Ali Hadizadeh is a co-founder and lead scientist who builds practical tools to accelerate deep learning, specializing in automated quantization and compression for LLMs and generative models. With a PhD from the University of Toronto and seven years of hands-on experience across academia and industry, he pioneered GOBO and its successor Mokey—early post-training quantization methods that shrink transformers without fine-tuning. He has driven system-level engineering at 1QBit and Irreversible, pairing algorithmic innovation with hardware-aware designs to cut memory traffic and energy for inference and training. Based in Toronto, he blends research rigor with startup execution, routinely translating paper ideas into deployable accelerators and model optimization toolchains. An uncommon strength is his end-to-end focus: from mathematical quantization techniques to FPGA and memory-compression implementations that maximize on-chip efficiency.
7 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Engineering, 4/4, Doctor of Philosophy - PhD, Computer Engineering, 4/4 at University of Toronto
Master of Science - MS, Digital Systems, Electrical Engineering, 4/4, Master of Science - MS, Digital Systems, Electrical Engineering, 4/4 at Sharif University of Technology
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Contributions:2 reviews, 3 comments, 2 issues in 1 year 3 months
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Ali Hadizadeh - Co-Founder, Lead Scientist at ByteShape