. This is a subtopic of machine learning in which machines learn to encode or embed raw data into representations (aka latent spaces or embeddings) that attempt to capture human notions of meaning and semantics, and disentangle the underlying factors that generated the data.

My research necessarily incorporates research into deep neural network models ("deep learning"), as well as the more general notion of differentiable programming. Much of my research focusses on representations of visual information, and hence crosses over into the field of computer vision. I have however also worked on representations of textual information and other data modalities, and I am particularly interested in representations at the convergence of different modalities of data. At the same time, I am also highly interested in how we can take inspiration from biological systems in the design of our models, and both use this to inform new model architectures (that for example have particular performance characteristics, or map particularly well to certain hardware), as well as to understand emergent representational properties inside those models.

My research has been published in over 100 articles in top peer-reviewed journals and conferences. I am one of only a small handful of UK-based academics to have successively published papers in both NeurIPS and ICLR, the top international conferences for neural network, machine-learning and representation-learning research.

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Research interests

My main research interests lie in the area of representation learning. The long-term goal of my research is to innovate techniques that can allow machines to learn from and understand the information conveyed by data and use that information to fulfil the information needs of humans.  Broadly speaking this can be broken down into the following areas:

Novel representation

I have worked on a number of different approaches to creating novel representations from data. These include:

  • New units for representing different data types: With Yan Zhang & Adam Prügel-Bennett, I’ve worked developing differentiable neural architectures for counting and working with unordered sets. 
  • Embedding and Disentanglement: I’ve worked on a number of aspects of learning joint embeddings of different modalities of data. Recently with Matthew Painter, Adam Prügel-Bennett and I have looked at how underlying latent processes might be disentangled.
  • Learning architectures under constraints: In recent work with Sulaiman Sadiq, Geoff Merrett and I have started to look at how neural architectures for representation might be themselves learned to optimise against certain hardware constraints. Also related to this theme is joint work with Enrique Marquez and Mahesan Niranjan on Cascade Learning of deep networks, which allows a network to be grown from the bottom up.
  • Representational translation: with Pavlos Vougiouklis I’ve worked a lot on the problem of translating structured data into a representational space that can then be translated into a human readable text; in short we trained neural networks to translate sets of <subject, predicate, object> triples into natural language descriptions.
  • Acceleration features: with Yan Sun and Mark Nixon, I’ve developed novel representations for image sequences based on higher orders of motion such as acceleration and jerk. These representations can for example be used as an intermediary in recognition tasks.
  • Soft Biometric representation: with Nawaf Almudhahka and Mark Nixon, I’ve developed new forms soft-biometric representation that allow photographs of people to be recognised from verbal descriptions.

Understanding representation and taking inspiration from biology

As we work towards the goal of building artificial intelligence, it is important that we understand how our models work internally, and perhaps even utilise knowledge of biological systems in their design. In this space, there are three main directions that stand out:

  • Relating behaviours and internal representations of deep networks to biology: with Daniela Mihai and Ethan Harris, we’ve been working to understand what factors of a neural architecture cause the emergence of particular cell-level properties. In recent work we’ve explored how neural bottlenecks in artificial visual systems give rise to colour opponent cellular properties observed in real biological systems.
  • Investigating the emergence of visual semantics: Daniela Mihai & I have been exploring what factors cause visual semantics to emerge from artificial agents parameterised by neural networks when they play a visual communication game.
  • In ongoing work with colleagues as part of the EPSRC International Centre for Spatial Computational Learning, we’re considering if certain observations from biological neural networks, such as sparsity and overall architecture, could be used to help develop new ways of designing network models to better fit existing hardware, and better inform the development of future neural network hardware.

Applications of representation

  • Learning representations of aerial imagery: With colleagues from Ordnance Survey and Lancaster University I’ve spent a lot of time investigating new ways of learning representations that have applications in the geospatial intelligence domain. With Iris Kramer, we’ve been investigating how deep learning and representation learning technologies can be applied to allow for the discovery of archaeological sites from aerial imagery and LiDaR data.
  • Learning representations of scanned text documents. I was the Investigator of the Innovate UK funded Transcribe AI project, which looked at ways of learning representations of scanned textual documents that would allow for automated information extraction and reasoning over the information that was conveyed. 

