Chris Gessner serves as the Director of Data Science at Data2Discovery Inc, where he leads a dedicated graph data science team focused on delivering innovative solutions tailored for the pharmaceutical industry. With over seventeen years of experience spanning academic, pharmaceutical, and chemical information sectors, Chris...
Chris Gessner serves as the Director of Data Science at Data2Discovery Inc, where he leads a dedicated graph data science team focused on delivering innovative solutions tailored for the pharmaceutical industry. With over seventeen years of experience spanning academic, pharmaceutical, and chemical information sectors, Chris has honed his expertise in algorithm design and cheminformatics, making him a pivotal figure in the intersection of data science and drug discovery.
In his current role, Chris oversees the development of custom graph-based solutions that address complex challenges throughout the drug discovery and development pipeline. His team's work ranges from early preclinical drug discovery to the analysis of real-world evidence, leveraging advanced graph data modeling and analytics to uncover insights that drive decision-making. By employing cutting-edge technologies such as Neo4j for graph database management and node2vec for graph embedding, Chris ensures that the team remains at the forefront of innovation in data science applications.
Chris’s technical proficiency spans a wide array of programming languages, including Java, C/C++, Python, and R, enabling him to design robust algorithms that enhance cheminformatics techniques. His experience with big data frameworks like Hadoop and Apache Spark further empowers his team to handle large datasets efficiently, ensuring that their solutions are both scalable and effective. As a thought leader in the field, Chris is committed to advancing the role of analytics and cloud computing in drug discovery, ultimately contributing to the development of safer and more effective therapeutics. Through his leadership, Data2Discovery Inc continues to push the boundaries of what is possible in the realm of pharmaceutical data science.
