Farri Mohajerani
AI Engineer 路 Computational Biophysicist
AI Engineer 路 Computational Biophysicist
I am a Senior AI Engineer at Wolters Kluwer AI Health, leading the design and delivery of Large Language Model (LLM)-powered solutions in healthcare and education, from the AI Clinical Fact Checker powered by UpToDate, to agentic AI systems for large-scale medical taxonomy labeling. My work centers on building intelligent systems that reason, retrieve, and explain.
A highlight of my work has been developing the AI-driven remediation engine powering Lippincott® Ready for NCLEX®, delivering personalized, multi-modal improvement plans that help nursing students and educators close knowledge gaps. (Press Release 路 Patent) I collaborate closely with product managers, domain experts, and engineers to transform LLM research into products that make a difference for learners and clinicians.
My research in computational biophysics at the Hagan Lab, Brandeis University focused on modeling biological systems, studying virus assembly, antiviral drug mechanisms, and nanocarriers. I developed machine learning and statistical models (such as Markov State Models) to create coarse-grained simulations that align with experiments and capture long-timescale biological behavior.
I鈥檓 most inspired by the intersection of science and intelligence, where building smarter systems helps us better understand the world. Feel free to explore my AI projects and computational biophysics research, and reach out if you'd like to connect!
Modeling self-organization from nanoscale assemblies to viral capsids.
Mapped HBV capsid assembly pathways and antiviral drug mechanisms.
Cargo-encapsulating nanoshells serve as potential drug delivery carriers. I used coarse-grained and theoretical modeling to uncover how cargo, scaffolds, and shell mechanics together control their size, morphology, and self-assembly pathways.
We developed a kinetic and thermodynamic framework showing how liquid-liquid phase separation can dramatically enhance reversible self-assembly by concentrating subunits within condensates and expanding the range of conditions for efficient assembly.
Retrieval-augmented generation toolkit for exploring and summarizing research papers.
Auto-generates concise and engaging research tweets using LLM prompts and templates.
Converts PDFs or text into natural-sounding audio for accessible, on-the-go learning.