Biomedical Data Science and Systems Modeling 

The lab's research aims to pioneer the integration of quantitative systems models with AI/machine learning approaches to uncover novel therapeutic strategies for cancer treatment. With a focus on deep learning techniques, we seek to seamlessly integrate large-scale datasets with patient outcome data, thereby elucidating intricate tumor dynamics and therapeutic responses. 

• Leverage innovative technologies like single-cell sequencing and deep learning
• Unravel the complexities of cancer biology and identify clinically relevant insights

We foster interdisciplinary collaboration through open communication and talent development, ensuring alignment among scientists across various fields. With a commitment to effective project management, our ultimate goal is to translate cutting-edge research findings into measurable improvements in patient outcomes, thereby advancing the forefront of cancer therapeutics.

Investigate patient outcome based on large-scale Omics datasets

• Deconvolute the complex interactions in the tumor microenvironment
• Characterize the tumor antigen and immune environments, both locally and systemically
• Predict outcome of treatments (e.g., with SRT, RLT, IO agents)
• Analyze patient journeys to support personalized medicine approaches

• Study biological mechanisms and processes of therapeutic relevance (e.g., signaling and transmembrane proteins)
• Develop novel approaches for mechanistic analysis using MassSpec data
• Enable learning across diverse data set by integrating different data modalities and modeling approaches

Does any of those areas interest you?

We have various topics for B.Sc., M.Sc. or Ph.D. thesis.

Contact us!