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 miscellaneous other reasons. His principal areas of interest and investigation are:

  • 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)
  • operations research: efficient spatial optimisation of critical public resources, mixed quadratic programming, pursuit/evasion
  • mathematics: zero-knowlege proofs of CBRN decommissioning with a specific emphasis on neutron spectroscopy
  • computer science: graph algorithms, sensor fusion computation, computer vision and deep learning, autonomous systems

Selected research papers by Chris von Csefalvay

Data sets by Chris von Csefalvay

Chris von Csefalvay is 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 by Chris von Csefalvay

  • 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

Chris von Csefalvay currently supervises 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.