RF/Bioelectrical/ Electrochemical Sensor design

Electromagnetic Fields and Waves/Bioelectromagnetic

Human body communication

Body sensor network / Healthcare internet of things

Wearable AI

Cloud computing in healthcare

Non-invasive glucose monitoring

Deep Learning and Machine Learning

Biometrics

2009.07-2013.01, Doctor degree, University of Chinese Academy of Sciences
2004.09-2007.07, Master degree, Wuhan University of Science and Technology
2000.09-2004.07, Bachelor degree, Wuhan University of Science and Technology

2007.05-now, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences

[1]   J. Li, J. Ma, O. M. Omisore, Y. Liu, H. Tang, P. Ao, Y. Yan, L. Wang, and Z. Nie, “Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach,” IEEE Trans Neural Netw Learn Syst, vol. Pp, Jun 8, 2023.

[2]   A. Kandwal, L. W. Liu, J. Li, Y. Liu, H. Tang, Z. Ju, T. Igbe, R. Jasrotia, and Z. Nie, “Designing Highly Sensitive Microwave Antenna Sensor with Novel Model for Noninvasive Glucose Measurements,” Progress In Electromagnetics Research, vol. 176, pp. 129-141, 2023.

[3]   J. Li, J. Lu, I. Tobore, Y. Liu, A. Kandwal, L. Wang, X. Ma, W. Lu, Y. Bao, J. Zhou, and Z. Nie, “Gradient variability coefficient: a novel method for assessing glycemic variability and risk of hypoglycemia,” Endocrine, 2022/01/23, 2022.

[4]   A. Kandwal, J. Li, T. Igbe, Y. Liu, R. Das, B. K. Kanaujia, and Z. Nie, “Young’s Double Slit Method-Based Higher Order Mode Surface Plasmon Microwave Antenna Sensor: Modeling, Measurements, and Application,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022.

[5]   J. Li, I. Tobore, Y. Liu, A. Kandwal, L. Wang, and Z. Nie, “Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3340-3350, 2021.

[6]   J. Li, J. Lu, I. Tobore, Y. Liu, A. Kandwal, L. Wang, J. Zhou, and Z. Nie, “Towards noninvasive and fast detection of Glycated hemoglobin levels based on ECG using convolutional neural networks with multisegments fusion and Varied-weight,” Expert Systems with Applications, vol. 186, pp. 115846, 2021/12/30/, 2021.

[7]   A. Kandwal, Z. Nie, T. Igbe, J. Li, Y. Liu, L. W. Liu, and Y. Hao, “Surface Plasmonic Feature Microwave Sensor With Highly Confined Fields for Aqueous-Glucose and Blood-Glucose Measurements,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021.

[8]   A. Kandwal, L. W. Liu, T. Igbe, J. Li, Y. Liu, R. Das, B. K. Kanaujia, L. Wang, and Z. Nie, “A Novel Method of Using Bifilar Spiral Resonator for Designing Thin Robust Flexible Glucose Sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-10, 2021.

[9]   T. Igbe, J. Li, A. Kandwal, O. M. Omisore, E. Yetunde, L. Yuhang, L. Wang, and Z. Nie, “An absolute magnitude deviation of HRV for the prediction of prediabetes with combined artificial neural network and regression tree methods,” Artificial Intelligence Review, 2021/08/24, 2021.

[10] I. Tobore, A. Kandwal, J. Li, Y. Yan, O. M. Omisore, E. Enitan, L. Sinan, L. Yuhang, L. Wang, and Z. Nie, “Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach,” Knowledge-Based Systems, vol. 209, pp. 106464, 2020/12/17/, 2020.

[11] J. Li, X. Ma, I. Tobore, Y. Liu, A. Kandwal, L. Wang, J. Lu, W. Lu, Y. Bao, J. Zhou, and Z. Nie, “A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes,” Journal of Diabetes Research, vol. 2020, pp. 8830774, 2020.

[12] A. Kandwal, J. Li, T. Igbe, Y. Liu, S. Li, L. Wang, Y. Hao, and Z. Nie, “Broadband Frequency Scanning Spoof Surface Plasmon Polariton Design with Highly Confined Endfire Radiations,” Scientific Reports, vol. 10, no. 1, Jan 10, 2020.

