index.json

[{"authors":["chen-chen"],"categories":null,"content":"Dr Chen (Cherise) Chen obtained her MSc and Ph.D. degree in Advanced Computing from Imperial College London. From 2022, she worked as a research associate at Imperial College London. She was also a research scientist at HeartFlow. After that, she joined Oxford BioMedIA group, University of Oxford in 2023. She joined University of Sheffield on November, 2023.\nDr. Chen’s research primarily revolves around the intersection of artificial intelligence (AI) and healthcare. Her focus is particularly strong in the domains of medical multi-modal data analysis (e.g, image, signal, text) with machine learning. Her work aims to develop and validate robust, data-efficient, and reliable machine learning algorithms that can enhance the scalability of AI-driven medical data analysis in practical applications.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"9172eaf349d6da57a2c668e2a2573ad5","permalink":"https://shef-AIRE.github.io/author/chen-chen/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/chen-chen/","section":"authors","summary":"Dr Chen (Cherise) Chen obtained her MSc and Ph.D. degree in Advanced Computing from Imperial College London. From 2022, she worked as a research associate at Imperial College London. She was also a research scientist at HeartFlow.","tags":null,"title":"Chen Chen","type":"authors"},{"authors":["haiping-lu"],"categories":null,"content":"Haiping is the Head of AI Research Engineering, a Professor of Machine Learning and AI Strategy Lead at the Department of Computer Science, the Turing Academic Lead, and Insigneo Research Director for Healthcare Data / AI, at the University of Sheffield. He is also the Machine Learning Theme Lead for Sheffield in the N8 Centre of Excellence in Computationally Intensive Research and the lead organiser of the Alan Turing Institute’s interest group on Meta-learning for multimodal data.\nHe received his Ph.D. degree in Electrical and Computer Engineering from the University of Toronto, Canada, in 2008. Prior to that, he received his Bachlor’s and Master’s degrees in Electrical and Electronics Engineering from Nanyang Technological University, Singapore, in 2001 and 2004, respectively. See his homepage for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"ab777c901708e58297c4b8065861f567","permalink":"https://shef-AIRE.github.io/author/haiping-lu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/haiping-lu/","section":"authors","summary":"Haiping is the Head of AI Research Engineering, a Professor of Machine Learning and AI Strategy Lead at the Department of Computer Science, the Turing Academic Lead, and Insigneo Research Director for Healthcare Data / AI, at the University of Sheffield.","tags":null,"title":"Haiping Lu","type":"authors"},{"authors":["haolin-wang"],"categories":null,"content":"Haolin is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHaolin received her Master’s degree in Data Analytics from the University of Sheffield in 2022. Prior to that, she received her Bachelor’s degree in Mathematics from the University of Oxford in 2021. See her LinkedIn for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"62e7858b08d7c933a0688fdcad1555a5","permalink":"https://shef-AIRE.github.io/author/haolin-wang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/haolin-wang/","section":"authors","summary":"Haolin is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHaolin received her Master’s degree in Data Analytics from the University of Sheffield in 2022.","tags":null,"title":"Haolin Wang","type":"authors"},{"authors":["jiayang-zhang"],"categories":null,"content":"Jiayang is an AI Research Engineer (AIRE) and the Deputy Assistant Head of AIRE at the University of Sheffield, under the lead of Prof. Haiping Lu.\nShe recieved her Bachelor’s and Master’s degree in Physics from Imperial College London. See her LinkedIn for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"fedb47a3f0c7ffe843a54ae9730ecbb1","permalink":"https://shef-AIRE.github.io/author/jiayang-zhang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jiayang-zhang/","section":"authors","summary":"Jiayang is an AI Research Engineer (AIRE) and the Deputy Assistant Head of AIRE at the University of Sheffield, under the lead of Prof. Haiping Lu.\nShe recieved her Bachelor’s and Master’s degree in Physics from Imperial College London.","tags":null,"title":"Jiayang Zhang","type":"authors"},{"authors":["mahjabin-islam"],"categories":null,"content":"Mahjabin Islam is a Neurology Registrar at Sheffield Teaching Hospitals and NIHR Academic Clinical fellow at SITraN (Sheffield Institute of Translational Neuroscience) with research interest in Motor Neurone Disease.