LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow - PowerPoint PPT Presentation

Oct 12, 2022 •252 likes •315 views

LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow RESEARCHERS PIRE Hackathon 2015 Finding an interdisciplinary researcher should be as easy as a Google search 1. Connect young faculty needing to take risks 2. Connect data

LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow RESEARCHERS PIRE Hackathon 2015
Finding an interdisciplinary researcher should be as easy as a Google search 1. Connect young faculty needing to take risks 2. Connect data scientists to long-tail researchers 3. Provide an objective collaborator metadata service
THE RENAISSANCE SCIENTIST IS DEAD One person, one lab , can no longer do everything 1. Tough Questions, Tougher Methods 2. Modern methods require modern statistical analysis for quality results 3. Connect Data to Data Scientists Through Data Commons
BRINGING A ‘DATA PERSPECTIVE’ TO SCIENTISTS Focus on ‘Hooking’ scientists to new ideas requiring minimal user input Low Energy, Simple Comprehensive, Organized Search Result Data Intensive Long-Tail Methodology Scientists
BEYOND A KEYWORD SEARCH: AGGREGATION AND METADATA User Name, Scientific Background and User Inputs Institution Interests Expanded Keyword searches Process Aggregate Results Generate Metadata Comprehensive, Customizable Outputs Suggestions

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LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow - PowerPoint PPT Presentation

LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow RESEARCHERS PIRE Hackathon 2015 Finding an interdisciplinary researcher should be as easy as a Google search 1. Connect young faculty needing to take risks 2. Connect data

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LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow - PowerPoint PPT Presentation

LASSO: CONNECTING Ryan Mork INTERDISCIPLINARY Genevieve Shattow RESEARCHERS PIRE Hackathon 2015 Finding an interdisciplinary researcher should be as easy as a Google search 1. Connect young faculty needing to take risks 2. Connect data

Ridge/Lasso Regression, Model selection Xuezhi Wang Computer Science Department Carnegie Mellon

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Using Stata 16s lasso features for prediction and inference Di Liu StataCorp August, 2019

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Complexity Analysis of the Lasso Regularization Path Julien Mairal and Bin Yu Inria, UC Berkeley

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