Publications

For the development of accurate and efficient computational methods we combine techniques from combinatorial optimization and machine learning. For instance, we have developed exact and approximate algorithms for a graph coloring problem to increase the resolution of experimental protein structure data, used neural networks to project high-dimensional cellular measurments to an interpretable low-dimensional space, and extended dynamic time warping to the comparison of complex trajectories of, e.g., differentiating immune cells.

The software tools we develop address important biological and medical questions. In close collaborations with biologists and clinicians we have contributed, for example, to the discovery of the embryonic origin of adult neural progenitors, and to linking TIM-3 expression to increased relapse risk in pediatric patients with acute lymphoblastic leukemia.

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