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"9a9781d100c50fc8ba89e57137d329db","permalink":"https://shef-AIRE.github.io/author/mahjabin-islam/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/mahjabin-islam/","section":"authors","summary":"Mahjabin Islam is a Neurology Registrar at Sheffield Teaching Hospitals and NIHR Academic Clinical fellow at SITraN (Sheffield Institute of Translational Neuroscience) with research interest in Motor Neurone Disease.","tags":null,"title":"Mahjabin Islam","type":"authors"},{"authors":["mohammod-suvon"],"categories":null,"content":"Mohammod Suvon is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHe received his Master’s degree in Computer Science with Speech and Language Processing from the University of Sheffield in 2022. Prior to that, he received Bachelor’s degree in Computer Science and Engineering from North South University, Bangladesh, in 2020. See his LinkedIn for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"5f64c51798678951ee9649c4a14284b5","permalink":"https://shef-AIRE.github.io/author/mohammod-suvon/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/mohammod-suvon/","section":"authors","summary":"Mohammod Suvon is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHe received his Master’s degree in Computer Science with Speech and Language Processing from the University of Sheffield in 2022.","tags":null,"title":"Mohammod Suvon","type":"authors"},{"authors":["oliver-bandmann"],"categories":null,"content":"Professor Oliver Bandmann is the professor of movement disorders neurology. He is also a Honorary Consultant Neurologist, and Co-Director of Neuroscience Institute.\nHis research focuses on movement disorders, in particular Parkinson´s Disease (PD) but also Huntington´s Disease, Wilson Disease and dystonia. He is particularly interested in working towards disease-modifying therapy for PD which would slow down disease progression.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"809df297755676aad86d8c2f846a22a8","permalink":"https://shef-AIRE.github.io/author/oliver-bandmann/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/oliver-bandmann/","section":"authors","summary":"Professor Oliver Bandmann is the professor of movement disorders neurology. He is also a Honorary Consultant Neurologist, and Co-Director of Neuroscience Institute.\nHis research focuses on movement disorders, in particular Parkinson´s Disease (PD) but also Huntington´s Disease, Wilson Disease and dystonia.","tags":null,"title":"Oliver Bandmann","type":"authors"},{"authors":["shuo-zhou"],"categories":null,"content":"Shuo is an Academic Fellow in Machine Learning at the Machine Learning Research Group, Department of Computer Science, and Deputy Head of AI Research Engineering at the Centre for Machine Intelligence, University of Sheffield.\nHe received his Ph.D. degree in Computer Science and his Master’s degree in Advanced Computer Science from the University of Sheffield, in 2022 and 2017, respectively. See his homepage for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"4772d675c00b2f31aaf8cb577204167c","permalink":"https://shef-AIRE.github.io/author/shuo-zhou/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/shuo-zhou/","section":"authors","summary":"Shuo is an Academic Fellow in Machine Learning at the Machine Learning Research Group, Department of Computer Science, and Deputy Head of AI Research Engineering at the Centre for Machine Intelligence, University of Sheffield.","tags":null,"title":"Shuo Zhou","type":"authors"},{"authors":["thomas-payne"],"categories":null,"content":"Tom is a NIHR Clinical Lecturer in Neurology at the Sheffield Institute for Translational Neuroscience (SITraN). His research interest is Parkinson’s Disease.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"0525939b4fcae52c82278e75437facba","permalink":"https://shef-AIRE.github.io/author/thomas-w.-payne/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/thomas-w.-payne/","section":"authors","summary":"Tom is a NIHR Clinical Lecturer in Neurology at the Sheffield Institute for Translational Neuroscience (SITraN). His research interest is Parkinson’s Disease.","tags":null,"title":"Thomas W. Payne","type":"authors"},{"authors":["venet-osmani"],"categories":null,"content":"","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"cc34c4ca5b381e387b7ef2d0341fa779","permalink":"https://shef-AIRE.github.io/author/venet-osmani/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/venet-osmani/","section":"authors","summary":"","tags":null,"title":"Venet Osmani","type":"authors"},{"authors":["wenrui-fan"],"categories":null,"content":"Wenrui Fan is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHe received his Master’s degree in Robotics from the University of Sheffield in 2022. Prior to that, he received Bachelor’s degree in Flight Vehicle Design from Beijing Institute of Technology, China, in 2021. See his homepage and LinkedIn for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"acadd11b9c9de74e1b9f5a52196602b6","permalink":"https://shef-AIRE.github.io/author/wenrui-fan/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/wenrui-fan/","section":"authors","summary":"Wenrui Fan is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHe received his Master’s degree in Robotics from the University of Sheffield in 2022.","tags":null,"title":"Wenrui Fan","type":"authors"},{"authors":["xianyuan-liu"],"categories":null,"content":"Xianyuan is an Assistant Head of AI Research Engineering at the Centre for Machine Intelligence and Senior AI Research Engineer at the University of Sheffield.\nHe received his Ph.D. degree in Signal and Information Processing from the University of Chinese Academy of Sciences, China, in 2023. Prior to that, he received his Bachelor’s degree in Measuring Control Technology and Instruments from Southeast University, China, in 2016. See his homepage and LinkedIn for more information.\n","date":1704067200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1704067200,"objectID":"8acea76f97d85ada6a9d70f0a9d1c54c","permalink":"https://shef-AIRE.github.io/author/xianyuan-liu/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/xianyuan-liu/","section":"authors","summary":"Xianyuan is an Assistant Head of AI Research Engineering at the Centre for Machine Intelligence and Senior AI Research Engineer at the University of Sheffield.