About me

I am a postdoc researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), in the research group of prof. Marc Langheinrich. My research field is AI with a focus on the 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. From 2014 to 2020, I was part of the Department of Intelligent Systems at the Jozef Stefan Institute, where I did my MSc and PhD studies under the supervision of prof. Matjaz Gams and prof. Mitja Lustrek. In the years 2019 and 2020, was also a teaching assistant at the Faculty of Computer and Information Science, University of Ljubljana. More details about my teaching activities are available here.

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

Full list available on Google Scholar

  • Piotr Romashov, Martin Gjoreski, Kacper Sokol, Vanina Martinez, and Marc Langheinrich. “BayCon: Model-agnostic Bayesian Counterfactual Generator.” IJCAI 2022. link, code
  • Stankoski, Simon, Ivana Kiprijanovska, Ifigeneia Mavridou, Charles Nduka, Hristijan Gjoreski, and Martin Gjoreski. “Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning.” Sensors 22, no. 6 (2022): 2079. link
  • Gjoreski, Martin, Bhargavi Mahesh, Tine Kolenik, Jens Uwe-Garbas, Dominik Seuss, Hristijan Gjoreski, Mitja Luštrek, Matjaž Gams, and Veljko Pejović. “Cognitive Load Monitoring With Wearables–Lessons Learned From a Machine Learning Challenge.” IEEE Access 9 (2021): 103325-103336. link
  • Gjoreski, Martin, Vito Janko, Gašper Slapničar, Miha Mlakar, Nina Reščič, Jani Bizjak, Vid Drobnič et al. “Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors.” Information Fusion 62 (2020): 47-62. link
  • Janko, Vito; Slapničar, Gašper; Dovgan, Erik; Reščič, Nina; Kolenik, Tine; Gjoreski, Martin; Smerkol, Maj; Gams, Matjaž; Luštrek, Mitja. 2021. “Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19” Int. J. Environ. Res. Public Health 18, no. 13: 6750. link
  • Piltaver, Rok, Luštrek, Mitja, Sašo Džeroski, Martin Gjoreski, and Matjaž Gams. “Learning comprehensible and accurate hybrid trees.” Expert Systems with Applications 164 (2021): 113980. link
  • Dzieżyc, Maciej, Martin Gjoreski, Przemysław Kazienko, Stanisław Saganowski, and Matjaž Gams. “Can we ditch feature engineering? end-to-end deep learning for affect recognition from physiological sensor data.” Sensors 20, no. 22 (2020): 6535. link, code
  • Gjoreski, Martin, Anton Gradišek, Borut Budna, Matjaž Gams, and Gregor Poglajen. “Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds.” IEEE Access 8 (2020): 20313-20324. link
  • 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. link
  • Gjoreski, Martin, Matja Ž. Gams, Mitja Luštrek, Pelin Genc, Jens-U. Garbas, and Teena Hassan. “Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals.” IEEE Access 8 (2020): 70590-70603. link
  • Gashi, Shkurta, Elena Di Lascio, Bianca Stancu, Vedant Das Swain, Varun Mishra, Martin Gjoreski, and Silvia Santini. “Detection of artifacts in ambulatory electrodermal activity data.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, no. 2 (2020): 1-31. link
  • Simjanoska, Monika, Martin Gjoreski, Matjaž Gams, and Ana Madevska Bogdanova. “Non-invasive blood pressure estimation from ECG using machine learning techniques.” Sensors 18, no. 4 (2018): 1160. link
  • 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. link
  • Gjoreski, Martin, Hristijan Gjoreski, Mitja Luštrek, and Matjaž Gams. “How accurately can your wrist device recognize daily activities and detect falls?.” Sensors 16, no. 6 (2016): 800. link
  • Janko, Vito, Martin Gjoreski, Gašper Slapničar, Miha Mlakar, Nina Reščič, Jani Bizjak, Vid Drobnič et al. “Winning the sussex-huawei locomotion-transportation recognition challenge.” In Human Activity Sensing, pp. 233-250. Springer, Cham, 2019. link

Services

  • Posters & Demos Chair at Ubicomp 2022: https://www.ubicomp.org/ubicomp2022/organizing-committee/
  • 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

  • Our paper Toward cognitive load inference for attention management in ubiquitous systems has won the 2020 Best-Paper Runner-up Award from “IEEE Pervasive Computing Magazine!”.
  • I was awarded the “Jožef Stefan golden emblem” (Slovenian: Zlati znak Jožefa Stefana), signifying an outstanding PhD thesis in Slovenia for the year 2020.
  • 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.