Dr. Khaled Alhawiti | Parkinson’s Monitoring | Best Researcher Award

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Dr. Khaled Alhawiti | Parkinson’s Monitoring | Best Researcher Award 

Dr. Khaled Alhawiti | Parkinson’s Monitoring | University of Tabuk | Saudi Arabia

Dr. Khaled M. Alhawiti is an accomplished Associate Professor in the Faculty of Computers and Information Technology at the University of Tabuk, recognized for his scholarly contributions in artificial intelligence, natural language processing, and Arabic language processing. He completed his Ph.D. in Computer Science from the University of Wales, Bangor University, where he focused on computational models and language technologies that support intelligent information processing. His academic path includes a Master of Science in Information Technology from the University of Technology Malaysia and a Bachelor’s degree in Computer Science from the University of Jordan, reflecting strong foundations in computing and higher education across multiple countries. Professionally, Dr. Khaled M. Alhawiti has built extensive experience in teaching, mentoring, research development, and academic leadership, actively contributing to curriculum enhancement and collaborative research initiatives within his institution and beyond. His research interests span artificial intelligence, data science, natural language processing, Arabic text modeling, speech-based systems, and intelligent educational technologies. He possesses strong research skills in machine learning, adaptive modeling, text compression techniques, rule-based systems, language preprocessing, and large-scale corpus analysis. His publications have been widely cited and indexed in Scopus and leading AI venues, demonstrating the impact of his contributions to computational linguistics and AI-driven text analysis. Dr. Khaled M. Alhawiti has collaborated on multiple international research activities, contributing to academic exchanges across Saudi Arabia, Malaysia, the United Kingdom, and Jordan, strengthening global partnerships in computer science. His awards and honors include recognition for high-impact publications, contributions to AI education research, and active participation in academic committees and professional societies. He is also associated with leading research communities such as IEEE and ACM, promoting engagement in emerging technological advancements.

Professional Profiles: ORCID  | Google Scholar

Featured Publications 

  1. Alhawiti, K. M. (2014). Natural language processing and its use in education. 161 citations.

  2. Alhawiti, K. M. (2015). Advances in artificial intelligence using speech recognition. 42 citations.

  3. Alhawiti, K. M. (2014). Adaptive models of Arabic text. 20 citations.

  4. Zerrouki, T., Alhawiti, K., & Balla, A. (2014). Autocorrection of Arabic common errors for large text corpus. 16 citations.

  5. Teahan, W. J., & Alhawiti, K. M. (2015). Preprocessing for PPM: Compressing UTF-8 encoded natural language text. 13 citations.

  6. Elfaki, A. O., Alhawiti, K. M., AlMurtadha, Y. M., Abdalla, O. A., & Elshiekh, A. A. (2014). Rule-based recommendation for supporting student learning-pathway selection. 13 citations.

  7. Alhawiti, K. M. (2014). Adaptive Arabic text modeling using computational techniques. (Derived from thesis-related work). 20 citations.

Mr. Mohamed Hamroun | Healthcare | Breakthrough Research Award

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Mr. Mohamed Hamroun | Healthcare | Breakthrough Research Award

Mr. Mohamed Hamroun | Healthcare | XLIM/ University of Limoges | France

Dr. Mohamed Hamroun is an accomplished computer scientist and engineer specializing in artificial intelligence, image processing, and multimodal information retrieval. Currently serving as a researcher and lecturer at the 3iL School and the XLIM Laboratory at the University of Limoges, France, he has made significant contributions to the fields of deep learning, computer vision, and semantic data indexing. His multidisciplinary expertise spans across AI, VR/AR systems, big data analytics, and intelligent information retrieval systems, positioning him as a leading researcher in computational intelligence and multimedia data analysis. Through his work, Dr. Hamroun has advanced both theoretical understanding and practical applications of machine learning and artificial intelligence for complex visual and semantic data challenges.

Professional Profile

Google Scholar

Summary of Suitability for the “Breakthrough Research Award” 

Dr. Mohamed Hamroun is an exceptionally qualified candidate for the Research for Breakthrough Research Award, demonstrating a strong academic foundation, extensive research experience, and impactful scientific contributions in the fields of artificial intelligence (AI), image processing, deep learning, and multimodal information retrieval.

