Ensemble Machine Learning Approaches in R

The Hacker Within


April 18, 2018
5:00pm to 6:30pm
190 Doe Library
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Nima Hejazi & Jeremy Coyle will lead this session focussing on "Ensemble Machine Learning Approaches in R."

Full details about this session can be found here: http://www.thehackerwithin.org/berkeley/upcoming.html.  All are welcome to join.   

The Hacker Within is a weekly peer learning group for sharing skills and best practices in research computation and data science. In these friendly sessions, peers at all levels of experience share information about tools for analyzing all types of data.  For more information, sign up for the THW mailing list or contact the organizers directly at thw-admin@berkeley.edu.


Nima Hejazi

PhD student, Biostatistics, UC Berkeley

Nima Hejazi is a PhD student in the Group in Biostatistics, where he is jointly supervised by Mark van der Laan and Alan Hubbard. Nima is also affiliated with the UC Berkeley NIH Biomedical Big Data training program and the Center for Computational Biology. Currently, his research centers around nonparametric statistical and causal inference, machine learning, and statistical computing – focusing on the development of robust techniques for inference and estimation in an eclectic collection of problem settings, with applications often arising in precision medicine, vaccine efficacy trials, computational biology, and public policy.

Jeremy Coyle

UC Berkeley

Jeremy Coyle is a recent PhD graduate in Biostatistics who continues working with the department to translate statistical theory to software. During his PhD studies, Jeremy worked with Alan Hubbard and Mark van der Laan on a series of projects broadly related to computational statistics, including more efficient cross-validation routines for ensemble machine learning and a software framework for cross-validation (origami). His current research interests include causal inference, model selection, re-sampling techniques, statistical software development, and statistical methods for assessing time series data from sensor systems.