Nirav Merchant is the director for the Data Science Institute at the University of Arizona. He is the co-principaI Investigator for the National Science Foundation CyVerse, a national scale cyberinfrastructure for life sciences and National Science Foundation Jetstream – the first user-friendly, scalable cloud environment for National Science Foundation Extreme Science and Engineering Discovery Environment (XSEDE).
Over the last two decades his research has been directed towards developing scalable computational platforms for supporting open science and open innovation, with emphasis on improving research productivity for geographically distributed interdisciplinary teams. His interests include data science literacy, large-scale data management platforms, data delivery technologies, managed sensor and mobile platforms for health interventions, workforce development and project-based learning.
Abstract: ML (Machine Learning) based methods are at the center of the data science revolution. These ML methods are enabling novel analysis which were not feasible just a few years ago These phenomenal advances are fueled by the availability of vast amounts of data, scalable computational platforms (GPU, Cloud), opensource software and open access publications.
The vibrant ecosystem of ML tools and techniques is constantly evolving, making it daunting for beginners and experts alike to keep up with these rapid advances, thus necessitating active collaboration amongst teams of subject matter experts, data scientists and engineers. Providing hands-on learning opportunities for exploring, tuning, and customizing these methods with users own datasets is central for improving the performance (accuracy) and ensuring pragmatic and appropriate utilization.
I will discuss our ongoing NSF (National Science Foundation) funded projects that democratize access to platforms and training, that allow teams of subject matter experts, students, and researchers with diverse computational skills to effectively collaborate, build and securely share their novel analysis and underlying data at scale.
- Introductions to key concepts of machine learning (ML) methods
- Learning how ML powered analysis platforms are designed, built and deployed
- Provide awareness of the limitations and opportunities of ML based analysis platforms
The University of Arizona College of Medicine - Tucson is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
The University of Arizona College of Medicine - Tucson designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)ä. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
All Faculty, CME Planning Committee Members, and the CME Office Reviewers have disclosed that they have no financial relationships with commercial interests that would constitute a conflict of interest concerning this CME activity.
CME Code - 897834