This interdisciplinary project bridges between artificial intelligence, computational linguistics, computational and cognitive neuroscience. Driven by innovations and progress in artificial intelligence and computer vision, cognitive computational neuroscience has recently used deep learning models to unravel the features represented at different levels of the cortical hierarchy. Combining deep learning and brain population responses such as fMRI or EEG revealed that higher cortical areas along the ventral visual processing stream represent features that emerge in higher layers of deep convolutional networks trained on object categorization (see Yamins and DiCarlo, Nature Neuroscience, 2016). Likewise research has started to correlate features emerging in neural networks trained on auditory source sounds with neural representations along the auditory processing stream of the human brain. While the extent to which deep neural networks truly mimic the computations performed by human observers is still debated (e.g. see Lake, Ullman, Tenenbaum Gershman, 2017), the combination of deep neural network and human neuroimaging has already proven a powerful approach to further our understanding of the human brain. Conversely, differences and similarities in learning/training, computations and representations between human and artificial systems will inform and inspire further advances in artificial intelligence. To our knowledge research combining deep learning with brain imaging data has focused predominantly on processing of signals from single sensory modalities. Yet, in our natural environment our senses are constantly bombarded with many different sensory signals. For effective interactions with the world, human and artificial agents need to integrate information across multiple senses into coherent and more reliable representations. Most prominently, speech comprehension in noise is greatly facilitated when the observer combines the auditory speech signal with concurrent visual input, i.e. temporally correlated articulatory facial movements. In this project we will exploit the synergies between artificial intelligence and computational neuroscience to investigate how artificial and human systems generate speech representations from vision and audition. 1. We will train deep neural networks on speech (e.g. words) recognition independently and together on visual (i.e. articulatory movements) and auditory speech signals. We expect that lower layers in the neural network predominantly represent unisensory representations, while higher layers generate representations that combine information from vision and audition. We will then investigate the representations elicited by unisensory and audiovisual inputs (e.g. partial observability) and at different levels of signal to noise ratios and various distortions. 2. Using psychophysics we will investigate the representational space formed by human observers. How do human observers recognize speech input under unisensory and audiovisual contexts? How is their performance affected by different levels of noise or distortions? Do neural networks and human observers make similar or different confusions (e.g. comparison of confusion matrices)? 3. Combining fMRI and EEG we will investigate the neural representations across the visual and auditory processing hierarchies and relate those to the features that emerged in different layers of the neural network. Comparing human and machine learning performance on speech recognition tasks will provide insights into similarities and differences in the representations, computations and learning of human and artificial systems for speech recognition. The results will advance our understanding of the neural mechanisms underlying audiovisual speech recognition and inspire innovations in algorithms and training schemes of artificial systems for audiovisual speech recognition.

Life Course theory, which examines the interactive roles of gender, age and status in the process of growingup, has fundamentally impacted historical approaches to Roman society (Harlow and Laurence 2002), but rarely features in Byzantine Studies. Envisaging the ageing process as more than the progression through a series of biologically-differential conditions, my thesis provides the first analysis of the Life Course in Late Byzantium (CE 1204-1453). It explores how cultural variants construct shifting personal, familial and wider social identities throughout the course of life, and thus shape forms of human social experience and interaction. That the single existing Byzantine Life Course study (Davies 2013) excluded the Late period reflects its historical and evidential distinctiveness. Late-period population fluctuation, increased social mobility, and fluid geo-political borders fostered the creation of a significantly altered Byzantine social world, which demanded the negotiation of new, dynamic social identities (Kondyli 2013). By harnessing a broad range of sourcetypes, both textual and material, this interdisciplinary project investigates these different identities' placement and development within the Life Course amongst various socioeconomic classes. It thus challenges modern tendencies to view Byzantine and other pre-modern societies as static, andcontributes new, class-sensitive material to the fields of medieval gender and social history, still heavily weighted toward studies of the elite. My BA and MA dissertations focus on gender's role in Byzantine romance, the latter emphasising its relation to age and social roles. During my BA, which I tailored towards gender and social history modules, I received prizes for excellence in Byzantine Studies and my dissertation. Through my MA I develop methodological skills, such as Greek language and the ability to analyse various source-types, pertinent to Byzantine Studies.

By using metamaterials and metasurfaces as the platform, this proposal focuses on the novel topological physics and applications introduced by Berry phase. The flexibility in engineering the artificial ‘atoms’ and ‘molecules’ of metamaterials provides unlimited possibilities to create new structural effect where symmetry (or symmetry breaking) and topology play critical roles. We are particularly interested in the role Berry phase plays in various nontrivial surface optical effects, including topological surface states and spin Hall effect of light. The investigation of the scattering immune surface states in a topological metamaterial, i.e. an effective medium approach, acts to unify the spin Hall effect of light with the more unconventional scheme of topological orders and protected surface states. We will further exploit Berry phase in the nonlinear regime, in particular harmonic generations, to control the nonlinear coefficients to an unprecedented level. Hence our study on Berry phase in the nonlinear regime will point to a new research direction on nonlinearity coefficient engineering, which will have important impact in the area of nonlinear optics. The proposal also investigates into practical applications brought by a novel type of geometrical metasurfaces, where the phase and hence the wavefront are finely controlled by the Berry phase in a highly robust manner. The proposal involves the development of innovative synthesis technologies, theoretical analysis, numerical simulations, experimental characterizations, and device development. The new symmetry and topological effects in this research will greatly impact a number of disciplines including material science, condensed matter physics and photonics.

Cardiovascular disease (CVD) affects nearly 18 million people globally (World Health Organization) as a result of heart attacks and strokes. The current treatment for CVD includes opening the restricted vessel through the use of stenting with a cylindrical tube made of a biocompatible metal or polymer, compressed and delivered by a catheter to the implant site. However, contemporary stents are severely limited as a result of poor biocompatibility, degradability, and manufacturing techniques. The 4D Stent project will utilize microstereolithography to produce biomaterials with controlled surface chemistries, bulk material properties, and possessing shape memory to give rise to 4D biomaterials, a potentially disruptive technological shift in medical device engineering. The produced stents will possess shape memory, controlled degradation and mechanical properties, and can be produced rapidly through photopolymerization. Thiol-ene click reactions, along with epoxide ring opening reactions, will be used to tailor biomaterial chemistries and engineer spatially-controllable printed prototypes, ultimately yielding stent surfaces that can be bio-orthogonally tailored to simulatenously recruit endothelial cells while preventing biofouling, all as post-polymerization processing. Here, Andrew Weems will combine his background in biomaterials engineering of shape memory materials with the synthetic expertise of Prof. Andrew Dove in the field of degradable polymers, and the practical cardiovascular surgical knowledge of Dr. Homer-Vanniasinkam to produce 4D stents of clinical relevance. Ultimately, 4D STENT has the potential to disrupt the medical device market, providing superior clinical support to European citizens and commercial entities by improving quality of life around the globe.