Research Interests

My research lies in the areas of mobile computing, sensing, wireless networking and the investigation of the interraction between the computing and the society. In particular I am interested in:

Current Projects

Mobile broadband measurement analysis

Mobile broadband (MBB) networks have revolutionised the way we communicate, yet our understanding and means of troubleshooting such complex systems remain modest. In our research we develop RICERCANDO (Rapid Interpretation and Cross-Experiment Root-Cause Analysis in Network Data with Orange), a tool for integrative exploration, visualization and interpretation of MBB data and meta-data across multiple experiments. RICERCANDO supports human experts in the process of detecting and understanding the root-cause of the network problems and performance degradation.

RICERCANDO was developed by an interdisciplinary team that includes data mining (prof. Blaz Zupan, Jernej Kernc) and networking experts (prof. Ricciato, prof. Pejovic, Ivan Majhen, and Dr. Miha Janez).

All tools developed as a part of RICERCANDO project are free and open-source, and can be found here: https://github.com/ivek1312/ricercando
We also invite you to find out more in our RICERCANDO paper


Finally, we used our tools, including RICERCANDO, to uncover Net neutrality violations. We analyzed almost two years worth of distributed crowdsourced measurement data from Agency for communication networks and services of the Republic of Slovenia and found examples of Net neutrality violation practices.

Port blocking
TCP port blocking by four major Slovenian ISPs.

Mobile User Attention Management

Attention is the most precious of the resources we have. From communication services (chat, SMS), to online social networks (Facebook, Twitter), and warning systems (e.g. "battery low"), different actors compete for our attention. In our research we aim to understand mechanisms that guide user attention and to develop technical solutions for attention management.
Our goal is to advance human-computer interaction to the point defined by Mark Weiser's 1991 quote:
machines that fit the human environment instead of forcing humans to enter theirs will make using computing as refreshing as taking a walk in the woods
From the practical point of view, attention management systems built towards the above vision will enable us to:
  • Reduce user frustration and application churn rate.
  • Increase user engagement with the delivered content.
  • Increase compliance with suggestions in mHealth and behaviour change intervention applications
  • Improve safety in ubiquitous computing environments (e.g. by not interrupting while a user is driving).
  • Reduce unwanted data traffic.
  • Open doors for anticipatory computing, where future activities could be intelligently steered through predictive attention management.
In our research, through mobile sensing, we have thoroughly investigated factors that impact a user's interruptibility (see our UbiComp'14, UbiComp'15, and CHI'16 papers, for example). Further, we built an Android library InterruptMe that uses sensors on a smartphone to recognize the user's context and infer if a user is interruptible or not. InterruptMe is available as open source software.

In our latest line of research we are investigating the ability of mobile and wireless sensors to recognize a user's cognitive load, as the information on cognitive load can greatly enhance attention management systems (see our UbiComp'18 survey for details). Our preliminary analysis with wearable and wireless methods for cognitive load inference shows the potential for unobtrusive recognition of a user's task engagement. We are currently working on in-depth inference of the actual level of cognitive load, as well as, on inferring cognitive load in real-world situations for automotive industry.
Activity - Interruptibility
Our Wi-Mind systems for wireless cognitive load inference.