Education

Dr. Hamroun’s academic journey reflects a deep commitment to advancing computer science and AI-driven data analysis. He earned his Ph.D. in Computer Science from the University of Bordeaux, where his doctoral research focused on “Indexing and retrieval by visual, semantic, and multi-level content of multimedia documents,” under the supervision of Professors Henri Nicolas and Ikram Amous. His doctoral work bridged the gap between computational semantics and large-scale multimedia information retrieval. He later completed his Habilitation to supervise research at the University of Limoges, where his postdoctoral contributions were consolidated into a major research theme titled “Contributions to indexing and information retrieval: application to generalist and medical multimodal data,” under the guidance of Professor Damien Sauveron. Before his doctoral studies, he obtained a Computer Engineering degree from the University of Sfax, Tunisia, and a Bachelor’s degree in Computer Science from the same institution. His undergraduate and graduate projects revolved around multilingual search engine development and database management systems, establishing his foundation in applied informatics and intelligent systems.

Professional Experience

Dr. Hamroun’s professional experience demonstrates a steady trajectory of academic excellence and applied innovation. He began his career as an R&D Engineer at SIM-SOFT in Tunisia, where he was involved in software development and data-driven industrial applications. Following this, he pursued his Ph.D. research jointly between the University of Bordeaux and the University of Sfax, working on hybrid semantic and visual content retrieval models. After completing his Ph.D., he joined the XLIM Laboratory at the University of Limoges as a Postdoctoral Researcher, where he focused on the integration of deep learning and ontology-based frameworks for medical and multimedia data analysis. Later, he was appointed as a Lecturer at EILCO Engineering School in France, contributing to both teaching and research in computer science and artificial intelligence. He now holds the position of Associate Professor at 3iL Engineering School, affiliated with the XLIM Laboratory, where he supervises research projects and mentors graduate students in AI, machine learning, and multimedia information systems.

Research Interests

Dr. Hamroun’s research interests cover a wide spectrum of computational and artificial intelligence domains. His core expertise includes image and signal processing, deep learning architectures for data classification and clustering, virtual and augmented reality applications, and semantic data mining. His studies often combine statistical learning, ontology modeling, and multimodal data fusion to enhance human-computer interaction and knowledge extraction. A significant part of his current research focuses on developing intelligent systems for multimodal medical data retrieval and applying AI to improve healthcare diagnostics and decision support. His recent work also extends to federated learning frameworks and semantic interpretation in multimedia environments, bridging applied computer science with real-world AI applications.

Awards

Dr. Hamroun has been recognized for his innovative research in artificial intelligence and multimedia information systems through various academic honors and nominations. His outstanding work in deep learning-based image analysis and computational semantics has earned him recognition among peers in the international AI research community. He has contributed as a co-author to several highly cited papers and participated in collaborative European research projects aimed at integrating AI into real-world industrial and medical systems. His nomination for the award highlights his leadership in combining artificial intelligence with practical problem-solving across domains such as emotion recognition, diabetic foot ulcer diagnosis, and semantic retrieval.

Publication Top Notes

  • Title: Emotion recognition from speech using spectrograms and shallow neural networks
    Authors: A. Slimi, M. Hamroun, et al.
    Year: 2020
    Citations: 47

  • Title: DFU-Siam: A novel diabetic foot ulcer classification with deep learning
    Authors: M. S. A. Toofanee, M. Hamroun, et al.
    Year: 2023
    Citations: 43

  • Title: A survey on intention analysis: successful approaches and open challenges
    Authors: M. Hamroun
    Year: 2020
    Citations: 21

  • Title: An interactive engine for multilingual video browsing using semantic content
    Authors: M. B. Halima, M. Hamroun, et al.
    Year: (arXiv preprint, circa 2013)
    Citations: 16

  • Title: DFU-Helper: Innovative framework for longitudinal diabetic foot ulcer evaluation using deep learning
    Authors: M. S. A. Toofanee, M. Hamroun, et al.
    Year: 2023
    Citations: 11

Dr. Wanderimam Tuktur | Medical | Best Researcher Award

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Dr. Wanderimam Tuktur | Medical | Best Researcher Award

