Mozhdeh Gheini is a machine learning research engineer and final-year PhD candidate in computer science at USC, specializing in transfer learning, machine translation, and parameter-efficient fine-tuning for data-scarce settings. With 11 years of industry and research experience, she has an established track record at Apple (multiple internships and now a research role) and at USC/ISI driving thesis work on inductive biases for data- and parameter-efficient transfer learning. She blends deep research rigor with product-minded engineering, having shipped research projects on gender debiasing, pseudo-labeling for speech translation, and weakly supervised entity linking. Based in New York, she is seeking roles at the intersection of product and research where her meta-learning and practical deep learning skills can accelerate real-world NLP systems. An interesting non-obvious detail: her thesis explicitly targets both data- and parameter-efficiency, signaling a focus on making large-model capabilities accessible under practical resource constraints.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Southern California
Bachelor of Science (B.Sc.), Computer Software Engineering, Bachelor of Science (B.Sc.), Computer Software Engineering at Sharif University of Technology
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