This interactive workshop session will delve deep into the multiple applications of AI & machine learning while addressing the bottlenecks and opportunities to assess success and failure of AI-driven drug discovery projects.
Dr. Gerald A. Higgins, Research Professor, Computational Medicine and Bioinformatics,
University of Michigan Medical School
|Dr. Gerald A. Higgins is Research Professor of Computational Medicine and Bioinformatics at the University of Michigan Medical School. His background includes chief of molecular neurobiology at the NIH, VP of R&D of Laerdal Medical Corporation, CIO of MedStar Health Hospital network, several start-up companies (e.g., SimQuest, Medscape), and VP of pharmacogenomic science at Assurex (now Myriad Genetics). His research combines expertise in pharmacogenomics, psychiatry, and computational analysis to understand human brain networks involved in psychotropic drug response. His work using artificial intelligence emphasizes pharmacology expertise combined with development of UX (user interface) design to enhance understanding by experimental biologists.|
The pharma industry is investing in AI & machine learning technologies as the drug discovery paradigm shifts towards safe, effective and value-based drug discovery. Our workshop focuses on how to leverage the large datasets currently available as well as how to successfully identify, prepare, assess and implement data into AI algorithms.
Leonardo Rodrigues, Associate Director, Advanced Analytics,
|Leonardo Rodrigues, Ph.D. is the Associate Director of Advanced Analytics at BERG. With more than 15 years of experience in data analysis and R&D, Dr. Rodrigues is a reference in applying AI and advanced analytical methods to extract actionable insights from clinical and biological data. At BERG, he leads the research and analysis of disruptive projects, as well as the development and deployment of innovative analytics technologies and IT platforms. Dr. Rodrigues holds a Ph.D. in Biochemistry and concluded his postdoctoral studies at the Whitehead Institute-MIT, where he applied statistics, bioinformatics, and molecular/cell biology in the study of cancer stem cells and metastasis initiation.|