Dr. Wanderimam Tuktur | Medical | DC Department of Health | United States

Dr. Wanderimam R. Tuktur, MBBS, MPH, PhD, is an accomplished and analytical public health professional with over twelve years of diverse experience in epidemiological research, geospatial analysis, health equity, and community-focused health interventions. Currently serving as a Public Health Analyst at the District of Columbia Department of Health, Office of Health Equity, he is dedicated to addressing the root causes of health disparities and advancing data-driven policies that promote equitable health outcomes for all communities. Dr. Tuktur’s professional journey reflects a seamless integration of clinical expertise and public health research, having previously held roles as an Epidemiologist at the Virginia Department of Health, a Research Analyst at Capital Area Health Network, and a Research Associate at Virginia Commonwealth University. His research and analytical proficiency span a wide spectrum, including predictive modeling, GIS-based health mapping, and the development of innovative tools like the DC Health Opportunity Index—an evidence-based framework for understanding social determinants of health.

Professional Profile

ORCID

Suitability Summary

Dr. Wanderimam R. Tuktur is exceptionally well-qualified for the Best Researcher Award, standing out as a seasoned and analytical public health professional with over 12 years of diverse experience spanning epidemiological research, geospatial analysis, health equity studies, and quantitative/qualitative data analytics. His multidisciplinary expertise bridges medicine, data science, and public health, demonstrating excellence in both applied research and community-focused interventions.

Education

  • Doctorate Degree in Public Health (Epidemiology) – Walden University, 2024

  • Master of Public Health (Global Health) – Liberty University, 2017

  • Bachelor of Medicine, Bachelor of Surgery (MBBS) – Ahmadu Bello University, 2012

Professional Experience

  • Public Health Analyst | District of Columbia Department of Health, Office of Health Equity (2022 – Present)
    Leads data-driven projects and research to address social determinants of health and health disparities. Oversees the DC Health Opportunity Index development, manages equity programs such as the Advancing Health Literacy Project (AHLP), and supports multi-sector collaborations aimed at improving population health outcomes and advancing health equity in Washington, D.C.

  • Epidemiologist | Virginia Department of Health, Office of Health Equity (2021 – 2022)
    Applied epidemiological and geospatial methods to identify health disparities, performed predictive modeling, and guided public health policy through data-informed recommendations for improving healthcare access across Virginia.

  • Data/Research Analyst | Capital Area Health Network (2021 – 2022)
    Ensured integrity of clinical and financial data systems, analyzed data for accurate reporting to HRSA, and trained staff in health information management software.

  • Research Analyst | Capital Area Analytica Inc. (capAHEC Data Center) (2018 – 2021)
    Conducted predictive modeling on healthcare disparities, managed large datasets, and generated insights for public health policies aimed at underserved communities.

  • Research Associate | Virginia Commonwealth University, Department of Family Medicine & Population Health (2017 – 2018)
    Contributed to a longitudinal cohort study on stress and social disparities in diabetes, managed community-based research partnerships, and supported manuscript preparation for publication.

  • Teaching Instructor | Liberty University, School of General Studies (2015 – 2016)
    Delivered undergraduate instruction, developed course materials, and provided academic mentorship to enhance student learning outcomes.

  • Teaching Assistant | Liberty University, Department of Public & Community Health (2016 – 2017)
    Supervised student projects, supported professors in course delivery, and participated in simulation-based teaching sessions.

  • Medical Officer | National Assembly Clinic & National Hospital, Abuja, Nigeria (2013 – 2015)
    Delivered inpatient and outpatient care, performed clinical evaluations, and participated in rotations across Surgery, Medicine, Pediatrics, and Obstetrics & Gynecology.

Achievements

  • Led the creation of the DC Health Opportunity Index, a comprehensive data visualization tool mapping health inequities and social determinants across Washington, D.C.

  • Directed the Advancing Health Literacy Project (AHLP), resulting in a model toolkit and curriculum for community-based organizations.

  • Provided epidemiological and analytic leadership in multiple CDC-funded Health Disparities Grants, improving COVID-19 vaccine outreach and equity strategies.

  • Developed predictive geospatial models to identify High Priority Health Target Areas in Virginia, influencing healthcare access and policy designations.

  • Published reports and guided evidence-based policymaking to address health disparities in marginalized populations.

