Professor Lyudmila Mihaylova

Department of Automatic Control and Systems Engineering

Professor of Signal Processing and Control

Lyudmila S Mihaylova
Profile picture of Lyudmila S Mihaylova
+44 114 222 5675

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Professor Lyudmila Mihaylova
Department of Automatic Control and Systems Engineering
Amy Johnson Building
Portobello Street
S1 3JD

Prof. Lyudmila Mihaylova and her team are working to develop novel methods for autonomous intelligent systems: for sensing, tracking and decision making, machine learning and their engineering applications. Prof. Mihaylova undertook pioneering work on traffic flow estimation with particle filtering for intelligent transportation systems which was followed later with developments for large scale systems, including large scale transportation and video processing systems. She has experience with a range of image modalities, including optical, thermal, LIDAR, SAR and hyperspectral image processing. Previously she had academic positions with Lancaster University (2006-2013), University of Bristol (2004-2006), and research visiting positions with the University of Ghent, Belgium, the Katholic University of Leuven, Belgium and the Bulgarian Academy of Sciences, Bulgaria.

Her interests are in the area of nonlinear filtering, sequential Monte Carlo Methods, statistical signal processing and sensor data fusion. Her work involves the development of novel techniques, e.g. for high dimensional problems (including vehicular traffic flow estimation and image processing) and localisation and positioning in sensor networks. Prof. Mihaylova is an Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems and an Associate Editor of Elsevier Signal Processing Journal. Prof. Mihaylova is a senior member of the IEEE, Signal Processing Society, the President of the International Society of Information Fusion (ISIF) and an ISIF board member.

Prof. Mihaylova has also been serving the scientific community as a member of the Programme/ Organising Committee of international conferences and symposia, including the International Conferences on Information Fusion, the American Control Conferences, EUSIPCO, conferences on Intelligent Transportation Systems and the German workshops on Multiple Sensor Data Fusion. She has given a number of invited talks, e.g. the keynote speech for the 5th IET International Conference on Wireless, Mobile and Multimedia Networks (2013), Beijing, China, and tutorials including for the EU Marie Curie ITN (2010, Sweden, 2012, Germany) and COST-NEARCTIS workshop (2010, Switzerland). Her research is funded by sponsors such as EPSRC, EU, MOD and industry.

Research interests

Broad research in the areas of signal processing, Bayesian methods, Monte Carlo methods, nonlinear estimation, target tracking, sensor data fusion, control, autonomous and complex systems (e.g. image and video processing, transportation systems, large scale systems) – both at theoretical and applied level. Assisted living and eHealth systems is another application area of my research.

My research group

We share knowledge, we grow. We actively work as a team.

Research group

Postdoctoral researchers

  • Peng Wang, Efficient Methods for Dealing with Big Data and Complex Large Scale Systems, funded by the CSI-Cobot and ShiRAS projects
  • Zhenglin Li, Machine Learning and IoT Methods for Automated Analysis of Medical Data, funded by the IoT4Healthy Sleep Pitch-In project
  • Waqas Aftab, Machine Learning Methods for CCTV Surveillance, funded by the In2Stempo project
  • James Douthwaite, Cooperative Robot Manufacturing, funded by the CSI-Cobot project

PhD students

  • Fodio Longman, Machine Learning Methods for Oil Spill Segmentation in Remote Sensing Images, revising the thesis, after a PhD viva, minor corrections
  • Kennedy Offor, Sensor Data Fusion for Improving Traffic Mobility in Smart Cities, PhD viva soon
  • Mahed Javed, Deep Learning Methods for Detection and Classification, a 3rd year PhD student
  • Richard Rudd-Orthne, Artificial Intelligence Methods to Mission Critical Systems and Automated Threat Analysis, second year PhD student
  • Yueda Lin, Machine Learning Methods for Autonomous Systems, start in September 2018
  • Yifei Zhu, Machine Learning Methods for Autonomous Systems, start in September 2018
  • Fethi Candan, Methods for Coordination and Control of a Swarm of UAVs, Start in September 2019
  • Rui Zhang, Simultaneous Localisation and Mapping of PipeBots for Pipe Networks, Start in November 2019
  • Jingxuan Su, Machine Learning Methods for Smart Farms, Start in November 2019
  • Rohit Chakraborty, Methods for Air Pollution Monitoring and Prediction, co-supervised with Prof. Martin Mayfield and Prof Tony Ryan, Start in October 2018
  • Brian Peach, Data Mining for Intelligent Transport Road Networks, co-supervised with Prof. Gwilym Pryce and Dr Mauricio Alvarez

