dr. D.V. Le Viet Duc (Duc)

Dr. Le Viet Duc is Assistant Professor at the Department of Computer Science at the University of Twente. He is broadly interested in bringing Machine Learning and Deep Learning into real-world behavior analytics. In addition to his academia research experience, he also has 10-years R&D experience on smart devices in industry. Inspired by the way human brain learns the world, he is eager to develop a cognitive sensing system that can minimize obtrusiveness and adapt itself to individuals and environments with limited prior knowledge. His focus is on employing recent advances in transfer learning and deep reinforcement learning to find hidden representations from long-term unlabeled data collected in unstructured environments.

Over the years, he has built up his experience in embedded systems, mobile sensing, localization, and deep learning. Examples include an infrastructure-independent framework for robust and reliable sensing in smart cities (best paper award 2012), a novel algorithm for context detection (best paper award 2014) to detect the transition of human activities, and an innovative localization algorithm (patented in 2017) for human and device tracking in unstructured environments.

Besides supervising PhDs and Masters on human and animal behavior monitoring based on location tracking and activity recognition in real world, he is doing research on vital sign detections for human mental monitoring and infrastructure condition monitoring.

Engineering & Materials Science

# Acoustic Waves
# Animals
# Pipe
# Polyvinyl Chlorides
# Sensors
# Smartphones
# Testbeds
# Water Pipelines

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Pervasive Systems (PS)

Nguyen, S. , Le, D. V. , & Havinga, P. J. M. (2023). Learning the world from its words: Anchor-agnostic Transformers for Fingerprint-based Indoor Localization. In 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 150-159) https://doi.org/10.1109/PERCOM56429.2023.10099376

Tran, Q. C. V. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (Accepted/In press). Self-diagnostic PVC pipes for capturing threats in un-derground water pipelines. 1-5. Poster session presented at 5th IFIP International Internet of Things Conference, IoT 2022, Amsterdam, Netherlands.

Tran, Q. C. V. , Nguyen, D. T. N. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (Accepted/In press). Efficient talking in underground water pipelines. 1-5. Poster session presented at 5th IFIP International Internet of Things Conference, IoT 2022, Amsterdam, Netherlands.

Tran, Q. C. V. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (Accepted/In press). Testbed Hardware Design to Collect Data for Underground PVC Water Pipe Crack Detection: Challenges and Solutions. 1-5. Paper presented at 5th International SenSys/BuildSys Workshop on Data, DATA 2022, Boston, Massachusetts, United States. https://doi.org/10.4121/21340608.v1, https://doi.org/10.1145/3560905.3567765

Nguyen, S., Tran, L. D. , Le, D. V., & Masayuki, A. (Accepted/In press). Self-Attention Generative Distribution Adversarial Network for Few- and Zero-Shot Face Anti-Spoofing. Paper presented at IEEE International Joint Conference on Biometrics, IJCB 2022, Abu Dhabi, United Arab Emirates.

Borsci, S. , Lehtola, V. V. , Nex, F. C. , Yang, M. Y. , Augustijn, P. W. M., Bagheriye, L. , Brune, C., Kounadi, O. , Li, J. J. , Rebelo Moreira, J. L. , van der Nagel, J. E. L. , Veldkamp, B. P. , Le, D. V. , Wang, M. , Wijnhoven, F. , Wolterink, J. M. , & Zurita-Milla, R. (2022). Embedding artificial intelligence in society: looking beyond the EU AI master plan using the culture cycle. AI & society. https://doi.org/10.1007/s00146-021-01383-x, https://doi.org/10.1007/s00146-021-01383-x

Tran, Q. C. V. , Le, D. V. , Yntema, D. R. , & Havinga, P. J. M. (2022). A Review of Inspection Methods for Continuously Monitoring PVC Drinking Water Mains. IEEE Internet of Things Journal, 9(16), 14336-14354. [2327-4662 ]. https://doi.org/10.1109/JIOT.2021.3077246

Shreya, S. , & Le, D. V. (2021). Completely Automated CNN Architecture Design Based on VGG Blocks for Fingerprinting Localisation. In 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN) [9662642] (International Conference on Indoor Positioning and Indoor Navigation (IPIN); Vol. 2021). IEEE. https://doi.org/10.1109/IPIN51156.2021.9662642

Kamminga, J. W. , Meratnia, N. , Le, D. V. , & Havinga, P. J. M. (2020). Towards Deep Unsupervised Representation Learning from Accelerometer Time Series for Animal Activity Recognition. Paper presented at 6th Workshop on Mining and Learning from Time Series, MiLeTS 2020.

Le, D. V. , Van Kleunen, W. A. P., Nguyen, T. , Meratnia, N. , & Havinga, P. J. M. (2018). SomBe: Self-Organizing Map for Unstructured and Non-Coordinated iBeacon Constellations. In 2018 IEEE International Conference on Pervasive Computing and Communications, PerCom 2018 [8444584] IEEE. https://doi.org/10.1109/PERCOM.2018.8444584

Le, D. V. , Kamminga, J. , Scholten, H. , & Havinga, P. J. M. (2018). A Framework to Measure Reliance of Acoustic Latency on Smartphone Status. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 (pp. 348-354). [8480354] IEEE. https://doi.org/10.1109/PERCOMW.2018.8480354

Le, D. V., Nguyen, T. , Scholten, H. , & Havinga, P. J. M. (2018). Probability of Task Completion and Energy Consumption in Cooperative Pervasive Mobile Computing. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018 (pp. 34-39). [8480135] IEEE. https://doi.org/10.1109/PERCOMW.2018.8480135

Tran, T. X. , Le, D. V., Yue, G., & Pompili, D. (2018). Cooperative Hierarchical Caching and Request Scheduling in a Cloud Radio Access Network. IEEE Transactions on Mobile Computing, 17(12), 2729-2743. [8323203]. https://doi.org/10.1109/TMC.2018.2818723

Le, D. V. , Meratnia, N. , & Havinga, P. J. M. (2018). Unsupervised Deep Feature Learning to Reduce the Collection of Fingerprints for Indoor Localization Using Deep Belief Networks. In IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation (pp. 1-7). [8533790] IEEE. https://doi.org/10.1109/IPIN.2018.8533790

Le Viet Duc, D. V. , van Kleunen, W. A. P. , & Havinga, P. J. M. (2018). Method and system for localizing a blind node. (Patent No. WO 2018/070863 A1).

Karuppiah Ramachandran, V. R., Alblas, H. J. , Le, D. V. , & Meratnia, N. (2018). Towards an Online Seizure Advisory System - An Adaptive Seizure Prediction Framework Using Active Learning Heuristics. Sensors (Switzerland), 18(6), [1698]. https://doi.org/10.3390/s18061698

Kamminga, J. W. , Le Viet Duc, D. V. , Meijers, J. P., Bisby, H. C. , Meratnia, N. , & Havinga, P. J. M. (2018). Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), [15]. https://doi.org/10.1145/3191747

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University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11), room 5013
Hallenweg 19
7522NH Enschede
The Netherlands

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University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling 5013
P.O. Box 217
7500 AE Enschede
The Netherlands

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Pervasive Systems (PS)

University of Twente Drienerlolaan 5
7522 NB Enschede

+31 (0)53 489 9111
info@utwente.nl
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