Danilo P. Mandic is a Professor in signal processing with Imperial College London, UK, and has been working in the area of nonlinear and multidimensional adaptive signal processing and bioengineering. He is a Fellow of the IEEE, member of the Board of Governors of the International Neural Networks Society (INNS) and has received several best paper awards in Brain Computer Interface. Prof Mandic runs the Smart Environments Lab at Imperial and has received President’s Award for excellence in postgraduate supervision at Imperial. His publication record includes more than 300 publications in journals and conferences and four research monographs

My research revolves around neural interfacing. In my work I have developed: a wireless implantable peripheral neural stimulation and recording system; a miniature FPGA headstage capable of neural recording, real-time spike sorting and long duration data logging; a new method of designing electrical neural stimulation waveforms; and am currently working on a miniature device capable of wirelessly monitoring all the body's vital signs (pulse, ECG, temperature, breathing, blood oxygen as well as EEG) that can be comfortably worn in your ear.

My research interest is in miniature neural interfaces and its applications. This includes full custom integrated implantable/wearable microsystems for neural recording/stimulation and other physiological signals acquisition. This involve many aspects in the filed of electronics including ASIC design, low power analogue/digital circuit design, RF circuit design, embedded system and PCB design. My long term research is towards successful applications of such interface in healthcare.

I am a postdoc working on statistical signal processing of quaternion- and complex-valued data. I received a Ph.D. degree in Electrical Engineering from Imperial College London in 2018 after obtaining M.Eng. and B.Eng. in Instrument Science and Technology from Beihang University, Beijing, China.

I’m pursuing my PhD degree in electrical engineering from Imperial College. My research interest is signal processing and pattern recognition for physiological data, such as electroencephalogram (EEG). I received the BEng. degree from the Department of Electrical Engineering and Bioscience, Waseda University, Japan, in 2014, and the MSc degree in communications and signal processing from Imperial College London, UK, in 2015.

I received an M.Eng degree in Electrical and Electronic Engineering from Imperial College London, UK in 2014. Since 2014, I have been working towards a Ph.D at Imperial College London in the department of Electrical and Electronic Engineering under professors Danilo Mandic and Tom Pike. His research interests include latent variable regression and instrumentation analysis for space applications. I am currently a Research Assistant at Imperial College London, working on the analysis and operations of the NASA InSight mission short period seismometer.

I received the M.Eng. degree in medical engineering from the University of Surrey, Guildford, U.K., in 2014. I have joined Prof. Mandic research team in 2015 and have been passionate about applying engineering principles to uncover preciously unseen physiological phenomena ever since.

I am currently pursuing PhD at the Department of Electrical and Electronic Engineering. In 2016, I received MRes in Advanced Computing from Imperial College London, UK, after obtaining a MSc in Intelligence Systems in Robotics and a BSc in Automation Control from ITMO University, Russia. My research focuses on analysis inherently N-dimensional arrays (tensors) with applications to biomedical and financial data. I have been the driving force behind HOTTBOX, a toolbox for tensor decompositions, visualisation, feature extraction and non-linear classification of multi-dimensional data.

I am currently working toward the Ph.D. degree in Electrical and Electronic Engineering, with a focus on statistical signal processing of nonstationary processes. I received the M.Eng. degree in Aeronautical Engineering from Imperial College London in 2016, which included an exchange semester at Massachusetts Institute of Technology. In parallel, I am also pursuing my passion for understanding the statistical determinants of global macroeconomic events. My research interests include deterministic systems, ergodic theory and multilinear algebra.

I obtained BSc degree in Telecommunication Engineering from the Polytechnic University of Turin in 2014 and an equivalent degree from the Tongji University of Shanghai in 2015. In 2016, I attained MSc degree in Communications and Signal Processing from Imperial College London, earning the Best Thesis Award. My research focuses on tensor decompositions for signal processing applications. In particular I study the integration of tensors and neural networks, support tensor machines for financial applications, and tensor networks and their taxonomy.

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My research interests are in finite element methods and signal processing for ultra-wearable physiological monitoring devices. I received an MSc Neurotechnology from Imperial College London in 2017 and a BSc Physics from the University of Leeds in 2015.

I received MEng degree in electrical and electronic engineering from Imperial College London in 2017 where I was on the Dean's list and was awarded the Governor's Prize for academic excellence. Currently, I am pursuing a PhD at Imperial College in Machine Learning and Signal Processing. My research is focused on the boundary between extracting latent information from multidimensional data and neural networks.

I am currently pursuing my PhD in non-Gaussian probabilistic models and generative models for signal processing in the Electrical and Electronic Engineering Department from Imperial College. I obtained both my MEng and BEng in the Electronic and Information Engineering Department in Beihang University, Beijing, China. My previous research focused on algorithm-based image and video signal processing, especially for the rate-distortion optimisation.

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I am a new PhD student in the department of Electrical and Electronic Engineering with a passion for biosignal processing and non-invasive wearable health technology. I received an MEng in Biomedical Engineering in 2018 from Imperial College, in which I specialised in the development of medical acoustic imaging algorithms. My current research is focused on in-ear electroencephalography and electrocardiography, and corresponding advances to fields such as sleep science.

I received my bachelor degree in Automation and master degree in Pattern Recognition and Intelligent Systems from National University of Defense Technology in China. Now I am a first-year PhD student at the Department of Electrical and Electronic Engineering, Imperial College London. My research interest lies in bio-electric signal processing and machine learning.

I am self-driven, ambitious and highly-motivated part-time PhD student at Imperial College London and data scientist associate at J.P. Morgan with previous front-office experience supporting several Rates trading desks. I graduated with a First-class MEng degree in the top 5% of the cohort. My current research lays in the intersection between Machine Learning and tensor decomposition/tensor network models with applications in finance. I am adaptable, always eager to learn and enjoy working in fast-paced and challenging environments.

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