Prof. Alina Nechyporenko | Healthcare Awards | Best Researcher Award

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Prof. Alina Nechyporenko | Healthcare Awards | Best Researcher Award

Prof. Alina Nechyporenko, Technische Hochschule Wildau, Germany

Dr. Alina Nechyporenko is an accomplished scientist and professor specializing in pattern recognition, biomedical signal processing, and data mining. Currently, she serves as a Scientist and Reader at the Technical University of Applied Sciences Wildau, Germany, where she works in the Department of Molecular Biotechnology and Functional Genome Analysis. She has also been a Professor at Kharkiv National University of Radio Electronics in Ukraine since 2018, contributing to the Faculty of Computer Science and the Department of Systems Engineering. Dr. Nechyporenko has over 70 publications in peer-reviewed journals and holds five patents. She is an expert evaluator for ISO/TC 276 Biotechnology and has been involved in several high-impact research projects, including Horizon2020, COST actions, and Erasmus+ initiatives. Her current research focuses on biomedical research, machine learning, and data management, with significant contributions to European life-science research and microbiome studies.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Alina Nechyporenko is a highly accomplished researcher in the fields of Pattern Recognition, Biomedical Signal Processing, and Machine Learning, with an extensive academic and professional background. She has demonstrated significant contributions to biomedical research, particularly in the application of data mining and computational techniques in cancer therapy, microbiome research, and deep learning. Given her work and leadership in her respective fields, she is highly suitable for the Best Researcher Award.

Education and Training

  • Expert in Evaluation Competences
    • Member of ISO/TC 276 Biotechnology, WG 2, WG 5, and national TC 166 “Clinical laboratory studies and systems for in vitro diagnostics.”
    • Technical Committee and Reviewer for the UKRCON IEEE conference.
  • Ph.D. in Computer Science
    • Specialization in Biomedical Signal Processing and Pattern Recognition
    • Thesis focused on data management and machine learning applications.
  • Publications and Patents
    • Over 70 publications in peer-reviewed scientific journals
    • Holder of 5 patents related to biomedical and computational applications.

Work Experience

2019 – Present

  • Scientist and Reader for Pattern Recognition, Biomedical Signal Processing
    • Technical University of Applied Sciences Wildau, Germany
    • Conducting research in areas such as data mining, machine learning, and data management within the Department of Molecular Biotechnology and Functional Genome Analysis.
    • Participates in Horizon2020 grant agreement ID: 654156 (RItrain – Research Infrastructures Training Programme), COST CA15110 (Harmonising standardisation strategies in European life-science research), and Erasmus + Capacity-building projects.
    • Engaged in COST CA18131 (Statistical and machine learning techniques in human microbiome studies) and DAAD “Digital Ukraine: Ensuring academic success in times of crisis” projects (2022 – 2025).

Since 2018

  • Professor
    • Kharkiv National University of Radio Electronics, Ukraine
    • Faculty of Computer Science & Department of Systems Engineering
    • Involved in teaching and research, focusing on pattern recognition, data processing, and systems engineering.

Publication top Notes:

Modeling and Computer Simulation of Nanocomplexation for Cancer Therapy

Comparison of CNN-Based Architectures for Detection of Different Object Classes

Comparison of CNN-Based Architectures for Detection of Different Object Classe

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition

Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation

Mr. Fulin Cai | Patient Monitor Award | Best Researcher Award

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Mr. Fulin Cai | Patient Monitor Award | Best Researcher Award 

Mr. Fulin Cai, Arizona State University, United States

Fulin Cai is a dedicated Ph.D. student in Computer Engineering at Arizona State University (ASU) under the supervision of Teresa Wu, with a research focus on deep learning, medical signals, and healthcare. He earned his M.S. in Computer Engineering from ASU with a GPA of 3.86/4.0 in 2023. Prior to this, he completed an M.S. in Management Science and Engineering and a B.S. in Information Management and Information System from Shenzhen University (SZU), ranking high in his class. Fulin’s research has led to numerous publications in prestigious journals such as IEEE Sensors Journal and Frontiers in Physiology, with topics ranging from radar sensing to respiratory function monitoring. He has also presented his work at notable conferences like the Institute of Industrial and Systems Engineers (IISE) Annual Conference.

