There is no registration fee. A light breakfast and box lunches will be provided.
Click here to register.
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.
Barings/Springfield Venture Fund
Burning Glass Technologies
Chan Zuckerberg Initiative
Department of Population Medicine of Harvard Medical School & Harvard Pilgrim Health Care Institute
Farm Credit Financial Partners
Holyoke Gas & Electric Dept
Lincoln Peak Partners
Mass Insight Global Partnerships
Massachusetts Green High Performance Computing Center
Systems & Technology Research
The MITRE Corporation
|8:00||Registration opens - Light breakfast and social networking|
|9:00 - 12:00||Faculty/Industry Lightning Talks
|12:00 - 1:00||Lunch / Transition to Workshops|
|1:00 - 5:00|| Research Workshops, parallel tracks
Foundations of Machine Learning and Data Science.This workshop explores the latest developments in machine learning theory and algorithms, along with applications of machine learning to text processing and image understanding tasks. Any data scientist using machine learning methods will find this workshop of interest. Co-Leaders: Subhransu Maji & Akshay Krishnamurthy
Models of Financial Well-Being and Systemic Risk. This workshop explores the challenges associated with modeling well-being and systemic risks in finance and healthcare applications. The audience for this workshop includes data scientists in financial services and digital health 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. Co-Leaders: Matt Rattigan & Andrew McCallum
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