MIT-Takeda Program

MIT-Takeda Program

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The MIT-Takeda Program concluded operations in 2024.

A collaboration between MIT’s School of Engineering and Takeda Pharmaceutical Company Limited, and centered within the Abdul Latif Jameel Clinic for Machine Learning in Health, the MIT-Takeda Program leveraged the combined expertise of both organizations. It supported faculty, students, researchers, and staff across the Institute who work at the intersection of AI and human health, ensuring that they could devote their energies to expanding the limits of knowledge and imagination.

  • 22

    22 projects funded over the course of the program

  • 80

    80 MIT students and faculty participated in the program

  • 125

    125 Takeda researchers and staff participated in the program

News

A man wearing glasses and a name tag stands in front of a digital display discussing research with a woman holding a drink at a science conference or academic event. Other people and displays are visible in the background.

June 2024: MIT-Takeda Program wraps up with 16 publications, a patent, and nearly two dozen projects completed.

The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.

MIT News

MIT-Takeda Fellows

While the MIT-Takeda Program has concluded operations, MIT-Takeda Fellowships are still active. Each year, current graduate students conducting research at the intersection of AI and health are supported with MIT-Takeda Fellowships.

Research Projects

Discovering human microbiome protein interactions with continuous distributed representation
  • Jim Collins, Termeer Professor of Medical Engineering and Science; Professor, Biological Engineering and MIT’s Institute for Medical Engineering and Science; Faculty Co-Lead, Jameel Clinic; Faculty Lead and Joint Steering Committee Member, MIT-Takeda Program
  • Andrew Krueger, Senior Scientist, Global Computational Biology
  • Timothy Lu, Associate Professor, Electrical Engineering and Computer Science and Biological Engineering
Machine learning for early diagnosis, progression risk estimation, and identification of non-responders to conventional therapy for inflammatory bowel disease
  • Peter Szolovits, Professor, Electrical Engineering and Computer Science
  • David Sontag, Hermann L. F. von Helmholtz Professor; Associate Professor, Electrical Engineering and Computer Science and MIT’s Institute for Medical Engineering and Science
  • Pravin Kamble, Associate Director, Global Evidence and Outcomes-Gastroenterology
  • Michelle Luo, Head, Global Evidence and Outcomes-Gastroenterology
Machine learning for image-based liver phenotyping and drug discovery
  • Polina Golland, Henry Ellis Warren (1894) Professor; Professor, Electrical Engineering and Computer Science
  • Brian W. Anthony, Principal Research Scientist, Mechanical Engineering
  • Peter Szolovits, Professor, Electrical Engineering and Computer Science
  • Jenifer Siegelman, Cell Therapy Translational Engine – Physician (Clinical) Lead, Pharmaceutical Sciences
  • Jim Hacunda, R&D Digital Strategy Lead, Data Sciences Institute
Predictive in silico models for cell culture process development for biologics manufacturing
  • Jackie Gonzalez, Staff Engineer CMC, Biologics Process Development.
  • Connor W. Coley, Henri A. Slezynger (1957) Career Development Professor; Assistant Professor, Chemical Engineering and Electrical Engineering and Computer Science
  • J. Christopher Love, the Raymond A. (1921) and Helen E. St. Laurent Professor of Chemical Engineering
  • Shan Tie, Senior Process Development Engineer* led year 1 of a 2 year project while working at Takeda
Automated data quality monitoring for clinical trial oversight via probabilistic programming
  • Jianchang Lin, Director Statistics, Oncology Stats
  • Wenwen Zhang, Associate Director, Statistical & Quantitative Sciences, Gastroenterology
  • Vikash Mansinghka, Principal Research Scientist in the Department of Brain and Cognitive Sciences
  • Tamara Broderick, Associate Professor, Electrical Engineering and Computer Science
  • David Sontag, Hermann L. F. von Helmholtz Professor; Associate Professor, Electrical Engineering and Computer Science and MIT’s Institute for Medical Engineering and Science
  • Sheela Kolluri, Senior Director, Statistics, Statistical and Quantitative Sciences* led year 1 of a 2 year project while working at Takeda
Time series analysis from video data for optimizing and controlling unit operations in production and manufacturing
  • Allan S. Myerson, Professor of the Practice, Chemical Engineering
  • George Barbastathis, Professor, Mechanical Engineering
  • Richard Braatz, Edwin R. Gilliland Professor of Chemical Engineering
  • Tolutola Oyetunde, Senior Scientist, Digital CMC
  • Michael Schwaerzler, Head, Computational Technology and Collective Intelligence
  • Bernhardt Trout, Raymond F. Baddour, Sc.D., (1949) Chemical Engineering Professor
