Lauri Koivunen
M.Sc. (Tech.)

Software Engineering

Office: 425

Available from 9 to 17

ORCID identifier:

Website (Presently empty)

Areas of expertise
Information security; DevOps; Software Engineering; Software Development; Python; Linux; Embedded; Data Visualization; network security; IoT; Data analysis; GDPR; software privacy; privacy; programming; software security; firmware security; networked systems; computer networks; networked systems security; cryptography in practice


Project researcher at the university since about 2019.

Worked on multiple projects ranging from medium-huge software/hardware/firmware/research efforts (Flavoria) to consultations to different organizations (Biology department software, different organization infrastructure concerns (AWS) and optimization, law firms (specifics of for example torrent traffic)).
External/Internal software/infrastructure development is close to heart for both research and teaching, how to improve "the process".
Helping people in all problem areas I can, ranging from debugging to new development. 
Teaching responsibilities are below.
Also interested in the hardware/firmware side of things, from the occasional PC building to choosing/developing/investigating/procuring new whole systems from software to hardware.


So far research has included software security, privacy and diversification since 2018. How to make software that is usable and does not make for example desire paths bypass security. The end goal is to find all the tools a software/system can deploy to make security as good as it can be and to balance with usability. Policies are good as long as they are observed (hard to enforce permanently!), but software/hardware changes should be the primary way to enforce any policies since humans are fallible and this is where we are still improving (2-factor, security keys, NFC payments, etc.).

On the Flavoria research restaurant side we have built a research platform with hundreds of sensors and also collaborated, for example, with Helsinki University on computer vision assisted food recognition based on our research platform. The aim in future is to figure out new ways we can affect the meal experience in a positive way and lift the research possibilities of everyday eating to a level where data is gathering itself as autonomously as possible. 


  • Full Stack course series teaching assistant 2019-2020
  • Engineering introduction course teaching assistant 2019-2022+
  • Capstone course research projects hardware/infrastructure procurement assistant 2018-2022+
  • Personal teacher-guide for first-year students for a new pilot in 2018-2019
  • Master's thesis supervisor (About 2-4 per year) 2020-2022+


Last updated on 2022-07-10 at 16:56