BIGMATH

Michiel Hochstenbach and Wil Schilders (Mathematics & Computer Science) will supervise 2 PhD students in Eindhoven as part of a European EID project (also involving the Universities of Milan, Novi Sad and Lisbon). The project aims at training in total seven young, creative mathematicians with strong theoretical and practical skills. This is needed to tackle the new challenges that the Big Data era poses to the mathematical methods currently used in the spectrum of data analysis. For the full article see here.

DayTiMe

Milan Petkovic (Mathematics & Computer Science) and Alp Akçay (Industrial Engineering & Innovation Sciences) will supervise 2 Ph.D. students and 1 Postdoc in a recently awarded ITEA 3 project, DayTiMe. The project, with an international consortium from 8 countries, will focus in The Netherlands on the following objectives in the domain of Predictive Maintenance:

• Data collection with distributed scalable processing and an analytics platform, delivering both user/usage profiles and anomalies of individual systems.
• Fact based advice system for users to improve performance and product longevity based on large scale machine learning.
• Fact based advice system for service engineers both off-site and on-site.
• Fact based advice system for product designers and engineers.

Scientists from TU/e will work in DayTiMe in close cooperation with scientists and engineers from Philips Healthcare, Philips Research and Philips Consumer Lifestyle.

SmartDataLake

The project “Sustainable Data Lakes for Extreme-Scale Analytics” has been funded by the H2020 program. George Fletcher, Nikolay Yakovets en Odysseas Papapetrou from the Database group (M&CS) will supervise 2 PhD students.

Data lakes are raw data ecosystems, where large amounts of diverse data are retained and coexist. They facilitate selfservice analytics for flexible, fast, ad hoc decision making. SmartDataLake enables extreme-scale analytics over sustainable big data lakes. It provides an adaptive, scalable and elastic data lake management system that offers: (a) data virtualization for abstracting and optimizing access and queries over heterogeneous data, (b) data synopses for approximate query answering and analytics to enable interactive response times, and (c) automated placement of data in different storage tiers based on data characteristics and access patterns to reduce costs.

The data lake’s contents are modelled and organized as a heterogeneous information network, containing multiple types of entities and relations. In this project, efficient and scalable algorithms will be provided for (a) similarity search and exploration for discovering relevant information, (b) entity resolution and ranking for identifying and selecting important and representative entities across sources, (c) link prediction and clustering for unveiling hidden associations and patterns among entities, and (d) change detection and incremental update of analysis results to enable faster analysis of new data. Interactive and scalable visual analytics are provided to include and empower the data scientist in the knowledge extraction loop. The results of the project are evaluated in real-world use cases from the business intelligence domain, including scenarios for portfolio recommendation, production planning and pricing, and investment decision making

REDUCESEARCH

Bart Jansen (Algorithms and Visualization, M&CS) is one of the four TU/e researchers who were awarded a prestigious ERC Starting Grant (European Research Council). Bart’s projects is about pre-processing techniques to accelerate time-consuming algorithms that are needed in the era of Data Science. More information

Geometric algorithms with uncertainty models

From the same group, also Kevin Buchin (Algorithms and Visualization, M&CS) received a grant: NWO TOP C2, where one PhD student will be supervised. Advances in tracking technology have resulted in an explosion of movement data being recorded for research, however many algorithms make unrealistic assumptions about the actual routes. The goal of Kevin’s project is to develop the theoretical foundation for integrating uncertainty models with geometric algorithms.  Full article

Veni grants

Two out of the eight Veni grants that talented young TU/e researchers have won are Data Science projects:

• The Artificial Ethicist - Elizabeth O'Neill (Philosphy & Ethics, IE&IS)
  This project examines the practical and philosophical implications of artificial ethicists.
• Information-Driven Modeling of Urban Activity-Travel Routines of Generic Households - Soora Rasouli (Real estate management & urban planning, BE)
  This project aims at developing a prototype of the next generation of travel demand models that incorporates new developments like shared cars and mobility-as-a-service.