Dr Faisal is a Senior Lecturer in Neurotechnology (US equivalent: Associate Professor, tenured) jointly at the Dept. of Bioengineering and the Dept. of Computing at Imperial College London. He is also Associate Group Head at the MRC Clinical Sciences Center (Hammersmith Hosptial) and is affiliated faculty at the Gatsby Computational Neuroscience Unit (University College London).
Note: It is strongly recommended to visit the Faisal Lab's web pages (www.FaisalLab.com) as that site is most up to date in terms of news and publications.
Dr Faisal's lab combines cross-disciplinary computational and experimental approaches to investigate how the brain and its neural circuits are designed to learn and control goal-directed movements. 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 techinques include on the computational side machine learning & stochastic modelling techniques and experimentally we use psychophysics, eyetracking & kinematics, non-invasive electrophysiolog, robotic (with Brain-Computer Interfaces) and funcational imaging. We have featured regularly across global media (such as BBC Today Show, CNN, WIRED, TED, TEDx, New Scientist, Guardian, Times of India, etc.). In the acemic year 2013/2014 the lab comprised 15 Post-Docs and PhD students (see www.FaisalLab.com for more).
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.
Aldo received a number of awards and distinctions, including being scholar of the German National Merit Foundation (Studienstiftung des Deutsche Volkes;Undergraduate and PhD training), a Fellow of the Böhringer-Ingelheim Foundation for Basic Biomedical Research and elected as a Junior Research Fellow at the University of Cambridge (Wolfson College).
Aldo serves on the editorial board of PLoS Computational Biology (Impact Factor 2012: 4.9) and the board of the EPSRC Centre for Doctoral Training in Neurotechnology at Imperial College. Aldo’s research publications can be found at the tab above, or more up to date on Google Scholar.
et al., 2015, Decoding of human hand actions to handle missing limbs in neuroprosthetics, Frontiers in Computational Neuroscience, Vol:9, ISSN:1662-5188, Pages:27-11
et al., 2014, Saltatory conduction in unrnyelinated axons: clustering of Na+ channels on lipid rafts enables micro-saltatory conduction in C-fibers, Frontiers in Neuroanatomy, Vol:8, ISSN:1662-5129
et al., 2014, The Complexity of Human Walking: A Knee Osteoarthritis Study, PLOS One, Vol:9, ISSN:1932-6203, Pages:e107325-e107325
et al., 2014, Acquisition of Paleolithic toolmaking abilities involves structural remodeling to inferior frontoparietal regions, Brain Structure & Function, Vol:220, ISSN:1863-2653, Pages:1-17
et al., 2014, Axonal Noise as a Source of Synaptic Variability, PLOS Computational Biology, Vol:10, ISSN:1553-734X, Pages:e1003615-e1003615
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
et al., 2013, Scaling-laws of human broadcast communication enable distinction between human, corporate and robot Twitter users., PLOS One, Vol:8, ISSN:1932-6203, Pages:e65774-e65774
et al., 2013, The effect of cell size and channel density on neuronal information encoding and energy efficiency, Journal of Cerebral Blood Flow and Metabolism, Vol:33, ISSN:0271-678X, Pages:1465-1473
et al., 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, Pages:046016-046016
et al., 2010, The Manipulative Complexity of Lower Paleolithic Stone Toolmaking, PLOS One, Vol:5, ISSN:1932-6203, Pages:e13718-e13718
et al., 2007, Stochastic Simulations on the reliability of action potential propagation in thin axons, PLOS Computational Biology, Vol:3, ISSN:1553-734X, Pages:783-795
et al., 2001, Coordinated righting behaviour in locusts, Journal of Experimental Biology, Vol:204, ISSN:0022-0949, Pages:637-648
et al., 2008, Noise in the nervous system, Nature Reviews Neuroscience, Vol:9, ISSN:1471-003X, Pages:292-303
et al., 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
et al., 2005, Ion-channel noise places limits on the miniaturization of the brain's wiring, Current Biology, Vol:15, ISSN:0960-9822, Pages:1143-1149
et al., 2002, Channel noise limits the minimum diameter of axons, Journal of Physiology - London, Vol:543, ISSN:0022-3751, Pages:21P-21P
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
et al., 2015, Haptic SLAM for context-aware robotic hand prosthetics - simultaneous inference of hand pose and object shape using particle filters, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:719-722, ISSN:1948-3546
et al., 2015, Towards neurobehavioral biomarkers for longitudinal monitoring of neurodegeneration with wearable body sensor networks, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:348-351, ISSN:1948-3546
et al., 2015, Towards a brain-derived neurofeedback framework for unsupervised personalisation of Brain-Computer Interfaces, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:162-165, ISSN:1948-3546
et al., 2015, The Moveable Feast of Predictive Reward Discounting in Humans, 2nd Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
et al., 2015, Gaussian Process Regression for accurate prediction of prosthetic limb movements from the natural kinematics of intact limbs, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:659-662, ISSN:1948-3546
et al., 2015, Dynamic forward prediction for prosthetic hand control by integration of EMG, MMG and kinematic signals, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:611-614, ISSN:1948-3546
et al., 2015, Data-efficient hand motor imagery decoding in EEG-BCI by using Morlet Wavelets & Common Spatial Pattern Algorithms, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:948-951, ISSN:1948-3546
et al., 2015, Towards Sparse Coding of Natural Movements for Neuroprosthetics and Brain-Machine Interfaces, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), IEEE, Pages:938-941, ISSN:1948-3546
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., 2014, A comparison of day-long recording stability and muscle force prediction between BSN-based mechanomyography and electromyography, 11th International Conference on Wearable and Implantable Body Sensor Networks, IEEE, Pages:69-74