 Alumni from Sheffield

  • Hayder Amer, Novel Internet of Vehicles Approaches for Smart Cities, PhD in July 2019
  • Danil Kuzin, Sparse Machine Learning Methods for Autonomous Decision Making, PhD degree, March, 2019
  • Maria Luisa Davila Garcia, Image Processing Methods for Automatic in-vitro Morphology Analysis, PhD degree, December, 2018
  • Chao Liu, Dealing with Incomplete Data for Localisation in Complex Envoronments, PhD degree, October 2018
  • Ruilong Chen, Machine Learning Methods for Autonomous Object Recognition and Restoration in Images, PhD degree, September 2018
  • Aroland Kiring, Shrinkage Based Particle Filters for Tracking in Wireless Sensor Networks with Correlated Sparse Measurements, 2014-2018, PhD degree, April 2018
  • Olga Isupova, Machine Learning Methods for Behaviour Analysis in Video, funded by the EU Marie Curie TRAX project, 2014-2017, PhD in August 2017
  • Allan de Freitas, Sequential Monte Carlo Methods for Tracking Groups an Extended Objects in Complex Environments, funded by the EU Marie Curie TRAX project, 2014-2017, PhD in July 2017
  • Youngjoo Kim, Efficient Methods for Dealing with Big Data and Complex Large Scale Systems, Postdoctoral researcher from June 2018 till February 2019
  • Matthew Hawes, Efficient Methods for Dealing with Big Data, funded by the BTaToT and EU SETA projects, Postdoctoral researcher, Sept. 2014 - Dec. 2017
  • Naveed Salman, Postdoctoral researcher, May 2014 - June 2016
  • Atta ur-Rehman, Postdoctoral researcher, Dec. 2013 - Jan. 2016, now Assistant Professor at the at University of Engineering and Technology, Lahore, Pakistan
  • Nikolay Petrov, PhD student (2009-2013) and Postdoctoral researcher, August 2013 - August 2014

Other graduated PhD students

  • Nickolay Petrov, Anna Zvikhachevskaya, Mahsa Honary, Chris Nemeth, Ali Arshad, Dennis Rodionov

I am grateful to the sponsors of my research: EPSRC, MoD/DSTL, EU, industrial and other partners. A brief description of projects of mine is given below.

  • NSF-EPSRC: ShiRAS. Towards Safe and Reliable Autonomy in Sensor Driven Systems, EP/T013265/1, PI, 2019-2023, £220,246.
    • Modern data-driven algorithms trained over enormous datasets have revolutionised contemporary autonomous systems with their accurate predictive power. However, due to technical limitations, it is a challenge to integrate large-scale data from many different and complex sensors. Capturing the confidence of these algorithms also remains a challenge. In response to this demand, ShiRAS will develop pioneering approaches that will introduce autonomy at different levels in sensor-driven systems.
    • The main focus is on machine learning methods with quantified uncertainty of the provided solutions. Within the field of machine learning, deep learning approaches have resulted in the state-of-the-art accuracy in visual object detection, speech recognition and translation, and many other domains. Deep learning can discover intricate structure in large data sets by using multiple levels of representation, where each level is a higher, more abstract representation of the data. However, a rigorous mathematical framework for uncertainty propagation and update in machine learning models has been largely underexplored.
    • Most current deep learning techniques process the raw data in a deterministic way and do not capture model confidence or trust. Uncertainty can emanate from the noise in the raw data and the parameters of the approach and this impact is a critical part for any predictive system's output. By representing the unknown parameters using distributions instead of point estimates and propagating these distributions from the input to the output of the system, we propose promising machine learning methods able to handle uncertainty in a unified way.
  • Confident safety integration for Cobots (CSI: Cobot), 2019-2020, funded by Lloyd's Register Foundation, co-I, PI Dr James Law, jointly with other co-Is Prof. John Clark, Dr Jonathan Aitken, Dr Radu Calinescu (University of York) and Dr Rob Alexander (University of York).
    • This project will demonstrate how novel safety techniques can be applied to build confidence in the deployment of uncaged collaborative robot systems operating in spaces shared with users. Existing collaborative processes provided by our industrial partners will act as case studies and demonstrators. These vary in complexity, but are suitably constrained in that they provide a tractable safety problem whilst providing a good representation of current industry applications and needs.
  • Internet of Things for Healthy Sleep, Pitch-In project (Oct. 2019-May 2020), PI, with Dr Mahnaz Arvaneh, co-I, £60,000. This is a joint project with the University of Oxford and Sheffield Teaching Hospital.
  • Internet of Things for Overcome Barriers in the Steel Rolling Measurement Technology, Knowledge Exchange project (Feb.- Oct. 2019), PI, co-Is Dr Peng Wang and Dr Mahnaz Arvaneh, £42,000
Teaching activities
  • AER324 Aircraft Dynamics and Control
  • ACS61012 Machine Vision

Past teaching

  • ACS6123 Intelligent and Vision Systems
  • ACS402 Industrial Training Program in Avionics and Autonomous Systems
Professional activities and memberships
  • Member, EPSRC Peer Review College, 2012 –
  • Elected as the president of the International Society of Information Fusion in March 2016, On the Board of Directors of ISIF, since 2011
  • Associate Editor for the IEEE Transactions on Aerospace and Electronic Systems, since 2011
  • Associate Editor of Elsevier Signal Processing Journal, since 2008
  • On the Editorial Board of Intelligent Decision Technologies. An International Journal, from 2011
  • Associate Editor for the IEEE Intelligent Transportation Systems Conference, Washington DC, USA, Oct. 5-7, 2011
  • Associate Editor for the American Control Conferences, since 2008 till present


Available code

Some recent invited talks

  • Plenary talk at the "First Joint International Conference on Design and Construction of Smart Cities Components", , Cairo, Egypt, 17-19 December 2019
  • Invited speaker, Science and Technology Organisation Specialists’ Meeting on “Artificial Intelligence for Military Multisensor Fusion Engines NATO-SET-262, 5-6 November 2018, Budapest, Hungary
  • Invited speaker, Northwestern Polytechnical University, Xi'an, and Chongqing University of Post and Telecommunications, China, July, 2017
  • Invited speaker, the 4th International Conference on Control Engineering & Information Technology (CEIT-2016)
  • Invited speaker for the Advances and Innovations on Traffic Modeling and Control, Control Summer School of GIPSA-Lab, CNRS, 12-16 Sept 2016, Grenoble, France
  • Invited talk at the Allan Turing Scoping Workshop: "Recursive Bayesian Estimation with Big Data", 10-12 February 2016, British Library, London
  • Invited talks for the "Advances and Innovations on Traffic Modeling and Control", Control Summer School of GIPSA-Lab, CNRS, 12-16 Sept 2016, Grenoble, France
  • Invited talk: "Sequential Monte Carlo Methods for Tracking and Inference in Intelligent Transportation Systems", Institute of Pure and Applied Mathematics (IPAM) Workshop II: Traffic Estimation, University of California, UCLA, Los Angeles, October 12 - 16, 2015
  • Plenary talk: "Recent Advances in Bayesian Methods for Tracking Groups, Extended Objects and Challenges with Big Data", IEEE Sensor Data Fusion Workshop, 6-8 October, Bonn, Germany 2015.
  • Invited Talk: "Machine Learning for Autonomous Systems and Knowledge Extraction from Big Data", Queen Mary University of London, UK, 23 Oct 2014
  • Plenary talk: "Advances and Challenges to Localisation in Wireless Sensor Networks'', The IET 5th International Conference on Wireless, Mobile and Multimedia Networks, (ICWMMN2013), Beijing, China, Nov. 22-25, 2013.
  • Invited talk: "Modelling, Estimation and Control for Intelligent Transportation Systems'', Jiatong University, Beijing, China, 25 November 2013.