Sophie is a 4th year PhD student in Jian Tang’s lab at Mila (Montréal), and focuses her thesis on graph machine learning. In January 2023, she came to the Computational Health Center at Helmholtz Munich for a research stay in the group of Dr. Anna-Lisa Marsico, one of the partnering teams of the Helmholtz International Lab Causal Cell Dynamics (CCD).
Sophie is a 4th year PhD student in Prof. Jian Tang’s lab at Mila, Montréal’s machine learning institute, and focuses her thesis on graph machine learning. In January 2023, she came to the Computational Health Center at Helmholtz Munich for a research stay in the group of Dr. Anna-Lisa Marsico, one of the partnering teams of the Helmholtz International Lab Causal Cell Dynamics (CCD).
1. How did this scientific stay impact your PhD project?
As a computer science and machine learning student, the stay has definitely added a new dimension to my PhD; namely, a better understanding of the challenges (e.g., technical noise) and needs (e.g., what answers we are aiming for from collected data) of biological and medical research. In particular, the research collaboration initiated with Dr. Lotfollahi has provided me with a valuable opportunity to leverage my expertise in graph machine learning and develop novel methods for the analysis of single-cell data. The scientific stay also fostered interdisciplinary collaborations and facilitated networking, opening exciting avenues for future interdisciplinary research at the interface of computer science and computational biology.
2. What are the things you like the most about your stay at Helmholtz Munich?
During my stay at the Institute of Computational Biology in Munich there were several aspects that I particularly enjoyed and appreciated. The institute's friendly and vibrant research environment undoubtedly stood out to me. The staff culture made it clear that it is okay to ask questions. For instance, my training has contained no biology for many years, meaning that many basic concepts in biology were foreign to me. I felt comfortable asking "basic" questions thanks to that same culture. This created an atmosphere conducive to learning in regard to how machine learning could help biology. Plus, the institute's strong emphasis on interdisciplinary collaboration was highly appealing.
Of course, it is worth mentioning that the city of Munich is incredibly well connected to the beautiful nature around it, enabling wonderful daytrips to go hiking and get away from screens. Overall, the supportive research environment, interdisciplinary collaborations, and the charming people I met made my stay at the Institute of Computational Biology an instructive and fun experience.
3. What will you take back from your scientific stay there?
The scientific stay has provided a much-improved understanding of the data available in biology, the problems of interest, and the bottlenecks of using the methods developed in machine learning so far. In addition, I will come out of the scientific stay with a new collaboration!
4. How will the project you started there continue now that you have returned?
The collaboration is continuing virtually with regular meetings to discuss technical challenges, the analysis of experiments, and the planning of next steps to ensure the success of the project.
5. What would you recommend to other PhD students who want to visit Helmholtz Munich?
I would say make sure to talk to as many people as possible about your potential visit as there are a variety of obstacles (that you can tackle in a number of ways), which of course anyone moving from abroad would face inevitably at any new location.
6. Any final words?
Thanks to all the wonderful people that made the stay possible and helped me feel welcome, in particular the group members of Prof. Marsico’s group! As well as a special thanks to my collaborators: Emy Yue Hu, Prof. Marsico, and Dr. Lotfollahi.