Mr. Joel Adams | Automation | Best Researcher Award

Mr. Joel Adams | Automation | Best Researcher Award 

Mr. Joel Adams, Florida International University, United States

Joel Adams is a robotics researcher and Ph.D. candidate in Mechanical Engineering at Florida International University, specializing in autonomous mobile and manipulator systems. With extensive experience in radiological surveillance, autonomous mission planning, and multi-robot coordination, he has developed innovative solutions integrating sensor technologies such as LiDAR, depth cameras, and IMUs. His expertise includes robotics middleware (ROS1, ROS2), simulation tools (Gazebo, PyBullet), and advanced programming in C++, Python, and MATLAB. As a Research Assistant at the Applied Research Center since 2019, he has contributed to cutting-edge projects in autonomous system development, multi-robot collaboration, and real-world testing of robotic platforms.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Joel Adams appears to be a strong candidate for the Best Researcher Award, particularly if the award recognizes contributions in robotics, autonomous systems, and applied research in radiological surveillance. His work aligns well with advanced robotics, AI-driven mission planning, and real-world applications in nuclear site monitoring.

🎓 Education

  • Florida International University
    • Ph.D. in Mechanical Engineering (Expected Summer 2025) 🎯 (GPA: 3.87)
    • Master of Science in Mechanical Engineering (Summer 2024) 🛠️ (GPA: 3.87)
    • Bachelor of Science in Mechanical Engineering (Honors College) (Fall 2019) 🏅 (GPA: 3.72)
  • Miami Dade College
    • Associate in Arts Degree (Highest Honors) (Summer 2015) 🏆 (GPA: 3.95)

💼 Work Experience

  • Applied Research Center, Florida International University (March 2019 – Present)
    Research Assistant
    • 🚀 Developed autonomous systems for radiological surveillance in nuclear sites, integrating LiDAR, depth cameras, and IMUs.
    • 🧠 Designed multi-robot mission planning solutions using network bridges and behavior-tree-based task allocation.
    • 🛠️ Conducted testing in simulation (Gazebo, PyBullet) and real-world robotic platforms for validation.

🏆 Achievements, Awards & Honors

  • 🎖️ Highest Honors Graduate – Miami Dade College
  • 🏅 Honors College Graduate – Florida International University
  • 🤖 Developed autonomous systems for radiological surveillance, enhancing safety in nuclear environments
  • 🏆 Contributed to multi-robot coordination research, advancing mission planning strategies in robotics
  • 🏅 Published research contributions in robotics intelligence and autonomous system optimization

Publication Top Notes:

A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award 

Mr. Fangzhou Lin, Hong Kong University of Science and Technology, Hong Kong

Fangzhou Lin is a Ph.D. researcher in Civil Engineering at the Hong Kong University of Science and Technology (HKUST), specializing in deep learning, machine vision, construction robots, and multimodal data fusion. He holds a Bachelor’s degree in Civil Engineering from Fuzhou University (2015-2019) and a Master’s degree in Structural Engineering from Southeast University (2019-2022). Fangzhou Lin’s research focuses on the integration of artificial intelligence and robotics in construction automation, with applications in fire safety inspection, resource management, visual measurement, and quality assessment. His work has been published in leading journals such as Automation in Construction, Computer-Aided Civil and Infrastructure Engineering, and Advanced Engineering Informatics. He has contributed to multiple cutting-edge studies on robotic systems for construction site management, vision-based measurement techniques, and reinforcement learning-based scheduling for electric concrete vehicles. As an emerging scholar in construction automation and AI-driven inspection technologies, Fangzhou Lin actively collaborates on multi-disciplinary research projects to enhance efficiency, safety, and sustainability in the built environment. His contributions to automated reality capture, rebar positioning, and construction robotics are shaping the future of intelligent construction and infrastructure development.

Professional Profile:

SCOPUS

Suitability of Fangzhou Lin for the Best Scholar Award

Fangzhou Lin is an outstanding early-career scholar with a strong background in deep learning, machine vision, construction robotics, and multimodal data fusion within the field of civil engineering. His academic trajectory, research productivity, and innovative contributions make him a compelling candidate for the Best Scholar Award. Below is a detailed assessment of his suitability based on key criteria.

