Imperial’s Data Science Institute
The Data Science Institute (DSI) is a major Imperial College London initiative that brings together Imperial’s existing data science activities and expertise and provides a focus and a catalyst for new partnerships.
The DSI supports multidisciplinary collaborations between the College’s 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 £50m in funding for data science research, technology and infrastructure and has published over 300 papers.
The Institute’s 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 Șerban is a Research Fellow at the Data Science Institute (DSI), Imperial College London. His current work includes real-time Natural Language Processing, Data Curation and Large Scale Visualisation Systems. Ovidiu’s research topics are Natural Language Processing, Machine Learning, Affective Computing and Interactive System Design. He holds a joint PhD from INSA de Rouen Normandy (France) and “Babeș-Bolyai” University (Romania), while working at LITIS Laboratory in France. In his youth, Ovidiu worked at the Institute for Security Science and Technology (ISST), Imperial College London; Computer Lab, University of Cambridge, UK and ISR Laboratory, 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, Imperial College London. I am affiliated with Biomedical Image Analysis Group 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 (World Meteorological Organization), 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 Imperial College London where she leads the Data Assimilation and Machine Learning (Data Learning) Group. Rossella has been with the Data Science Institute at Imperial 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 Imperial and other Universities in UK and Europe. Since February 2022, she is the elected speaker of the Artificial Intelligence Network of Excellence at Imperial College, where she represents ~250 academics working on AI. She collaborates with the Leonardo Centre at Imperial 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. |
