Professor Aldo Faisal is the Professor of AI & Neuroscience at the Dept. of Computing and the Dept. of Bioengineering at Imperial College London. He was awarded a UKRI Turing AI Fellowship. Aldo is the Founding Director of the £20Mio. UKRI Centre for Doctoral Training in AI for Healthcare. He is the Elected Speaker of the Cross-Faculty Network in Artificial Intelligence representing AI in College on behalf of over 200 academic members.
At his two departments, Aldo leads the Brain & Behaviour Lab focussing on AI & Neuroscience and the Behaviour Analytics Lab at the Data Science Institute. He is Associate Investigator at the MRC London Institute of Medical Sciences and is affiliated faculty at the Gatsby Computational Neuroscience Unit (University College London).
Aldo serves as an Associate Editor for Nature Scientific Data and PLOS Computational Biology and has acted as conference chair, program/area chair, chair in key conferences in the field (e.g. Neurotechnix, KDD, NIPS, IEEE BSN). In 2016 he was elected into the Global Futures Council of the World Economic Forum.
Aldo received a number of awards and distinctions, including Scholar of the German National Merit Foundation (Studienstiftung des Deutsche Volkes; Undergraduate & PhD), a PhD Fellow of the Böhringer-Ingelheim Foundation for Basic Biomedical Research, elections as a Junior Research Fellow at the University of Cambridge (Wolfson College), and a number of research prizes and award such as the Toyota Mobility Foundation $50,000 Research Discovery Prize in 2018.
Aldo's lab featured regularly across global media (such as BBC, CNN, TED, TEDx, Wall Street Journal, Guardian, Financial Times , WIRED, Scientific American, New Scientist, etc.), e.g. in 2016 Scientific American voted his research on gaze-based control as 1st of 10 most transformative ideas of year.
Dr Faisal's labs are located in the Royal School of Mines building and combine cross-disciplinary computational and experimental approaches to investigate how the brain and behaviour evolved to learn and control goal-directed behaviour. The neuroscientific findings enable the targeted development of novel technology for clinical and research applications (Neurotechnology) for a variety of neurological/motor disorders and amputees. Key techniques include on the computational side are data-driven methods from machine learning & stochastic modelling techniques and experimentally we use sensorimotor expeirments, eye-tracking & kinematics (full-body, hands), non-invasive brain imaging (EEG, fNIRS), robotics (hand & arm robots).
Dr Faisal's Behaviour Analytics lab located in the Data Science Institute objective is the data-driven analysis of human behaviour and pioneering development of methods & algorithms to move in a principled manner from Big Data to Big Knowledge. Keys goals are, understand and predict human behaviour from ubiquitous sensors & digital data, predict and evaluate human performance, Infer internal or cognitive state (stress, risk) of individuals from behavioural dynamics, develop behavioural biomarkers of physiological and psychological well-being and bottom-up analysis of group and social dynamics from the decisions of individuals.
Biographical sketch: Aldo read Computer Science and Physics in Germany, where he wrote his Diplomarbeit (M.Sc. thesis) in non-linear dynamical systems and neural networks (with Helge Ritter). He moved on to study Biology at Cambridge University (Emmanuel College) and wrote his M.Phil. thesis on the electrophysiological and behavioural study of a complex motor behaviour in freely moving insects with Tom Matheson in the group of Malcolm Burrow FRS. For his Ph.D. he joined Simon Laughlin FRS group at the Zoology Department in Cambridge investigating the biophysical sources of neuronal variability. He was elected a Junior Research Fellow at Cambridge University (Wolfson College) and joined the Computational & Biological Learning Group (Engineering Department) to work with Daniel Wolpert FRS on human sensorimotor control. Between and after his studies he gained insights into strategic mangement consulting with McKinsey & Co. and as a "quant" with the investment bank Credit Suisse. In winter 2009 Aldo setup the Brain & Behaviour Lab at Imperial College to pursue a research program that aims at understanding the brain with principles from engineering which often immediately translates into direct technological applications for patients and society.
Note: It is strongly recommended to visit the Faisal Lab's web pages (www.FaisalLab.org) as that site is most up to date in terms of news and publications. Aldo’s research publications can be found at the tab above, or more up to date on Google Scholar.
