Master's Program in Smart Medicine and Health Informatics
Working within Genenet's flat startup structure gave me valuable insight into operational agility and interdisciplinary collaboration. A major professional takeaway was bridging the gap between wet-lab biologists and dry-lab computational engineers. While algorithms demand structured, clean datasets and mathematical ground truth, living biological systems inherently exhibit variation, environmental sensitivity, and non-linear dynamics. To reconcile these differences, I maintained open communication with wet-lab researchers to understand cell types, imaging protocols, and experimental controls, while articulating how imaging conditions directly impacted model accuracy. Working in an agile startup also taught me that individual initiative is paramount when there is no predefined manual. Interfacing directly with C-level leadership and aligning computational models with biological realities ensured that our engineering solutions directly served active drug discovery pipelines.
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