Professional Profile:

ORCID

Education 🎓

  • Arizona State University (ASU), Tempe, USA
    • Ph.D. Student in Computer Engineering (08/2020 – Present)
    • Supervisor: Teresa Wu
    • Research Interests: Deep Learning, Medical Signals, Healthcare
  • Arizona State University (ASU), Tempe, USA
    • M.S. in Computer Engineering, GPA: 3.86/4.0 (05/2023)
  • Shenzhen University (SZU), Shenzhen, China
    • M.S. in Management Science and Engineering, GPA: 86/100 (Rank 3) (06/2019)
    • Supervisors: Li Li and Xianghua Chu
    • Research Interests: Meta Learning, Reinforcement Learning, Optimization
  • Shenzhen University (SZU), Shenzhen, China
    • B.S. in Information Management and Information System, GPA: 3.56/4.0 (Rank 4) (06/2016)

Teaching Experience 👨‍🏫

  • Arizona State University, Tempe, USA
    • Information Systems Engineering (Spring 2024)
  • Shenzhen University (SZU), Shenzhen, China
    • Lecturer, College of Continuing Education:
      • Management Information System Analysis and Design (03/2017-06/2017)
      • Website Construction and Management (09/2017-12/2017)
      • E-commerce Technology (03/2018-06/2018)
      • Management Information System (03/2019-06/2019)
    • TA, Online Course: Living with Etiquette (03/2017-06/2018)

Working Experience 💼

  • Arizona State University, Tempe, USA (08/2020-Present)
    • Position: Graduate Research Assistant
    • Research Topic: Enhanced representation learning for human biosensing applications
    • Responsibilities:
      • Apply computer vision models to human biosensing applications (e.g., ECG for sleep apnea, radar data for physiological measurement and motion detection).
      • Improve representation learning of DL models from time and frequency perspectives when bio signal is treated as a spectrogram (1-channel image).
  • Huawei Technologies Co., Ltd, Shenzhen, China (07/2019-07/2020)
    • Position: Algorithm Engineer
    • Responsibilities:
      • Implementation of automatic channel selection algorithm.
      • Development of channel simulation software based on NS-3.

Academic Services 📝

  • Journal Reviewer:
    • Computers in Biology and Medicine
    • Biomedical Signal Processing and Control
    • Computers & Industrial Engineering
    • International Journal of Production Research
    • Quality and Reliability Engineering International

Publication top Notes:

E-BDL: Enhanced Band-Dependent Learning Framework for Augmented Radar Sensing

Corrections to “STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning” [May 23 10998-11006]

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning

Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

Prof. Shing-Hong Liu | Biomedical Award | Best Researcher Award

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Prof. Shing-Hong Liu | Biomedical Award | Best Researcher Award 

Prof. Shing-Hong Liu, Chaoyang University of Technology, Taiwan

Shing-Hong Liu is an esteemed academic and researcher in the field of biomedical engineering and computer science. He obtained his B.S. degree in Electronic Engineering from Feng-Jia University, Taiwan, in 1990, followed by an M.S. degree in Biomedical Engineering from National Cheng-Kung University in 1992. In 2002, he earned his Ph.D. from the Department of Electrical and Control Engineering at National Chiao-Tung University, Taiwan. Since August 1994, Dr. Liu has been actively involved in academia, initially as a Lecturer in the Department of Biomedical Engineering at Yuanpei University, Taiwan. He progressed to become an Associate Professor from 2002 to 2008. Currently, he holds the position of Distinguished Professor in the Department of Computer Science and Information Engineering at Chaoyang University of Technology. Dr. Liu’s research focuses on biomedical signal processing, artificial intelligence applications in mobile health (mHealth), and the design of biomedical instruments. He has been recognized for his contributions, being named one of the World’s Top 2% Scientists in 2020. His research projects have received substantial funding, totaling NT$36,329,914, and he has authored 59 papers in SCI journals.

 

Professional Profile:

ORCID

 

Education:

  • B.S. in Electronic Engineering
    • Feng-Jia University, Taizhong, Taiwan, R.O.C.
    • Year of Completion: 1990
  • M.S. in Biomedical Engineering
    • National Cheng-Kung University, Tainan, Taiwan, R.O.C.
    • Year of Completion: 1992
  • Ph.D. in Electrical and Control Engineering
    • National Chiao-Tung University, Hsinchu, Taiwan, R.O.C.
    • Year of Completion: 2002

Work Experience:

  • Lecturer
    • Department of Biomedical Engineering, Yuanpei University, Hsinchu, Taiwan, R.O.C.
    • August 1994 – 2002
  • Associate Professor
    • Department of Biomedical Engineering, Yuanpei University, Hsinchu, Taiwan, R.O.C.
    • 2002 – 2008
  • Distinguished Professor
    • Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taiwan, R.O.C.
    • 2020 – Present

Achievements:

Shing-Hong Liu has been recognized as one of the World’s Top 2% Scientists in 2020. His research interests focus on biomedical signal processing, artificial intelligence for mHealth applications, and the design of biomedical instruments. He has successfully led projects with a total budget of NT 36,329,914 and has published 59 papers in SCI journals.

Publication top Notes:

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

A Wearable Assistant Device for the Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing

A Wearable Assistant Device for Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing