Exploring Open Data and Collaborative Research with Philip
Philip touches on his transition from software engineering to research, his data projects, and his project collaborations.
Hello Philip, tell us about yourself.
Hi, my name is Philip Heltweg. I used to be a professional software engineer/product manager before I returned to academia, and now I am a doctoral student at Friedrich-Alexander-Universität Erlangen-Nürnberg. I am part of a research project, called JValue, that aims to make the use of open data easier and faster for everyone. With my research, I contribute to the design, implementation, and evaluation of a domain-specific language to model data pipelines that enables collaborative data engineering between users from diverse backgrounds and skill sets. Most things I work on eventually find their way to my homepage at heltweg.org, so that is the best way to connect with me.
To escape the monitor, I've discovered running for myself. I love that it is a sport that everyone can participate in and that allows you to compete against yourself only. I am working on completing a marathon one day, so far I've cleared the 10 and 21 km.
What fascinates you about your field of research?
Coming from a software engineering background, I had to first spend time getting familiar with data engineering, data science and open data ecosystems. My experience with people working with open data has been wonderful: Everyone I spoke with has been kind, welcoming, and patient with my questions.
I also enjoy the variety that my research touches so different areas. High-quality data is fundamental for many fields and I have had the chance to explore a variety of topics from public transport to materials science and artificial intelligence.
What is your project about and with whom are you collaborating on it?
For Software Campus, I collaborate with Springer Nature and more specifically with their curated materials science database product SpringerMaterials. Working with SpringerMaterials allows me to connect with leading subject-matter experts in a very complicated data domain and gain insights into their challenges with data quality and data engineering. Our goal is to implement and evaluate solutions to these challenges to increase the quality of materials science data and easier to use for scientists and industry. During the initial problem identification, I was able to talk to subject-matter experts like professors or experts from industry working with data and AI solutions. I learned how complicated seemingly simple challenges in data engineering for complex data domains can be, but also how much I can contribute with my background in software engineering.
How is the Software Campus program contributing to your personal development?
Thanks to Software Campus, I've been able to build a strong network of contacts in industry and academia that has let me talk to interesting people from different fields. During the regular meetups, I have also had the opportunity to talk to other doctoral students on a similar path to mine, which is a great way to compare work, meet exciting people and talk about potential collaborations.
What collaboration and network opportunities have you had during the program?
During the program, we are allowed to participate in trainings for future tech leaders by industry partners. I have found them of high quality and with relevant topics like diversity or guidelines for new leaders. Additionally, Holtzbrinck is very proactive and has connected me with internal mentors that match my personal goals very well and have supported me in one-on-one sessions. Of course, I also learned a lot by writing a plan and budget for a research project of 100.000 EUR and managing the employees it allowed me to hire.