EY today announces the launch of the initiative to connect Science, Technology, Engineering and Mathematics (STEM) students worldwide, with the winners of the first EY NextWave Global Data Science Challenge.

The Challenge provides STEM students with the opportunity to analyze real-world problems through data and develop initiatives that will help build a better working world. Top participants received an EY Badge, recognizing their knowledge and skills in data science. The EY Badges program supports EY people in shaping their own careers by earning credentials in skills that differentiate them in the market, such as data analytics or artificial intelligence (AI).

For this year’s Challenge, the students used real-world data from Skyhook, a pioneer in location technology, to help tackle the task of smart mobility in cities as the urban population rises across the globe. Future challenges will continue to mirror the EY focus on building a better working world for all people, clients and communities.

The EY NextWave Global Data Science Challenge attracted more than 4,500 students from nearly 500 universities. A judging panel comprised of EY leadership and data science professionals assessed in-person presentations of more than 50 of the top performing teams in the competition. The three global winners were chosen among those who combined the best results with in-depth insights and contributions to building a better working world. Of the 12,000 entries received, the 2019 EY NextWave Data Science Challenge winners are:

Advertisement

First place: Sergio Banchero. Sergio is from Argentina, pursuing his Master’s degree in Data Science at the University of Western Australia in Perth. Sergio analyzed the data created by devices as they moved from the city center to the city border. He created models that could be used for real-time decision making, such as identifying issues in transportation networks; identifying blocked roads and the impact on city traffic; and identifying common travel patterns to extend and enhance the public transportation network.

Second place: Katherine Edgley and Philipp Barthelme. Katherine is from the US and is pursuing her Master’s degree in Computational Applied Mathematics at the University of Edinburgh; Philipp is from Germany and is pursuing his Master’s degree in Statistics with Data Science at the University of Edinburgh. Katherine and Philipp used recurrent neural networks – which use sequential formation to make predictions – to create models that could provide companies and people with optimal traffic routes and parking, for example.

Third place: Chia Yew Ken. Chia is from Singapore and is currently pursuing an Engineering Bachelor’s degree in Information Systems Technology and Design with a specialization in Artificial Intelligence at the Singapore University of Technology and Design. Chia used long-short-term-memory (LSTM), a special form of recurrent neural networks that can retain memory over long periods of time and are used to classify, process and make predictions. Chia developed models that could be used to identify over-crowded roads and intersections, reschedule traffic or schedule road work.

Future for the EY NextWave Data Science Challenge

Moving forward, EY teams are implementing an enhanced EY NextWave Data Science Challenge that deepens their relationship with students and will enable students to participate in potentially world-transforming initiatives and earn EY Badges.

Advertisement

The new program will publish challenges on an ongoing basis and students will achieve EY Badges by solving the issues and participating in innovative crowdsourcing initiatives. For example, an online marketplace and a discussion board will help students connect and create inclusive, global teams to tackle the challenges. University faculty can also create challenges to motivate students with real-world issues, either as an assignment or to propose competitions to the global community of students.

Beatriz Sanz Saiz, EY Global Advisory Data Analytics Leader, says:

“Our goal is to mobilize millions of students worldwide through this network, so that they contribute, together with EY teams, to build a better working world, with a sense of purpose, belonging and reciprocity as they develop their skills in data science. The EY NextWave Data Science Challenge connects students with academic leaders, industry professionals and real-world challenges to foster future career development. We are proud of today’s EY NextWave Data Science Challenge winners, who have demonstrated innovative approaches to creating smarter cities and improving mobility. We hope that their projects, and the new Challenge we’re announcing today, will inspire even more students to embark on STEM careers.”