SORA-TABA-DLSPH Workshop 2020

Dalla Lana School of Public Health, 155 College Street, Toronto, 6th floor Auditorium, HS610
Vendredi, 22 mai, 2020

Statistical Machine Learning for Biomedical Data

Date: May 22, 2020
Time: 8:00 am to 4:00 pm
Instructor: Dr. Noah Simon, University of Washington

The Registration link below will be activated soon. The registration fee (TBA) includes lunch and coffee breaks.

The Division of Biostatistics at the Dalla Lana School of Public Health is pleased to host the SORA-TABA Workshop & DLSPH Biostatistics Research Day. The event brings together regional and local statistical communities who are interested in biostatistics, financial statistics and other applied areas of statistics. Please join us in making this event a great success!

In addition to the lecture presentation, the workshop will include poster presentations by participants; students and post-docs are particularly encouraged to present their research or practicum work, and three poster awards will be given at the closing ceremony. There will also be career advice mentors in attendance who will be available to provide career-building advice, especially for graduate students.

Please note that on-site registration is not available and ALL registration must be completed by May 15th. Space is limited and registration will be first come first served.

 

Statistical Machine Learning for Biomedical Data

Instructor: Dr. Noah Simon, University of Washington

Dr. Noah Simon from the University of Washington will present a 1-day intensive workshop. You’ll learn about how traditional statistical methods like logistic regression can be used for high dimensional data (data with many variables) and then move onto machine learning methods such as random forests, support vector machines, (gradient) boosting using trees and neural networks.

New machine learning methods will be related to more classical statistical approaches – all designed for an audience with familiarity with statistical approaches and discussed in the context of biomedical big data and predictive modeling.

Throughout the course, Dr. Simon will focus on common pitfalls in the supervised analysis of Biomedical Big Data and how to avoid them. The course will include interactive discussions/”Challenge Questions”, to help participants actively engage with applying these tools in biomedical scenarios.

By the end of the workshop, participants will be able to….

  1. Understand the bias/variance trade-off and its various applications;
  2. Understand the use of split-sample validation for tuning bias/variance and evaluating performance;
  3. Have some intuition for the various regression/classification methods, as well as model aggregation;
  4. Understand the main ideas in deep learning, how they relate to classical statistical ideas, and some scenarios where they may be useful.

 

About the Instructor

Dr. Noah Simon received his PhD in Statistics from Stanford University under the supervision of Professor Robert Tibshirani. He is an Associate Professor in the Department of Biostatistics at the University of Washington and has affiliate appointments at the Therapeutics Development Network of Seattle Children’s Hospital and the Kaiser Permanente Health Research Institute. His work is at the intersection of biostatistics, machine learning, and computational biology. He develops methodology that engages with machine learning, biomarker discovery, and clinical trial design. His collaborative work includes applications in immunology, oncology, and cystic fibrosis, among other areas.

Workshop Committee Members

  • TBA

If you have any question regarding the workshop, please send your inquiry to Ryan Rosner at biostat.dlsph@utoronto.ca.