\nHe received his Ph.D. degree in Signal and Information Processing from the University of Chinese Academy of Sciences, China, in 2023.","tags":null,"title":"Xianyuan Liu","type":"authors"},{"authors":["joshua-berry"],"categories":null,"content":"","date":1702512e3,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1702512e3,"objectID":"ceb36785e8b6ba033168e0d0d22ac317","permalink":"https://shef-AIRE.github.io/author/joshua-berry/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/joshua-berry/","section":"authors","summary":"","tags":null,"title":"Joshua Berry","type":"authors"},{"authors":["katerina-christofidou"],"categories":null,"content":"Katerina (Kathy) Christofidou is a Senior Lecturer in Metallurgy. She is currently the departmental UCAS admissions tutor and leads the Materials Discovery and Prototyping technology platform for the Royce Institute.\nKathy’s research focuses on bridging high performance alloy design and advanced manufacturing following two key strands, which are a) accelerated digitalised methods of alloy development with emphasis on manufacturing performance and b) advanced diffraction methods for non-destructive alloy evaluation applied to high performance components.\n","date":1702512e3,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1702512e3,"objectID":"cdb62e2d06137ea4eb476374e9d66804","permalink":"https://shef-AIRE.github.io/author/kathy-christofidou/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/kathy-christofidou/","section":"authors","summary":"Katerina (Kathy) Christofidou is a Senior Lecturer in Metallurgy. She is currently the departmental UCAS admissions tutor and leads the Materials Discovery and Prototyping technology platform for the Royce Institute.","tags":null,"title":"Kathy Christofidou","type":"authors"},{"authors":["nicola-morley"],"categories":null,"content":"Nicola Morley is the Head of Department of Materials Science and Engineering. She was appointed as Lecturer to the department in May 2005 and promoted to Senior Lecturer in January 2014 and Professor in January 2019.\nHer research centres on functional magnetic materials, including the understanding and development of magnetic films to be used in magnetic devices and sensors.\n","date":1702512e3,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1702512e3,"objectID":"e79a94e86f1ff7890bf769b2f8a797a8","permalink":"https://shef-AIRE.github.io/author/nicola-morley/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/nicola-morley/","section":"authors","summary":"Nicola Morley is the Head of Department of Materials Science and Engineering. She was appointed as Lecturer to the department in May 2005 and promoted to Senior Lecturer in January 2014 and Professor in January 2019.","tags":null,"title":"Nicola Morley","type":"authors"},{"authors":["donghwan-shin"],"categories":null,"content":"","date":1699228800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1699228800,"objectID":"b303f930d00768ab8a6e522cc267e369","permalink":"https://shef-AIRE.github.io/author/donghwan-shin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/donghwan-shin/","section":"authors","summary":"","tags":null,"title":"Donghwan Shin","type":"authors"},{"authors":["wei-xing"],"categories":null,"content":"","date":1699228800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1699228800,"objectID":"95e3c276302f7a9d61be1eec2ed6e9c5","permalink":"https://shef-AIRE.github.io/author/wei-xing/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/wei-xing/","section":"authors","summary":"","tags":null,"title":"Wei Xing","type":"authors"},{"authors":["andrew-segerdahl"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"798d4a8ed0ade368b132be003074f6e2","permalink":"https://shef-AIRE.github.io/author/andrew-segerdahl/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/andrew-segerdahl/","section":"authors","summary":"","tags":null,"title":"Andrew Segerdahl","type":"authors"},{"authors":["david-bennett"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"b4f9cfae8e33958c82286a35d575c228","permalink":"https://shef-AIRE.github.io/author/david-bennett/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/david-bennett/","section":"authors","summary":"","tags":null,"title":"David Bennett","type":"authors"},{"authors":["dinesh-selvarajah"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"618082d09e92a61173bdfc76db3fb6a3","permalink":"https://shef-AIRE.github.io/author/dinesh-selvarajah/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/dinesh-selvarajah/","section":"authors","summary":"","tags":null,"title":"Dinesh Selvarajah","type":"authors"},{"authors":["douglas-steele"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"8656cafc6a807d6f2c24efd7c58a8e41","permalink":"https://shef-AIRE.github.io/author/douglas-steele/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/douglas-steele/","section":"authors","summary":"","tags":null,"title":"Douglas Steele","type":"authors"},{"authors":["jim-wild"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"ea2ca0b68ae9f0d304414b345672fcaa","permalink":"https://shef-AIRE.github.io/author/jim-wild/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jim-wild/","section":"authors","summary":"","tags":null,"title":"Jim Wild","type":"authors"},{"authors":["leslie-colvin"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"afb4af23f5643cb836e36f5672793f47","permalink":"https://shef-AIRE.github.io/author/leslie-colvin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/leslie-colvin/","section":"authors","summary":"","tags":null,"title":"Leslie Colvin","type":"authors"},{"authors":["prasun-tripathi"],"categories":null,"content":"Prasun commenced his role as a Visiting Researcher at the University of Sheffield in February 2024. Prior to this, he served as a Postdoctoral Research Associate and a Senior AI Research Engineer at the University of Sheffield, from July 2022 to January 2024, under the leadership of Prof. Haiping Lu. He worked on a Wellcome Trust project for the diagnosis, prognosis, and treatment assessment of cardiovascular diseases from cardiac MRI.