Automating adverse effect assessments and scientific literature review
  • Regina Barzilay, School of Engineering Distinguished Professor for AI and Health; Professor, Electrical Engineering and Computer Science; Jameel Clinic faculty co-lead
  • Tommi Jaakkola, Thomas M. Siebel Distinguished Professor; Professor, Electrical Engineering and Computer Science and MIT’s Institute for Data, Systems, and Society
  • Jacob Andreas, X-Window Consortium Career Development Professor; Assistant Professor, Electrical Engineering and Computer Science
  • Nathan Nyein, Associate Director, Global Patient Safety & Evaluation
  • Antonia Panayi, Senior Director Medical Information, Global Medical Affairs Medical Function
  • Amir Benhadji-Schaff, Head, GMA Business Solutions & Analytics, Global Medical Affairs Medical Function
  • Aleksandra Seifert, Senior Director, Global Patient Safety & Evaluation
Automated analysis of speech and language deficits for frontotemporal dementia
  • James Glass, Senior Research Scientist, MIT’s Computer Science and Artificial Intelligence Laboratory
  • Sanjay Sarma, Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering; Vice President, Open Learning
  • Brian Subirana, Research Scientist; Director, MIT Auto-ID Laboratory; Director, MIT and Accenture Convergence Initiative for Industry and Technology
  • Brian Tracey, Scientific Fellow, Quantitative Sciences
AI-enabled, automated inspection of lyophilized products in sterile pharmaceutical manufacturing
  • Duane Boning, Clarence J. LeBel Professor in Electrical Engineering and Computer Science
  • Luca Daniel, Professor, Electrical Engineering and Computer Science
  • Gerhard Muhrer, Senior Director, Head of R&D Quality Compliance and Systems
  • Sanjay Sarma, Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering; Vice President, Open Learning
  • Brian Subirana, Research Scientist; Director, MIT Auto-ID Laboratory; Director, MIT and Accenture Convergence Initiative for Industry and Technology
  • Linda Wilding, Portfolio Manager Digital & Data Science
AI – enabled, automated inspection of lyophilized products in sterile pharmaceutical
  • Linda Wildling, Head of Digital Innovation Success Management – Global GMSGQ DD&T
  • Antonio Burazer, Global Head Visual Inspection and Particle LCM – Analytical Services & Support
  • Duane Boning, Clarence J. LeBel Professor in Electrical Engineering and Computer Science
  • Sanjay Sama, Fred Fort Flowers (1941) and Daniel Fort Flowers (1941) Professor in Mechanical Engineering; Vice President, Open Learning
  • Luca Daniel, Professor, Electrical Engineering and Computer Science
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
  • Peter Szolovits, Professor of Computer Science and Engineering
  • Rahul Mazumder, Associate Professor in the Operations Research and Statistics Group
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
  • Jonas Oddur Jonasson, Robert G. James Career Development Associate Professor in Operations Management
  • Vivek Farias, Patrick J. McGovern (1959) Professor
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
  • Hermano Igo Krebs, Principal Research Scientist
Interpretable discovery of clinical features using transformer networks
  • Marco Vilela, Associate Director, Statistics-Quantitative Sciences
  • Jim Glass, Senior Research Scientist, MIT’s Computer Science and Artificial Intelligence Laboratory
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
  • J. Christopher Love, Raymond A. (1921) and Helen E. St. Laurent Professor of Chemical Engineering
  • Connor W. Coley, Henri A. Slezynger (1957) Career Development Professor; Assistant Professor, Chemical Engineering and Electrical Engineering and Computer Science
Optimal treatment strategies and decision making in real-world with machine learning
  • Jianchang Lin, Sr Director, Statistics-Oncology Stats
  • Marzyeh Ghassemi, Herman L. F. von Helmholtz Career Development Professor
  • Ashia Wilson, Assistant Professor of Electrical Engineering and Computer Science
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)
  • Allan S. Myerson, Professor of the Practice, Chemical Engineering
  • Richard Braatz, Edwin R. Gilliland Professor; Professor, Chemical Engineering
  • George Barbastathis, Professor, Mechanical Engineering
  • Wojciech Matusik, Professor of Electrical Engineering and Computer Science at the Computer Science
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
  • Resef Levi, J. Spencer Standish (1945) Professor of Operations Management
  • Anthony Sinskey, Professor of Biology
AI automated diagnosis of Fabry disease using electrocardiogram (ECG)
  • Subir Roy, Global Medical Lead for Metachromatic Leukodystrophy-GMA LSD
  • Dina Katabi, Thuan and Nicole Pham Professor
  • Elazar Edelman, Edward J. Poitras Professor in Medical Engineering and Science
  • Piotr Indyk, Thomas D. and Virginia W. Cabot Professor
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