Bryce Liesemer
Manager, IT Operations & Business Continuity
Calgary, Alberta, Canada
Summary
👤
Senior🎓
Top SchoolBryce Liesemer is an IT operations and business continuity manager with over a decade of experience delivering resilient infrastructure for healthcare and community organizations. He combines hands-on engineering—servers, Windows Server, Cisco, virtualization, Veeam/Azure backups and WiFi—with proven leadership running service desks and large migrations, including ERP consolidation and Office 365 at scale. Based in Calgary, he also teaches SQL Server to industry professionals, reflecting a commitment to knowledge transfer and practical training. Uncommonly for an operations leader, Bryce has contributed to medical imaging ML tooling by implementing a BalancedSampler for CNN training, showing an ability to bridge infrastructure, data, and applied machine learning workflows.
9 years of coding experience
16 years of employment as a software developer
High School Diploma, usual stuff, High School Diploma, usual stuff at Cochrane High SchoolDiploma, Programming / Network Administration, Diploma, Programming / Network Administration at CDI College
Github Skills (14)
mask-rcnn10
faster-rcnn10
machine-learning10
image-segmentation10
subsampling10
deep-learning10
tensorflow10
segmentation10
sampling10
medical-image-analysis10
python10
resampling10
numpy9
unit-testing9
Programming languages (6)
Github contributions (5)
NifTK/NiftyNet
Apr 2018 - Apr 2018
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
Role in this project:
ML Engineer
ML EngineerContributions:6 commits in 1 day
Contributions summary:Bryce implemented and tested a `BalancedSampler` class, a critical component for training convolutional neural networks in medical image analysis. This sampler addresses class imbalance issues by ensuring each label has an equal chance of being selected during training. The user also integrated the `BalancedSampler` into the `SegmentationApplication` and developed thorough unit tests to validate its functionality, ensuring robustness in the context of medical image analysis. This contribution directly improves the training process of segmentation models.
image-analysistherapymedical-imagedeep-learningconvolutional
Besler/ITKBoneEnhancement
Nov 2017 - Nov 2018
Various filters for enhancing cortical bones in quantitative computed tomography.
Contributions:108 commits, 17 PRs, 60 pushes in 11 months
pythonbrain-computer-interfacefeature-extractionfilterscomputed-tomography
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