Ambient intelligence Search pathology Bioinformatics Tourism
Games and search Machine learning Genre classification Other

Ambient intelligence

I use the term ambient intelligence for all the research on the interpretation of sensor data, mobile and context-sensitive applications. This research area corresponds to (or maybe intersects) the areas of ubiquitous and pervasive computing. I am particularly interested in analyzing human physical and psychological condition and behavior with sensors, particularly in the domain of health. However, I occasionally also work on building automation and related topics.

I am currently the coordinator of the HeartMan H2020 project, which is developing a personal health system for congestive heart failure patients. The system comprises (wearable) devices for unobtrusive patient monitoring, a mobile application featuring a decision support system to advise on disease management, and data management components in the cloud. I am also a principal investigator in the Fit4Work AAL project, where we are developing methods to monitor older workers' physical activity, stress and work environment (temperature, humidity, CO2 level ...) with wearable and ambient sensors. Based on this, we provide recommendation on physical exercise, stress relief and ways to improve the work environment.

I was a principal investigator in the Commodity12 FP7 project, where we developed methods to monitor physical activity of diabetic patients with a mobile phone and an optional ECG monitor. The project as a whole included further methods for reasoning about patients' condition and managing their data. We reused the monitoring methods in the e-Gibalec application, which tracks and encourages the physical activity of children using gamification.

I was a principal investigator in the Chiron Artemis project, where we developed methods to monitor physical activity of congestive heart failure patients with wearable sensors, and a decision support system to assess their health risk. My first project in the area of ambient intelligence was the Confidence FP7 project. The goal of this project was to detect falls and other health problems of the elderly by recognizing anomalies in their posture and movement. This was done using location sensors that detected one to four tags worn on the user' bodies.

A repository of ambient-intelligence datasets used in our research

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2010 and earlier

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Search pathology

Search pathology is a phenomenon best known from minimax search, where under seemingly reasonable conditions, the deeper one searches, the worse he plays - the opposite of what happens in practice. Similar behavior was observed in real-time single-agent heuristic search. Search pathology was the subject of my Ph. D. thesis under the supervision of Ivan Bratko and Matjaž Gams. My research on the pathology in single-agent search was done in collaboration with Vadim Bulitko.

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Bioinformatics

I started working in this area as a postdoc at the Institute for Biostatistics and Informatics in Medicine and Ageing Research. I developed a machine-learning method for epitope prediction based on peptide array data, which is relevant to vaccine design and diagnostics. I am also involved in the research on pluripotency.

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Tourism

We have developed an intelligent electronic tourist guide as a web and mobile application. The guide prepares a personalized itinerary for each user and then guides him on his trip. The personalization relies on knowledge-based recommendations and collaborative filtering.

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Games and search

Computer game playing was the subject of my B. Sc. thesis, for which I wrote a program for playing tarok called Silicon Tarokist. The program was substantially improved afterwards. I later also collaborated with Domen Marinčič, who worked on a Bayesian decision model for bidding in tarok. I have also done some research on real-time single-agent heuristic search, where I was investigating methods to determine the optimal lookahead depth. This research is an offshoot of the research on search pathology and was also done in collaboration with Vadim Bulitko.

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Machine learning

While I use machine learning in most of my research, I do not often research machine learning itself. My main interest in this area is building classifiers that are both comprehensible and accurate.

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Genre classificationp

The ability to classify web pages into genres can be a helpful addition to a search engine. We developed machine learning methods for genre classification in Alvis EU FP6 project, but the development continues after the end of the project. I collaborate on this with Vedrana Vidulin.

The web genre dataset used in our research

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Other

Occasionally I also dabble in other areas.

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