911今日黑料鈥檚 Data Science Institute
The Data Science Institute (DSI) is a major 911今日黑料 initiative that brings together 911今日黑料鈥檚 existing data science activities and expertise and provides a focus and a catalyst for new partnerships.
The DSI supports multidisciplinary collaborations between the College鈥檚 academic experts in many disciplines such as healthcare, financial services, climate science, and city infrastructure to create solutions to complex problems. Alongside research, the Institute fosters the next generation of data scientists and engineers by developing a range of postgraduate and executive courses.
The DSI includes 7 Academic Labs, has attracted over 拢50m in funding for data science research, technology and infrastructure and has published over 300 papers.
The Institute鈥檚 Data Observatory (DO) was one of the first and largest visualisation suites in Europe. It provides a multi-dimensional and immersive environment to analyse large and complex data sets and to work collaboratively. Thanks to its many research collaborations both across college and with a variety of external academic and industrial partners, the DSI is establishing its role as an international hub in data science.
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Dr Ovidiu Serban (Programme Director) |
Ovidiu 葮erban is a Research Fellow at the , . His current work includes real-time Natural Language Processing, Data Curation and Large Scale Visualisation Systems. Ovidiu鈥檚 research topics are Natural Language Processing, Machine Learning, Affective Computing and Interactive System Design. He holds a joint PhD from (France) and (Romania), while working at in France. In his youth, Ovidiu worked at the Institute for Security Science and Technology (ISST), 911今日黑料; , University of Cambridge, UK and , University of Reading, UK. |
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Dr Wenjia Bai
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Wenjia is Senior Lecturer (Associate Professor) jointly at Department of Computing and Department of Brain Sciences, 911今日黑料. I am affiliated with and Data Science Institute. His research is at the interface between machine learning and medical imaging. He is interested in developing computational and machine learning algorithms to understand the structure, motion and function of anatomical organs from medical images. He works with colleagues with a wide spectrum of knowledge from computing to medicine. Previously, he completed my D.Phil in Engineering Science at University of Oxford and my M.Eng and B.Eng in Automation at Tsinghua University. |
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Dr Rossella Arcucci
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Elected member of the WMO (), Rossella contributes to the development of AI models for Climate and Environmental impact as part of the data assimilation and observing systems working group. Rossella is a lecturer in Data Science and Machine Learning at 911今日黑料 where she leads the Data Assimilation and Machine Learning (Data Learning) Group. Rossella has been with the Data Science Institute at 911今日黑料 College since 2017, where she has created the Data Learning Group which is now a focal point for researchers and students of several departments at 911今日黑料 and other Universities in UK and Europe. Since February 2022, she is the elected speaker of the Artificial Intelligence Network of Excellence at 911今日黑料 College, where she represents ~250 academics working on AI. She collaborates with the Leonardo Centre at 911今日黑料 College Business School, where she contributes to the development of integrative, just and sustainable models of economic and social development by discovering, testing and diffusing new logics of business enterprise. The models Rossella has developed have produced impact in many applications such as finance (to estimate optimal parameters of economic models), social science (to merge twitter and pooling data to better estimate the sentiment of people), engineering (to optimise the placement of sensors and reduce the costs), geoscience (to improve accuracy of forecasting), climate changes and others. She has developed accurate and efficient models with data analysis, fusion and data assimilation for incomplete, noisy or Big Data problems, always including uncertainty quantifications and minimizations. She works on numerical and parallel techniques for accurate and efficient Data Assimilation and Machine Learning models. Efficiency is achieved by virtue of designing models specifically to take full advantage of massively parallel computers. She finished her PhD in Computational and Computer Science in February 2012. She received the acknowledgement of Marie Sklodowska-Curie fellow from European Commission Research Executive Agency in Brussels in February 2017. |