I have also been involved in numerous other projects involving both machine learning and computer vision. For example, I was part of a team that innovated a system for scanning archaeological artefacts in the form of ancient Near Eastern cylinder seals using structured light.

Through all my research, I have made a commitment to open science and reproducible results. The published outcomes of almost all my work is accompanied with open source implementations that others can view, modify and run. A large body of the outcomes of my earlier research can be found in the OpenIMAJ software project (see http://openimaj.org), which won the prestigious 2011 ACM Multimedia Open Source Software Competition. OpenIMAJ is now used by researchers and developers across the globe, and in a variety of national and international organisations.

Teaching

I currently lead and teach on our popular research-led undergraduate Computer Vision and fourth year/MSc Differentiable Programming/Deep Learning modules. I am also part of the teaching team (with Adam Prügel-Bennett) on the Advanced Machine Learning module. In the past I have also taught the fourth year/MSc Data Mining module which I designed, and Scripting Languages/Cloud Application Development.

My work on designing and delivering these modules has been widely recognised: For Computer Vision, the students nominated me for the 2013-14 Excellence in Teaching Awards, and I won the faculty award for innovative teaching. In 2015 I was awarded a Vice Chancellors Teaching Award. The Computer Vision module was also shortlisted in the Blackboard and VLE Awards in 2017 and 2020, and my Data Mining module was shortlisted in 2016. 

I am actively involved in matters surrounding teaching in the School, such as engaging with education away days, education committee, and School reviews. In particular I was heavily involved in the review of machine learning teaching which led to the creation of new modules on Deep LearningNatural Language Processing and Machine Learning Technologies.

As Doctoral Programme Director, I am currently also responsible for the education of over 150 PhD students. Through these activities I contribute directly to education policy. I am also heavily involved in the organisation of outreach activities, including the ECS Taster Course

Together with Adam Prügel-Bennett and Mahesan Niranjan, I am also regularly involved in contract teaching of short courses on Machine Learning to both academia and industry.

I am a Fellow of the HEA and act as a mentor to ECS colleagues undertaking PGCAP and PREP.

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Email:
j.grundy@soton.ac.uk

 

CDCES executive committee member

 

Previously a chemist, BSc. Chemistry Exeter 1996, teacher, P.G.C.E. Durham 1997, then back in to chemistry, PhD in organometallic Chemistry from Sussex  2003 with Prof M.P. Coles, then Research fellow in Phosphorous Chemistry with Prof. Mathey at UC Riverside 2004-5. After brief (10 year) gap, teaching A level Chemistry and Maths, having kids and learning programming, did an MSc in Arfitificial Intelligence at Southampton, worked as a Teaching Fellow in CS for one year, now in the position of Research Fellow in 'Machine Learning on Advanced Sensor Array Data'.

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Research interests

Machine learning, genetic algorithms, outlier detection, time series data

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Telephone:
+442380593125
Email:
lh@ecs.soton.ac.uk

 FREng, FIEEE, FIET, EURASIP Fellow

Personal homepage
https://ieeexplore.ieee.org/search/searchresult.jsp?action=search&newsearch=true&matchBoolean=true&queryText=(%22All%20Metadata%22:hanzo)
https://www-mobile.ecs.soton.ac.uk/
https://en.wikipedia.org/wiki/Lajos_Hanzo)
https://qcit.committees.comsoc.org/officers/
https://vtsociety.org/about/board-of-governors/

Chair of Telecommunications, Head of Next-Generation Wireless, FREng, FIEEE, FIET, Fellow of EURASIP

 

 

Lajos Hanzo (http://www-mobile.ecs.soton.ac.uk, https://en.wikipedia.org/wiki/Lajos_Hanzo) (FIEEE'04) received his Master degree and Doctorate in 1976 and 1983, respectively from the Technical University (TU) of Budapest. He was also awarded the Doctor of Sciences (DSc) degree by the University of Southampton (2004) and Honorary Doctorates by the TU of Budapest (2009) and by the University of Edinburgh (2015).  He is a Foreign Member of the Hungarian Academy of Sciences and a former Editor-in-Chief of the IEEE Press.  He has served several terms as Governor of both IEEE ComSoc and of VTS.  He has published 2000+ contributions at IEEE Xplore https://ieeexplore.ieee.org/search/searchresult.jsp?action=search&newsearch=true&matchBoolean=true&queryText=(%22All%20Metadata%22:hanzo) , 19 Wiley-IEEE Press books and has helped the fast-track career of 123 PhD students. Over 40 of them are Professors at various stages of their careers in academia and many of them are leading scientists in the wireless industry. He is also a Fellow of the Royal Academy of Engineering (FREng), of the IET and of EURASIP. His citations can be found at https://scholar.google.com/citations?user=p0jnEW0AAAAJ