🎓 Education

  • 2015.09 – 2019.06 | Fuzhou UniversityBachelor’s Degree in Civil Engineering
  • 2019.09 – 2022.06 | Southeast UniversityMaster’s Degree in Structural Engineering
  • 2022.09 – Present | Hong Kong University of Science and TechnologyPh.D. in Civil Engineering

🏗️ Work & Research Experience

  • Expertise in: Deep learning, machine vision, construction robots, multimodal data fusion
  • Published in top journals such as Automation in Construction and Computer-Aided Civil and Infrastructure Engineering
  • Conducting research on:
    • 🔥 Fire Safety Inspection using AI-driven visual inspection
    • 🤖 Robotics for Construction Management with multi-task planning and automatic grasping
    • 🏗️ BIM-integrated Reality Capture for indoor inspection using multi-sensor quadruped robots
    • 🎯 Vision-based Monitoring for assembly alignment of precast concrete bridge members

🏆 Achievements & Awards

  • Published multiple high-impact journal papers 📚
  • Lead researcher on innovative construction technology projects 🔍
  • Contributed to advanced AI-driven automation for civil engineering 🤖
  • Research works under review in prestigious engineering journals 🏅
  • Collaborated with leading experts in civil engineering and robotics 🤝

Publication Top Notes:

Efficient visual inspection of fire safety equipment in buildings

 

Dr. Minh-Khang Le | Artificial Intelligence Awards | Best Researcher Award

Dr. Minh-Khang Le | Artificial Intelligence Awards | Best Researcher Award 

Dr. Minh-Khang Le, Cedars-Sinai Medical Center, United States

Minh-Khang Le, M.D., Ph.D., is a Postdoctoral Research Scientist in the Department of Pathology and Computational Biomedicine at Cedars-Sinai Medical Center in Los Angeles, California. He obtained his Doctor of Medicine degree from the University of Medicine and Pharmacy at Ho Chi Minh City, graduating in the top 10% of his class, and completed his Ph.D. in Pathology at the University of Yamanashi in Japan. His research focuses on integrating histopathology, molecular profiles, and clinicopathological features to characterize human cancers, particularly lymphoid and hematopoietic neoplasms. Dr. Le has contributed to several projects involving histopathology, molecular analyses, and the development of clinicopathological machine-learning models. As a strong advocate for the transformative potential of artificial intelligence in pathology, he aims to enhance the understanding and treatment of cancer. In addition to his postdoctoral position, he has held research roles at various institutions, including the University of Iowa Hospitals and Clinics and the University of Oklahoma Health Sciences Center. Dr. Le’s work has led to impactful advancements in digital pathology and cancer research.

Professional Profile:

SCOPUS

Researcher Suitability Summary for Best Researcher Award: 

Minh-Khang Le is an exemplary candidate for the Best Researcher Award, showcasing a profound commitment to advancing the field of digital pathology and computational biomedicine. His research is particularly focused on integrating histopathological and molecular profiles to enhance the understanding and characterization of human cancers, especially lymphoid and hematopoietic neoplasms. This multidisciplinary approach not only reflects his extensive knowledge but also his dedication to translating complex data into meaningful clinical insights.

Education 🎓

  • Postdoctoral Research Scientist
    Cedars-Sinai Medical Center, Department of Computational Biomedicine and Pathology
    July 2024 – Present
    8700 Beverly Blvd, Los Angeles, CA, USA
  • Ph.D. Student
    University of Yamanashi, Department of Pathology
    April 2020 – March 2024
    GPA: 3.5/4.0
    1110 Shimokato, Chuo, Yamanashi, Japan
  • Doctor of Medicine
    University of Medicine and Pharmacy at Ho Chi Minh City
    October 2013 – September 2019
    Degree Classification: Good (Top 10% of the Course)
    Ho Chi Minh City, Vietnam

