• TBA

  • Jan
    8:00am - 4:30pm

    Advance-CTR Mentoring Training Program

    University of Rhode Island

    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

    Advising, Mentorship, Biology, Medicine, Public Health, Careers, Recruiting, Internships, Research, Training, Professional Development
  • Jan

    Scientific Machine Learning

    121 South Main Street

    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.

    Mathematics, Technology, Engineering
  • Theory and Practice in Machine Learning and Computer Vision Feb 18 - 22, 2019 Recent advances in machine learning have had a profound impact on computer vision. Simultaneously, success in computer vision applications has rapidly increased our understanding of some machine learning techniques, especially their applicability. This workshop will bring together researchers who are building a stronger theoretical understanding of the foundations of machine learning with computer vision researchers who are advancing our understanding of machine learning in practice. Much of the recent growth in the use of machine learning in computer vision has been spurred by advances in deep neural networks. At the same time, new advances in other areas of machine learning, including reinforcement learning, generative models, and optimization methods, hold great promise for future impact. These raise important fundamental questions, such as understanding what influences the ability of learning algorithms to generalize, understanding what causes optimization in learning to converge to effective solutions, and understanding how to make optimization more efficient. The workshop will include machine learning researchers who are addressing these foundational questions. It will also include computer vision researchers who are applying machine learning to a host of problems, such as visual categorization, 3D reconstruction, event and activity understanding, and semantic segmentation.

    Mathematics, Technology, Engineering
  • Limin Peng, PhD
    Professor, Biostatistics and Bioinformatics
    Rollins School of Public Health
    Emory University
    Abstract and Title to be announced

    Biology, Medicine, Public Health, Education, Teaching, Instruction, Graduate School, Postgraduate Education, Research, Training, Professional Development


DSI Colloquia take place in CIT 241 (Swig Boardroom), at 4:00 PM unless otherwise noted.

september 21

Michael Littman

Professor of Computer Science, Brown University
Joseph Cappelleri
Executive Director of Biostatistics, Pfizer Inc.
Adjunct Professor of Biostatistics, Brown University
Thomas Serre
Associate Professor of Cognitive, Linguistic, and Psychological Sciences; Brown University
DeDe Paul
Director of Statistics Research, AT&T Labs
november 28 
Patrick Steele
Operations Research Scientist, Wayfair
December 5
Ellie Pavlick​
Assistant Professor, Department of Computer Science; Brown University