Research

Research interests

Multimedia communications, quantum  communications

Teaching

Multimedia Communications, signal processing

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Telephone:
+442380599305
Email:
R.Huang@soton.ac.uk

 

Personal homepage
  • Committee member of the IOP Semincodcutor Physics Group
  • Member of the management group of the ECS Centre for Internet of Things and Pervasive Systems (C-IoT)
  • Member of the mangement group of the ECS Centre for Flexible Electronics and E-Textiles (C-FLEET)

Ruomeng studied Physics in China, receiving a BSc in 2008 and a MEd in 2009. He came to Southampton in 2009 where he obtained a MSc in Nanoelectronics and Nanotechnology in 2010 and a PhD investigating the confined nanoscale chalcogenide phase change material and memory in 2015.

Following his PhD, Ruomeng was awarded an EPSRC Doctoral Prize fellowship in 2015 to start his independent research in functional chalcogenides and metal oxides with particular focus on application of novel non-volatile memory technologies (phase change memory and resistive switching memory) and thermoelectric (TE) materials and energy harvester. In 2018, he was appointed a lectureship in the Sustainable Electronic Technologies Group in the School of Electronics and Computer Science in the University of Southampton. He was a Co-Investigator in a STFC grant (Selective Chemical Vapour Deposition for Production of Thermoelectric Micro-Generators for Energy Harvesting, £363k) for the development of thin film thermoelectric generators. He is also working in a EPSRC program grant (ADEPT – Advanced Devices by ElectroPlaTing, £6.33m) which explores the state of the art of electrodeposition and device design at the nanoscale in the areas of thermoelectrics, infrared detection, and phase change materials.

Ruomeng has published over 40 journal papers (Google Scholar) and delivered over 40 oral/poster presentations at national and international conferences. He is a regular reviewer of several journals from ACS, RSC and IEEE.

Grants:

EPSRC IAA: Flexible thin-film thermoelectric for electricity generation from heat pipes, PI

STFC IAA: Chemical deposition of earth abundant tellurium-free materials for thermoelectric micro-generator applications, PI.

EPSRC eFutures Sandpit: NanoWire for MegaPower: Silicon nanowire based micro-thermoelectric generator, PI

STFC IPS: Selective chemical vapour deposition for production of thermoelectric micro-generators for energy harvesting, Co-I.

Latest news:

Fully funded PhD positions available! Please contact me (email: r.huang@soton.ac.uk) for more information.

Research

Research interests

1. Thermoelectric materials and generators

  • High performance thermoelectric binary and ternary materials (e.g. SnSe, Bi2Te3, Sb2Te3, BiSeTe, etc.)
  • Thermoelectric generator design and optimisation enabled by AI technologies (e.g. deep learning)

2. Novel non-volatile memory and neuromorphic devices

  • Chalcogenide materials (e.g. GeSbTe) based phase change and resistive switching memory via electrodeposition
  • Tunable metal oxdie thin film (e.g. ZnO, ZrOx, HfO2) based resistive switching memory
  • Back-end-of-line SiC thin film based resistive switching and neuromorphic devices
  • Solution based resistive switching and neuromorphic devices on flexible substrates

3. Deep-learning enabled structural colour design and optimisation

  • Inverse design of F-P cavity based colour filter by deep learning technology
  • Thermal and electrical controlled dynamic structural colour 

Team members

Post-Doctoral Research Associate:

  • Dr Ayoub H. Hamdiyah 
  • Mr Daniel Newbrook

PhD students:

  • Tongjun Zhang (September 2020-)
  • Dongkai Guo (Sepetember 2020-)
  • Yuxiao Zhu (September 2020-)
  • Peng Dai (September 2019-)
  • Vikesh Sethi (September 2019-)