Awards and Honors

  • Certificate of Practicing License as a General Practitioner

  • Certificate of Completion – Collaborative Institutional Training Initiative (CITI) in Responsible Conduct of Research (RCR) and Human Subjects Research (HSR), Liberty University

  • Program Evaluation Training Certification, Ahmadu Bello University

  • Sphere Training Certification on Humanitarian and Disaster Relief

  • Virginia Commonwealth University Medication Management Training

  • American Red Cross First Aid and CPR Certification

  • Guest Lectureship Recognition, Liberty University (2015–2017)

Professional Memberships and Affiliations:

  • Active Member, Rotary Club of Richmond, Virginia

  • Member, Academy of Nutrition and Dietetics

  • Member, American Public Health Association

Publication Top Notes

A Geographic Weighted Regression Analysis of the Health Opportunity Index and Stroke Prevalence in Health and Human Services Region 3
Metachronous Malignancy (Pancreatic Endocrine Neoplasm And Renal Cell Carcinoma): Case Report.

Ms. Soree Hwang | Healthcare Intelligence Awards | Best Sensor for Health Monitoring Award

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Ms. Soree Hwang | Healthcare Intelligence Awards | Best Sensor for Health Monitoring Award 

Ms. Soree Hwang, Korea Institute of Science and Technology (KIST), South Korea

So Ree Hwang is a dedicated researcher in the field of biomedical engineering currently pursuing her Ph.D. at Korea University. She holds a Master’s degree in Design and Engineering from Seoul National University of Science and Technology and a Bachelor’s degree in Mechanical Engineering from Korea Aerospace University. Since May 2022, she has been a student researcher at the Korea Institute of Science and Technology (KIST), where she contributes to the development of AI-based health management platforms, including lifelog acquisition systems and fatigue and stress detection technologies. Her research also focuses on gait analysis and stroke assessment using motion signal processing and wearable devices. So Ree has published numerous papers as a main and co-author in reputable journals such as Sensors, Frontiers in Human Neuroscience, and IEEE journals. Her work integrates machine learning and biomedical signal analysis to advance rehabilitation technologies and health monitoring systems.

Professional Profile:

GOOGLE SCHOLAR

SCOPUS

Summary of Suitability for Best Researcher Award – So Ree Hwang

Dr. So Ree Hwang is a highly suitable candidate for the Best Researcher Award in the domain of health monitoring and biomedical engineering, with a strong multidisciplinary background and an impressive portfolio of impactful, AI-integrated sensor-based research.

🎓 Education

  • Ph.D. in Biomedical Engineering
    Korea University, Seoul, Republic of Korea (2021 – Present)

  • M.S. in Design and Engineering
    Seoul National University of Science and Technology, Seoul, Republic of Korea (2018 – 2020)

  • B.S. in Mechanical Engineering
    Korea Aerospace University, Goyang-si, Republic of Korea (2011 – 2017)

💼 Work Experience

  • Student Researcher – Korea Institute of Science and Technology (KIST)
    📍 Seoul, Republic of Korea (2022.05.01 – Present)

    • 🧠 Developed a lifelog system and AI-based fatigue/stress management platform

    • 🚶‍♂️ Contributed to gait analysis tech for knee disorder recovery

    • 🧪 Worked on motion signal-based stroke assessment technologies

  • Research Intern – Korea Institute of Science and Technology (KIST)
    📍 Seoul, Republic of Korea (2020.03.01 – 2021.12.31)

    • 🧠 Focused on stroke assessment using motion signal analysis

🏆 Achievements & Research Contributions

  • 📝 8 SCI-indexed papers as main or co-author, including in top journals like Sensors, Frontiers in Human Neuroscience, and IEEE

    • 📊 Topics: Gait phase classification, stroke severity assessment, fatigue detection using AI, wearable systems

  • ⚙️ First-author of applied engineering papers on 3D printing and IMU validation

  • 🤖 Integrated machine learning models (CNN-LSTM-Attention, RNNs) into biomedical signal analysis

  • 🧩 Contributed to the advancement of intelligent health monitoring and gait recovery systems

Publication Top Notes:

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Mr. Foad Zahedi | Digital Twin Awards | Best Researcher Award

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Mr. Foad Zahedi | Digital Twin Awards | Best Researcher Award 

