Paul Sebexen grew up in Staten Island, New York and studied Mathematics at the City University of New York College of Staten Island and later Computer Science and Nanomaterials at the Georgia Institute of Technology. While in college, he developed an interest in structural biology, including molecular dynamics and other computational modeling techniques, and eventually genetic engineering. The latter motivated his participation in the 2011 Georgia Tech iGEM team (read about that project here). Inspired by the potential he saw in synthetic biology and wanting to contribute to the field, Paul started collaborating with his iGEM teammate (and roommate), Kettner Griswold, on addressing limitations of de novo DNA synthesis technology (Simply stated, de novo synthesis is the chemical creation of new DNA from scratch according to a design. It may be analogized to the use of 3D printing in generating physical prototypes - synthetic DNA includes novel genes and other constructs which can be transformed into organisms to encode new or modified functionality). Aiming to make DNA synthesis technology as rapid and powerful as programming computers, he accepted the 2012 Thiel Fellowship and received additional funding from the inaugural SynBio Launchpad accelerator program at Singularity University, now part of SU Labs, to work on the technology full time.

Evolutionary Solutions, cofounded in 2011 with Kettner Griswold, is focused on building the next generation of DNA synthesis technology with the vision of accelerating research in synthetic biology and encouraging the future of "apps" to be synthetic and genetically-engineered organisms. Paul and Kettner conducted research at the Molecular Foundry at Lawrence Berkeley National Laboratory and are always happy to discuss ideas with interested students and researchers. Learn more at evosol.co.

You can read more about the background story here: http://www.quora.com/What-is-the-story-behind-Evolutionary-Solutions.

Neuroscience - Active exploration of functional imaging techniques, with a focus on magnetic and hybrid modalities. Also interested in applications of theoretical neuroscience, such as neural field theory, and how these may interact with physical continuum models. 

REX Computing, started by Thomas Sohmers, aims to revolutionize the space of High Performance Computing. Initial work is focused on improving efficiency, performance and scalability of computation. This relies on a vertical of efforts, from innovations in processor architecture design to system design and software tools which will allow the capabilities of the new hardware to be used. Learn more about REX at rexcomputing.com.

Software Consulting - Paul works as a software contractor as a means of maintaining his coding skills and to support his other projects. Software projects have included Linux kernel and driver development, networking, FPGA development, and system architecture. He has also recently collaborated with an academic group to provide assistance with computer vision tool development for automated video analysis in biomechanics experiments. To suggest a contract opportunity to Paul, please use the provided Contact Form.

Research Interests

Some of Paul's current and past research interests include:

• Modeling of Physical Systems: Structural biology, molecular dynamics; quantum chemistry, DFT variants and similar; theoretical ecology and systems biology, population dynamics. Preference for semi-analytical, hybrid, and multi-scale approaches.
  
  
• Computer Hardware: Semiconductors, VLSI, nanofab, new materials research, photonics in communication. 
  
  
• Software: Simulation, kernel development, compiler design and optimization, language theory, static analysis and binary translation, computer vision, machine learning. 
  
  
• Biology: Molecular biology, synthetic biology, genetic engineering, bioinformatics. 
  
  
• Theoretical Physics: Broadly - QM, QFT, some QED. Applications of recent interest include descriptions of superconductivity (e.g. BCS theory) and electron-neutron coupling.
  
  
• Mathematics: Interval-vector polytopes, applied statistics.