Research

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 (and non-national security…) reasons. His principal areas of interest and investigation are

  • operations research: efficient spatial optimisation of critical public resources, mixed quadratic programming, pursuit/evasion
  • mathematics: elliptic curve cryptography, zero-knowlege proofs of CBRN decommissioning with a specific emphasis on neutron spectroscopy
  • epidemiology: computational epidemiology, especially modeling disease-avoidant behaviour and general modeling, as well as signal identification from mining passive reporting pharmacovigilance systems (specific emphasis on VAERS)
  • computer science: graph algorithms, sensor fusion computation, computer vision and deep learning, autonomous systems

Selected research papers by Chris von Csefalvay

Data sets

I’m a strong supporter of open data and have curated and/or contributed to several public data sets for research use.

  • Starschema COVID-19 Data Set DOI
  • 2019 Samoan measles outbreak epidemiological data DOI
  • 2018 DRC Ebola outbreak epidemiological data DOI
  • CODRAS (COnsolidated Diabetic Retinopathy Assessment Set) DOI

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)

Thesis supervision

I currently supervise thesis students for the Faculty of Mathematics, Budapest University of Technology and Economics. Aspiring third-years should get in touch via the contact panel as early as possible. There are typically two thesis slots per semester, and preference goes to students on the Data Science track.