Bret Nestor



I am researching ways to ensure that machine learning models are robust to data distribution changes after deployment in healthcare.

Patient information recorded in electronic health records (EHR) are recorded as asynchronous events. For machine learning models, it is often convenient to convert these representations into synchronous blocks of time. This transformation results in discretised data with exceptionally high amounts of missing data (i.e. blood samples are not drawn hourly). I am interested in ways to represent patient trajectories as a single vector in a robust manner so that we can fine tune models for automated clinical decisions, ensure performance when transporting models across multiple institutes, and create decision performance that endures the chaotic evolution of medicine.

Relevant literature:

For a list of my publications, please check my Google Scholar page.


PhD in Computer Science, University of Toronto,
Advisors: Dr. Marzyeh Ghassemi (MIT) and Dr. Anna Goldenberg

3.67 GPA


M.Eng. in Bioengineering, University of California, Berkeley,
Advisor: Dr. Seung Wuk Lee

3.80 GPA


B.A.Sc. in Mechanical Engineering, University of British Columbia, Okanagan,
Advisors: Dr. Mina Hoorfar and Dr. Hadi Mohammadi

3.80 GPA



2021 Visiting Student
CSAIL, Massachusetts Institute of Technology
2018/09 Student Researcher
Vector Institute & SickKids Hospital, Toronto, Canada.
Dr. Marzyeh Ghassemi and Dr. Anna Goldenberg
  • Representation learning for electronic health records and time series data
2019/04 Visiting Student Researcher
LKS CHART, St.Michael’s Hospital, Toronto, Canada
Dr. Muhammad Mamdani and Dr. Amol Verma
  • Early warning system for general internal medicine
  • Multi-site generalisability for underdiagnosed disease classification.
Teaching Assistant
CSC311, Intro to Machine Learning, University of Toronto
Dr. Murat Erdogdu and Dr. Richard Zemel
Teaching Assistant
CSC2541, Machine Learning for Healthcare, University of Toronto
Dr. Marzyeh Ghassemi
Teaching Assistant
CSC311, Intro to Machine Learning, University of Toronto
Dr. Roger Grosse
Microengineering Research Assistant
Wyss Institute for Bioinspired Engineering at Harvard, Boston, USA
Dr. Donald Inger & Dr. Richard Novak
  • Created computer vision algorithms and hardware to infer omics in whole animal infectious disease screens (executed on AWS instance)
  • Created a cognitive screening platform for analysing behavioural effects of pharmaceuticals
  • Created an affordable and manufacturable optogenetic stimulation box for Dr. Mike Levin’s Lab at Tufts University
  • Designed image processing pipeline incorporating AI algorithms to measure oxygen concentration in microfluidic devices
  • Created Custom printed circuit boards (PCBs) for various hardware and sensing capabilities
Graduate Student Instructor (Volunteer)
BIOE221, Advanced BioMEMS and Bionanotechnology, University of California, Berkeley
Dr. Luke Lee
Student Researcher
Bio-Nanomaterials Lab, Lawrence Berkeley National Laboratory, University of California, Berkeley, USA
Dr. Seung-Wuk Lee
  • Producing bio-nano surfaces from Bacteriophages for portable diagnostics
  • Applying computer vision for automatic colourimetric sensor analysis
Student Researcher
Advanced Thermofluidics Laboratory and Integrated Bio-Micro/Nanotechnology Laboratory, University of British Columbia, Kelowna, Canada
Dr Mina Hoorfar and Dr. Keekyoung Kim
  • Characterized dielectrophoretic patterning of mammalian cells in hydrogel on digital microfluidic platforms. The paper can be found here.
  • Produced custom PCB and chip designs, and fabricated devices using photolithography
Student Researcher
Heart Valve Performance Laboratory, University of British Columbia, Kelowna, Canada
Dr. Hadi Mohammadi
  • Designed manufacturing process for biomimetic hydrogel microvessels
  • Surgically simulated aortic root replacement with coronary artery bypass grafts on the models


  • Pytorch
  • Tensorflow
  • python (opencv, scikit-learn, pandas)
  • AWS/GCP/Slurm
  • Control system design/Robotics
  • C++ (Arduino, OpenCV)
  • MATLAB/Simulink
  • Clean-room microfabrication
  • Microfluidic Electrode/PCB/Sensor design
  • Bacterial and mammalian cell culture
  • Viral amplification (M13 phage)
  • Nano-surface fabrication
  • Solidworks/Fusion360 design and rapid prototyping
  • Project planning, designing, implementing, monitoring, and evaluation
  • Academic writing
  • Root cause analysis