Eric Sabo
Georgia Institute of Technology
About Me

I am currently a graduate student in the School of Mathematics at the Georgia Institute of Technology (GT). My advisors are Dr.’s Kenneth Brown (physics - Duke University), Andrew Cross (IBM), Evans Harrell (mathematical physics - School of Math, GT), Adam Meier (coding theory - GT Research Institute), and Prasad Tetali (coding theory - School of Math, GT). My research splits into two main projects: classical coding theory (Meier, Tetali) and quantum computation (Brown, Cross). I also have a couple of smaller projects related to machine learning. The icons at the bottom of the page also link to my email, LinkedIn, GitHub, and curriculum vitae.

Past projects I have been involved with include the exact and numerical scientific computation of quantum invariants via knot theory and Feynman diagrams (Stavros Garoufalidis - School of Math, GT), path planning for autonomous robots (Sung Ha Kang - School of Math, GT), mathematical modeling of microelectromechanical systems (John Pelesko - Department of Mathematics, University of Delaware), reverse draining of magnetic thin films (John Pelesko - Department of Mathematics, University of Delaware), and particle physics models of primordial inflation (Qaisar Shafi - Bartol Research Institute, University of Delaware).


On the classical side, I am the lead information theorist for IARPA's new Molecular Information Storage (MIST) initiative. This large-scale project spans several academic institutions and numerous (large and small) corporations. My main task (Technical Area 3 - Operating System) is to model the errors inherent to Technical Areas 1 (Storage) and 2 (Retrieval) and develop an operating system that coordinates addressing, data compression, encoding, error-correction, and decoding in a manner that supports efficient random access at scale. Please see the link for more details.

On the quantum side, I am mainly interested in the quantum error correction and its related quantum information theory. Specific interests include approximate error correction, all things color codes, double color codes, and the unitary decomposition problem.