The Calgary Machine Learning Lab is a research group led by Yani Ioannou within the Schulich School of Engineering at the University of Calgary. The lab has a research focus on improving Deep Neural Network (DNN) training and models. Topics of research include: Sparse Neural Network Training, Bias and Robustness of Efficient Deep Learning methods and Efficient Inference with Large Language Models.
Lab photo from May 2026 outside the ICT building at the University of Calgary.
news
| Apr 30, 2026 | Adnan Mohammed’s paper, “SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training” (Adnan et al., 2026), has been accepted at the International Conference on Machine Learning (ICML), 2026. This work introduces SparseOpt, a novel optimization algorithm designed to mitigate the issue of gradient skew caused by normalization in sparse training. SparseOpt effectively balances the gradient updates across all parameters, leading to improved convergence and performance in sparse neural networks. |
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| Jan 26, 2026 | Mike Lasby’s collaborative work with Cerebras, “REAP the experts: Why pruning prevails for one-shot moe compression” (Lasby et al., 2026), has been accepted at the International Conference on Learning Representations (ICLR), 2026. This work explores the compression of Sparse Mixture of Experts (SMoE) models through expert compression techniques, demonstrating that REAP (Router-weighted Expert Activation Pruning) outperforms existing expert merging and pruning methods in terms of compressed model quality retention. |
| Dec 01, 2025 | Tejas Pote was awarded the Alberta Innovates Graduate Student Scholarship in the 2025 Graduate Award Competition. The Government of Alberta funds Alberta Innovates Graduate Studentship Scholarships to support top-quality, research related to Information and Communications Technology (ICT), Nanotechnology, and other technology areas. |
