Ali Shahmohammadi, Ph.D.

Takeda Pharmaceutical Inc. · Greater Boston, MA

• Lead Takeda's R&D FAIR data strategy, data governance, and master data management (MDM) roadmap, defining policies, capabilities, and operating models that ensure data is Findable, Accessible, Interoperable, and Reusable across Research data domains.
• Drive enterprise deployment of FAIR Studio, the centralized platform that operationalizes FAIR assessments, embeds governance directly into digital products and workflows, and replaces manual oversight with scalable, automated, policy-driven controls.
• Architect Takeda's next-generation R&D knowledge graph and data catalog strategy, defining requirements for automated data quality checks, lineage, metadata management, ontology-driven classification, and seamless integration with FAIR Studio.
• Designed and built an Agentic AI system for ontology curation, knowledge graph integration, and ontology term mapping, providing first-pass automation for entity registration, term mapping, and stewardship workflows — accelerating curation cycle times while preserving human-in-the-loop governance approval.
• Partner with product, engineering, and architecture leaders to embed governance frameworks, data quality controls, and FAIR practices directly into platforms, AI pipelines, and prioritized AI use cases.
• Led the Cell Therapy workflow automation team to design and deliver a scalable, end-to-end digital data backbone connecting laboratory instruments, AWS data services, LabKey, and JMP.
• Built the cell therapy data model, data governance framework, and data catalog, including controlled vocabularies, master data definitions, data ownership/stewardship roles, and quality rules.
• Delivered the flow cytometry automation initiative with Tetrascience, eliminating manual handoffs and embedding QC checkpoints in the workflow.
• Developed and deployed the end-to-end mechanistic model for continuous manufacturing of a small molecule API, integrating unit-operation models into a digital twin used to support process understanding, scale-up decisions, and CMC strategy.
• Built a first-principles model of the continuous hydrogenation step for a pharmaceutical compound in development, providing a validated in-silico platform that informed process parameters and accelerated development.
• Delivered an AI/ML predictive model for product-level CO₂ emissions in support of Takeda's sustainability strategy.
• Built strategic academic partnerships with MIT, BYU, Brown, and Purdue to advance Physics-Informed Neural Networks (PINNs), mechanistic modeling, and in-silico–first development capabilities.

Moderna Inc. · Greater Boston, MA

• Collaborated with the CMC statistics technical team to develop fundamental and machine learning models in accordance with ICH guidelines, predicting the stability and shelf-life of mRNA drug substances and drug products.
• Led comparability and product specification projects, ensuring compliance with specification limits and maintaining comparability throughout different phases of process scale-up.
• Leveraged digital transformative tools such as machine learning and artificial intelligence, combined with fundamental modeling, to optimize the IVT reaction for mRNA process characterization.
• Conducted statistical analysis and hypothesis testing for factor analysis, providing support to the mRNA and LNP process development functional team.
• Implemented statistical process control strategies to establish specification limits for raw materials, drug products, and drug substances.
• Effectively communicated and documented statistical analysis results to senior leadership, contributing to IND and BLA submissions.

The University of Texas at Austin · Austin, TX

• Developed fundamental models to predict the thickness of thin film gallium phosphate on silicon, enabling rapid model-based optimization of plasma etching processes.
• Designed model-based Design of Experiments (DoE) and performed statistical analysis to obtain informative data during thin film deposition.
• Built R Shiny user interface to provide model-based DoE for optimization of plasma etch processes and conducting statistical analysis.
• Articulated a fundamental model to predict the linear and nonlinear viscoelastic properties of adhesive soft particles in aqueous solutions.
• Led a process engineering team in developing mathematical models and implementing statistical techniques to predict the gelation time of adhesive soft materials.
• Performed Molecular Dynamic Simulations with over 100,000 particles to investigate microstructural changes in soft matter.