\nHe received his Ph.D. degree in Computer Science from Indian Institute of Technology, Dhanbad, in 2022. See his LinkedIn for more information.\n","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"61f26c892a7a09ff18dd94961c9c3483","permalink":"https://shef-AIRE.github.io/author/prasun-tripathi/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/prasun-tripathi/","section":"authors","summary":"Prasun commenced his role as a Visiting Researcher at the University of Sheffield in February 2024. Prior to this, he served as a Postdoctoral Research Associate and a Senior AI Research Engineer at the University of Sheffield, from July 2022 to January 2024, under the leadership of Prof.","tags":null,"title":"Prasun Tripathi","type":"authors"},{"authors":["solomon-tesfaye"],"categories":null,"content":"","date":1696118400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1696118400,"objectID":"cd19a57a5e609f5810d96286726cb4bf","permalink":"https://shef-AIRE.github.io/author/solomon-tesfaye/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/solomon-tesfaye/","section":"authors","summary":"","tags":null,"title":"Solomon Tesfaye","type":"authors"},{"authors":["alan-thomas"],"categories":null,"content":"Alan is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHe received his Master’s degree in Computer Science from the University of Manchester. His research focuses on the application of generative AI and foundation models across textual, visual, audio and spatial data types.\n","date":1688601600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688601600,"objectID":"2f28232c27c20f945fbbb7076cd54988","permalink":"https://shef-AIRE.github.io/author/alan-thomas/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/alan-thomas/","section":"authors","summary":"Alan is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nHe received his Master’s degree in Computer Science from the University of Manchester.","tags":null,"title":"Alan Thomas","type":"authors"},{"authors":["michael-pidd"],"categories":null,"content":"","date":1688601600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688601600,"objectID":"604c7aeceac408b6b45344ebd4080476","permalink":"https://shef-AIRE.github.io/author/michael-pidd/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/michael-pidd/","section":"authors","summary":"","tags":null,"title":"Michael Pidd","type":"authors"},{"authors":["robert-gaizauskas"],"categories":null,"content":"","date":1688601600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688601600,"objectID":"1b23802dbc1e44924bf554ffb22062e3","permalink":"https://shef-AIRE.github.io/author/robert-gaizauskas/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/robert-gaizauskas/","section":"authors","summary":"","tags":null,"title":"Robert Gaizauskas","type":"authors"},{"authors":["robert-shoemaker"],"categories":null,"content":"","date":1688601600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688601600,"objectID":"8e9465e0fd23814b5491828ef8878052","permalink":"https://shef-AIRE.github.io/author/robert-shoemaker/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/robert-shoemaker/","section":"authors","summary":"","tags":null,"title":"Robert Shoemaker","type":"authors"},{"authors":["valeria-vitale"],"categories":null,"content":"","date":1688601600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688601600,"objectID":"7e335b0798cb1d6a00595066b806c760","permalink":"https://shef-AIRE.github.io/author/valeria-vitale/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/valeria-vitale/","section":"authors","summary":"","tags":null,"title":"Valeria Vitale","type":"authors"},{"authors":["andrew-swift"],"categories":null,"content":"","date":1688515200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688515200,"objectID":"64f18b4817c89b6b8a652e4303d67216","permalink":"https://shef-AIRE.github.io/author/andrew-swift/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/andrew-swift/","section":"authors","summary":"","tags":null,"title":"Andrew Swift","type":"authors"},{"authors":["pete-metherall"],"categories":null,"content":"","date":1688515200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688515200,"objectID":"6d7c20b4daec60032a639984f0f584fa","permalink":"https://shef-AIRE.github.io/author/pete-metherall/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/pete-metherall/","section":"authors","summary":"","tags":null,"title":"Pete Metherall","type":"authors"},{"authors":["samer-alabed"],"categories":null,"content":"","date":1688515200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1688515200,"objectID":"0f96f0bca3c6c2a8ed933e29642c8a2a","permalink":"https://shef-AIRE.github.io/author/samer-alabed/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/samer-alabed/","section":"authors","summary":"","tags":null,"title":"Samer Alabed","type":"authors"},{"authors":["jose-casamayor"],"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"81dd2e8973e7ea0e7ffb7a94cad7ff23","permalink":"https://shef-AIRE.github.io/author/jose-l.