Work Experience 💼

  • Postdoctoral Research Scientist
    Cedars-Sinai Medical Center, Department of Computational Biomedicine and Pathology
    July 2024 – Present
  • Part-time Researcher
    New Energy and Industrial Technology Development Organization (NEDO)
    April 2022 – Present
  • Part-time Researcher
    Department of Pathology, The University of Iowa Hospitals and Clinics, Iowa, USA
    April 2022 – Present
  • Research Assistant
    Department of Pathology, University of Yamanashi
    April 2020 – Present
  • Teaching Assistant
    Department of Pathology, University of Yamanashi
    April 2020 – Present
  • Part-time Researcher
    Department of Neurosurgery, The University of Oklahoma Health Sciences Center, Oklahoma, USA
    April 2021 – March 2022

Achievements, Awards, and Honors 🏆

  • Top 10% of the Course in Doctor of Medicine program at the University of Medicine and Pharmacy at Ho Chi Minh City
  • GPA of 3.5/4.0 in Ph.D. studies at the University of Yamanashi

Publication Top Notes:

Clinical implication of PRAME immunohistochemistry in differentiating melanoma in situ and dysplastic nevus in non-acral nevus-associated melanoma in situ: An institutional experience and meta-analysis

A Novel Artificial Intelligence-Based Parameterization Approach of the Stromal Landscape in Merkel Cell Carcinoma: A Multi-Institutional Study

Comprehensive analysis of distinct circadian clock subtypes of adult diffuse glioma and their associations with clinicopathological, genetic, and epigenetic profiles

CXCL5 expression is associated with active signals of macrophages in the microenvironment of papillary thyroid carcinoma

Severe asthmatic airways have distinct circadian clock gene expression pattern associated with WNT signaling

Akmal Jahan Mohamed Abdul Cader | Artificial Intelligence | Best Researcher Award

Akmal Jahan Mohamed Abdul Cader | Artificial Intelligence | Best Researcher Award

Dr. Akmal Jahan Mohamed Abdul Cader, South Eastern University, Sri Lanka.

Dr. Akmal Jahan Mohamed Abdul Cader is a distinguished academic and researcher currently serving as a Senior Lecturer in Computer Science at the South Eastern University of Sri Lanka. With extensive experience in higher education, he is a Visiting Research Fellow at QUT, Australia. His research interests include artificial intelligence, data science, and document image analysis. Dr. Cader has published numerous high-impact articles and is actively involved in academic development and curriculum design. He is committed to advancing education and research in the field of computer science. 📚💻🌍

Publication Profiles 

Googlescholar

Education and Experience

  • Visiting Research Fellow – QUT Momentum Visiting Fellow, QUT, Australia (2021 – Present) 🎓
  • Senior Lecturer (Computer Science) – South Eastern University of Sri Lanka (2020 – Present) 🏫
  • Sessional Academic – School of Electrical Engineering & Computer Science, QUT (2016 – 2019) 📖
  • Lecturer (Computer Science) – South Eastern University of Sri Lanka (2012 – 2015) 🧑‍🏫
  • Assistant Lecturer – South Eastern University of Sri Lanka (2010 – 2012) 🔍
  • Demonstrator in Computer Science – South Eastern University of Sri Lanka (2009 – 2010) 👨‍🔬

Suitability For The Award

Dr. Mac Akmal Jahan Mohamed Abdul Cader, Senior Lecturer in Computer Science at the South Eastern University of Sri Lanka, is a highly accomplished academic and researcher, making him an exemplary candidate for the Best Researcher Award. With a career spanning over a decade, Dr. Cader has consistently demonstrated leadership in research, teaching, and academic development, particularly in the fields of artificial intelligence, computer science, and digital technologies. His research contributions, coupled with his active involvement in academic service, professional organizations, and international collaborations, solidify his standing as a leading figure in his domain.