Belic JJ, Faisal AA, 2015, Decoding of human hand actions to handle missing limbs in neuroprosthetics, Frontiers in Computational Neuroscience, Vol:9, ISSN:1662-5188
Neishabouri A, 2014, Saltatory conduction in unmyelinated axons: clustering of Na channels on lipid rafts enables micro-saltatory conduction in C-fibers, Frontiers in Neuroanatomy, ISSN:1662-5129
et al., 2014, The Complexity of Human Walking: A Knee Osteoarthritis Study, PLOS One, Vol:9, ISSN:1932-6203
Neishabouri A, Faisal AA, 2014, Axonal Noise as a Source of Synaptic Variability, PLOS Computational Biology, Vol:10, ISSN:1553-734X
et al., 2014, Acquisition of Paleolithic toolmaking abilities involves structural remodeling to inferior frontoparietal regions, Brain Structure & Function, ISSN:1863-2653, Pages:1-17
et al., 2013, The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas, Expert Review of Medical Devices, Vol:10, ISSN:1743-4440, Pages:661-673
Tavares G, Faisal A, 2013, Scaling-laws of human broadcast communication enable distinction between human, corporate and robot twitter users, PLOS One, Vol:8, ISSN:1932-6203
et al., 2013, The effect of cell size and channel density on neuronal information encoding and energy efficiency, Journal of Cerebral Blood Flow & Metabolism
Abbott WW, Faisal AA, 2012, Ultra-low-cost 3D gaze estimation: an intuitive high information throughput compliment to direct brain-machine interfaces, Journal of Neural Engineering, Vol:9, ISSN:1741-2560
et al., 2010, The Manipulative Complexity of Lower Paleolithic Stone Toolmaking, PLOS One, Vol:5, ISSN:1932-6203
Faisal AA, Wolpert DM, 2009, Near Optimal Combination of Sensory and Motor Uncertainty in Time During a Naturalistic Perception-Action Task, Journal of Neurophysiology, Vol:101, ISSN:0022-3077, Pages:1901-1912
Faisal AA, Selen LPJ, Wolpert DM, 2008, Noise in the nervous system, Nature Reviews Neuroscience, Vol:9, ISSN:1471-0048, Pages:292-303
Faisal AA, Laughlin SB, 2007, Stochastic Simulations on the reliability of action potential propagation in thin axons, PLOS Computational Biology, Vol:3, ISSN:1553-734X, Pages:783-795
Faisal AA, White JA, Laughlin SB, 2005, Ion-channel noise places limits on the miniaturization of the brain's wiring, Current Biology, Vol:15, ISSN:0960-9822, Pages:1143-1149
Faisal AA, Laughlin SB, 2004, The effect of ion channel noise on the propagating action potential wave form and its potential impact on synaptic transmission, Journal of Physiology - London, Vol:555P, ISSN:0022-3751, Pages:C49-C49
Faisal AA, Laughlin SB, 2002, Channel noise limits the minimum diameter of axons, Journal of Physiology - London, Vol:543, ISSN:0022-3751, Pages:21P-21P
Faisal AA, Matheson T, 2001, Coordinated righting behaviour in locusts, Journal of Experimental Biology, Vol:204, ISSN:0022-0949, Pages:637-648
et al., 2015, The Moveable Feast of Predictive Reward Discounting in Humans, 2nd Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
Ferrante A, Gavriel C, Faisal AA, 2015, Towards a brain-derived neurofeedback framework for unsupervised personalisation of Brain-Computer Interfaces, IEEE Neural Engineering (NER), Pages:162-165
Thomik AAC, Fenske S, Faisal AA, 2015, Towards sparse coding of natural movements for neuroprosthetics and brain-machine interfaces, IEEE/EMBS Neural Engineering (NER), IEEE Engineering in Medicine & Biology Society, Pages:938-941
et al., 2015, Towards neurobehavioral biomarkers for longitudinal monitoring ofneurodegeneration with wearable body sensor networks, IEEE Neural Engineering (NER), Pages:348-351
et al., 2015, Gaussian Process Regression for accurate prediction of prosthetic limb movements from the natural kinematics of intact limbs, IEEE Neural Engineering (NER), Pages:659-662
et al., 2015, Dynamic forward prediction for prosthetic hand control by integrationof EMG, MMG and kinematic signals, IEEE Neural Engineering (NER), Pages:611-614
Ferrante A, Gavriel C, Faisal AA, 2015, Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet Wavelets & Common Spatial Pattern Algorithms, IEEE Neural Engineering (NER), Pages:948-951
et al., 2015, Towards steering the chronnectome - on the potential of dynamic functional connectivity-based neurofeedback of large scale brain networks, Real-time Functional Imaging and Neurofeedback Conference
et al., 2015, Haptic SLAM for context-aware robotic hand prosthetics – simultaneous inference of hand pose and object shape using particle filters, 7th International IEEE EMBS Conference on Neural Engineering, IEEE, Pages:719-722
Gavriel C, Faisal A, 2014, A Comparison of Day-Long Recording Stability and Muscle Force Prediction between BSN-Based Mechanomyography and Electromyography, Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on, Pages:69-74