I am Zarifeh, an AI Research Scientist working at the intersection of computational biology, systems immunology, and interpretable machine learning. My work bridges high dimensional data and advanced artificial intelligence to better understand complex biological systems and translate data into meaningful biomedical insights.

    My journey into artificial intelligence and computational biology began with a deep curiosity about how complex systems can be understood, modeled, and ultimately improved. I pursued my Master’s and later my PhD in Control and Systems Engineering, specializing in Computational and Systems Biology. During this time, I developed a strong foundation in mathematical modeling, dynamical systems, and data-driven approaches to biological complexity—tools that would later define my scientific path.

    As a Visiting Scholar and subsequently a Postdoctoral Researcher, I expanded my focus toward large-scale multi-omic data analysis and gene regulatory network modeling. I became increasingly interested in how interpretable machine learning and mechanistic modeling could work together to move beyond prediction toward causal understanding. My work integrated single-cell genomics, transcriptomics, and advanced AI methods—including deep learning, graph neural networks, and language models—to extract meaningful biological insights from high-dimensional data.

    Today, as an AI Research Scientist, I design scalable, reproducible computational frameworks that translate complex biological signals into actionable insights for precision medicine and therapeutic discovery. My research is driven by a central question: how can we make AI not only powerful, but biologically meaningful and mechanistically grounded?

    Across academia and translational research environments, I have collaborated with multidisciplinary teams, contributed to software development for biochemical modeling, and built high-performance predictive systems that enable causal inference and translational insight. I am passionate about bridging disciplines, mentoring emerging scientists, and advancing data-driven biomedical innovation.