Chris von Csefalvay has been active in the research and development of new technologies, ideas and solutions for perennial problems. Of these, a significant part is unfortunately subject to non-disclosure and governmental secrecy provisions for national security reasons. His principal areas of interest and investigation are
- mathematics: elliptic curve cryptography (research currently classified), mathematical physics (esp. explosive lensing, research currently classified), zero-knowlege proofs of CBRN decommissioning
- epidemiology: computational epidemiology, especially modeling disease-avoidant behaviour and general modeling (research currently classified)
- virology: host-pathogen interactions, V(D)J recombination, sarbecoviruses (reserach currently classified), filoviridae (research currently classified)
- computer science: graph algorithms (research currently classified), sensor fusion computation (research currently classified), computer vision and deep learning
Selected research papers by Chris von Csefalvay
- Foldi, T., von Csefalvay, C. and Perez, N.A. (2020). JAMPI: Efficient matrix multiplication in Spark using Barrier Execution Mode.
- von Csefalvay, C. and Foldi, T. (2020). PAWS: Towards a globally integrated outbreak surveillance system for public health. doi:10.5281/zenodo.3782877
- von Csefalvay, C. (2019). Novel quantitative indicators of digital ophthalmoscopy image quality. arXiv:1903.02695
- von Csefalvay, C. (2015). Ecological metrics of diversity in understanding social media. arXiv:1501.07621.
- von Csefalvay, C. (2009). On Good Intentions and Poor Outcomes: A Critical Retrospective on Chester v Afshar. 9 U. C. Dublin L. Rev. 46.
Selected patents & applications
- A Method, Software and Architecture for the Monitoring of Data Flows in ETL Systems, GB1420320.2 (15 November 2014)
- A method, software and architecture for a graphical user interface for creating, editing, visualizing and authenticating smart contracts, GB1605154.2 (11 May 2016)
- Preventing repeated malicious online communications using a proof of work approach, GB1607128.4 (08 June 2016)