📍 San Francisco, CA | ✉️ mfazli@stanford.edu | 🌐 Stanford Research Portfolio | 🖇️ LinkedIn

I’m Mojtaba S. Fazli, a Lead AI Scientist and Computer Vision Engineer with extensive experience in Computer Vision, Biomedical AI, Drug Discovery, Generative AI, and Big Data Analytics. I’m passionate about harnessing data-driven approaches and scalable solutions to solve complex challenges in healthcare and drug discovery. My academic and industry experience spans renowned institutions like Stanford, Harvard, and Novartis.

With a background in Computer Science, Strategic Management, and hands-on expertise in Machine Learning, I specialize in developing cutting-edge AI solutions. My work integrates AI and computer vision for biomedical imaging, data analysis, and clinical research, contributing to advancements in rheumatology, ophthalmology, and drug discovery.

• Machine Learning & AI: Deep learning, multi-task models, contrastive learning, computer vision, NLP, and generative AI.
• Data Science & Big Data: Processing massive datasets for scalable analyses and efficient data pipelines.
• Cloud & Distributed Computing: AWS, GCP, Azure; frameworks like Apache Spark, Dask, and Kubernetes.
• Visualization & Presentation: Creating impactful visualizations with Tableau, PowerBI, and custom Python libraries.

• Postdoctoral Research Fellow - Stanford University
• Postdoctoral Research Fellow - Harvard University
• Ph.D. in Computer Science - University of Georgia
• Doctorate in Strategic Management - University of Montesquieu Bordeaux IV
• MSc. & BSc. in Artificial Intelligence and Robotics - University of Tehran

Objective: Built an advanced rheumatology clinic database with multimodal patient data to enhance diagnostic accuracy for Rheumatoid Arthritis (RA).

Technologies: Python, PyTorch, SQL, Docker, Google Cloud Platform

Impact: Contributed to successful grant applications and improved RA diagnostic workflows.

Objective: Led a multi-task ML project under the Gates Foundation to accelerate drug discovery through predictive modeling.

Technologies: Python, RDKit, Databricks, AWS, Dask

Impact: Boosted drug discovery efficiency by 30%, providing faster, accurate predictions across multiple assays.

Objective: Applied deep learning to 3D OCT image analysis, improving accuracy in ophthalmic diagnostics and therapeutic guidance.

Technologies: PyTorch, ResNet-3D, V-Net, Docker

Impact: Reduced UI errors by 20% and boosted research efficiency by 30%.

• Shi, M., et al. Artifact-tolerant clustering-guided contrastive embedding learning for ophthalmic images in glaucoma. IEEE Journal of Biomedical and Health Informatics, 2023.
• Fazli, M. S., et al. Ornet-a python toolkit to model the diffuse structure of organelles as social networks. Journal of Open Source Software, 2020.
• Li, X., et al. Scalable fast rank-1 dictionary learning for fMRI big data analysis. ACM SIGKDD International Conference, 2016.

For a full list of my publications, please visit my Google Scholar or ResearchGate.

Languages: Python, R, Java, C++, Scala
ML Libraries: TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV
Cloud Platforms: AWS, GCP, Microsoft Azure
Containerization: Docker, Kubernetes
Visualization: Tableau, PowerBI, Advanced Matplotlib

I’m always open to discussing potential collaborations, innovative research ideas, or connecting with fellow scientists in the field. Feel free to reach out!