Martin Gjoreski – UbiComp @ USI Lugano

I am a postdoc researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), in the research group of Research group of prof. Marc Langheinrich and prof. Silvia Santini. My research field is AI with focus on development of standard machine learning and deep learning methods for sensor data. I am particularly interested in applications in fields such as mobile and wearable computing, behavioural analytics, affective computing, and mobile healthcare. I did my PhD at the Department of Intelligent Systems at the Jozef Stefan Institute. I was also a teaching assistant at the Faculty of Computer and Information Science, University of Ljubljana.

Education

PhD in Information and Communication Technologies (2016-20)
Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
PhD thesis title: “A fusion of classical and deep machine learning for mobile health and behavior monitoring with wearable sensors” link

M.Sc. in Information and Communication Technologies (2014-16)
Jožef Stefan International Postgraduate School, Ljubljana, Slovenia
M.Sc. thesis title: “Continuous stress monitoring using wrist device and smartphone” link

B.Sc. in Computer Science and Engineering (2010-14)
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius, Skopje, Macedonia
B.Sc. thesis title: “Emotion Classification by Using Features Extracted from Speech” link

Selected Publications

GJORESKI, Martin, JANKO Vito, SLAPNIČAR, Gašper, MLAKAR, Nejc, REŠČIČ, Nina, BIZJAK, Jani, MARINKO, Matej, MLAKAR, Miha, DROBNIC, Vid, LUŠTREK, Mitja, GAMS, Matjaž. Classical and Deep Learning Methods for Recognizing Human Activities and Modes of Transportation with Smartphone Sensors. Information Fusion, 2020. link
DZIEŻYC, Maciej, GJORESKI, Martin, KAZIENKO, Przemysław, SAGANOWSKI, Stanisław, GAMS, Matjaž. Can we ditch feature engineering? End-to-end deep learning for affect recognition from physiological sensor data. Sensors, 2020. link, code
GJORESKI, Martin, GRADIŠEK, Anton, BUDNA, Borut, GAMS, Matjaž, POGLAJEN, Gregor. Machine Learning and End-to-end Deep Learning for the Detection of Chronic Heart Failure from Heart Sounds. IEEE Access, 2020. link
PEJOVIĆ, Veljko, GJORESKI, Martin, ANDERSON, Christoph, DAVID, Klaus, LUŠTREK, Mitja. Towards Telepathic Computing: Cognitive LoadInference for Attention Management in Ubiquitous Systems. IEEE Pervasive Computing, 2020. link
GJORESKI, Martin, GAMS, Matjaž, LUŠTREK, Mitja, GENC Pelin, GARBAS, Jens-Uwe, HASSAN, Teena. Machine Learning and End-to-end Deep Learning for Monitoring Driver Distractions from Physiological and Visual Signals. IEEE Access, 2020. link
GASHI, Shkurta, DI LASCIO, Elena, STANCU, Bianca, DAS SWAIN, Vedant, GJORESKI, Martin, MISHRA Varun, SANTINI, Silvia. Automatic Detection of Artifacts in Electrodermal Activity Sensor Data. IMWUT 2020. link
SIMJANOSKA Monika, GJORESKI, Martin, GAMS, Matjaž, BOGDANOVA, Ana. Non-invasive Blood Pressure Estimation from ECG using Machine Learning. Sensors, 2018. link
GJORESKI, Martin, LUŠTREK, Mitja, GAMS, Matjaž, GJORESKI, Hristijan. Monitoring Stress with a Wrist Device Using Context. Journal of Biomedical Informatics, 2017. link
GJORESKI, Martin, GJORESKI, Hristijan, LUŠTREK, Mitja, GAMS, Matjaž. How accurately can your wrist device recognize daily activities and detect. MDPI-Sensors, 2016. link
JANKO Vito, GJORESKI Martin et al. Winning the Sussex-Huawei Locomotion-Transportation Recognition Challenge. Human Activity Sensing – Corpus and Applications”. Chapter, Springer Nature, 2020. link

Service

Guest editor for the special issue “Artificial Intelligence and Ambient Intelligence” at the at MDPI Electronics journal: https://www.mdpi.com/journal/electronics/special_issues/AIs_electronics
Workshop Organising Committee at “UbiTtention 2020: 5th International Workshop on Smart & Ambient Notification and Attention Management” UbiComp 2020 Workshop, Cancun: (https://www.ubittention.org/2020)
Program Committee at “ML for Mental Health – Machine Learning for the Diagnosis and Treatment of Affective Disorders (ML4AD)” ACII 2019 Workshop, Cambridge: (http://mlformentalhealth.com)

News

We received a best paper award at the Slovenian Conference on Artificial Intelligence 2020 (https://is.ijs.si/) paper link
Our team won first place at the “Cooking Activity Recognition Challenge” at the ABC Conference, Kitakyushu, 2020: https://abc-research.github.io/cook2020/
We organize an ML challenge for congitive load monitoring from physiological signals as part of the UbiTtention workshop at UbiComp 2020: https://www.ubittention.org/2020
Our team won first place at the “Challenge UP – Multimodal Fall Detection” at the International Joint Conference on Neural Network, Budapest, 2019
Our team won first place at the “Emteq – Activity Recognition Challenge” at the International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp, London, 2019
Our team won first place at the “Sussex-Huawei Locomotion Challenge 2019” at the International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp, London 2019

Datasets

Labeled datasets for cognitive-load monitoring with wearable device: link
- The datasets can be used only for research purposes
- References:
  1. Gjoreski, Martin, Tine Kolenik, Timotej Knez, Mitja Luštrek, Matjaž Gams, Hristijan Gjoreski, and Veljko Pejović. “Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits.” Applied Sciences 10, no. 11 (2020): 3843.
  2. Pejović, Veljko, Martin Gjoreski, Christoph Anderson, Klaus David, and Mitja Luštrek. “Toward Cognitive Load Inference for Attention Management in Ubiquitous Systems.” IEEE Pervasive Computing 19, no. 2 (2020): 35-45.
  3. Gjoreski, Martin, Mitja Luštrek, and Veljko Pejović. “My watch says I’m busy: Inferring cognitive load with low-cost wearables.” In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 1234-1240. 2018.
Labeled datasets for stress monitoring with wearable device
- The datasets can be used only for research purposes
- Laboratory data (pass: cogload2015jsi): link
- Real-life data (pass: emoStress2015jsi): link
- References:
  1. Gjoreski, Martin, Mitja Luštrek, Matjaž Gams, and Hristijan Gjoreski. “Monitoring stress with a wrist device using context.” Journal of biomedical informatics 73 (2017): 159-170.
  2. Gjoreski, Martin, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. “Continuous stress detection using a wrist device: in laboratory and real life.” In proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: Adjunct, pp. 1185-1193. 2016.