Mrs. Sabatina Criscuolo | Signal Processing Awards | Young Scientist Award

Mrs. Sabatina Criscuolo | Signal Processing Awards | Young Scientist Award 

Mrs. Sabatina Criscuolo, University of Naples Federico II, Italy

Sabatina Criscuolo is an Italian biomedical engineer currently pursuing a Ph.D. in Information and Communication Technology for Health at the University of Naples Federico II, where she is affiliated with the National Research Council’s Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA). Her research focuses on the development of advanced artificial intelligence techniques to support precision medicine, with specific applications in neurodegenerative diseases, type 1 diabetes, and colorectal surgery. Sabatina has also collaborated internationally, including a visiting PhD position at the Applied Intelligence Research Centre in Dublin, where she worked on EEG artifact removal using variational autoencoders. With a strong academic background, she holds a Master’s degree in Biomedical Engineering, specializing in Bionic and Biorobotics, and has been involved in various research projects and initiatives aimed at enhancing health monitoring and rehabilitation technologies. In addition to her research activities, Sabatina contributes to the scientific community as a reviewer for multiple journals and has organized significant conferences in her field.

Professional Profile:

ORCID

Research for Young Scientist Award Evaluation for Sabatina Criscuolo

Sabatina Criscuolo is a promising candidate for the Research for Young Scientist Award, given her strong academic background, ongoing research initiatives, and contributions to the field of biomedical engineering and artificial intelligence. Here are several key points that highlight her suitability for this award.

Academic Experience

Sabatina is currently engaged in research at the National Research Council – STIIMA in Lecco, Italy, focusing on advanced artificial intelligence techniques to support precision medicine. Her work involves developing AI algorithms for applications in electroencephalographic (EEG) analysis related to neurodegenerative diseases, type 1 diabetes, and colorectal surgery.

PhD Studies: Since January 2022, she has been pursuing her PhD, with her thesis submission planned for December 2024 and expected graduation in March 2025. She has also been a visiting PhD student at the Applied Intelligence Research Centre in Dublin, Ireland, where she worked on EEG artifact removal using variational autoencoders and explainable AI.

Education

Sabatina holds a Master’s degree in Biomedical Engineering with a focus on Bionic & Biorobotics, where she developed a wearable Brain-Computer Interface system for robot-assisted rehabilitation in children with ADHD. She also completed a Bachelor’s degree in Biomedical Engineering, focusing on innovative enzyme immobilization methods.

Research Collaborations

Her collaborative research spans several institutions, including the University of Salento, Temple University, and the Interdepartmental Research Centre on Management and Innovation in Healthcare at her home university.

Scientific Impact

As of July 2024, Sabatina has an H-index of 5 on Scopus, reflecting her contributions to topics such as EEG signal analysis and diabetes management.

Publication Top Notes

Interpreting the latent space of a Convolutional Variational Autoencoder for semi-automated eye blink artefact detection in EEG signals

Improving Multiscale Fuzzy Entropy Robustness in EEG-Based Alzheimer’s Disease Detection via Amplitude Transformation

Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

A Novel Metric for Alzheimer’s Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy

EEG complexity-based algorithm using Multiscale Fuzzy Entropy: Towards a detection of Alzheimer’s disease

Assist Prof Dr. Hwa-Dong Liu | Signal Processing | Best Researcher Award

Assist Prof Dr. Hwa-Dong Liu | Signal Processing | Best Researcher Award

Assist Prof Dr. Hwa-Dong Liu, Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taiwan

Hwa-Dong Liu is an Assistant Professor at National Taiwan Normal University (NTNU) in Taipei, Taiwan, specializing in power electronics, microcontrollers, rail vehicle power systems, and solar power systems. He holds a Ph.D. in Electrical Engineering from National Taiwan University of Science and Technology (NTUST). His research interests include the development of advanced power converters, control strategies for renewable energy systems, and innovative solutions for electric vehicle charging. Dr. Liu has authored numerous papers in reputable journals, with a focus on improving the efficiency and performance of power electronic systems and renewable energy technologies. His recent work includes contributions to energy management systems, high-gain boost converters, and novel MPPT algorithms for solar power generation.

Professional Profile:

Summary of Suitability for Best Researcher Award 

Hwa-Dong Liu has expertise in several cutting-edge fields including power electronics, microcontrollers, rail vehicle power systems, and solar power systems. This diversity indicates a broad impact on multiple important areas of research.

Education

  • Ph.D. in Electrical Engineering from National Taiwan University of Science and Technology (NTUST).

Work Experience

  • Assistant Professor at National Taiwan Normal University (NTNU).

Expertise

  1. Power Electronics
  2. Microcontroller
  3. Rail Vehicle Power Systems
  4. Solar Power Systems

Publication top Notes:

An improved solar step-up power converter for next-generation electric vehicle charging

Hybrid Management Strategy for Outsourcing Electromechanical Maintenance and Selecting Contractors in Taipei MRT

An Improved High Gain Continuous Input Current Quadratic Boost Converter for Next-Generation Sustainable Energy Application

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

High-Voltage Autonomous Current-Fed Push-Pull Converter with Wireless Communication Applied to X-Ray Generation