Faculty Profiles

De, B. K., Sakevych, M., & Metsis, V. (2024). The Impact of Data Augmentation on Time Series Classification Models: An In-Depth Study with Biomedical Data. In International Conference on Artificial Intelligence in Medicine. Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-66538-7_20

Katrompas, A., & Metsis, V. (2024). Many-to-Many Prediction for Effective Modeling of Frequent Label Transitions in Time Series. In Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments (pp. 265–272). https://doi.org/10.1145/3652037.3652049

Irani, H., & Metsis, V. (2024). Enhancing Time-Series Prediction with Temporal Context Modeling: A Bayesian and Deep Learning Synergy. In The 37th International FLAIRS Conference. https://doi.org/10.32473/flairs.37.1.135583

Li, X., Sakevych, M., Atkinson, G., & Metsis, V. (2024). Biodiffusion: A versatile diffusion model for biomedical signal synthesis. Bioengineering, 11(4). https://doi.org/10.3390/bioengineering11040299

Hinkle, L. B., Pedro, T., Lynn, T., Atkinson, G. M., & Metsis, V. (2023). Assisted Labeling Visualizer (ALVI): A Semi-Automatic Labeling System For Time-Series Data. In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (pp. 1–5). IEEE Exlplore. https://doi.org/10.1109/ICASSPW59220.2023.10193169

Hinkle, L. B., & Metsis, V. (2023). An LLVM-Inspired Framework for Unified Processing of Multimodal Time-Series Data. In 16th ACM International Conference on PErvasive Technologies Related to Assistive Environments (pp. 91–94). ACM. https://doi.org/https://doi.org/10.1145/3594806.3594812

Hinkle, L. B., Atkinson, G. M., & Metsis, V. (2023). Fusion of Learned Representations for Multimodal Sensor Data Classification. In 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2023) (pp. 404–415). Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-34111-3_34

Li, X., Nabati, R., Singh, K., Corona, E., Metsis, V., & Parchami, A. (2023). EMOD: Efficient Moving Object Detection via Image Eccentricity Analysis and Sparse Neural Networks. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops (pp. 51–59). IEEE Xplore. https://doi.org/https://doi.org/10.1109/WACVW58289.2023.00010

Atkinson, G. M., Li, X., & Metsis, V. (2023). Conditional Diffusion with Label Smoothing for Data Synthesis from Examples with Noisy Labels. In 31st European Signal Processing Conference (EUSIPCO 2023) (pp. 1300–1304). IEEE. https://doi.org/10.23919/EUSIPCO58844.2023.10289794

Katrompas, A., & Metsis, V. (2023). Temporal Attention Signatures for Interpretable Time-Series Prediction. In 32nd International Conference on Artificial Neural Networks (ICANN 2023) (pp. 268–280). United States: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-031-44223-0_22

Li, X., Metsis, V., & Ngu, A. H. (2023). Generating Realistic Multi-class Biosignals with BioSGAN: A Transformer-based Label-guided Generative Adversarial Network. In The 25th International Conference on Artificial Intelligence (ICAI’23). USA: IEEE.

Byers, M., Trahan, M. H., Nason, E. E., Eigege, C., Moore, N., Washburn, M., & Metsis, V. (2023). Detecting Intensity of Anxiety in Language of Student Veterans with Social Anxiety Using Text Analysis. Journal of Technology in Human Services, 41(2), 125–147. https://doi.org/https://doi.org/10.1080/15228835.2022.2163452

Li, X., Metsis, V., Wang, H., & Ngu, H. H. (2022). TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network. https://doi.org/0.1007/978-3-031-09342-5_13

Byers, M., Hinkle, L. B., & Metsis, V. (2022). Topological Data Analysis of Time-Series as an Input Embedding for Deep Learning Models. In 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2022) (pp. 402–413). Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-031-08337-2_33

Li, X., & Metsis, V. (2022). SPP-EEGNET: An Input-Agnostic Self-supervised EEG Representation Model for Inter-dataset Transfer Learning. In 18th International Conference on Computing and Information Technology (IC2IT 2022). Springer. https://doi.org/https://doi.org/10.1007/978-3-030-99948-3_17