-casamayor/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jose-l.-casamayor/","section":"authors","summary":"","tags":null,"title":"Jose L. Casamayor","type":"authors"},{"authors":["riza-rizky"],"categories":null,"content":"Riza is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nRiza recieved his Master’s degree in Artificial Intelligence from The University of Edinburgh. See his LinkedIn for more information.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"8e5a8210fa3b9e79edd016e11e8bd781","permalink":"https://shef-AIRE.github.io/author/lalu-muhammad-riza-rizky/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/lalu-muhammad-riza-rizky/","section":"authors","summary":"Riza is an AI Research Engineer (AIRE) at the University of Sheffield, under the lead of Prof. Haiping Lu.\nRiza recieved his Master’s degree in Artificial Intelligence from The University of Edinburgh.","tags":null,"title":"Lalu Muhammad Riza Rizky","type":"authors"},{"authors":null,"categories":null,"content":"Nelson Bighetti is a professor of artificial intelligence at the Stanford AI Lab. His research interests include distributed robotics, mobile computing and programmable matter. He leads the Robotic Neurobiology group, which develops self-reconfiguring robots, systems of self-organizing robots, and mobile sensor networks.\nLorem ipsum dolor sit amet, consectetur adipiscing elit. Sed neque elit, tristique placerat feugiat ac, facilisis vitae arcu. Proin eget egestas augue. Praesent ut sem nec arcu pellentesque aliquet. Duis dapibus diam vel metus tempus vulputate.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://shef-AIRE.github.io/author/nelson-bighetti/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/nelson-bighetti/","section":"authors","summary":"Nelson Bighetti is a professor of artificial intelligence at the Stanford AI Lab. His research interests include distributed robotics, mobile computing and programmable matter. He leads the Robotic Neurobiology group, which develops self-reconfiguring robots, systems of self-organizing robots, and mobile sensor networks.","tags":null,"title":"Nelson Bighetti","type":"authors"},{"authors":["Wenrui Fan","Thomas W. Payne","Mohammod Suvon","Xianyuan Liu","Mahjabin Islam","Haolin Wang","Jiayang Zhang","Venet Osmani","Chen Chen","Shuo Zhou","Oliver Bandmann","Haiping Lu"],"categories":null,"content":"University of Sheffield Collaborating Faculties: Sheffield Institute for Translational Neuroscience (SITraN) and Faculty of Engineering\nOverview: Our project is dedicated to the development of artificial intelligence tools for Parkinson’s Disease, designed to elucidate the underlying mechanisms of the condition and predict its progression. This initiative integrates data from multiple modalities—including genetic data, biomarkers, environmental factors, and medical examinations—utilizing advanced AI methodologies such as contrastive learning, foundation models, and causal discovery.\nMotivation: The growing interest in applying artificial intelligence (AI) to tackle Parkinson’s Disease reflects a comprehensive appreciation of the condition’s widespread impact, its escalating incidence, the existing gaps in our comprehension, and the extraordinary research opportunities afforded by current data repositories.\nAs the second most prevalent neurodegenerative disorder in the UK, Parkinson’s Disease presents a formidable public health challenge. Its widespread nature underlines the pressing demand for novel treatment and management strategies, positioning AI as an ideal candidate to drive forward innovative solutions. Globally, the frequency of Parkinson’s Disease is on the rise, affecting an ever-growing number of individuals either directly or putting them at a significant risk of developing the condition. This increasing trend accentuates the urgent necessity for interventions that are both scalable and efficacious, areas where AI technology shines with potential. Despite thorough research efforts, the core mechanisms behind Parkinson’s Disease remain a mystery. Here, AI’s ability to sift through and analyze large, complex datasets could unlock new understanding, setting the stage for transformative developments in how we treat and prevent the disease.\nThe UK Biobank, with its comprehensive data, emerges as a pivotal resource for AI research. The extensive scope of this data lays a robust groundwork for the creation and refinement of AI models, fostering considerable progress in our grasp and handling of Parkinson’s. Leveraging AI, researchers are not just shedding light on the elusive causes of Parkinson’s Disease but are also crafting predictive models that foresee the disease’s trajectory, pinpoint therapeutic targets, and ultimately, instill hope in the countless lives touched by this afflictive illness.\nThus, the convergence of AI and Parkinson’s Disease research heralds an exciting era in the battle against neurodegenerative diseases. It offers a beacon of hope for enhancing patient outcomes, marking a vital step forward in our journey towards understanding, managing, and eventually overcoming such conditions.