ESR14

Jia Qian

• Education:
  • Master Degree: Information Engineering in University Of Padova (Italy)
  • Bachelor Degree: Software Engineering in Fudan University (China)
• Research Interests:
  • Federated Learning
  • Deep Learning
  • Graph neural Network
  • Data Privacy (Differential privacy, Adversarial Attack)
  • Active Learning

Research project status:

Ongoing

PhD Student

lainey.qian@gmail.com
jiaq@dtu.dk
LinkedIn
Google Scholar

Distributed real-time operational data analytics

Host: Technical University of Denmark (DTU), Cognitive System (CogSys) section.

• Main supervisor: Prof. Lars Kai Hansen, lkai@dtu.dk (contact person)
• Co-supervisor: Prof. Xenofon Fafoutis, xefa@dtu.dk

Objectives:

1. Propose data analytics for Industry 4.0 that exploit the connectivity and data access features of the Fog Nodes.
2. Implement a distributed real-time data analytics solution based on Machine-Learning-as-a-Service.
3. Demonstrate the data analytics solution on an industrial use case.

visits and collaboration:

• University of Kaiserslautern: work on the data privacy issue under Federated Learning.

Publications:

• Jia Qian, Sarada Prasad Gochhayat, and Lars Kai Hansen. Distributed active learning strategies on edge computing. In 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) pages 221–226. IEEE, 2019. [pdf]
• Jia Qian, Lars Kai Hansen, Xenofon Fafoutis, Prayag Tiwari, and Hari Mohan Pandey. Robustness analytics to data heterogeneity in edge computing. Computer Communications , 164:229–239, 2020. [pdf]
• Jia Qian, Prayag Tiwari, Sarada Prasad Gochhayat, and Hari Mohan Pandey. A noble double-dictionary-based ecg compression technique for ioth. IEEE Internet of Things Journal, 7(10):10160–10170, 2020. [pdf]
• Mohammadreza Barzegaran, Nitin Desai, Jia Qian, Koen Tange, Bahram Zarrin, Paul Pop, and Juha Kuusela. Fogification of electric drives: An industrial use case. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), volume 1, pages 77–84. IEEE, 2020. [pdf]
• Mohammadreza Barzegaran, Nitin Desai, Jia Qian, and Paul Pop. Electric drives as fog nodes in a fog computing-based industrial use case. The Journal of Engineering, 2021. [pdf]
• Prayag Tiwari, Jia Qian, Qiuchi Li, Benyou Wang, Deepak Gupta, Ashish Khanna, Joel JPC Rodrigues, and Victor Hugo C de Albuquerque. Detection of subtype blood cells using deep learning. Cognitive Systems Research, 52:1036–1044, 2018. [pdf]
• Jia Qian, Hiba Nassar, and Lars Kai Hansen. Minimal conditions analysis of gradient-based reconstruction in federated learning (submitted). arXiv preprint arXiv:2010.15718 , 2020. [pdf]
• Bebortta, S., Senapati, D., Rajput, N.K., Singh, A.K., Rathi, V.K., Pandey, H.M., Jaiswal, A.K., Qian, J. and Tiwari, P., 2020. Evidence of power-law behavior in cognitive IoT applications. Neural Computing and Applications, 32(20), pp.16043-16055.[pdf]
• Jaiswal, A.K., Tiwari, P., Rathi, V.K., Qian, J., Pandey, H.M. and Albuquerque, V.H.C., 2020. Covidpen: A novel covid-19 detection model using chest x-rays and ct scans. Medrxiv. [pdf].

For the full publication list, please check Here.