Professional Development

Dr. Cader has participated in several professional development programs focused on effective communication, teaching and learning, and project-based learning. He has completed various certifications at QUT, enhancing his skills in pedagogy and curriculum development. His commitment to continuous improvement in education is evident in his active engagement in workshops and training sessions aimed at promoting best practices in teaching. As a Fellow of the Higher Education Academy, he champions high standards in academic instruction and student engagement. 🏅📈📚

Research Focus

Dr. Cader’s research primarily focuses on artificial intelligence, data science, and document image analysis. He explores the synthesis and application of synthetic metals, aiming to develop innovative solutions in electronics and energy storage. His work on TCNQ chemistry has significant implications for biotechnology and medicine, including the construction of electrochemical sensors and drug delivery systems. By synthesizing novel compounds, he contributes to advancements in both theoretical and practical aspects of computer science and materials research. 🔬⚙️🌐

Awards and Honors

  • Senate Honours Award for High Impact Publications – SEUSL (2022 & 2023) 🏆
  • Queensland University of Technology Postgraduate Award (QUTPRA) (2015) 📜
  • Faculty Write Up (FWU) Scholarship – QUT, Australia (2019) 📚
  • Effective Communication in Teaching and Learning – QUT, Australia (2019) 🗣️
  • Foundation of Teaching and Learning – QUT (2018) 🎓

Publication Top Notes 

  • Locating tables in scanned documents for reconstructing and republishing | Cited by: 46 | Year: 2014 📄🔍
  • Plagiarism Detection on Electronic Text based Assignments using Vector Space Model (ICIAfS14) | Cited by: 37 | Year: 2014 📊✏️
  • AntiPlag: Plagiarism Detection on Electronic Submissions of Text Based Assignments | Cited by: 34 | Year: 2014 📄🛡️
  • Plagiarism detection tools and techniques: A comprehensive survey | Cited by: 23 | Year: 2021 🔎📚
  • Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges | Cited by: 11 | Year: 2023 🖐️📷
  • Contactless finger recognition using invariants from higher order spectra of ridge orientation profiles | Cited by: 10 | Year: 2018 ✋📏
  • Accelerating text-based plagiarism detection using GPUs | Cited by: 10 | Year: 2015 ⚡💻
  • Contactless multiple finger segments based identity verification using information fusion from higher order spectral invariants | Cited by: 9 | Year: 2018 🖐️🔗

Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Assoc. Prof. Dr. Mahmoud Bayat | Artificial intelligence Award | Best Researcher Award

Assoc. Prof. Dr. Mahmoud Bayat, Research Institute of Forests and Rangelands, Iran

Mahmoud Bayat is an Assistant Professor at the Research Institute of Forests and Rangelands, part of the Agricultural Research, Education, and Extension Organization (AREEO) in Tehran, Iran. He earned his B.A., M.Sc., and Ph.D. degrees from the University of Tehran, specializing in forestry science. Mahmoud has collaborated with renowned researchers, including Dr. Charles P.-A. Bourque, Dr. Pete Bettinger, Dr. Eric Zenner, Dr. Aaron Weiskittel, Dr. Harold Burkhart, and Dr. Timo Pukkala. His research focuses on forest modeling and inventory, with particular interest in applying artificial intelligence and machine learning techniques in forestry. Currently, he is working on projects related to growth and yield models for uneven-aged and mixed broadleaf forests using neural networks and the monitoring and mapping of tree species richness in northern Iran’s forests through symbolic regression and artificial neural networks. Mahmoud is proficient in statistical tools such as SPSS and MATLAB, and he is eager to share his expertise and discuss potential collaborations. For more information, his profiles can be found on ResearchGate, Google Scholar, and Scopus.

Professional Profile:

SCOPUS

 

Mahmoud Bayat’s Suitability for the Research for Best Researcher Award

Based on the provided details, Mahmoud Bayat demonstrates a strong candidacy for the Research for Best Researcher Award due to his extensive academic and professional contributions. Below is a summary supporting his suitability

Education 🎓

  • Ph.D. in Forestry Science
    University of Tehran, Iran
  • M.Sc. in Forestry Science
    University of Tehran, Iran
  • B.A. in Forestry Science
    University of Tehran, Iran

Work Experience 🏢

  • Assistant Professor
    Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO)
    Tehran, Iran
    Year: [Specify Year] – Present
  • Research Collaborator
    Worked with:

    • Dr. Charles P.-A. Bourque
    • Dr. Pete Bettinger
    • Dr. Eric Zenner
    • Dr. Aaron Weiskittel
    • Dr. Harold Burkhart
    • Dr. Timo Pukkala