\n","date":1704067200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1704067200,"objectID":"72c55507bdbb0b1ee307a95b1ea1106e","permalink":"https://shef-AIRE.github.io/project/parkinson/","publishdate":"2024-01-01T00:00:00Z","relpermalink":"/project/parkinson/","section":"project","summary":"Utilizing advanced artificial intelligence to process multimodal data to enhance the diagnosis and prognosis of Parkinson's Disease, paving the way to uncover the underlying mechanisms of Parkinson's Disease and improve its prediction.","tags":["Healthcare and medicine","Multimodal AI"],"title":"Multimodal AI for Parkinson's Disease","type":"project"},{"authors":["Xianyuan Liu","Joshua Berry","Kathy Christofidou","Nicola Morley","Haiping Lu"],"categories":null,"content":"University of Sheffield Collaborating Faculties: Faculty of Engineering, Royce Institute\nOverview: This project uses AI to accelerate the discovery of new materials crucial for green technologies. It explores both data and modelling aspects to predict material composition and properties, through two case studies: permanent magnetic materials and novel corrosion-resistant coatings. The project uses machine learning algorithms for predictions, drawing on both existing and self-generated databases enriched by natural language processing. It addresses inherent challenges like data scarcity and imbalance through data augmentation and the integration of machine learning with physics-based models. The project will deliver high-quality methodologies, open-source code, and a user-friendly interface to broaden the application of these predictive capabilities. This has the potential to revolutionize materials discovery by enabling the software to autonomously predict material properties from specified compositions, paving the way for significant breakthroughs.\nMotivation: The demand for sustainable technologies requires the discovery of novel materials with superior properties. Traditional methods for materials discovery are time-consuming and resource-intensive due to their reliance on physical experiments and iterative testing, limited by both practical laboratory setups and human imagination. This project bridges this gap by harnessing the power of AI, using machine learning algorithms, digital databases, and computational tools. This significantly reduces the time it takes to translate a concept into a material with desired properties. By enabling autonomous prediction of material properties, this project has the potential to revolutionize the field of materials science. In the short term, it can accelerate the discovery cycle, leading to faster development of solar cells and energy-efficient materials. In the long term, this project could pave the way for entirely new materials with unforeseen properties, ultimately propelling advancements across diverse fields like aerospace, medicine, and electronics.\n","date":1702512e3,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1702512e3,"objectID":"6886d47a8ec06aed81d6b0b9c1d91212","permalink":"https://shef-AIRE.github.io/project/digital-materials-discovery/","publishdate":"2023-12-14T00:00:00Z","relpermalink":"/project/digital-materials-discovery/","section":"project","summary":"Using digital databases and advanced AI to predict new, sustainable materials for green technologies, accelerating the shift towards net-zero emissions and driving substantial progress in global sustainability initiatives.","tags":["Multimodal AI","Foundation Model"],"title":"Digital Materials Discovery","type":"project"},{"authors":["Haolin Wang","Wei Xing","Donghwan Shin","Jose L. Casamayor","Haiping Lu"],"categories":null,"content":"University of Sheffield Collaborating Faculties: Faculty of Science, Faculty of Engineering and AMRC\nOverview: Our project aims to investigate novel methods to leverage a special type of multimodal data—multi-fidelity data—to improve AI model accuracy and efficiency, leading to scalable solutions in computationally intensive optimisation problems in various engineering disciplines.\nMotivation: Our goal is to create innovative techniques and toolsets capable of assimilating and being able to use multi-fidelity data effectively (i.e., providing the same outputs of high-fidelity datasets using lower-fidelity datasets), such as simulation outcomes from both coarse and dense meshes, and signals from high-cost precision sensors and low-cost basic sensors. We aim to enhance AI model accuracy without relying on highly accurate data obtained from high-cost precision sensors only, whilst improving efficiency metrics such as training time and memory cost. We will implement the proposed method in simulation-based testing for autonomous driving systems (ADS) to demonstrate the capability for the safety and reliability of self-driving cars with low-cost computation.\n","date":1699228800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1699228800,"objectID":"8fd1f03b77728deb1bc95f9d104e2a6d","permalink":"https://shef-AIRE.github.io/project/multifidelity-fusion/","publishdate":"2023-11-06T00:00:00Z","relpermalink":"/project/multifidelity-fusion/","section":"project","summary":"Exploring innovative methods to optimize AI models using multi-fidelity data, aiming to enhance accuracy and efficiency across engineering disciplines by leveraging diverse data sources to improve model performance while reducing reliance on high-cost precision data.","tags":["Multimodal AI"],"title":"Multi-fidelity Fusion and Optimization Theory and Applications","type":"project"},{"authors":["Jiayang Zhang","Prasun Tripathi","Mohammod Suvon","Dinesh Selvarajah","Jim Wild","Solomon Tesfaye","David Bennett","Andrew Segerdahl","Leslie Colvin","Douglas Steele","Shuo Zhou","Haiping Lu"],"categories":null,"content":"University of Sheffield Collaborating Faculties: Faculty of Medicine, Dentistry and Health, Faculty of Engineering\nExternal Partners: University of Oxford, University of Dundee, AstraZeneca\nOverview: Neuropathic pain, arising from damage or disease impacting the nervous system, stands as a significant health concern. Our project aims to develop an AI-based neuroimaging model capable of predicting treatment responses and clinical phenotypes in patients afflicted with neuropathic pain. We plan to conduct extensive external validation studies across multiple sites and conditions to ensure the development of a robust and objective AI-based neuroimaging model.