Michael Alvarez
University of Puerto Rico-Rio Piedras, PR·Biomedical Engineering
Stem cells could treat many diseases, but growing them reliably and cheaply at large scale is hard. Small changes in culture conditions can lower yield or quality, and problems are often caught too late.
- Fields
Biomedical Engineering
Engineering
Computer Science
- Topics
biomanufacturing
cell manufacturing
mesenchymal stem cells
- Grants
- 1 known grant, through Jul 2030
What they work on
- Develop a scalable, AI-enabled bioreactor platform for growing mesenchymal stem cells (adult stem cells used in therapies) and collecting their extracellular vesicles, small particles cells release.
- Create noninvasive sensing methods, including biosensors and connected Internet of Things devices, that track the condition of stem cell cultures in real time without disturbing the cells.
- Build physics-informed digital twins, which are virtual models of the bioreactor, and pair them with AI agents. Use them to predict optimal culture conditions and detect anomalies during cell manufacturing.
- Test whether safety-constrained AI decision-making, fed with imaging, sensor and quality-control data, can automatically adjust culture settings to maximize stem cell yield and consistency.
More about the research
This collaborative center, shared among several Puerto Rico institutions, asks whether artificial intelligence (AI) can watch cell cultures in real time and steer them toward better, more consistent results.
Why it matters
Making stem cells and their vesicles more cheaply and consistently could lower the cost and improve the quality of cell-based biological products. Patients who may benefit from stem cell therapies could gain from this, as could biomanufacturers. The center also aims to build Puerto Rico's biomanufacturing industry through partnerships with companies, startups and a trained local workforce in AI and cell manufacturing.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Steven Brunton
University of Washington, WA·Engineering
Develop physics-aware machine learning methods that build physical laws and stability constraints into the models, producing interpretable models that work reliably for aerospace design and control.
Mitsunori Ogihara
University of Miami, FL·Computer Science
Develop accelerated methods for analyzing medical images, using AI/ML together with coupled software and programmable hardware so results arrive fast enough for real-time use.
Johnna Temenoff
Georgia Institute of Technology, GA·Biomedical Engineering
Test whether attaching transactivator of transcription (TAT) peptides, which help cargo enter cells, to donor mitochondria increases their uptake and bone formation by precursor cells in culture.
The school
University of Puerto Rico-Rio Piedras: 2 labs on PI Pages, $34M a year on research, 90 research doctorates a year.
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