Ali Motie Nasrabadi | Biomedical Engineering | Best Researcher Award

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Prof. Ali Motie Nasrabadi | Biomedical Engineering | Best Researcher Award

Professor at Shahed University, Iran

Prof. Ali Motie Nasrabadi is a renowned academic and researcher in the field of Biomedical Engineering, specializing in the intelligent processing of EEG signals, computational neuroscience, and artificial intelligence in healthcare applications. He earned his Ph.D. in Biomedical Engineering from Amirkabir University of Technology in 2004, where his research focused on evaluating consciousness and hypnosis depth through EEG signal analysis. With a career spanning decades, Prof. Nasrabadi has contributed extensively to EEG/ERP signal processing, nonlinear and chaotic systems, and physiological modeling. He has published over 100 peer-reviewed papers and continues to influence the fields of biomedical signal processing and neuroscience.

Profile

Education

Prof. Ali Motie Nasrabadi has a distinguished academic background in Biomedical Engineering, with his education centered at the Amirkabir University of Technology in Tehran, Iran. He earned his Ph.D. in Biomedical Engineering in March 2004, with a dissertation titled “Quantitative and Qualitative Evaluation of Consciousness Variation and Depth of Hypnosis through Intelligent Processing of EEG Signals.” His doctoral research focused on advanced signal processing techniques applied to biomedical data. Prior to that, he completed his M.Sc. in Biomedical Engineering in September 1998, where he specialized in the “Design and Implementation of an 8-channel PC-Based Electroencephalograph.” This work demonstrated his early interest in biomedical instrumentation and signal analysis. Prof. Nasrabadi began his academic journey with a B.Sc. in Electronic Engineering in February 1994, focusing on the “Design and Implementation of Control of Satellite Dish to Get Better Signal.” His strong foundation in electronic engineering and biomedical systems has been integral to his success in applying engineering principles to healthcare challenges.

Professional Experience

Prof. Ali Motie Nasrabadi has had a distinguished career across both academic and industrial sectors. He has served as the Vice Chancellor for Support and Resource Management at Shahed University from 2017 to 2021, where he played a pivotal role in overseeing resource management. Prior to this, he was the Director General of Equipment at Shahed University, a position he held from 2004 to 2021. His expertise in biomedical signal processing has led to consultancy roles, including being an EEG Signal Processing Consultant at the National Brain Mapping Laboratory (NBML) since 2016. In this capacity, he has also designed and organized brain signal processing workshops at NBML. Prof. Nasrabadi has been actively involved in industrial and technology consulting as well. Since 2014, he has been a Technology and E-commerce Consultant at Shenasa Venture Capital and Trigup Accelerator. Earlier in his career, he worked as a researcher at the Research Center for Intelligent Signal Processing (RCISP) from 2001 to 2006 and the Laser and Optical Group at the Research Center for Science and Technology in Medicine (RCSTIM) from 1998 to 2004. His wide-ranging experience reflects his strong influence in both academia and industry, particularly in the fields of biomedical engineering and signal processing.

Research Interest

Prof. Ali Motie Nasrabadi’s research interests are deeply rooted in the intersection of biomedical engineering and signal processing. His primary focus is on artificial intelligence in healthcare applications, where he explores the potential of AI to enhance medical diagnostics and treatments. He is also heavily involved in computational neuroscience, studying the brain’s complex systems through computational models. Prof. Nasrabadi specializes in chaotic and nonlinear signal processing, applying advanced techniques to understand irregular biological signals. A significant portion of his research is dedicated to the recording and processing of EEG/ERP/LFP/ECoG signals, where he analyzes brain activity patterns for medical and cognitive insights. Additionally, he is interested in the modeling of physiological systems, where he creates mathematical representations of biological processes to better understand and predict their behavior. This broad array of research areas positions Prof. Nasrabadi as a leading figure in applying advanced computational methods to solve complex challenges in healthcare and neuroscience.