Teaching

ELEC1206: Electrical Materials and Fields

ELEC2230: Semiconductor Devices, Materials and Sensors

ELEC3207/6256: Nanoelectronic Devices

ELEC3208: Analogue and Signal Processing

ELEC6200: Group Design Project

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Telephone:
+442380598660
Email:
Adriane.Chapman@soton.ac.uk

 

I am a Professor of Computer Science, in the Web and Internet Science Group (WAIS), in Electronics and Computer Science at the University of Southampton. My research is in the area of database systems, focusing on using data appropriately and effectively. This involves solving problems that span the areas of databases, information discovery and retrieval, provenance, and algorithmic accountability. I work closely with clinical practitioners and other health-deliverers in order to understand their needs, refine my research and apply it.  I have worked closely with the US Federal government, and influenced the Office of the National Coordinators (ONC) report on the usage of provenance within electronic health systems. I have advised the US Food and Drug Agency (FDA) , the National Geospatial-Intelligence Agency (NGA), and the Department of Homeland Security (DHS) on data management problems. 

I chair the steering committee of the Theory and Practice of Provenance (TaPP) and ProvenanceWeek. I am the recipient of the 2016 ACM SIGMOD Test of Time Award for my work on provenance. I run a Science Fair for the local primary schools to encourage the joy of research in budding scientists.

To apply for a PhD with me, please provide a research proposal that aligns with my interests and follow the ECS PhD application process.

Research

Research interests

Artificial Intelligence and Data Science are making huge strides in improving the human condition, helping us to make decisions and utilize resources better. We are creating a new economy - the data economy. In support of this, my research revolves around how to ensure that data required for artificial intelligence and data science can be found and used appropriately. This includes:

  1. Dataset retrieval: How can we discover datasets and rapidly assess which is suitable for a given task?
  2. Data improvement: Can we provide additional information that allows data to be better understood, such as provenance and annotations?
  3. Appropriate data usage: How can we ensure that the data chosen is used responsibly? To support algorithmic accountability, can we provide mechanisms that allow end users to understand the algorithmic impact of their data choices (e.g. to support the machine learning algorithms in the criminal justice system). Can we ensure that users's consent on their personal data is honored? 

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Telephone:
+442380593777
Email:
hmhc@ecs.soton.ac.uk

 

Professor Harold Chong plays an active role in the School of Electronics and Computer Science by contributing to;

  • Research excellence through publications in high quality journals, conference presentations, research grants, national and international research collaborations
  • Teaching excellence in module development and management, lecturing, tutoring, lab teaching and project supervision of undergraduate, postgraduate and PhD students
  • Admintration in undergraduate student recruitment, School laser safety management, Faculty level Collaborative Programme Sub-Committee
  • Enterprise in term of consultancy and research collaboration with industrial partners

Professor Harold Chong is a member of Sustainable Electronic Technologies Group in the School of Electronics and Computer Science at the University of Southampton. He is leading the research in thin film electronic and photonic devices technology using atomic layer deposition and hot-wire chemical vapour deposition method. His work has led to the lowest optical loss amorphous silicon material platform for multi-layer photonic circuits and novel development of oxide semiconductor nanowire electronics for environmental sensing and RF integrated circuits. Professor Chong has published more than 160 journal and conference papers in the areas of semiconductor nanowire electronic devices, Micro-Electro-Mechanical System, multi-layer photonic integrated circuits and microfabrication technology. Besides reserach, he lectures in the subject of semiconductor devices, electronics and microfabrication to undergraduate and postgraduate students. He is a visiting Professor at Japan Advanced Institute of Science and Technology.

Research

Research interests

Professor Harold Chong research areas are;

  • Ultra thin film and structured devices based on Group IV and oxide semiconductor materials for environmental sensing and high performance electronic circuit systems
  • Multi-layer and three dimensional photonic integrated circuits for high density optical routing and distribution network
  • Micro and nanoscale fabrication technology for integrated Micro-Electro-Mechanical System in resilient electronic applications

He maintains a team of 5 PhD students and 4 postdoctoral researchers.

Teaching

Professor Harold Chong is the module leader and lecturer of;

  • ELEC2201 Devices (Undergraduate level)
  • ELEC2205 Electronic Design (Undergraduate lab teaching)
  • ELEC6201 Microfabrication (Master level)
  • ELEC6226 Power Electronics for DC Transmission (Master level)
  • ELEC3200 Industrial Studies

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