CV
Academic and industry experience across machine learning, LLMs, and astrophysics.
Contact Information
| Name | Mohammad Safarzadeh |
| Professional Title | Principal Applied Scientist |
| mtsafarzadeh@gmail.com |
Professional Summary
Machine learning researcher and engineer working across large language models, generative AI, efficient neural networks, and scientific applications of machine learning.
Experience
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2022 - present Remote
Principal Applied Scientist
Oracle
Leading work on large language models and generative AI, with an emphasis on domain adaptation and production-oriented research.
- Fine-tuned and pre-trained LLMs for specialized domains including healthcare.
- Built research and applied ML workflows that connect model development with practical deployment needs.
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2021 - 2022 Remote
Senior Machine Learning Engineer
Perceive
- Developed efficient neural network inference solutions for edge deployment.
- Worked on lightweight model architectures and performance optimization.
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2019 - 2021 Remote
Scientist II
FICO
- Built machine learning models for credit card fraud detection.
- Applied ML methods in high-stakes financial decision systems.
Education
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2011 - 2016 Baltimore, MD
PhD
Johns Hopkins University
Astrophysics
- Research on gravitational waves and high-energy astrophysics.
- Developed AI-aided approaches to identify astrophysical sources of gravitational wave events.
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2016 - 2019 Cambridge, MA
Postdoctoral Fellow
Harvard University
Astrophysics
- Conducted postdoctoral research in astrophysics with a focus on gravitational waves, dark matter, and high-energy phenomena.
Skills
Machine Learning & AI (Expert): Large Language Models, Fine-tuning, Pre-training, Generative AI, Neural Networks, Deep Learning, PyTorch, TensorFlow
Programming (Expert): Python, C++, SQL, Bash
Research (Expert): Astrophysics, Gravitational Waves, Dark Matter, High-Energy Astrophysics, Scientific Machine Learning
Interests
Applied AI: Large Language Models, Medical AI, Efficient Inference, Generative AI
Scientific Computing: Astrophysics, Scientific Modeling, Gravitational Waves, Data-Driven Discovery