Research Interests 🔍

  • Forest modeling and inventory
  • Application of artificial intelligence and machine learning in forestry

Current Projects 📊

  1. Growth and Yield Models for Uneven-Aged and Mixed Broadleaf Forest
    • Method: Neural Network
  2. Monitoring, Mapping, and Modeling Variation in Tree Species Richness
    • Method: Symbolic Regression and Artificial Neural Networks
    • Location: Northern Iran Forests

Publication Top Notes:

Comparison of Random Forest Models, Support Vector Machine and Multivariate Linear Regression for Biodiversity Assessment in the Hyrcanian Forests

Projected biodiversity in the Hyrcanian Mountain Forest of Iran: an investigation based on two climate scenarios

Recreation Potential Assessment at Tamarix Forest Reserves: A Method Based on Multicriteria Evaluation Approach and Landscape Metrics

Comparison between graph theory connectivity indices and landscape connectivity metrics for modeling river water quality in the southern Caspian sea basin

Development of multiclass alternating decision trees based models for landslide susceptibility mapping

Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests

 

Prof. Dr. Shih-Lin Chang | Artificial Intelligence Awards | Best Researcher Award

Prof. Dr. Shih-Lin Chang | Artificial Intelligence Awards | Best Researcher Award 

Prof. Dr. Shih-Lin Chang, National Yang Ming Chiao Tung University, Taiwan

Dr. Shih-Lin Chang, is a distinguished cardiologist and academic leader in the field of cardiovascular medicine. He is currently the Chief of the Department of Experimental Examination at Taipei Veterans General Hospital and the Director of the Intelligent Medicine and Telehealth Center within the Cardiovascular Center. Dr. Chang is also a Professor of Medicine at National Yang Ming Chiao Tung University, where he has contributed significantly to research and education in cardiology. Dr. Chang completed his M.D. at China Medical University in 1998 and earned his Ph.D. from National Yang Ming Chiao Tung University in 2012. He underwent extensive training, including a residency in Internal Medicine and fellowships in cardiology and electrophysiology at Taipei Veterans General Hospital. His professional journey includes significant roles such as Staff Cardiologist and Associate Director of the Cardiovascular Research Center at National Yang Ming Chiao Tung University.

Professional Profile:

Suitability for Best Researcher Award: Shih-Lin Chang, M.D., Ph.D.

Shih-Lin Chang exemplifies the qualities and achievements that make him an outstanding candidate for the Best Researcher Award. With a robust educational background, including an M.D. from China Medical University and a Ph.D. from National Yang Ming Chiao Tung University, Dr. Chang has established himself as a leading figure in cardiology and electrophysiology.

🎓 Education

  • M.D.: China Medical University, Taiwan (1991–1998)
  • Ph.D.: National Yang Ming Chiao Tung University, Institute of Clinical Medicine, Taiwan (2007–2012)

💼 Work Experience

  • 2023.8: Chief, Department of Experimental Examination, Taipei Veterans General Hospital Healthcare and Services Center
  • 2023.1: Director of Intelligent Medicine and Telehealth Center, Department of Cardiovascular Center
  • 2022.7: Associate Director, Cardiovascular Research Center, National Yang Ming Chiao Tung University
  • 2019.8: Professor of Medicine, National Yang Ming Chiao Tung University, School of Medicine
  • 2017.10–2020.10: Director, Heart Rhythm Center, Taipei Veterans General Hospital
  • 2016.8–2019.8: Associate Professor of Medicine, National Yang Ming Chiao Tung University
  • 2015.3–2017.3: Secretary-General, Taiwan Heart Rhythm Society
  • 2009.3–Present: Staff Cardiologist, Division of Cardiology, Taipei Veterans General Hospital
  • 2006–2009.3: Staff Cardiologist, Division of Cardiology, Suao Veterans Hospital
  • 2004–2006: Fellowship, Clinical and Basic Electrophysiology Laboratory, Taipei Veterans General Hospital
  • 2003–2005: Fellowship, Division of Cardiology, Taipei Veterans General Hospital
  • 2000–2003: Resident, Department of Internal Medicine, Taipei Veterans General Hospital

🏆 Awards and Honors

  • Poster Award: 2nd Asia-Pacific Atrial Fibrillation Symposium (2006) 🖼️
  • First Prize: Young Investigator Award, Taiwan Society of Cardiology (2010) 🥇
  • Young Investigator Award: 3rd Asia-Pacific Heart Rhythm Society (2010) 🏅
  • Best Oral Presentation Award: Taiwan Society of Cardiology (2011) 🎤
  • Best Poster Presentation Award: Taiwan Society of Cardiology (2013) 🖼️
  • Best Teacher Award: National Yang Ming University (2014, 2016, 2019) 🎓
  • Best Paper Award: Veterans General Hospitals and University System of Taiwan Joint Research Program (2015, 2018, 2019, 2021) 📝
  • PBL Tutor Award: National Yang Ming University (2017) 👩‍🏫
  • Outstanding Journal Paper Special Excellence Award: Taiwan Society for Simulation in Healthcare (2021) 🌟
  • Gold Award: Outstanding Academic Research Paper in Medical Education, Taipei Veterans General Hospital (2022) 🥇
  • National Healthcare Quality Award: Smart Services Category (2022) 🏥
  • Clinical Teaching Excellence Award: Taipei Veterans General Hospital (2023) 📚

🌟 Achievements

  • Active roles as editor for Acta Cardiologica Sinica (2015–Present) and Clinical Medicine (2014–Present).
  • Member of APHRS EP Ablation and Digital Health Committees (2024).
  • Numerous oral and poster presentations at international cardiology conferences.
  • Invited faculty/speaker at prestigious global cardiology events, including the European Society of Cardiology Congress and Heart Rhythm Society Annual Scientific Sessions.

Publication Top Notes:

Performance of the novel ANTWERP score in predicting heart function improvement after atrial fibrillation ablation in Asian patients with heart failure

Three-dimensional mapping and superior approach for catheter ablation in patients without inferior vena cava access

Effectiveness and safety of non-vitamin K antagonist oral anticoagulants in low-weight patients with atrial fibrillation

Multistep Algorithm to Predict RVOT PVC Site of Origin for Successful Ablation Using Available Criteria: A Two-Center Cross-Validation Study

Frailty and Its Associated Factors in Patients With Atrial Fibrillation: A Cross-Sectional Study

Vasyl Martsenyuk | Data Science | Best Researcher Award

Vasyl Martsenyuk | Data Science | Best Researcher Award

Prof. Vasyl Martsenyuk, University of Bielsko-Biala, Poland.

Prof. Vasyl Martsenyuk is a prominent academic in the field of Computer Science and Automation, currently serving as a Full Professor and Head of the Department at the University of Bielsko-Biała, Poland. With a strong background in data science, machine learning, and cybernetics, he has significantly contributed to educational reforms and various research projects in Europe. An active member of international academic communities, Prof. Martsenyuk has published extensively and engaged in numerous collaborations, fostering advancements in digital pedagogy and applied artificial intelligence. His dedication to education and research continues to inspire students and colleagues alike. 🎓🌍💻

Publication Profile

Scopus
Orcid

Education and Experience

Education:
  • Ph.D. in Technical Sciences (2005)
    Kyiv Taras Shevchenko National University, Faculty of Cybernetics
    Specialty: System Analysis and Decision Making 📚
  • Diploma of Full Professor in Medical Informatics with Biophysics (2005)
    Ministry of Education and Sciences of Ukraine 🇺🇦
  • Candidate of Physical and Mathematical Sciences (Ph.D.) (1996)
    Kyiv Taras Shevchenko National University, Faculty of Cybernetics
    Specialty: System Analysis and Decision Making 🎓
  • Diploma in Applied Mathematics (1993)
    Kyiv Taras Shevchenko National University, Faculty of Cybernetics, Ukraine 🧮
  • Diploma in Pharmacy (2010)
    Kharkiv National Pharmaceutical University, Ukraine 💊
Experience:
  • Full Professor & Head of Department (2020-Present)
    University of Bielsko-Biała, Department of Computer Science and Automation 🏫
  • Associate Professor (2015-2020)
    University of Bielsko-Biała, Department of Computer Science and Automation 👨‍🏫
  • Scientific Supervisor (2014-2016)
    Institute of Information Technologies and Learning Tools, National Academy of Pedagogical Sciences of Ukraine 🔬
  • Full Professor & Head of Medical Informatics Department (2005-2016)
    Ternopil Medical University, Ukraine 🏥
  • Vice-Rector (2005-2015)
    Ternopil Medical University, Ukraine 🎖️

Suitability For The Award

Prof. Vasyl Martsenyuk is a distinguished academic with a strong educational foundation, extensive professional experience, and significant contributions to computer science, medical informatics, and cybernetics. He has excelled in research areas such as data science, artificial intelligence, and system analysis, alongside leadership roles in academia and international projects. With numerous awards, publications, and innovative initiatives, he has demonstrated exceptional expertise and dedication. His impactful work and commitment to advancing knowledge make him highly deserving of the Best Researcher Award.

Professional Development

Prof. Martsenyuk has engaged in numerous international traineeships, focusing on advancements in digital pedagogy, applied artificial intelligence, and big data innovations. Notably, he participated in the Erasmus+ program and collaborated with institutions such as the University of Montenegro and Charles University in Prague. His efforts have emphasized fostering digital learning environments and enhancing educational methodologies in the fields of medicine and technology. These experiences reflect his commitment to continuous professional growth and adaptation to emerging technological trends in education. 🌐📚✨

Research Focus

Prof. Martsenyuk’s research centers on data science, encompassing machine learning, artificial intelligence, and big data analysis. He explores the application of reinforcement learning within cybernetics, focusing on control theory and system analysis. His mathematical investigations include functional-differential equations, population dynamics, and stability analysis. By integrating these disciplines, he aims to develop innovative solutions that advance scientific computing and enhance decision-making processes in complex systems. Prof. Martsenyuk’s work significantly contributes to the advancement of knowledge in computer science and its practical applications. 🔍📊🤖

Awards and Honors

  • Award of the Supreme Council of Ukraine (2007) 🏅
  • Award of the Prime Minister of Ukraine (2009) 🎖️

Publication Top Notes

  • A Method to Improve the Accuracy of Bridge Cranes Overload Protection Using the Signal Graph (2024) 📖
  • Neural Networks Toward Cybersecurity: Domain Map Analysis of State-of-the-Art Challenges (2024)
  • On Model of Recurrent Neural Network on a Time Scale: Exponential Convergence and Stability Research (2024)
  • The Influence of the Load Modelling Methods on Dynamics of a Mobile Crane (2024) 📖
  • Amperometric Biosensor Based on Glutamate Oxidase to Determine Ast Activity (2024) 📰
  • Аналітичний огляд підходів до проектування виробничих мереж (2024)
  • Designing a Competency-Focused Course on Applied AI Based on Advanced System Research on Business Requirements (2024)

Prof. Fabio Caldarola | Neural Network Awards | Best Paper Award

Prof. Fabio Caldarola | Neural Network Awards | Best Paper Award 

Prof. Fabio Caldarola, Università della Calabria, Italy

Dr. Fabio Caldarola is an accomplished mathematician and researcher, currently serving as an Assistant Professor in the Department of Environmental Engineering (DIAm) at the University of Calabria, Italy, a position he has held since January 2022. He earned his Ph.D. in Mathematics and Computer Science from the University of Calabria in December 2013, specializing in Algebraic Number Theory with a focus on Iwasawa Theory. Dr. Caldarola also holds a Laurea in Mathematics, graduating cum laude in 2003 with a thesis in Algebraic Geometry. His academic career includes several postdoctoral research fellowships, contributing to projects such as “Smart Secure & Inclusive Communities” and “I-BEST,” where he applied advanced mathematical concepts to environmental and land engineering challenges. His research interests extend to the study of complex networks, including symmetries and symmetry groups in graphs and quivers. With a strong background in pure and applied mathematics, Dr. Caldarola combines theoretical expertise with practical applications in environmental and computational sciences.

