DSI Colloquia take place Wednesdays, 4-5 pm, at 164 Angell, 3rd floor
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.
Data are not objective; algorithms have biases; machine learning doesn’t produce truth. These realities have uneven effects on people’s lives, often serving to reinforce existing systemic biases and social inequalities. At the same time, data can be used in the service of social justice, and taking control of the data produced about people and its use is more and more important in our data-centric world. Brown’s Center for the Study of Race and Ethnicity in America (CSREA) and Data Science Initiative (DSI) invite you to a panel discussion about algorithms, race, and justice—the problems and the possibilities.
Presented by the Data Science Initiative and the Center for the Study of Race and Ethnicity in America at Brown University.
Free and open to the public. Light reception to follow.
Limin Peng, PhD
Professor, Biostatistics and Bioinformatics
Rollins School of Public Health
Title: Quantile Regression Modeling of Latent Trajectory Features with Longitudinal Data
Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under multilevel modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.
Register now for, “Better Together: Putting Team Science Theory into Practice to Enhance Your Research,” a workshop presented by Debbie Cornman, PhD, and Katie Sharkey, MD, PhD.
Medical research continues to move away from the traditional “independent investigator” model, and toward collaborative approaches that integrate diverse disciplines with traditional biomedical sciences.
The National Institutes of Health established team science approaches as a major goal in its 2003 Roadmap . Yet, despite educational efforts and funding incentives, barriers toward achieving this goal remain. Many investigators are not aware of how to implement team science approaches into their research, or utilize available support.
In this workshop, Debbie Cornman, PhD and Katie Sharkey, MD, PhD will discuss the latest findings in the “science of team science,” and teach investigators how to implement strategies and techniques for successful team science approaches into their own research.
By the conclusion of the workshop, attendees will:
Who should attend:
All faculty, researchers, and affiliated staff at Brown, URI, and the affiliated hospital systems who are interested in team-based science approaches. Physician scientists and junior faculty are encouraged to attend.
The Warren Alpert Medical School of Brown University is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
Physicians: The Warren Alpert Medical School of Brown University designates this live activity for a maximum of 1.5 AMA PRA Category 1 Credits™ . Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Susan Athey, PhD
The Economics of Technology Professor
Stanford Graduate School of Business
Title and abstract to be posted