Research Skills

Prof. Ali Motie Nasrabadi possesses a diverse set of skills that span across various domains of biomedical engineering and computational analysis. He is an expert in MATLAB and C programming, which he uses for designing and implementing algorithms for signal processing and neural network modeling. His proficiency with EEGLAB and SPM software makes him adept at processing and analyzing EEG and brain signals, essential for neuroscience research. Additionally, Prof. Nasrabadi is skilled in SPSS, which he uses for statistical data analysis, allowing him to interpret complex biomedical data. His deep understanding of chaos theory and nonlinear dynamics further complements his expertise, enabling him to analyze and quantify irregular physiological signals. These technical and computational skills are integral to his contributions in fields such as artificial intelligence, signal processing, and healthcare.

Award and Recognition

Prof. Ali Motie Nasrabadi has received several awards and recognitions throughout his distinguished academic and research career. He achieved First Rank in his B.Sc. program from the Department of Electrical Engineering at Amirkabir University of Technology, with an impressive average score of 18.16 out of 20. Similarly, he was awarded First Rank in his M.Sc. program from the Department of Biomedical Engineering at Amirkabir University of Technology, with an outstanding average score of 18.63 out of 20. His significant contributions to the field of biomedical engineering, particularly in signal processing and artificial intelligence applications, are reflected in his high H-index of 31 in Google Scholar and H-index of 23 in Scopus, underscoring the impact of his research in the global scientific community.

Conclusion

Considering Prof. Ali Motie Nasrabadi’s extensive academic achievements, innovative research in biomedical signal processing, significant publication record, industry-related activities, and leadership roles, he is highly suitable for the Research for Best Researcher Award. His work has had a profound impact on the field of biomedical engineering, particularly in the analysis and processing of EEG signals, making him a deserving candidate for this prestigious recognition.

Publications Top Notes

  • Title: EEG Classification of ADHD and Normal Children Using Non-linear Features and Neural Network
    • Citations: 220
    • Year: 2016
  • Title: Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes During Simulated Driving
    • Citations: 153
    • Year: 2015
  • Title: A New Automatic Sleep Staging System Based on Statistical Behavior of Local Extrema Using Single Channel EEG Signal
    • Citations: 110
    • Year: 2018
  • Title: Determining Fault’s Type and Accurate Location in Distribution Systems with DG Using MLP Neural Networks
    • Citations: 105
    • Year: 2009
  • Title: A Review on EEG Signals Based Emotion Recognition
    • Citations: 103*
    • Year: 2018
  • Title: The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals
    • Citations: 83
    • Year: 2014
  • Title: EEG-based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery
    • Citations: 69
    • Year: 2006
  • Title: Emotion Classification Through Nonlinear EEG Analysis Using Machine Learning Methods
    • Citations: 68
    • Year: 2018

 

Ursula Hubner | Medical Informatics Award | Best Researcher Award

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Prof Dr. Ursula Hubner | Medical Informatics Award | Best Researcher Award

Chief Scientist at Microgravity, Space Pharma, Israel

Mrs. Orit Ecker Cohen is a highly experienced professional with a background in biology and extensive expertise in various fields, including biotechnology, medical informatics, and healthcare technology. She has a strong research background, as evidenced by her involvement in numerous studies and projects related to wound care, healthcare digitalization, and AI in healthcare. Mrs. Cohen has held various positions, including CEO of a biotech and media development consultancy, director of regulation and quality at a food tech company, and head of preclinical and cell culture media departments at pharmaceutical companies. She has also been involved in projects with international collaborations, such as those with NASA. Mrs. Cohen’s skills include project management, research and development, regulatory affairs, quality control, and technical support. She is fluent in English and Hebrew and has a strong commitment to advancing healthcare through innovative technologies and research.

Professional Profiles:

Education:

Ursula Hübner has a degree in Psychology, Biology, and Brain Research (Dipl.-Psychologin) from the University of Mainz and the University of Düsseldorf, Germany. She obtained her Doctorate (Dr. rer. nat.) from the University of Düsseldorf, Department of Natural Science and Mathematics, Germany.

Experience:

Ursula Hübner has a diverse professional background. She started as a research fellow at University Hospital Düsseldorf (DFG Sonderforschungsbereich) from 1984 to 1987. She then worked as a software developer, project manager, and principal investigator at the computer company Group Bull S.A. in Paris and Cologne from 1987 to 1997. Since 1997, she has been a Professor at Osnabrück University of Applied Sciences and the founder of the Health Informatics Research Group, where she currently leads 15 research fellows.

