Ali Heydari
Student Researcher at University of California, Merced
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolAli Heydari is a Senior Research Scientist and applied mathematician with eight years of experience bridging machine learning research and real-world systems across Google, Microsoft, Amazon, and national labs. A fourth-year applied math PhD candidate with a background in cancer systems biology, he specializes in deep learning, computer vision, NLP, and bioinformatics, applying rigorous math to practical problems like VM risk characterization and large-scale ad ranking. At Microsoft and Amazon he translated theoretical advances—generalizing ML to semi-metric spaces and optimizing LLM training/inference—into measurable production gains and state-of-the-art results. His trajectory at Google progressed from intern to Senior Research Scientist, reflecting an ability to move ideas rapidly from prototype to impact within large engineering organizations. Based in San Francisco, he blends academic rigor with product-minded engineering, often reframing applied problems through novel mathematical lenses that improve model scalability and deployment.
8 years of coding experience
1 year of employment as a software developer
Bachelor of Science (B.S.), Applied Mathematics, Bachelor of Science (B.S.), Applied Mathematics at University of California, DavisDoctor of Philosophy - PhD, Applied Mathematics, Doctor of Philosophy - PhD, Applied Mathematics at University of California, MercedUniversity of California, Irvine
Arabic, Persian, English
Github Skills (64)
variational-autoencoder10
audio10
loss-balancing10
generative-model10
pytorch9
llm9
python9
machine-learning9
spatial-transcriptomics9
natural-language-processing9
sequencing9
deep-learning9
synthetic-dataset-generation9
nlp9
speech-recognition9
Programming languages (4)
Github contributions (5)
dr-aheydari/ScientificComputing-Math233
Sep 2020 - Dec 2021
Contributions:80 commits, 102 pushes, 1 branch in 1 year 3 months
SindiLab/N-ACT
May 2022 - Oct 2022
The public repository for N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification
Contributions:4 reviews, 13 commits, 5 PRs in 5 months
deep-learning
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