[13] A. Kandwal, T. Igbe, J. Li, Y. Liu, S. Li, L. W. Y. Liu, and Z. Nie, “Highly Sensitive Closed Loop Enclosed Split Ring Biosensor With High Field Confinement for Aqueous and Blood-Glucose Measurements,” Scientific reports, vol. 10, no. 1, pp. 4081-4081, 2020 Mar, 2020.

[14] I. Tobore, J. Li, Y. Liu, Y. Al-Handarish, A. Kandwal, Z. Nie, and L. Wang, “Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations,” Jmir Mhealth and Uhealth, vol. 7, no. 8, Aug 2, 2019.

[15] I. Tobore, J. Li, A. Kandwal, Y. Liu, Z. Nie, and L. Wang, “Statistical and spectral analysis of ECG signal towards achieving non-invasive blood glucose monitoring,” Bmc Medical Informatics and Decision Making, vol. 19, Dec 19, 2019.

[16] L. W. Y. Liu, A. Kandwal, Q. Cheng, H. Shi, I. Tobore, and Z. Nie, “Non-Invasive Blood Glucose Monitoring Using a Curved Goubau Line,” Electronics, vol. 8, no. 6, Jun, 2019.

[17] A. Kandwal, Z. Nie, L. Wang, L. W. Y. Liu, and R. Das, “Realization of Low Profile Leaky Wave Antennas Using the Bending Technique for Frequency Scanning and Sensor Applications,” Sensors, vol. 19, no. 10, May 2, 2019.

[18] A. Kandwal, Z. Nie, J. Li, Y. Liu, L. W. Y. Liu, and R. Das, “Bandwidth and Gain Enhancement of Endfire Radiating Open-Ended Waveguide Using Thin Surface Plasmon Structure,” Electronics, vol. 8, no. 5, May, 2019.

[19] T. Igbe, J. Li, Y. Liu, S. Li, A. Kandwal, Z. Nie, and W. Lei, "Analysis of ECG Segments for Non-Invasive Blood Glucose Monitoring." pp. 1-6.

[20] N. Zeng, J. Li, T. Igbe, Y. Liu, C. Yan, Z. Nie, and Ieee, Investigation on Dielectric Properties of Glucose Aqueous Solutions at 500 KHz-5MHz for Noninvasive Blood Glucose Monitoring, 2018.

Body Area network, Non-invasive glucose monitoring, Human body communication, AI in wearable devices.

1，Electromagnetic Fields and Waves；

2，Biomedical Engineering

3，Electric，Computer，Communication，Control，Mechatronics

4，Mathematics

5，Chemical, Biochemistry, Materials

PIFI/Postdoc in AI healthcare

The Postdoctoral Research Associate will apply his/her technical skills toward development and implementation of machine learning,deep learning, and other algorithms for analysis of physiolical signlas  and prognostication as well as to other tasks related to this research program in order to solve important clinically relevant/werable healthcare problems. 

Required Qualifications: 

The candidate for this position will be a highly motivated individual with a track record of academic achievements and a Ph.D. degree (or will be in the process of completing a Ph.D. program) in computer science, electrical engineering, biomedical engineering, biomedical informatics, biostatistics，mathmatics or a related discipline.

Required qualifications include (1) expertise and experience in machine learning, (2) experience with deep learning, (3) proficiency in computer programming, (4) good verbal and written communication skills, (5) more than two first-author journal papers published or accepted for publication in a good international journal or multiple papers in top conferences in computer science.

PIFI/Postdoc in RF sensor

We are seeking a driven and results-oriented Postdoctoral Appointee to join our hardworking and dynamic team to model, simulation, design, testing Radio Frequency /Microwave sensors for physiological signals sensing ，The core theme of the research would be to develop an wearable ,continuous and non-invasive biomedical applicaiton devices .

Required Qualifications: 

Ph.D. in Electrical Engineering with emphasis on antennas, RF systems and/or electromagnetics.

Expertise of electromagnetic simulation software, such as, HFSS, Designer, ADS.

Strong record in journal and conference publications in the areas of antennas, RF systems and/or electromagnetics.

Strong academic record on courses related to electromagnetics, fields & waves, antennas, RF design, microwave engineering or related courses.