Mr. Foad Zahedi, Washington State University, Iran

Foad Zahedi is a seasoned Procurement Director with over 18 years of comprehensive experience in procurement management, technical management, and contract management across a diverse range of projects, including multipurpose complexes, dams, roads, tunnels, and industrial structures. Based in Tehran, Iran, he has successfully led procurement and purchase engineering efforts to ensure optimal logistics, quality, and cost-effectiveness. Foad’s expertise encompasses tender management, contract oversight, and project management, where he skillfully navigates the complexities of EPC, PC, and E projects from both the employer and contractor perspectives. He holds a Master’s degree in Civil Engineering (Construction Management) and another Master’s in Civil Engineering (Marine Structures Engineering) from Islamic Azad University, along with a Bachelor’s degree in Civil Engineering. A certified Project Management Professional (PMP) and a Professional Engineer, Foad is proficient in areas such as cost estimation, value engineering, and building information modeling. His strategic insights and consultancy roles for boards and CEOs have been pivotal in aligning project goals with organizational objectives.

Professional Profile:

GOOGLE SCHOLAR

Research for Best Researcher Award – Foad Zahedi

Profile Overview: Foad Zahedi has over 18 years of extensive experience in procurement and project management across a variety of large-scale civil engineering projects, including multipurpose complexes, dams, and tunnels. His diverse skill set encompasses technical management, contract management, and procurement engineering, showcasing his ability to lead complex projects effectively.

Education 🎓

  • M.S. Civil Engineering (Construction Management)
    Islamic Azad University of Central Tehran Branch, Tehran, IR
    GPA: 3.53 | Year: 2020
  • M.S. Civil Engineering (Marine Structures Engineering)
    Islamic Azad University of Science and Research Branch, Tehran, IR
    GPA: 3.72 | Year: 2016
  • B.S. Civil Engineering
    Islamic Azad University of Shahr-e-kord Branch, Shahrekord, IR
    Year: 2004

Work Experience 💼

  • Procurement Director
    Iran Mall, Tehran, Iran
    Years: 20XX – Present

    • Led procurement and purchase engineering management for various complex projects, ensuring high quality and timely logistics support.
    • Managed the technical office, handling invoices, quantity surveying, and as-built drawings in EPC, PC, and E projects.
    • Conducted national and international tenders, preparing comprehensive technical and financial submissions.
    • Oversaw contract management in diverse roles (Employer, Contractor, Consultant) for EPC, PC, and E projects.
    • Provided consultancy to boards and CEOs, developing strategic plans to achieve project goals.

Achievements 🌟

  • Successfully managed procurement for multiple large-scale projects, including multipurpose complexes, dams, and industrial structures.
  • Developed effective strategies for cost estimation and value engineering, resulting in significant savings for projects.
  • Implemented advanced Building Information Modelling (BIM) and soil-structure interaction modelling techniques to enhance project outcomes.

Awards and Honors 🏆

  • Project Management Professional (PMP)
    Licensure #: 3203610 | Year: 2022
  • Professional Engineer (Grade 2: Supervision)
    Licensure #: 17-31-12174 | Year: 2014
  • Professional Engineer (Grade 1: Construction)

Publication Top Notes:

Global BIM Adoption Movements and Challenges: An Extensive Literature Review
Development of a BIM Implementation Roadmap: The Case of Iran
Robot-BIM integration for underground canals life-cycle management
Digital Twins in the Sustainable Construction Industry
BIM Implementation for PMBOK Enhancement in the Construction Industry

Prof. Dr. Mahmoud Abulmeaty | Remotecare Awards | Best Researcher Award

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Prof. Dr. Mahmoud Abulmeaty | Remotecare Awards | Best Researcher Award 

Prof. Dr. Mahmoud Abulmeaty, King Saud University, Saudi Arabia

Mahmoudd Mustafa Ali Abulmeaty is an esteemed Egyptian academic and physician specializing in clinical nutrition and metabolism. Dakahlia Governorate, Egypt, he earned his M.B. B.Ch. from Zagazig University in 2003 with honors. He further pursued advanced studies, obtaining a Master’s degree in Basic Medical Sciences (Physiology) in 2007 and an M.D. in Medical Physiology in 2012, both from Zagazig University. Abulmeaty has also earned multiple certifications, including those in obesity management, acupuncture, and clinical nutrition. He has held various academic positions, starting as an intern at Zagazig University Hospitals in 2004, then progressing through roles as demonstrator, assistant lecturer, and clinical nutritionist. In 2012, he joined King Saud University in Riyadh, Saudi Arabia, where he has served as an assistant professor, associate professor, and is currently a professor of clinical nutrition and metabolism. His professional expertise extends to weight reduction clinics and therapeutic nutrition, where he also serves as a physician consultant. With a wealth of experience and expertise in obesity management and clinical nutrition, Abulmeaty is recognized for his contributions to both research and clinical practice in these fields.

Professional Profile:

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award – Dr. Mahmoudd Mustafa Ali Abulmeaty

Dr. Mahmoudd Mustafa Ali Abulmeaty stands out as a distinguished academic and researcher in the field of clinical nutrition, obesity management, and metabolism. His academic qualifications, extensive experience, and significant contributions to the medical and scientific community make him a strong contender for the Best Researcher Award.

Education:

  • October 2003: M.B. B.CH. (Total grade: Excellent with Honors), Faculty of Medicine, Zagazig University, Egypt
  • November 2007: M.Sc. in Basic Medical Sciences (Physiology), Faculty of Medicine, Zagazig University, Egypt
  • August 2008: Professional Certificate in Obesity Management (Children & Adults), Cairo University, Egypt
  • January 2009: Professional Certificate in Acupuncture, Zagazig University, Egypt
  • November 2009: Professional Certificate in Office Management of Obesity, American Medical Association, USA
  • April 2011: Diploma in Endocrinology and Metabolism, Faculty of Medicine for Girls, Al Azhar University, Egypt
  • July 2011: ESPEN Diploma in Clinical Nutrition & Metabolism, Faculty of ESPEN, European Union
  • March 2012: M.D. in Medical Physiology, Zagazig University, Egypt
  • September 2012: Diploma in Clinical Nutrition, AICPD, Egypt
  • November 2017: Fellowship FACN, American College of Nutrition, USA

Work Experience:

  • March 2004: Intern at Zagazig University Hospitals
  • July 2005: Demonstrator of Physiology, Faculty of Medicine, Zagazig University
  • April 2008: Assistant Lecturer in the Endocrine Research Unit, Physiology Department, Faculty of Medicine, Zagazig University
  • July 2011: Clinical Nutritionist in Obesity Management and Research Unit, Faculty of Medicine, Zagazig University
  • April 2012: Lecturer in Medical Physiology Department and Obesity Management and Research Unit, Faculty of Medicine, Zagazig University
  • September 2012: Assistant Professor, Clinical Nutrition Program, and Senior Registrar, Weight Reduction Clinic, Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University
  • January 2018–2022: Associate Professor of Clinical Nutrition and Metabolism, Clinical Nutrition Program, and Physician Consultant at Primary Care Clinic and Therapeutic Nutrition Clinic, Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University
  • June 2022–Present: Professor of Clinical Nutrition and Metabolism, Clinical Nutrition Program, and Physician Consultant at Primary Care Clinic and Therapeutic Nutrition Clinic, Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University
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Mr. Mohammad Ahmadi | physiological Sensors | Best Researcher Award

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Mr. Mohammad Ahmadi | physiological Sensors | Best Researcher Award 

Mr. Mohammad Ahmadi, University of Auckland, New Zealand

Ted Ahmadi is a seasoned game developer based in Toronto, with a strong focus on designing Mixed/Augmented/Virtual Reality (MR/AR/VR) games using Unity3D and C#. With over 6 years of experience, he is proficient in utilizing the Microsoft Mixed Augmented Reality Toolkit (MRTK) and has expertise in designing Mixed Reality games for platforms such as Magic Leap, Vive/Vive Pro Eye, Oculus Quest/Quest 2&3/Quest Pro, HP Omnicept, Hololens 2, and Apple Vision Pro. Ted’s career spans across various aspects of game development, including 2D game design for Android using Unity3D, game networking with Photon and Ubiq, and integrating technologies like OpenGL, Blender, and iClone 3D animation toolkit. He is also skilled in using Leap Motion for enhancing interactive experiences in game applications. Beyond game development, Ted is proficient in C++/C# programming across different applications and has experience in Agile/Rapid development methodologies, Waterfall, and Continuous Integration. His expertise extends to embedded systems such as ROS in Linux/Windows, particularly in VR applications for robotics, and enterprise web server applications where he excels in Java programming, software optimization, debugging, and troubleshooting.

