Christoph Ogris

Leading the computational biology strategy to map the cell surfaceome and identify novel target pairs for next-generation cancer therapeutics. • Strategic Vision: Building a scalable computational engine that bridges high-fidelity biological data with AI/ML to unlock "undruggable" targets in hard-to-treat tumors and beyond. • Pipeline Acceleration: Directing the computational identification of target pairs for bispecific ADCs and T-cell engagers. • Leadership: Fostering an interdisciplinary culture where data science and wet-lab biology work in sync to drive genuine innovation in oncology. Core Focus: Surfaceome Mapping, Target Discovery, AI/ML in Biotech, Oncology.

Led the computational biology strategy for early drug discovery in Inflammation and Oncology. Focused on bridging the gap between large-scale data generation and biological validation. • Leadership & Strategy: Managed an international team of computational biologists, driving the integration of data science into the R&D value chain. • Target Discovery: Spearheaded computational initiatives to identify and validate novel targets, leveraging multi-omics data to improve clinical Probability-of-Success. • Decision Support: Translated complex computational findings into clear strategic guidance for senior stakeholders, directly influencing portfolio decisions and project prioritization. Core Focus: Oncology, Inflammation, Target Identification, R&D Strategy.

Transitioned into a fully independent leadership role, driving the scientific roadmap for AI/ML applications in biomedicine. • Strategic Leadership: Defined and presented computational strategies to executive leadership (C-suite/Scientific Board), ensuring alignment with broader institutional goals. • Funding & Sustainability: Successfully secured grant funding by translating complex technical proposals into compelling value propositions for funding agencies. • Innovation Management: Directed a team of scientists in pioneering machine learning applications, moving from theoretical models to applied innovation. • Operational Excellence: Optimized budget and resource allocation to ensure project efficiency and long-term sustainability.

Stepped into leadership to co-manage the group's operations and scientific output. • Scientific Management: Oversaw project delivery and quality control for the Computational Cell Maps group, ensuring high-fidelity results. • Talent Development: Mentored junior scientists and PhD students, fostering a culture of professional growth and technical excellence. • Cell Mapping: Contributed to the strategic focus on spatial biology and cell mapping, laying the groundwork for advanced data integration.

Hands-on development of the core algorithms driving our research. • Algorithm Development: Architected innovative ML/AI workflows for complex data integration and multi-omics analysis. • Discovery: Applied state-of-the-art data analysis to identify hidden disease mechanisms and novel biological insights. • Scientific Communication: Authored high-impact publications and presented novel findings at global conferences to position Helmholtz Munich at the forefront of the field.

Bridged the gap between academic theory and practical application for Master's level students. Focus on conveying complex computational concepts to the next generation of bioinformaticians. • Curriculum Delivery: Lectured on Translational Bioinformatics and Statistical Learning, focusing on the application of ML in biological contexts. • Network Analysis: Taught advanced methodologies in Multi-Modal Network Analysis and Inference. • Mentorship: Provided academic guidance and technical mentorship to students bridging biology and computer science. Core Skills: University Teaching, Machine Learning Algorithms, Statistical Learning.

Conducted doctoral research at the intersection of technology development and network biology. Played a key role in early-stage innovations that are now standard in the field. • Spatial Transcriptomics: Collaborated directly with engineering teams to co-develop seminal methods for Spatial Transcriptomics, bridging the gap between hardware engineering and computational interpretation. • Network Algorithms: Developed novel algorithms for predicting functional association networks and network-based pathway annotations to decipher complex biological systems. • Tool Development: Implemented full-stack solutions (backend to frontend) and deployed multiple public-facing web servers to make these algorithms accessible to the global research community. Core Skills: Spatial Transcriptomics, Network Biology, Algorithm Design, Full-Stack Development.

Early adoption and teaching of Machine Learning methodologies. • Instruction: Developed and conducted hands-on exercises covering both supervised and unsupervised learning methods. • Personalized Support: Tailored practical exercises to individual student needs, ensuring a solid grasp of algorithmic foundations.