Hinkle, L. B., & Metsis, V. (2022). Individual Convolution of Ankle, Hip, and Wrist Data for Activities-of-Daily-Living Classification. In 18th International Conference on Intelligent Environments (IE 2022). United States: IEEE. https://doi.org/10.1109/IE54923.2022.9826781

Katrompas, A., Ntakouris, T., & Metsis, V. (2022). Recurrence and Self-attention vs the Transformer for Time-Series Classification: A Comparative Study. In 20th International Conference on Artificial Intelligence in Medicine (AIME 2022). Springer. https://doi.org/https://doi.org/10.1007/978-3-031-09342-5_10

Blakeney, C., Atkinson, G., Huish, N., Yan, Y., Metsis, V., & Zong, Z. (2022). Measuring Bias and Fairness in Multiclass Classification.

Ngu, A., Metsis, V., Coyne, S., Srinivas, P., Mahmud, T., & Chee, K. (2022). Personalized Watch-based Fall Detection Using a Collaborative Edge-Cloud Framework. International  Journal of Neural Systems, 32(12). https://doi.org/10.1142/s0129065722500484

Atkinson, G. M., & Metsis, V. (2021). A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data. In 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 479--493).

Katrompas, A., & Metsis, V. (2021). Enhancing LSTM Models with Self-attention and Stateful Training. In Proceedings of SAI Intelligent Systems Conference (IntelliSys 2021) (pp. 217--235).

Hinkle, L. B., & Metsis, V. (2021). Model Evaluation Approaches for Human Activity Recognition from Time-Series Data. In 19th International Conference on Artificial Intelligence in Medicine (AIME 2021) (pp. 209--215).

Katrompas, A., & Metsis, V. (2021). Rate My Professors: A Study Of Bias and Inaccuracies In Anonymous Self-Reporting. In 2021 2nd International Conference on Computing and Data Science (CDS) (pp. 536--542).

Byers, M., & Metsis, V. (2021). Text Analysis for Understanding Symptoms of Social Anxiety in Student Veterans. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15958--15959.

Atkinson, G. M., & Metsis, V. (2021). TSAR: a time series assisted relabeling tool for reducing label noise. In 14th PErvasive Technologies Related to Assistive Environments Conference.

Ngu, H. H., Coyne, S., Srinivas, P., & Metsis, V. (. (2021). Collaborative Edge-Cloud Computing for Personalized Fall Detection. In Artificial Intelligence Applications and Innovations. AIAI 2021 (pp. 323–336). Springer, Cham: Springer International Publishing. https://doi.org/https://doi.org/10.1007/978-3-030-79150-6_26

Trahan, M. H., Morley, R. H., Nason, E. E., Rodrigues, N. A., Huerta, L., & Metsis, V. (2021). Virtual reality exposure simulation for student veteran social anxiety and PTSD: A case study. Clinical Social Work Journal, 49(2), 220–230. https://doi.org/https:/doi.org/10.1007/s10615-020-00784-7

Mauldin, T. R., Ngu, A. H. H., Metsis, V., & Canby, M. (2020). Ensemble Deep Learning on Wearables Using Small Datasets. ACM Transactions on Computing for HealthCare, 2. https://doi.org/https://doi.org/10.1145/3428666

Byers, M., & Metsis, V. (n.d.). Text Analysis for Understanding Social Anxiety Symptoms of Student Veterans. Retrieved from 601 University Dr

Atkinson, G. M., & Metsis, V. (2020). Identifying label noise in time-series datasets. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers (pp. 238–243). ACM. https://doi.org/https://doi.org/10.1145/3410530.3414366

Ngu, H. H., Metsis, V., Coyne, S., Chung, B., Pai, R., & Chang, J. (2020). Personalized Fall Detection System. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (pp. 1–7). https://doi.org/10.1109/PerComWorkshops48775.2020.9156172

Koutitas, G., Kumar, V., & Metsis, V. (2019). In-Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing. Sensors Journal, 2020, 20, 690.

