There is no registration fee. A light breakfast and box lunches will be provided.
PURPOSE. Sponsored by the Center for Data Science at UMass Amherst, the Data Science Research Symposium showcases active university-industry research partnerships, provides a forum for technical exchange and professional networking among data science researchers across industry and academia, and facilitates new collaborative efforts to tackle emerging research challenges of practical importance.
AUDIENCE. Industrial data scientists conducting basic and applied research; data science faculty and students from UMass Amherst and the Five College consortium; current and prospective data science project managers and technical team leaders; research sponsors and consumers of data science research results.
HIGH-LEVEL AGENDA. The morning plenary session, running from 9:00am until noon, will consist of short talks summarizing ongoing industry-sponsored research, emphasizing novel findings and practical applications. After lunch, attendees will select one of four workshops to participate in during the afternoon. Workshops will provide a forum for deeper exploration of particular research topics, and/or offer tutorial material on cutting-edge data science techniques and practices.
Modeling Wellness in Health and Finance. This workshop explores the challenges associated with modeling “wellness” in health and finance domains. How might indicators of physical wellness predict changes in financial wellness, and vice versa? The audience for this workshop includes data scientists in healthcare-IT and financial services domains working on risk modeling and forecasting, or those seeking to make predictions based on analysis of patient records or financial transactions.
Workforce Analytics. This workshop will focus on the data, methods, and fundamental research challenges involved in applying advanced data science methods to massive‐scale workforce data. Achievements in this area promise to help grow the economy, reduce economic inequality, and accelerate progress in STEM by making workforce decision‐making more effective and evidence‐based. The audience for this workshop includes government leaders; those responsible for hiring, educating and training; resume/job companies; and those with data bearing on career-path development.
Machine Learning Foundations. This workshop explores the latest developments in machine learning, their applications to problems of practical interest, and the new work needed to make machine learning algorithms more fair and explainable. Any data scientist using machine learning methods will find this workshop of interest.
Data Science Systems. This workshop explores the scientific challenges involved in designing and operating big data analytics pipelines, and processing data at speed and massive scales. Any data scientist doing true “big data” will find this workshop of interest.