`

Timezone: »

 
Tutorial
Machine Learning for Computational Biology and Health
Anna Goldenberg · Barbara Engelhardt

Mon Dec 09 11:15 AM -- 01:15 PM (PST) @ West Ballroom A + B

Questions in biology and medicine pose big challenges to existing ML methods. The impact of creating ML methods to address these questions may positively impact all of us as patients, as scientists, and as human beings. In this tutorial, we will cover some of the major areas of current biomedical research, including genetics, the microbiome, clinical data, imaging, and drug design. We will focus on progress-to-date at the intersection of biology, health, and ML. We will also discuss challenges and open questions. We aim to leave you with thoughts on how to perform meaningful work in this area. It is assumed that participants have a good grasp of ML. Understanding of biology beyond high school level is not required.

Author Information

Anna Goldenberg (SickKids/University of Toronto)

Dr Goldenberg is a Senior Scientist in Genetics and Genome Biology program at SickKids Research Institute, recently appointed as the first Varma Family Chair in Biomedical Informatics and Artificial Intelligence. She is also an Associate Professor in the Department of Computer Science at the University of Toronto, faculty member and an Associate Research Director, Health at Vector Institute and a fellow at the Canadian Institute for Advanced Research (CIFAR), Child and Brain Development group. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a post-doctoral focus in computational biology and medicine. The current focus of her lab is on developing machine learning methods that capture heterogeneity and identify disease mechanisms in complex human diseases as well as developing risk prediction and early warning clinical systems. Dr Goldenberg is a recipient of the Early Researcher Award from the Ministry of Research and Innovation. She is strongly committed to creating responsible AI to benefit patients across a variety of conditions.

Barbara Engelhardt (Princeton University)

Barbara E. Engelhardt is an associate professor in the Princeton Computer Science Department, on leave in 2019-2020 working as a principal scientist at Genomics Plc. Previously, she was an assistant professor at Duke University in Biostatistics and Bioinformatics and Statistical Sciences. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms and dynamics of complex phenotypes and human disease.

More from the Same Authors

  • 2022 Workshop: Learning from Time Series for Health »
    Sana Tonekaboni · Thomas Hartvigsen · Satya Narayan Shukla · Gunnar Rätsch · Marzyeh Ghassemi · Anna Goldenberg
  • 2021 : Offline Reinforcement Learning for Hospital Patients When Every Patient is Different »
    Barbara Engelhardt
  • 2021 : Invited Speaker Panel »
    Sham Kakade · Minmin Chen · Philip Thomas · Angela Schoellig · Barbara Engelhardt · Doina Precup · George Tucker
  • 2020 Workshop: Learning Meaningful Representations of Life (LMRL.org) »
    Elizabeth Wood · Debora Marks · Ray Jones · Adji Bousso Dieng · Alan Aspuru-Guzik · Anshul Kundaje · Barbara Engelhardt · Chang Liu · Edward Boyden · Kresten Lindorff-Larsen · Mor Nitzan · Smita Krishnaswamy · Wouter Boomsma · Yixin Wang · David Van Valen · Orr Ashenberg
  • 2020 Poster: What went wrong and when? Instance-wise feature importance for time-series black-box models »
    Sana Tonekaboni · Shalmali Joshi · Kieran Campbell · David Duvenaud · Anna Goldenberg
  • 2019 : Anna Goldenberg Talk »
    Anna Goldenberg
  • 2018 : Poster Session I »
    Aniruddh Raghu · Daniel Jarrett · Kathleen Lewis · Elias Chaibub Neto · Nicholas Mastronarde · Shazia Akbar · Chun-Hung Chao · Henghui Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adityanarayanan Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Tzu Ming Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang
  • 2018 Poster: PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits »
    Bianca Dumitrascu · Karen Feng · Barbara Engelhardt
  • 2015 Workshop: Machine Learning in Computational Biology »
    Nicolo Fusi · Anna Goldenberg · Sara Mostafavi · Gerald Quon · Oliver Stegle
  • 2014 Workshop: Machine Learning in Computational Biology »
    Oliver Stegle · Sara Mostafavi · Anna Goldenberg · Su-In Lee · Michael Leung · Anshul Kundaje · Mark B Gerstein · Martin Renqiang Min · Hannes Bretschneider · Francesco Paolo Casale · Loïc Schwaller · Amit G Deshwar · Benjamin A Logsdon · Yuanyang Zhang · Ali Punjani · Derek C Aguiar · Samuel Kaski
  • 2013 Workshop: Machine Learning in Computational Biology »
    Jean-Philippe Vert · Anna Goldenberg · Sara Mostafavi · Oliver Stegle
  • 2012 Workshop: Machine Learning in Computational Biology »
    Jean-Philippe Vert · Anna Goldenberg · Christina Leslie
  • 2011 Workshop: Machine Learning in Computational Biology »
    Jean-Philippe Vert · Gunnar Rätsch · Yanjun Qi · Tomer Hertz · Anna Goldenberg · Christina Leslie
  • 2010 Workshop: Networks Across Disciplines: Theory and Applications »
    Edo M Airoldi · Anna Goldenberg · Jure Leskovec · Quaid Morris