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Organization profile

Centre for Doctoral Training in Data Science

The EPSRC Centre for Doctoral Training (CDT) in Data Science, based at the School of Informatics, University of Edinburgh, is a world-class PhD programme training a new generation of data scientists with the technical skills and interdisciplinary awareness necessary to become R&D leaders in this dynamic area.  It comprises 50 PhDs over five intake years and the first cohort started the programme in September 2014.

Data science is the study of the computational principles, methods, and systems for extracting knowledge from data. Large data sets are now generated by almost every activity in science, society, and commerce — ranging from molecular biology to social media, from sustainable energy to health care. 

Data science asks: How can we efficiently find patterns in these vast streams of data? Many research areas have tackled parts of this problem: machine learning and artificial intelligence provide methods for finding patterns and making predictions and decisions from data; databases are needed for efficiently accessing data and ensuring its quality; statistics and optimization provide fundamental mathematical ideas and methods; ideas from algorithms are required to build systems that scale to big data streams; and natural language processingcomputer vision, and speech processing are each needed for analysis of different types of unstructured data. Recently, these distinct disciplines have begun to converge into a single field called data science.


Dive into the research topics where Data Science CDT is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.


Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
  • The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning

    EUCLID Consortium, Cabayol, L., Eriksen, M., Carretero, J., Casas, R., Castander, F. J., Fernández, E., Garcia-Bellido, J., Gaztanaga, E., Hildebrandt, H., Hoekstra, H., Joachimi, B., Miquel, R., Padilla, C., Pocino, A., Sanchez, E., Serrano, S., Sevilla, I., Siudek, M., Tallada-Crespí, P., & 107 othersAghanim, N., Amara, A., Auricchio, N., Baldi, M., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Kümmel, M., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Mei, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Riccio, G., Rosset, C., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E., Valenziano, L., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Scottez, V. & Tramacere, A., 21 Mar 2023, In: Astronomy and Astrophysics. 671, p. 1-23 23 p., A153.

    Research output: Contribution to journalArticlepeer-review

    Open Access
  • Names, Nicknames, and Spelling Errors: Protecting Participant Identity in Learning Analytics of Online Discussions

    Farrow, E., Moore, J. & Gasevic, D., 13 Mar 2023, Proceedings of the 13th International Conference on Learning Analytics and Knowledge (LAK23). Association for Computing Machinery (ACM), Vol. LAK23. p. 145-155 11 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
  • Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn

    Bohdal, O., Tian, Y., Zong, Y., Chavhan, R., Li, D., Gouk, H., Guo, L. & Hospedales, T., 27 Feb 2023, (Accepted/In press) 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 18 p. (Conference on Computer Vision and Pattern Recognition (CVPR)).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution