TBA

Join Advance-CTR for the next installment of our highly rated Mentoring Training Program on January 22, 2019 at the University of Rhode Island. 

Faculty who mentor junior investigators are encouraged to take advantage of this professional development opportunity. Participants will learn how to improve their relationships with mentees and become more effective mentors to investigators as they grow their research careers. 

The program targets faculty who mentor junior investigators who conduct clinical and translational research, which is broadly defined. Preference will be given to more senior mentors.

Learn more about the training program on AdvanceCTR.org.

The machine learning revolution is already having a significant impact across the social sciences and business but it is also beginning to change computational science and engineering in fundamental and very varied ways. We are experiencing the rise of new and simpler data driven methods based on techniques from machine learning such as deep learning. This revolution allows for the development of radical new techniques to address problems known to be very challenging with traditional methods and suggests the potential dramatic enhancement of existing methods through data informed parameter selection, both in static and dynamic modes of operation. Techniques are emerging that allows us to produce realistic solutions from non-sterilized computational problems in diverse physical sciences. However, the urgent and unmet need to formally analyze, design, develop and deploy these emerging methods and develop algorithms must be addressed. Many central problems, e.g. enforcement of physical constraints in machine learning techniques and efficient techniques to deal with multiscale problems, are unmet in existing methods. The primary goal of this Hot Topic workshop is to bring together leading researchers across various fields to discuss recent results and techniques at the interface between traditional methods and emerging data driven techniques to enable innovation in scientific computing in computational science and engineering.