Academic degree and current position in TSI: professor, leading researcher of the Engineering Faculty, Director of MSc Program “Transport and Logistics”
Previous experience: more than 10 years was Vice-rector for Research and Development, Transport and Telecommunication Institute (TSI), founder of the Laboratory of Applied Systems and chair of Mathematical Methods and Modelling Department in TSI
Membership: From 01.2017 member of ECTRI Board and from 2019 – vice-president of ECTRI; an external scientific expert in Association of Paneuropa Coach Terminals from 2009; has been chair or member of the organizing and the programme committees of the International Conference on Applied Statistics (6 events), Transportation Research (15 events), Complex Systems (4 events), etc. and reviewer in transportation journals and conference proceedings. Member of Editorial Board of Journals: Transport (WoS, SCOPUS); Maintenance and Reliability (SCOPUS), Transport and Telecommunication (WoS, SCOPUS), etc. Guest redactor of Volumes of Lecture Notes in Networks and Systems and Lecture Notes in Intelligent Transportation and Infrastructure in Springer; Procedia Engineering published by Elsevier etc.
Academic experience: Author more than 170 publications, incl. 9 books and textbooks, chapters in the books “From Transit Systems to Models: Purpose of Modelling” in book: “Modelling Public Transport Passenger Flows in the Era of Intelligent Transport Systems” (Springer Verlag, 2016), “3D City Models and urban information: Current issues and perspectives” (edpsciences, 2014), papers in International Journal of Transport, Transport and Telecommunication, Advances in Intelligent Systems and Computing, etc. Guest redactor of Volumes of Lecture Notes in Networks and Systems and Lecture Notes in Intelligent Transportation and Infrastructure in Springer; Procedia Engineering published by Elsevier etc. An Expert in Transport and Logistics (2008-2021) and Civil Engineering (from 2021) at the Latvian Council for Science; member of the PhD promotion committee
Teaching at post- and graduate levels: Data Analysis (PhD in Business Management, RSEBAA-BA); Multivariate Statistical Analysis, Transport Modelling, Scientific Seminar (Dr. Ing. in Telematics and Logistics, TSI); Data Mining (MSc in Computers Science, MIS, TSI); Research Seminar; Methods of Data Analysis and Business Forecasting (MSc in Transport and Logistics, TSI); etc.
Participation in projects: as leading researcher has participated in more than 20 European and Latvian research projects and more than 14 national transportation studies, and was scientific coordinator in 8 of them. Is/was national representative and management committee member of COST Actions CA16222 (WISE-ACT), TU1306 (CYBERPARKS), TU1004, TU0903, TU0804 and TU0801. Has great experience in coordinating different kind of projects in frame of Horizon 2020, FP, INTERREG, BSR programmes, she was coordinator of Horizon 2020 project “Enhancing excellence and innovation capacity in sustainable transport interchanges” (ALLIANCE)
Research interest: Data analysis, data mining, statistical analysis, mathematical modelling and simulation with application in different fields (transportation sphere, business, economics, networks, logistics and others)
Supervised Doctoral, Master and Bachelor Theses: Promoted 6 Doctoral, more than 90 Master theses and 70 Bachelor theses an official reviewer of 7 PhD theses
Awards: (2019) by Ministry of Education and Science; (2017, 2010, 2008) Latvian Education Funds; (2011, 2010, 2008) Riga Municipality
Acronym of the project: D-CoDE
Short description of the project:
A fundamentally new kind of design competence is needed to anticipate the digital transformation of society and create the conditions for responsible and sustainable futures. For this, D-CoDE will train a cohort of 15 PhD students in design, design anthropology, media studies, science and technology studies and data science, and equip them with the holistic understanding needed for the human-centric design of product service systems powered by Big Data, Machine Learning and Artificial Intelligence.
New foundations for design will require the interdisciplinary integration of five key research challenges identified in both engineering and the social sciences: (1) anthropological study and principled engineering of algorithms as foundation for shaping digital futures; (2) design of personally meaningful and socially appropriate forms of interaction with and across decentralised systems; (3) inclusive approaches to value creation in designing data-driven products, services and business models; (4) principles and mechanisms for public deliberation and governance of data flows across systems; (5) future design practices upholding anticipatory, deliberative and responsive innovation approaches.
To foster this holistic understanding – and the integration of knowledge across disciplines it requires – D-CoDE introduces a post-disciplinary mode of working called ‘prototeams’: teams of PhD students working in real-world contexts to develop and prototype future professional design roles and practices, including the scientific knowledge needed to support them. As these research challenges require both deep disciplinary expertise and knowledge that cuts across sectors, D-CoDE brings together an exceptional team of internationally leading researchers in the required subject areas, and non-academic partners that bring societal, economic and political practice to the project and provide multiple forums for the dissemination of knowledge, results and best practices.
Transport and Telecommunication Institute (TSI) will have a role in the following tasks:
WP1 – Data engineering methods for machine behaviour design. It will develop novel methods and tools for the design of machine behaviour through principled data engineering. The goal is to provide designers with meaningful and effective data-driven tools to shape, test and evolve machine behaviour. In particular, the research will investigate the adaptation of approaches drawn from data science and software engineering (e.g. semantic data modelling, data integration, requirements specification) for the principled design of machine learning training data.
Machine learning methods for sustainable design futures. It will explore the development of machine learning methods suitable for design tasks instead of the typical focus on efficiency and optimisation. In particular, the research will investigate what machine learning metrics are most apt for guiding these tasks and for navigating the associated design trade-offs (e.g., between accuracy and fairness) with human empowerment in mind.
WP2 – Designing for multi-intentional interaction. It will explore how human-machine interfaces can manifest the concurrent and conflicting needs of multiple users (“multi-intentionality”) of a data driven
product service system. The research will focus on how the use of techniques for scraping, visualising and interacting with data about the unstable interdependencies that exist among the system, its multiple users and the other systems to which it is connected, can provide an additional layer of legibility of a system’s behaviour and enable trust.
Total budget of the project: 4206200,40