About me

PhD candidate at the Department of Intelligent Systems at the Jozef Stefan Institute. 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 affective computing, ambient intelligence, mobile healthcare and wearable computing. I am also a teaching assistant at the Faculty of Computer and Information Science, University of Ljubljana. Subjects: Mobile Computing and Platform Based Development.

Education

PhD candidate in Information and Communication Technologies (2016-Present)
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”

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”

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”

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
  • 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 [pre-print].
  • 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

Services

News

  • We organize an ML challenge for congitive laod monitorring 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