About the Laboratory

An important area of data analysis is the construction of predictive models. The field devoted to researching automated methods for building such models is called machine learning. Universality is an important feature to such methods, as model-building approaches can be used to do things like search for rare events in particle physics or rank pages for search queries. Founded in 2015, the Laboratory for Methods of Big Data Analysis (LAMBDA) focuses on developing model-building methods capable of solving tasks in both online and offline data processing, as well as on adapting models for various subject areas and application spheres.

Goal

The laboratory aims to create of a world-class research centre capable of supplying the academic staff needed to solve fundamental theoretical and practical tasks in computer science, in particular those related to big data processing techniques.

Tasks

• Integrate the laboratory's research results into HSE's academic programmes;
• Organize and participate in international academic partnerships centred on the laboratory’s research interests, and integrate LAMBDA’s staff into global research networks;
• Integrate research alongside HSE's other research laboratories;
• Conduct fundamental research on developing methods and technologies for big data analysis;
• Conduct applied research on developing methods and technologies for big data analysis;
• Apply methods, methodologies, and technologies of big data processing to resolve practical tasks within basic and applied science and modern industries;
• Publish qualitative research in international peer-reviewed journals and conferences;
• Attract researchers from the international market, including leading foreign specialists;
• Provide instrumental support and integrate the results of work with HSE's 'subjects' – economics, psychology, and sociology;
• Coordinate HSE's cooperation with Yandex on data access issues.