About Me
Mojtaba S. Fazli, Ph.D., is a Postdoctoral Research Scholar and Lead AI Scientist at Stanford University, where his work pushes the boundaries of AI-driven medical research and biomedical imaging. Dr. Fazli's research integrates advanced artificial intelligence techniques with medical sciences, driving innovation in data processing and analysis to unlock new possibilities in healthcare.
Before joining Stanford, Dr. Fazli held the role of Senior Research Data Scientist and Open Innovation Scholar at the Novartis Institute for Biomedical Research, where he played a pivotal role in advancing scalable biomedical data processing pipelines. In addition to his work at Novartis, he served as a Fellow with the Gates Foundation under the MalDA Consortium, contributing to impactful global health research. Dr. Fazli also pursued postdoctoral research at Harvard University, where he led pioneering advancements in biomedical AI. He continues to collaborate with the Harvard Ophthalmology Artificial Intelligence Lab, further expanding the frontiers of medical research. His contributions have been extensively recognized and published in prestigious academic outlets, including IEEE Transactions on Medical Imaging and the esteemed KDD Conference.
He holds a Ph.D. in Computer Science, with a minor in Mathematics, from the University of Georgia, USA. Dr. Fazli also brings a unique interdisciplinary perspective, having earned a Doctorate in Business Administration alongside an M.Sc. in Economics and Management from the University of Montesquieu Bordeaux IV, France. Furthermore, his solid technical foundation stems from an M.Sc. in Artificial Intelligence and Robotics and a B.Sc. in Computer Engineering, from the University of Tehran, Iran.
Dr. Fazli's research interests lie at the intersection of Machine Learning, Computer Vision, Generative AI, Big data analytics and Statistical Analysis mainly in the context of medicine and biomedical applications. His contributions have significantly advanced the understanding of complex biological systems through AI, with a special focus on creating scalable solutions for large datasets in the biomedical field.
Selected Publications
- Shi, Min, et al. "Artifact-tolerant clustering-guided contrastive embedding learning for ophthalmic images in glaucoma." IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 9, 2023, pp. 4329--4340.
- Triana, Miryam A. Hortua, et al. "Regulation of calcium entry by cyclic GMP signaling in Toxoplasma gondii." Journal of Biological Chemistry, vol. 300, no. 3, 2024.
- Eslami, Mohammad, et al. "PyVisualFields: A Python Package for Visual Field Analysis." Translational Vision Science & Technology, vol. 12, no. 2, 2023, pp. 6--6.
- Zebardast, Nazlee, et al. "Deep unsupervised discovery of OCT phenotypes enables genome-wide analyses." Investigative Ophthalmology & Visual Science, vol. 63, no. 7, 2022, pp. 1844--1844.
- Saini, Chhavi, et al. "Assessing surface shapes of the optic nerve head and peripapillary retinal nerve fiber layer in glaucoma with artificial intelligence." Ophthalmology Science, vol. 2, no. 3, 2022, p. 100161.
- Pulagam, Neelima, et al. "Classification of Diffuse Subcellular Morphologies." 2021.
- Stasic, Andrew J., et al. "Ca2+ entry at the plasma membrane and uptake by acidic stores is regulated by the activity of the V-H+-ATPase in Toxoplasma gondii." Molecular Microbiology, vol. 115, no. 5, 2021, pp. 1054--1068.
- Hill, Marcus, et al. "Spectral Analysis of Mitochondrial Dynamics: A Graph-Theoretic Approach to Understanding Subcellular Pathology." SciPy, 2020, pp. 91--97.
- Fazli, Mojtaba, et al. "Ornet-a python toolkit to model the diffuse structure of organelles as social networks." Journal of Open Source Software, vol. 5, no. 47, 2020, p. 1983.
- Fazli, Mojtaba Sedigh, et al. "Lightweight and scalable particle tracking and motion clustering of 3D cell trajectories." 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019, pp. 412--421.
- Fazli, Mojtaba S., et al. "Toward simple & scalable 3D cell tracking." 2018 IEEE International Conference on Big Data (Big Data), 2018, pp. 3217--3225.
- Durden, Andrew, et al. "Dynamic Social Network Modeling of Diffuse Subcellular Morphologies." 2018.
- Fazli, Mojtaba S., et al. "Unsupervised discovery of toxoplasma gondii motility phenotypes." 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018, pp. 981--984.
- Fazli, Mojtaba Sedigh, et al. "Computational motility tracking of calcium dynamics in toxoplasma gondii." arXiv preprint arXiv:1708.01871, 2017.
- Lin, Binbin, et al. "Distributed Rank-1 Dictionary Learning." 2016.
- Li, Xiang, et al. "Scalable fast rank-1 dictionary learning for fMRI big data analysis." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 511--519.
For the complete list of my publications, please visit my profiles on Google Scholar or ResearchGate.
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