Research Interest:

Ursula Hübner’s research interests are primarily focused on health informatics. She is particularly interested in the adoption and determinants of assistive technologies, formative evaluation of IT projects in healthcare, innovation capabilities of healthcare organizations, professionalism of information management in healthcare, and eHealth adoption and policies. Her research also covers topics such as complex eHealth innovations, clinical information logistics, and nursing informatics.

Award and Honors:

Ursula Hübner, a distinguished professional in the field of health informatics, has garnered numerous accolades for her impactful contributions. She received the Medical Informatics Certificate from the German Association for Medical Informatics, Biometry and Epidemiology (GMDS) in 2012, recognizing her expertise and commitment to advancing healthcare through technology. In 2012, she was also awarded a prestigious three-year grant as a Lower Saxony Research Professor, highlighting her research excellence and leadership in academia. Her dedication and achievements were further acknowledged in 2019 when she was elected as a Fellow of the International Academy of Health Sciences Informatics (IAHSI) of the International Medical Informatics Association (IMIA). Most recently, Ursula Hübner was honored with the “Most Influential Women in Health IT” award by the Healthcare Information Management Systems Society (HIMSS) in 2021, a testament to her significant impact and leadership in the field.

Skills:

Ursula Hübner possesses a diverse skill set honed through years of experience and dedication to her field. Her expertise includes proficiency in health informatics, where she has demonstrated a deep understanding of complex eHealth innovations and their measurement. She is skilled in conducting both summative and formative evaluations under real-world conditions, providing valuable insights into the adoption and determinants of assistive technologies. Additionally, Ursula is experienced in software development and project management, having served as a principle investigator at a leading computer company. Her skills extend to academic leadership, as evidenced by her role as the founder and head of the Health Informatics Research Group at Osnabrück University of Applied Sciences. Ursula is also adept at academic supervision, having successfully guided several PhD students in their research. Her skills in research, leadership, and academia make her a valuable asset in the field of health informatics.

Publications:

Title: Predilection sites of pyoderma gangrenosum: Retrospective study of 170 clearly diagnosed patients Authors: Moelleken, M., Erfurt-Berge, C., Ronicke, M., Przysucha, M., Dissemond, J. Year: 2023

Title: Trust in Digitalization and AI: Findings from a Qualitative Study on Healthcare Professionals in Germany Authors: Babitsch, B., Hannemann, N., Kutza, J.-O., Hübner, U. Year: 2023

Title: German Medical Data Sciences 2023 Preface for the 7th Volume Authors: Röhrig, R., Haag, M., Beißbarth, T., Sedlmayr, M., Zapf, A. Year: 2023

Title: Adoption and Determinants of Assistive Technologies in the Real World: Results from the VdK Study Authors: Hübner, U., Yalymova, I., Przysucha, M., Büscher, A. Year: 2023

Title: Design and Implementation of an ETL Process to Transfer Wound-Related Data into a Standardized Common Data Model Authors: Przysucha, M., Hüsers, J., Liberman, D., Dissemond, J., Hübner, U. Year: 2023

Title: The Representation of Trust in Artificial Intelligence Healthcare Research Authors: Kutza, J.-O., Hannemann, N., Hübner, U., Babitsch, B. Year: 2023

Title: How Do User Participation and IT Self-Efficacy Influence User Attitudes Towards Smart Hospital Technology? Authors: Kröner, S., Hassmann, J., Esdar, M., Maischak, J., Hübner, U. Year: 2023

Title: Can Synthetic Images Improve CNN Performance in Wound Image Classification? Authors: Malihi, L., Hübner, U., Richter, M.L., Hendriks, A., Hüsers, J. Year: 2023

Title: Effects of Hospital Digitization on Clinical Outcomes and Patient Satisfaction: Nationwide Multiple Regression Analysis Across German Hospitals Authors: Von Wedel, P., Hagist, C., Liebe, J.-D., Hübner, U., Pross, C. Year: 2022

Title: Developing a professional-practice-model-based nursing organizational informatics competency model Authors: Chen, Y., Cai, Z., Lin, B., Hübner, U., Chang, P. Year: 2022