Professor Silvia Cirstea

Silvia Cirstea For media enquiries please contact the Press Team
Deputy Head of School

Advanced Computing Research Centre; Vision and Eye Research Institute

Faculty:
Faculty of Science and Engineering
School:
Computing and Information Science
Location:
Cambridge
Areas of Expertise:
Vision and eye research , Computer Science , Artificial Intelligence
Research Supervision:
Yes

Silvia carries out cross-faculty research in the areas of computer modelling, simulation and optimization for medical and engineering applications.

[email protected]

Background

Silvia’s research interests centre around the use of computational modelling and AI methods to understand, analyse and optimise complex systems and processes pertaining to physics, engineering and health sciences. Analysing and predicting the behaviour of complex systems paves the way to developing autonomous decision-making at scale for real world applications.

Her areas of expertise include data, signal and image processing, statistical, machine learning and AI methods, neural networks, numerical modelling and optimization of physical and industrial processes (electromagnetics, acoustics, quantum mechanics, workflows). Since 2009, she has worked with the Vision and Eye Research Institute (VERI) on the role of acoustic cues, like level and reverberation, as conveyors of information about the environment and to facilitate better navigation and assisted living of the visually impaired. She has a keen interest in digital healthcare and in the role responsible AI can play in prediction of disease, personalised care and assistive technologies.

She has worked on research projects funded by Innovate UK, the EU, UK Central Laboratory of the Research Councils, Radiocommunications Agency, Medical Research Council and British industry.

Research interests
  • Computer modelling and simulation
  • Artificial Intelligence (AI); explainable AI
  • Signal processing techniques for multimodal data fusion
  • Applications of AI and big data in health and medicine
  • Virtual environments
  • Acoustic modelling and echolocation
  • Assisted living for the visually impaired and the elderly
  • Navigation aids for the visually impaired
Areas of research supervision
  • Computational Modelling for engineering and medical applications
  • Applications of AI (medical imaging, forensic chemistry, digital healthcare)

Find out more about our Computing and Information Science PhD and exciting PhD project opportunities.

Teaching

Modules:

  • Introduction to Mathematical Techniques for AI
  • Advanced Analytical Techniques for AI
  • Analytical Techniques
  • Final Year Project

Courses:

Qualifications
  • PhD Imaging Technologies, De Montfort University
  • BSc and MSc Mathematics, University of Bucharest, Romania
  • PGCE Learning & Teaching in Higher Education, Anglia Ruskin University
Memberships, editorial boards
  • Member, Institution of Engineering and Technology (MIET)
  • Fellow, Higher Education Academy (FHEA)
Research grants, consultancy, knowledge exchange
  • Innovate UK CyberASAP ‘AI360Degree: Elevating FinTech Security with Advanced AI Protection and Automated Compliance’ (2024-2025)
  • Innovate UK CyberASAP ‘Enhancing AI Trustworthiness and security with AI360Degree: A comprehensive framework for ethical and secure AI systems’ (2024)
  • Innovate UK CyberASAP ‘QuantoSniff - a next generation cyber defense using quantum cryptography’ (2023)
  • Innovate UK 'Adaptive Learning for Zero Defects in Building Construction', TR Control Solution Ltd (2019-2020)
  • ERDF Innovation Bridge 'Investigate technical requirements to deploy Ophta software into a standalone portable diagnostic tool for diabetic retinopathy', Effective Solutions Ltd (2018) 
  • ERDF Innovation Bridge 'Investigate DSP (Digital Signal Processing) solutions to the triboelectric effect, and apply the chosen solution to a non-contact infrared temperature sensor', Irisense Ltd (2018)
  • EU FP7 Capacities Research for SMEs project ‘Echo2eco’ (FP7-SME-2011-286155, 'A novel sound absorption technology to enable energy efficient construction techniques and promote the health and wellbeing of occupants'), 2012-2014

Patent
US Patent  US10190312B2: Sound absorbing material, a method for production of the same and device for cutting apertures in the sound absorbing material; Inventors: Bjorn Andre Flotre, Silvia Cirstea, Edwin Robert Toulson: https://patents.google.com/patent/US10190312B2/en.

Selected recent publications

Yordanov D, Chakraborty A, Hasan MM, Cirstea S. A Framework for Optimizing Deep Learning-Based Lane Detection and Steering for Autonomous Driving. Sensors. 2024; 24(24):8099. https://doi.org/10.3390/s24248099

Yankova Y, Warren J, Cole MD, Cirstea S (2024), Use of optimized 1H selTOCSY for identification and individualization of petrol samples from fire debris, Forensic Chemistry,100614, ISSN 2468-1709, https://doi.org/10.1016/j.forc.2024.100614.

Pardhan, S; Raman, R; Moore, BCJ.; Cirstea, S; Velu, S; Kolarik, AJ. (2024) Effect of early versus late onset of partial visual loss on judgments of auditory distance, Optometry and Vision Science. 101(6):393-398, DOI: 10.1097/OPX.0000000000002125, Optometry and Vision Science (lww.com).

Yankova, Y, Cirstea, S, Cole, M, Warren, J (2024), Identification and Discrimination of Petrol Sources by Nuclear Magnetic Resonance Spectroscopy and Machine Learning in Fire Debris Analysis, Applied Sciences 14 (12), 5177, https://doi.org/10.3390/app14125177

Yankova, Y, Cole, M, Cirstea, S, Warren, J (2024), Individualization of petrol sources by high field Nuclear Magnetic Resonance Spectroscopy, Forensic Science International, 112103, https://doi.org/10.1016/j.forsciint.2024.112103

Nanwani, R, Hasan, Md, Cirstea, S (2023), Techniques used to predict climate risks: a brief literature survey, Natural Hazards, 118, 925-951, doi: https://doi.org/10.1007/s11069-023-06046-2

Ray, J., Wijesekera, L., Cirstea, S., 2022. Machine learning and clinical neurophysiology. J Neurology, doi: https://doi.org/10.1007/s00415-022-11283-9.

Kolarik, A. J., Moore, B. C. J., Cirstea, S., Raman, R., Gopalakrishnan, S., Pardhan, S., 2022. Partial visual loss disrupts the relationship between judged room size and sound source distance. Experimental Brain Research, 240:81–96, doi: https://doi.org/10.1007/s00221-021-06235-0.

Hameed, N., Shabut, A., Hameed, F., Cirstea, S., Hossain, A., 2021. Achievements of neural network in skin lesions classification, in State of the Art in Neural Networks and Their Applications, Elsevier Academic Press, London, ISBN: 978-0-12-819740-0.

Aggius-Vella, E., Kolarik, A.J., Gori, M., Cirstea, S., Campus, C., Moore, B.C.J., Pardhan, S., 2020. Comparison of auditory spatial bisection and minimum audible angle in front, lateral, and back space. Scientific Reports 10:6279, doi: https://doi.org/10.1038/s41598-020-62983-z.

Kolarik, A.J., Raman, R., Moore, B.C.J., Cirstea, S., Gopalakrishnan, S., Pardhan, S., 2020. The accuracy of auditory spatial judgments in the visually impaired is dependent on sound source distance. Scientific Reports 10:7169, doi: https://doi.org/10.1038/s41598-020-64306-8.

Hameed, N., Shabut, A., Hameed, F., Cirstea, S., Harriet, S., Hossain, A., 2020. Mobile-based Skin Lesions Classification Using Convolution Neural Network. Annals of Emerging Technologies in Computing (AETiC), 4(2):26-37, doi: 10.33166/AETiC.2020.02.003.

Hameed N., Hameed F., Shabut A., Khan S., Cirstea S., Hossain A., 2019. An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions. Computers, 8(3), 62; https://doi.org/10.3390/computers8030062.

Kolarik, A. J., Pardhan, S., Cirstea, S., Moore, B. C. J., 2017. Auditory spatial representations of the world are compressed in blind humans. Experimental Brain Research, 235(2), 597-606. doi: 10.1007/s00221-016-4823-1.

Kolarik, A. J., Moore, B. C. J., Zahorik, P., Cirstea, S., Pardhan, S., 2016. Auditory distance perception in humans: a review of cues, development, neuronal bases, and effects of sensory loss. Attention, Perception, and Psychophysics, 78(2), 373-395. doi: 10.3758/s13414-015-1015-1.

Kolarik, A. J., Cirstea, S., Pardhan, S., Moore, B. C. J., 2014. A summary of research investigating echolocation abilities of blind and sighted humans. Hearing Research, 310, 60-68. doi: 10.1016/j.heares.2014.01.010.

Recent presentations and conferences

Chakraborty, A, Hubbard, T, Cirstea, S (2024) A Deep Transfer Learning Approach For Lung Tumour Detection With Resilience Testing Under Suboptimal Conditions, IEEE International Conference on Industrial Technology (ICIT), 1-8, doi: 10.1109/ICIT58233.2024.10540913.

Hasan, Md, Plamthottathil, RK, Morshed, J, Sarkar, D, Hameed, N, Cirstea, S, (2023) Circulogy: An AI-Enabled Blockchain-Based e-Waste Management Framework Using Non-Fungible Tokens (NFT) to Achieve Net Zero and Imply the Circular Economy, IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-3, Doi: 10.1109/ICBC56567.2023.10174985.

Kolarik. A. J., Moore, B. C. J., Raman, R., Cirstea, S., Gopalakrishnan, S., Pardhan, S., 2020. Greater severity of visual loss is associated with larger auditory distance judgments, with poorer accuracy for closer sounds, J. Investigative Ophthalmology & Visual Science, 61(7), 4268, ARVO.

O’Reilly, J., Cirstea, S., Zhang, J., Cirstea, M., 2019. A novel development of acoustic SLAM, Proceedings of 2019 Joint International Conference OPTIM-ACEMP, p. 525-531, IEEE.

Bagnoli, M., Cirstea, S., 2017. Dynamic geometry- and material-dependent simulation of room impulse responses in a virtual gaming environment. Joint International Conference OPTIM-ACEMP, IEEE. doi: 10.1109/OPTIM.2017.7975118.