We make use of technology on the cutting edge of science to improve the lives of patients everywhere
With the use of the foremost AI implementations and architectures we're building a future where the power of technology can be leveraged to the fullest.
We've developed algorithms and processes that allow for 1:1 scale, 0.001mm accuracy anatomical prints on consumer hardware.
With our fast iteration pipeline and knowledge of consumer grade hardware, we are able to implment cost effective hardware and machines to support the highest quality healthcare.
Our familiarity of AWS and toolkit of analysis algorithms allow us to gleen insights from even the largest amounts of data.
Our diverse team, with expertise in the worlds of medicine, computer science, phyisics, and business, bring valuable perspectives to any project.
Dr. Ehwerhemuepha's experience encompasses advanced statistical and biostatistical analyses, machine learning, and artificial intelligence (AI) in medicine. He leads the data science program at CHOC Children’s with first, last, and other authorship on applications of electronic health records for improvement in quality of care and as a decision support tools. His work on predicting hospital readmissions and development of clinical interventions has resulted significant drops in the readmission rates of children at CHOC Children’s Hospital in Orange, California.
Greg Tyler is a computer scientist, mathematician, and masters student in computational and data sciences. With projects in over a dozen programming languages and a certificate in systems biology from Johns Hopkins, interdisciplinary solutions are the future of medicine. With work on the first versions of Android and Cloud Computing, he has a knack for finding new technologies and creating useful products with them.
With a B.S. in Mathematics and Finance, and a lifelong passion for physics, Andrew Papilion's focus is applying high level computational solutions to complex problems. With 4 years of experience using remote sensing data for earth systems modeling, and industry experience in financial engineering, he is interested in applying AI in combination with mathematical modeling to bring unique insight to big data projects. From earth systems to finance, his aim now is to help revolutionize big data analytics in the field of medicine.
As a Computational and Data Science Masters Student with a background in Business Administration, Oliver Mathias is well versed in the analysis of problems and the development of solutions from both a business and technological standpoint. His experience includes Machine Learning and Data Science internships at the Chapman Brain Institute and Children's hospital of Orange County respectively. His personal portfolio spans work from numpy based Neural-Net python packages, LSTM data funnels, Self Driving Simulations, and Instagram Bots.
Most of the projects listed below are exploritory endevours we embarked on to test the limits of whats possible in healthcare
By using open source datasets from research institutions around the world, we have created algorithms that can segment brain tumors with similar accuracy to human radiologists but in a more scalable way.
By utilizing custom algorithms and pipelines, we have build a process to automatically segment tissue from MRI and CT scans and 3D print human anatomical models, allowing for improved case visualization and taking the first steps towards bioprinting.
One of the most exciting uses of 3D printing in the medical field today is for implant shaping and operation planning in orthopedics. By having a 1:1 scale model outside the body, the surgeon can shape implants beforehand, saving time and pain for the patient.
A collection of public articles, papers, and posts diving further into our tech.