\nMotivation: Chronic neuropathic pain afflicts one in ten adults over 30, stemming from injuries to the sensory nervous system. The incidence of this debilitating pain is anticipated to escalate due to ageing, a surge in diabetes cases, and improved cancer survival rates. Neuropathic pain markedly impairs daily functioning, manifesting symptoms like burning or ’electric-shock’ sensations, potentially leading to depression and a significant diminution in quality of life. Present medications provide only marginal relief to roughly half of the affected individuals, and are associated with side effects. Over the past quarter-century, the quest for more efficacious drugs for neuropathic pain has stagnated, a likely consequence of the diverse sub-types of the condition and the unpredictable nature of treatment responses. Collaborating with AstraZeneca and other universities, our endeavor is to unearth new biomarkers for neuropathic pain, aiming to invigorate drug development initiatives and enhance their effectiveness.\n","date":1696118400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1696118400,"objectID":"50e331bd54f121ea26a4f8373e180a91","permalink":"https://shef-AIRE.github.io/project/nerve-pain-detection/","publishdate":"2023-10-01T00:00:00Z","relpermalink":"/project/nerve-pain-detection/","section":"project","summary":"Harnessing advanced AI technology to discern novel biomarkers, paving the way for enhanced chronic nerve pain treatments and revolutionising healthcare outcomes.","tags":["Healthcare and medicine","Multimodal AI","Explainable AI","Domain Adaptation"],"title":"AI Brain Imaging for Nerve Pain Detection (UKRI funded)","type":"project"},{"authors":["Alan Thomas","Robert Gaizauskas","Valeria Vitale","Michael Pidd","Robert Shoemaker","Haiping Lu"],"categories":null,"content":"University of Sheffield Collaborating Faculties: Faculty of Arts \u0026amp; Humanities, Faculty of Engineering, Digital Humanities Institute (DHI)\nExternal Partner: British Library\nRelated Links: https://www.dhi.ac.uk/text-correction-for-mining-historical-documents/\nOverview: This project addresses the critical issue of correcting noisily OCR’d historical documents, focusing on the British Library Newspapers (BLN) collection. BLN is a major corpus of over 200 years of scanned British newspapers from over 240 newspapers with textual data, visual data, and metadata available. Scanned newspaper images have undergone OCR (optical character recognition) processing, resulting in inaccurate transcriptions due to the degradation of the original documents. The project aims to employ advanced deep-learning techniques to improve the quality of these transcriptions. The final outputs will be high-quality corrected transcriptions of BLN and open-source code for OCR text correction, both of which would serve as valuable resources for humanities researchers.\nMotivation: Since the early 2000s, significant digitisation efforts have been undertaken to preserve and make accessible historical primary sources such as newspapers, early printed books, and handwritten documents. While these efforts have been instrumental in advancing humanities research, the low quality of OCR transcriptions remains a significant barrier to discovering new historical insights. The successful completion of this project promises both short-term and long-term benefits. In the short term, it will significantly enhance the transcription quality of BLN, enabling accurate and efficient searching within the collection as well as unlocking the potential for text mining, which was previously impractical due to low transcription quality. In the long term, the project’s success could revolutionise research on other large collections of historical documents, allowing researchers to track content changes, language evolution, and shifts in thought across different time periods. By being language-independent, the impact could extend to historical documents worldwide, advancing research on a global scale.\n","date":1688601600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688601600,"objectID":"06443932ea0e212949ec417ea1a963bb","permalink":"https://shef-AIRE.github.io/project/historical-doc-text-correction/","publishdate":"2023-07-06T00:00:00Z","relpermalink":"/project/historical-doc-text-correction/","section":"project","summary":"Leveraging deep learning to refine OCR transcriptions of the extensive British Library Newspapers collection to overcome the barrier of inaccurate text data, unveiling a rich resource for exploring centuries of historical narratives and advancing global humanities research.","tags":["Digital Humanities","Multimodal AI","Foundation Model"],"title":"Text Correction for Historical Documents","type":"project"},{"authors":["Mohammod Suvon","Wenrui Fan","Prasun Tripathi","Andrew Swift","Venet