“The flagship research projects of the MIT-Takeda Program offer real promise to the ways we can impact human health. We are delighted to have the opportunity to collaborate with Takeda researchers on advances that leverage AI and aim to shape health care around the globe.”

— Jim Collins, MIT-Takeda Program faculty lead

AI – enabled, automated inspection of lyophilized products in sterile pharmaceutical

  • Linda Wildling, Senior Director, Outcomes Research-US Medical Economics and Outcomes Research
  • Antonio Burazer, Global Scientific Training Lead-Medical Excellence and Scientific Training (MEST)

AI for the Diagnosis of Autoimmune Gastrointestinal Disorders

  • Jeanne Jiang, Associate Director Patient Data Domain Expert-Data Strategy and Governance
  • Tao Fan, Director, Data Networks for External Partnerships-Data Services & Content Delivery

Causal Inference and Optimization for Patient and HCP Engagement

  • Kyle Dillon, Sr Director, Quantitative Clinical Pharmacology, ONC-Quantitative Clinical Pharmacology
  • Jillian Berry Jaeker, Sr. Director, Statistics-Oncology Stats

Machine learning for early identification and assessment of gross motor function deterioration in metachromatic leukodystrophy (MLD) (TAK-611)

  • Javier Gervas, Head of Digital & Industry 4.0 ad interim-GMS Digital & Data Analytics

Interpretable discovery of clinical features using transformer networks

  • Marco Vilela, Associate Director, Statistics-Quantitative Sciences

Developing a framework and tools for machine-learning based disease identification and classification in administrative health data with application to narcolepsy diagnosis

  • Dana Teltsch, Director, GME Head, NS & Hematology-GMA Medical Evidence Generation

Predictive Modeling for Downstream Process Development for Biologics Manufacturing

  • George Parks, Sr Staff Engineer, Process Development-Mab Derived Biologics Development
  • Raghu Shivappa, Head, Biologics Process Development-Pharmaceutical Sciences

Optimal treatment strategies and decision making in real-world with machine learning

  • Jianchang Lin, Sr Director, Statistics-Oncology Stats

AI-enabled Transfer-Learning methods for video data analysis models for optimizing and controlling manufacturing process

  • Chris Mitchell, Sr Dir, Head Signal Management-Global Patient Safety Operations (GPSO)

Predictive Signal Detection and Analyses – PRISM (Patients Really are first In Signal Management)

  • Dona M. Ely, Director, Health Economics and Outcomes Research-US Medical Outcomes Research – GI

AI automated diagnosis of Fabry disease using electrocardiogram (ECG)

  • Subir Roy, Global Medical Lead for Metachromatic Leukodystrophy-GMA LSD

 

Improving multiple myeloma clinical trial design and identification of heterogeneous treatment effects using machine learning

  • Neeraj Gupta, Assoc. Dir. Quality Control-Analytical Services & Support
  • Guohui Liu, Senior Staff Engineer, Process Chemistry & Development-Process Chemistry