Professional Profile:

ORCID

 

Education

University of Auckland

  • Bachelor of Science in Computer Science
    Date: Graduated in 2018

Work Experience

Design School, University of Auckland
Teaching and Tutoring Assistant
July 2022 – Nov 2022

  • Responsibilities: Assisted in teaching and tutoring the course “Designing Mix Realities” at the School of Design.
  • Skills: Unity3D, Blender (3D modeling and animation for rapid prototyping), Adobe Aero (3D modeling).

Skills

  • Game Design: Unity3D, MRTK and XR SDK, AR Kit, AR Core, Leap Motion, OpenGL, Vuforia, Blender, iClone 7.
  • Programming: C++/C#, Java, JavaScript, PHP/CSS/HTML, jQuery, mySQL, JSON/XML, Matlab.
  • HMD: Vive/Vive Pro Eye, Oculus Quest/Quest 2/Quest 3/Quest Pro, HP Omnicept, Magic Leap, Hololens 2, Apple Vision Pro.
  • API: WebGL, OpenGL.
  • Web API: .Net/ASP.Net MVC.
  • J2EE API: Java Servlet and EJB.
  • Version Control: git and GitHub.
  • OS: Linux, Windows.
  • Embedded Systems: ROS.

Employment History

🏫 Design School, University of Auckland
Teaching and Tutoring Assistant (July 2022 – Nov 2022)

  • Teaching and tutoring assistant for the course “Designing Mix Realities” at the school of design.
  • Skills: Unity3D, Blender (3D modeling and animation for rapid prototyping), Adobe Aero (3D modeling).

Publication top Notes:

EEG, Pupil Dilations, and Other Physiological Measures of Working Memory Load in the Sternberg Task

Cognitive Load Measurement with Physiological Sensors in Virtual Reality during Physical Activity

Comparing Performance of Dry and Gel EEG Electrodes in VR using MI Paradigms

PlayMeBack – Cognitive Load Measurement using Different Physiological Cues in a VR Game

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

Best Medical Sensing Technology

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Introduction Best Medical Sensing Technology

The Best Medical Sensing Technology Award recognizes groundbreaking innovations that revolutionize medical sensing, enabling accurate, non-invasive, and real-time monitoring of patient health parameters. This prestigious award celebrates advancements that have the potential to significantly improve healthcare outcomes globally.

About the Award:
The Best Medical Sensing Technology Award is open to individuals, research teams, and companies worldwide that have developed cutting-edge technologies in medical sensing. Applicants must demonstrate exceptional creativity, innovation, and impact in the field of medical sensing.

Eligibility:
There are no age limits for applicants. The award is open to researchers, engineers, inventors, and entrepreneurs who have made significant contributions to the field of medical sensing. Applicants must have a proven track record of excellence in their respective fields.

Qualifications:
Applicants must have developed a medical sensing technology that demonstrates exceptional innovation, effectiveness, and potential for improving healthcare outcomes. The technology should be supported by strong scientific evidence and have the potential for widespread adoption in the medical field.

Publications:
Applicants are encouraged to submit any relevant publications, research papers, or patents that support their application. These publications should demonstrate the novelty and impact of the medical sensing technology.

Evaluation Criteria:
Applications will be evaluated based on the following criteria:

  • Innovation and creativity of the medical sensing technology
  • Impact on healthcare outcomes
  • Scientific rigor and validity of the technology
  • Potential for widespread adoption
  • Overall quality and clarity of the application

Submission Guidelines:
Applicants must submit a detailed description of their medical sensing technology, along with any supporting documents, such as publications, patents, or videos. The submission should clearly demonstrate the innovation, effectiveness, and potential impact of the technology.

Recognition:
The winner of the Best Medical Sensing Technology Award will receive a prestigious award certificate, recognition on our website and social media channels, and an opportunity to present their technology at a major medical conference.

Community Impact:
The award-winning medical sensing technology should demonstrate a positive impact on the healthcare community, improving patient outcomes, reducing healthcare costs, or advancing medical research.

Biography:
Applicants should provide a brief biography highlighting their relevant experience and achievements in the field of medical sensing.

Abstract:
A concise abstract summarizing the key features and benefits of the medical sensing technology should be included in the application.

Supporting Files:
Applicants may include any additional supporting files, such as videos, images, or technical specifications, to enhance their application.