Ali ShahmohammadiPh.D.
Associate Director, Applied AI Engineering & Scientific Data
Summary
Applied AI engineer with a decade in pharmaceutical R&D, building the MCP servers, agentic pipelines, and evaluation harnesses that make scientific data trustworthy for AI. Ships production systems in regulated life-sciences environments, grounded in a background in physics-informed and mechanistic modeling.
Experience
Associate Director, Applied AI Engineering & Scientific Data
Sep 2022 – PresentTakeda Pharmaceutical Inc. · Greater Boston, MA
- Designed and deployed a production agentic AI system for ontology curation — a multi-agent LangGraph pipeline with human-in-the-loop governance — cutting curation cycle times significantly.
- Built MCP-style scientific data connectors integrating ELN, LIMS, and AWS into a validated, agent-ready data layer across R&D domains.
- Architected a next-generation R&D data catalog with automated quality checks, lineage, and metadata management that gates data before it reaches AI pipelines.
- Led enterprise FAIR data strategy and delivered FAIR Studio (Pistoia Alliance), an industry-alliance product operationalizing FAIR assessment at scale.
- Built a digital twin for continuous manufacturing of a small-molecule API, integrating unit-operation mechanistic models.
- Established strategic research partnerships with MIT, BYU, Brown, and Purdue on PINNs and in-silico development.
Senior CMC Scientist
Aug 2021 – Sep 2022Moderna Inc. · Greater Boston, MA
- Developed ML models for mRNA drug-substance and drug-product stability and shelf-life prediction (ICH-compliant).
- Optimized the IVT reaction for mRNA process characterization using combined ML and fundamental modeling.
- Led comparability and product-specification projects through process scale-up phases.
- Contributed to IND and BLA submissions through statistical analysis and documentation.
Post-Doctoral Research Fellow
Oct 2019 – Aug 2021The University of Texas at Austin · Austin, TX
- Developed fundamental models for thin-film gallium phosphate on silicon for plasma-etch optimization.
- Built model-based DoE tools in R Shiny for plasma-etch process optimization.
- Created a first-principles model for the viscoelastic properties of adhesive soft particles.
- Performed molecular-dynamics simulations with 100,000+ particles.
Education
Ph.D., Chemical Engineering
Queen's University · Kingston, ON, Canada
Process systems engineering & statistical design of experiments — developed a new design-of-experiments algorithm for building mathematical models via a process-automation system.
2019 (verify year)
M.Sc., Chemical Engineering
Tarbiat Modares University · Tehran, Iran
2013
B.Sc., Chemical Engineering
Tehran University · Tehran, Iran
2011