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Scaled Biolabs Inc.

Accelerating next generation cell-based medicines.

In 2016, I took a deep breath, and co-founded a biotech company in San Francisco, California. At Scaled Biolabs we combined microfluidics, stem cell engineering and computer science to discover and develop novel cell-based medicines. Put simply, we find intelligent ways to regenerate or grow damaged cells, tissues and organs. In one project we grow neurons as treatment for Parkinson's Disease, for example.

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Computational Astrophysics

Using supercomputers to understand the origin and evolution of our Universe.

Prior to starting Scaled Biolabs, I spent nearly a decade devoted to academic research in the fields of computational astrophysics, cosmology and theoretical physics. Whilst I have taken a hiatus from formal astrophysics research I still have on-going collaborations relating to The Caterpillar Project of which I was the project lead at MIT. I also contribute to The Illustris Project development pipeline at Harvard.

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Computer Vision & Machine Learning

Also known as applied statistics and linear algebra but that isn't a catchy heading.

Providing a quantitative window into complex cellular environments underpins much of discovery and development of new cell-based medicines. To study these environments I deploy various statistical and image processing techniques to process large volumes of image data typically found in these biological settings.

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Trying to make collections of atoms do useful things.

I spend a fair chunk of time making various electronic systems do useful tasks. This includes programming liquid handling robots, Raspberry Pi/Arduino-type micro-controllers, interactive interfaces and other general purpose systems to automate work flows. A lot of this work is devoted toward the systems I build at Scaled Biolabs.

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Data-driven frameworks for learning in the 21st century.

I periodically consult for the Center For Curriculum Redesign focused where I help build ontology frameworks for powering a new generation of school curricula. Using natural language processing, semantic networks and other data crunching tools, we strive to determine more effective ways of learning.

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