Solna, Stockholm County, Sweden
I bring an engineering mindset to modern life science. With a background in Chemical Engineering and a Master’s in Molecular Techniques in Life Science, I focus on building data-driven tools and workflows that make complex biological data easier to visualize, interpret, and act on. My experience spans proteomics, transcriptomics, data integration, and machine learning. I’ve developed bioinformatics software such as R packages and web applications, and contributed to biomarker discovery projects using mass-spectrometry and affinity-based datasets. I enjoy designing workflows that turn raw, noisy data into clear, reproducible outputs that help researchers work faster and with greater confidence. I’m now a PhD student in the Human Protein Atlas (HPA) Alpha Cell Program, working on computational omics and large-scale data integration. My current work focuses on mapping gene and protein expression across human cell types and tissues to support the long-term goal of building a virtual model of the human cell. Beyond the technical side, I value collaboration and clear communication. I have experience presenting my work, coordinating teams, and working in interdisciplinary research groups, whether with biologists, engineers, or computational scientists.
Human Protein Atlas - Alpha Cell Program (Computational Proteomics & Data Integration) • Map gene and protein expression across 100+ human cell types and tissues using large-scale omics integration • Develop computational methods for aligning transcriptomic and proteomic datasets in support of the virtual human cell model • Contribute to analysis workflows and visualization tools for the HPA public resource used by thousands of users • Work with Python, R, machine learning models and FAIR-compliant data practices to ensure reproducibility
Alpha Cell Program — Cell-Specific Expression Integration • Developed a cluster-comparison pipeline to align 80+ bulk tissue with 100+ single-cell clusters, enabling unified cell-type annotation based on 10M+ datapoints • Contributed these results to a peer-reviewed publication strengthening the mapping of cell-type–resolved gene expression across the human body
Modeling Time-Resolved Cellular Drug Responses • Adapted an RNN framework to model phosphoproteomic dynamics across multiple drugs • Incorporated 35k+ curated signaling interactions to guide model behavior and improve interpretability • Demonstrated that the model successfully zero-shot predicted responses for 3 out of 4 drugs outperforming naive estimates despite limited data • Results contributed to a peer-reviewed publication
• Managed a network of 200+ alumni and students increasing engagement through events and digital outreach • Organized 2 career-oriented events for current master’s students to enhance networking and professional development
• Simulated nanoparticle movement in cardiovascular geometries using CFD/CFPD • Evaluated magnetic-field targeting strategies to improve simulated drug-delivery efficiency
• Completed 30+ projects involving recording, editing and production • Built templates that cut production time by ~20%