Nason, E. E., Trahan, M. H., Smith, K. S., Metsis, V., & Selber, S. K. (2019). Virtual treatment for veteran social anxiety disorder: A comparison of 360° video and 3D virtual reality. Journal of Technology in Human Services, 38(3), 288–308. https://doi.org/10.1080/15228835.2019.1692760

Mauldin, T., Ngu, A., Metsis, V., Canby, M., & Tesic, J. (2019). Experimentation and Analysis of Ensemble Deep Learning in IoT Applications. Open Journal of Internet Of Things (OJIOT), 5(1), 133–149. Retrieved from http://nbn-resolving.de/urn:nbn:de:101:1-2018080519304951282148

Koutitas, G., Metsis, V., Lawrence, G. B., Smith, K. S., & Trahan, M. H. (2019). A Virtual and Augmented Reality Platform for the Training of First Responders of the Ambulance Bus. ACM PETRA ’19: Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive EnvironmentsJune 2019 Pages 299–302 https://doi.org/10.1145/3316782.3321542.

Hinkle, L. B., Khoshhal, K., & Metsis, V. (2019). Physiological Measurement for Emotion Recognition in Virtual Reality. In Proceedings of the 2nd International Conference on Data Intelligence and Security (ICDIS 2019). https://doi.org/https://doi.org/10.1109/ICDIS.2019.00028

Metsis, V., Lawrence, G. B., Trahan, M. H., Smith, K. S., Tamir, D., & Selber, S. K. (2019). 360 Video: A Prototyping Process for Developing Virtual Reality Interventions. Journal of Technology in Human Services, 37, 32–50. https://doi.org/https://doi.org/10.1080/15228835.2019.1604291

Metsis, V., Lawrence, G. B., Trahan, M. H., Smith, K. S., & Tamir, D. (2019). Virtual Reality Environments for Returning Combat Veteran Social Anxiety and PTSD: Rapid Prototyping Methodologies for Intervention. SSWR - Society for Social Work and Research. Retrieved from https://sswr.confex.com/sswr/2019/webprogram/Paper34417.html

Malhotra, A., Schizas, I. D., & Metsis, V. (2018). Correlation Analysis-Based Joint Segmentation and Classification of Human Activity. IEEE Sensors Journal, 18(19), 8085–8095. https://doi.org/https://doi.org/10.1109/JSEN.2018.2864207

Trahan, M. H., Ausbrooks, A. R., Smith, S., Metsis, V., Berek, A., Trahan, L. H., & Selber, K. (2018). Experiences of student veterans with social anxiety: A qualitative study. Social Work in Mental Health, 17(2), 197–221. https://doi.org/10.1080/15332985.2018.1522607

Mauldin, T. R., Canby, M. E., Metsis, V., Ngu, H. H., & Rivera, C. C. (2018). Smartfall: A smartwatch-based fall detection system using deep learning. Sensors (Switzerland), 18(10). https://doi.org/10.3390/s18103363

Huber, M., & Metsis, V. (2018). PerCom Workshops 2018 Welcome Page. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018. https://doi.org/10.1109/PERCOMW.2018.8480261

Gobert, D., Metsis, V., & Smith, K. S. (2018). Integration of virtual reality with an omnidirectional treadmill system for multi-directional balance skills intervention. IEEE. https://doi.org/doi: 10.1109/WEROB.2017.8383831

Gobert, D., Metsis, V., & Smith, K. (2018). Integration of Virtual Reality with an Omnidirectional Treadmill System for Multi-directional Balance Skills Intervention. IEEE XPLORE. https://doi.org/https://ieeexplore.ieee.org/document/8383831

Metsis, V., Smith, K. S., & Gobert, D. V. N. (n.d.). Integration of Virtual Reality with an Omnidirectional Treadmill System for Multi-directional Balance Skills Intervention.

Anderson, A., Hsiao, T., & Metsis, V. (2017). Classification of Emotional Arousal During Multimedia Exposure. ACM.

Roudposhti, K. K., Dias, J., Peixoto, P., Metsis, V., & Nunes, U. (2016). A Multilevel Body Motion-based Human Activity Analysis Methodology. IEEE Transactions on Cognitive and Developmental Systems.

Ngu, A. H., Gutierrez, M., Metsis, V., Nepal, S., & Sheng, M. Z. (2016). IoT Middleware: A Survey on Issues and Enabling technologies. IEEE Internet of Things Journal.

Espiritu, H., & Metsis, V. (2015). Automated detection of sleep disorder-related events from polysomnographic data. In Healthcare Informatics (ICHI), 2015 International Conference on (pp. 562–569).

Ebert, D., Metsis, V., & Makedon, F. (2015). Development and evaluation of a unity-based, kinect-controlled avatar for physical rehabilitation. In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments (p. 88).

Papakostas, M., Staud, J., Makedon, F., & Metsis, V. (2015). Monitoring breathing activity and sleep patterns using multimodal non-invasive technologies. In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments (p. 78).

Metsis, V., Schizas, I. D., & Marshall, G. (2015). Real-time subspace denoising of polysomnographic data. In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments (p. 77).

Lioulemes, A., Sassaman, P., Gieser, S. N., Karkaletsis, V., Makedon, F., & Metsis, V. (2015). Self-managed patient-game interaction using the barrett wam arm for motion analysis. In Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments (p. 34).

Paulk, D., Metsis, V., McMurrough, C., & Makedon, F. (2014). A supervised learning approach for fast object recognition from RGB-D data. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments (p. 5).

Phan, S., Lioulemes, A., Lutterodt, C., Makedon, F., & Metsis, V. (2014). Guided physical therapy through the use of the barrett wam robotic arm. In Haptic, Audio and Visual Environments and Games (HAVE), 2014 IEEE International Symposium on (pp. 24–28).

Gieser, S. N., Metsis, V., & Makedon, F. (2014). Quantitative evaluation of the kinect skeleton tracker for physical rehabilitation exercises. In Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments (p. 48).

Lioulemes, A., Galatas, G., Metsis, V., Mariottini, G. L., & Makedon, F. (2014). Safety challenges in using AR. Drone to collaborate with humans in indoor environments. In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments (p. 33).

Metsis, V., Makedon, F., Shen, D., & Huang, H. (2014). DNA copy number selection using robust structured sparsity-inducing norms. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 11(1), 138–181.

Metsis, V., Kosmopoulos, D., Athitsos, V., & Makedon, F. (2014). Non-invasive analysis of sleep patterns via multimodal sensor input. Personal and Ubiquitous Computing, 18(1), 19–26. https://doi.org/10.1007/s00779-012-0623-1

Zikos, D., Galatas, G., Metsis, V., & Makedon, F. (2013). A web ontology for brain trauma patient computer-assisted rehabilitation. In ICIMTH (pp. 100–102).

Papangelis, A., Gatchel, R., Metsis, V., & Makedon, F. (2013). An adaptive dialogue system for assessing post traumatic stress disorder. In Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments (p. 49).

Metsis, V., Jangyodsuk, P., Athitsos, V., Iversen, M., & Makedon, F. (2013). Computer aided rehabilitation for patients with rheumatoid arthritis. In Computing, Networking and Communications (ICNC), 2013 International Conference on (pp. 97–102).

Gardner, M., Metsis, V., Becker, E., & Makedon, F. (2013). Modeling the effect of attention deficit in game-based motor ability assessment of Cerebral Palsy patients. In Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments (p. 65).

McMurrough, C. D., Metsis, V., Kosmopoulos, D., Maglogiannis, I., & Makedon, F. (2013). A dataset for point of gaze detection using head poses and eye images. Journal on Multimodal User Interfaces, 7(3), 207–215.

Vyas, K. B., Metsis, V., & Makedon, F. (2013). Aria: Getting started quickly. Computer Science Undergraduate Research Journal, 1.

McMurrough, C. D., Metsis, V., Kosmopoulos, D., Maglogiannis, I., & Makedon, F. (2013). A Dataset for Point of Gaze Detection using Head Poses and Eye Images. Journal on Multimodal User Interfaces, 7(3), 207–215. https://doi.org/http://dx.doi.org/10.1007/s12193-013-0121-4

Metsis, V., Makedon, F., Shen, D., & Huang, H. (2013). DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 11(1), 168–181. https://doi.org/http://dx.doi.org/10.1109/TCBB.2013.141

Zhang, Z., Liu, W., Metsis, V., & Athitsos, V. (2012). A viewpoint-independent statistical method for fall detection. In Pattern Recognition (ICPR), 2012 21st International Conference on (pp. 3626–3630).

