| layout | title |
|---|---|
default |
About Me |
Hello, I’m Thomas Rauter, a PhD researcher in Bioinformatics at the University of Salzburg. My PhD focuses on statistical evaluation of time-series omics data, CHO cell modeling, and interpretable deep learning for molecular networks.
I hold a bachelor’s degree in Molecular Biology from Graz and a master’s degree in Biotechnology, where I developed a strong interest in computational methods and statistics. This passion led me to specialize in bioinformatics for my PhD research.
As part of my PhD, I aim to:
- Develop statistical methods for analyzing time-series omics data.
- Build interpretable machine learning models for molecular networks.
- Model CHO cells to improve biotechnological processes.
- Autodidactic Learning: I have a proven ability to teach myself complex
technical topics through independent study. With a formal background in
biology, I transitioned into bioinformatics without relying on additional
university coursework. By leveraging online resources, technical
documentation, and hands-on experimentation, I developed expertise in
statistical modeling, data analysis, and machine learning using Python and R. This self-directed approach allows me to efficiently master new tools and concepts in fast-evolving domains. - Structured Thinking: I’m highly organized by nature—whether it’s my desk, desktop, phone, browser tabs, emails, or codebase, everything is intuitively named, neatly arranged, and well-documented. I use to-do lists extensively and rarely lose track of data or files. This structure isn’t just a habit—it’s a conscious strategy. Long-term projects live or die by their organization, and I make sure mine stay alive.
- Patience: Structured work over time requires patience, and I’ve always had a strong sense of that. Progress in research is often slow and incremental, so staying focused on the bigger picture is essential.
- Creativity: I tend to think in unconventional ways, which often leads to efficient or elegant solutions. For example, back in school, I’d sometimes forget the "proper" formulas for math problems but still solve them using graphical reasoning or approximation. That mindset stuck—I look for insight, not just instructions.
- Prioritization: I sometimes gravitate toward tasks I find interesting, even when they aren’t the most urgent. This can delay higher-priority work. I’ve become more aware of this tendency and am now building routines that help me stay aligned with what matters most to the project—without losing the drive for exploration.
- Focus: I can get distracted by noise and interruptions, especially in shared office environments. To counter this, I use earplugs, limit notifications, and set clear focus blocks to stay fully immersed in one task at a time.
Whenever I work with machine learning, I use Python as the to go language for that. Further, I wrote a Python package that allows to train a special type of interpretable neural network that has the architecture of a molecular network (not yet made available publicly at the time of writing this).
R is the number one language in bioinformatics and therefore I use it a lot in my daily work. I did a multitude of statistical analyses with it, and also wrote an R package, SplineOmics (see section: software-packages).
Overview:
Presenting results and topics is a key skill when someone pursues a scientific
education. Since highschool, I had to make many different presentations, which
includes more formal ones at seminars and conferences, but also more informal
ones in group meetings. I can say with confidence that over the years I
became a very good presenter, when I have the time to sufficiently prepare.
For more details, you can check out: