Canal
Solving BioEngineering Problems Using Predictive Modeling and Machine Learning Approaches (303769)
*Ahmad M. Haider, AGCO Corporation
Keywords: machine learning, modeling, predictive models, biostatistics, data science
This session will introduce application of machine learning to fundamental problems in field of bioengineering. Machine learning methods will be used to design a data collection and segmentation strategy in order to improve accuracy of energy reconstructions for single molecule biological interactions. The presentation will be broken down as follows: (a) Motivation: Description of how a traditional bioengineering based problem statement was framed as a data science use-case (b) Modeling: Use of data science techniques to design features which represent specific data patterns and choice of appropriate statistical algorithms to model patterns (c) Application: Deployment of predictive models to obtain highly accurate energy reconstructions. The modeling section is designed to be interactive in nature and involves audience participation. This session will help participants to create their own machine learning applications to solve relevant problems at work across different industries, especially when the usage of data science is not immediately obvious. Author has held lead data science positions at multiple Fortune 500 companies and has a PhD and papers in quantitative data analysis.