McMurrough, C. D., Metsis, V., Rich, J., & Makedon, F. (2012). An eye tracking dataset for point of gaze detection. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 305–308).

McMurrough, C., Rich, J., Metsis, V., Nguyen, A., & Makedon, F. (2012). Low-cost head position tracking for gaze point estimation. In Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments (p. 22).

Metsis, V. (2012). A Computational Framework For Human-Centered Multimodal Data Analysis.

Dela Rosa, K., Metsis, V., & Athitsos, V. (2012). Boosted ranking models: a unifying framework for ranking predictions. Knowledge and Information Systems, 30(3), 543–568.

Metsis, V., Huang, H., Andronesi, O. C., Makedon, F., & Tzika, A. (2012). Heterogeneous data fusion for brain tumor classification. Oncology Reports, 28(4), 1413.

Ferdous, S., Metsis, V., Galatas, G., Huang, H., Basco, M., Fegaras, L., & Makedon, F. (2011). Automatic Depression Assessment from Speech Records. ICICTH.

Metsis, V., Andronesi, O., Huang, H., Mindrinos, M., Rahme, L., Makedon, F., & Tzika, A. (2011). Combination of sparse and wrapper feature selection from multi-source data for accurate brain tumor typing. In Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM) 19th Annual Meeting Exhibition (pp. 7–13).

Metsis, V., Galatas, G., Papangelis, A., Kosmopoulos, D., & Makedon, F. (2011). Recognition of sleep patterns using a bed pressure mat. In Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments (p. 9).

Doukas, C., Metsis, V., Becker, E., Le, Z., Makedon, F., & Maglogiannis, I. (2011). Digital cities of the future: Extending@ home assistive technologies for the elderly and the disabled. Telematics and Informatics, 28(3), 176–190.

Park, K., Lin, Y., Metsis, V., Le, Z., & Makedon, F. (2010). Abnormal human behavioral pattern detection in assisted living environments. In Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments (p. 9).

Papangelis, A., Metsis, V., Shawe-Taylor, J., & Makedon, F. (2010). Sensor placement and coordination via distributed multi-agent cooperative control. In Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments (p. 14).

Ahmad, I., Arora, R., White, D., Metsis, V., & Ingram, R. (2009). Energy-constrained scheduling of dags on multi-core processors. In International Conference on Contemporary Computing (pp. 592–603).

Metsis, V., Huang, H., Makedon, F., & Tzika, A. (2009). Heterogeneous data fusion to type brain tumor biopsies. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 233–240).

Metsis, V., Mintzopoulos, D., Huang, H., Mindrinos, M., Black, P., Makedon, F., & Tzika, A. (2009). Multi-Source feature selection to improve multi-class brain tumor typing.

Arora, R., Metsis, V., Zhang, R., & Makedon, F. (2009). Providing QoS in ontology centered context aware pervasive systems. In Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments (p. 8).

Becker, E., Metsis, V., Arora, R., Vinjumur, J., Xu, Y., & Makedon, F. (2009). SmartDrawer: RFID-based smart medicine drawer for assistive environments. In Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments (p. 49).

Metsis, V., Le, Z., Lei, Y., & Makedon, F. (2008). Towards an evaluation framework for assistive environments. In Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments (p. 12).

Stamatakis, K., Metsis, V., Karkaletsis, V., Ruzicka, M., Svátek, V., Amigó, E., … Spyropoulos, C. (2007). Content collection for the labelling of health-related web content. In Conference on Artificial Intelligence in Medicine in Europe (pp. 341–345).

Villarroel, D., Mayer, M., Leis, A., Karkaletsis, V., Stamatakis, K., Metsis, V., … others. (2007). Assisting Quality Assessment (AQUA)–a system based on semantic web and information extraction technologies to support medical quality labelling agencies. Citeseer.

Karkaletsis, V., Stamatakis, K., Metsis, V., Redoumi, V., & Tsarouhas, D. (2006). Health-related Web Content: quality labelling mechanisms and the MedIEQ approach. In Proc 4th International conference on Information Communication Technologies in Health (ICICTH 2006.

Metsis, V., Androutsopoulos, I., & Paliouras, G. (2006). Spam filtering with naive bayes-which naive bayes? In CEAS (Vol. 17, pp. 28–69).