Osmani","Samer Alabed","Pete Metherall","Xianyuan Liu","Shuo Zhou","Chen Chen","Haiping Lu"],"categories":null,"content":"University of Sheffield Collaborating Faculties: Faculty of Medicine, Dentistry and Health, Faculty of Social Science, and Faculty of Engineering\nExternal Partner: Sheffield Teaching Hospitals NHS Foundation Trust\nOverview: Our project aims to develop a sophisticated Artificial Intelligence (AI) system which can process multimodal, multi-vendor, multi-centre, and multi-pathophysiological cardiothoracic data, such as Chest Radiographs (CXR), Echocardiogram (ECG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Electronic Health Record (EHR), to segment and classify pathophysiological features and improve the diagnosis, prognosis, and therapeutic response prediction of Cardiothoracic Disease (CTD) such as Pulmonary Hypertension (PH), Chronic Obstructive Pulmonary Disease (COPD) and Abnormal Heart Rhythms (AHR) to a level at which advanced methods such as contrastive learning, foundation model, meta-learning, few-shot and zero-shot learning can successfully extract interpretable clinical parameters.\nMotivation: Cardiothoracic disease refers to a variety of conditions that affect the heart and lungs, including coronary artery disease, heart failure, lung cancer, and diseases of the chest wall. These illnesses can impact the overall function and structure of the heart and lungs, and they often require specialised care, potentially including surgery. Despite substantial progress in medical technology, the early diagnosis of these diseases remains a challenge due to the complexity of disease development and the vague symptoms produced in the early stages. In our initial study, we will utilise the MIMIC datasets, which comprise various data modalities within a comprehensive open-access database. This dataset has been the foundation for high-quality research in areas ranging from intensive care and mortality prediction to disease classification in pathology. Additionally, we will use the in-house dataset, ASPIRE registry, which also offers multiple data modalities, to further test our model. In the short term, the project’s success could lead to improved diagnosis and monitoring of cardiothoracic diseases, potentially reducing the need for invasive procedures and facilitating personalised treatment plans. In the long term, our AI system could be adapted to more cardiovascular and respiratory diseases, revolutionising the approach to cardiothoracic medicine and benefiting countless patients worldwide.\n","date":1688515200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688515200,"objectID":"26ee8c2a6308dd09c0a0d88a1ae15c00","permalink":"https://shef-AIRE.github.io/project/cardiothoracic-disease-prediction/","publishdate":"2023-07-05T00:00:00Z","relpermalink":"/project/cardiothoracic-disease-prediction/","section":"project","summary":"Utilising advanced AI to process multimodal cardiothoracic data for enhanced diagnosis and prognosis of Cardiothoracic Disease (CTD), paving the way for personalised medical care and transformative approaches in heart and lung health.","tags":["Healthcare and medicine","Multimodal AI","Explainable AI"],"title":"Multimodal Cardiothoracic Disease Prediction","type":"project"},{"authors":null,"categories":null,"content":" Register TODAY via https://lnkd.in/eJv76fRg to attend the hybrid information session on our FIVE 3-year (Senior) AI Research Engineer positions at University of Sheffield from 13:30 to 14:30 (BST, GMT+1) on 31st March. Profession Haiping Lu will explain who we are looking for, how we will work together, and what opportunities we can offer, and answer any questions you may have, either submitted via the form or to be asked during the session. To build a team with five full-time staff, we are very keen to have female members on board so we strongly encourage female candidates to apply and highly appreciate if you could share this opportunity with potential female candidates in your network. Thank you! Job application deadline: 24th April 2023. Application link: https://lnkd.in/eJyE5eH4\n","date":1680220800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680220800,"objectID":"18af8778e5224ca10eaefd16a1b5eb10","permalink":"https://shef-AIRE.github.io/news/23-03-31-information-session/","publishdate":"2023-03-31T00:00:00Z","relpermalink":"/news/23-03-31-information-session/","section":"news","summary":"Register TODAY via https://lnkd.in/eJv76fRg to attend the hybrid information session on our FIVE 3-year (Senior) AI Research Engineer positions at University of Sheffield from 13:30 to 14:30 (BST, GMT+1) on 31st March.","tags":null,"title":"Registration for information session on AI Research Engineer openings","type":"news"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"8e7bc052bdfc6746ea2bb6595e8093eb","permalink":"https://shef-AIRE.github.io/home/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/home/","section":"","summary":"","tags":null,"title":"","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"8b17fa86b3beca71fff728746974b793","permalink":"https://shef-AIRE.github.io/q-and-a/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/q-and-a/","section":"","summary":"","tags":null,"title":"","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"3bf44c81f2a197de01edde90bcd77783","permalink":"https://shef-AIRE.github.io/team/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/team/","section":"","summary":"","tags":null,"title":"","type":"widget_page"}]