Professional Profile:

ORCID

Summary of Suitability for the Best Paper Award: Fabio Caldarola

Research Contributions
Fabio Caldarola is a distinguished researcher in mathematics and computer science, with a strong focus on innovative applications that address contemporary challenges. His significant contributions are showcased through his research publications, especially in the areas of neural fairness, blockchain protocols, and mathematical theories. Notable works include.

Education 🎓

  • Ph.D. in Mathematics and Computer Science (December 2013)
    • Università della Calabria
    • Thesis: Capitulation and Stabilization in various aspects of Iwasawa Theory for Zp-extensions (Algebraic Number Theory)
    • Advisor: Dott. A. Bandini
  • Laurea in Mathematics (May 2003)
    • Università della Calabria
    • 110/110 cum laude
    • Thesis: Rivestimenti Abeliani di Varietà Algebriche (Algebraic Geometry)
    • Advisor: Prof. P. A. Oliverio
  • Maturità Scientifica (July 1998)
    • Liceo Scientifico G.B. Scorza, Cosenza
    • Score: 60/60

Work Experience 💼

  • Assistant Professor (SSD MAT/07)
    • Department of Environmental Engineering, Università della Calabria
    • January 2022 – December 2024
  • Postdoctoral Research Fellowships 📚
    • Smart Secure & Inclusive Communities Project (SSD MAT/02 – INF/01)
      • Department of Mathematics and Computer Science, Università della Calabria
      • August 2020 – October 2021 (15 months)
    • I-BEST Project (SSD MAT/02 – ICAR/02)
      • Department of Environmental and Land Engineering and Chemical Engineering
      • June 2019 – May 2020
    • I-BEST Project (SSD MAT/03 – ICAR/02)
      • Department of Civil Engineering, Università della Calabria
      • May 2018 – April 2019
  • Research Collaboration Contract 🔬
    • Study of complex networks, focusing on symmetries and symmetry groups in graphs and quivers emerging from real contexts
    • Department of Physics, Università della Calabria
    • March 2016 – June 2016 (4 months)

Achievements & Awards 🏆

  • Academic Excellence: Laurea in Mathematics with highest honors (110/110 cum laude) 🎖️
  • Research Impact: Contributed to advanced research in Algebraic Number Theory, Algebraic Geometry, and complex network analysis.
  • Ph.D. Scholarship: Funded by Università della Calabria for excellence in doctoral research

Publication Top Notes:

Neural Fairness Blockchain Protocol Using an Elliptic Curves Lottery

Algebraic Tools and New Local Indices for Water Networks:Some Numerical Examples

Combinatorics on n-sets: Arithmetic Properties and Numerical Results

New Approaches to Basic Calculus: An Experimentation via Numerical Computation

Numerical Experimentations for a New Set of Local Indices of a Water Network

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award 

Assoc. Prof. Dr. Mohammed Farag, Alexandria University, Egypt

Dr. Mohammed M. Farag is an accomplished Associate Professor of Electrical Engineering with extensive academic experience spanning over two decades. Currently affiliated with King Faisal University, Saudi Arabia, and Alexandria University, Egypt, he specializes in the fields of machine learning, signal processing, and cybersecurity. His research is particularly focused on the development of innovative solutions for edge computing and cyber-physical systems. Dr. Farag holds a Ph.D. in Computer Engineering from Virginia Tech, where he conducted groundbreaking research on enhancing trust in cyber-physical systems. His academic journey also includes a Master’s and Bachelor’s degree in Electrical Engineering from Alexandria University, both achieved with distinction. A prolific researcher, he has an impressive publication record in high-impact journals and has secured numerous research grants. Beyond his research contributions, Dr. Farag is dedicated to advancing the field through excellence in teaching, mentorship, and quality assurance, actively contributing to program development and accreditation processes.

Professional Profile:

SCOPUS

ORCID

GOOGLE SCHOLAR

Summary of Suitability for Best Researcher Award: Dr. Mohammed M. Farag

Dr. Mohammed M. Farag’s academic and professional profile reflects significant accomplishments in research, teaching, and academic leadership. Based on his qualifications and achievements, he is a strong candidate for the Best Researcher Award for the following reasons.

🧑‍🎓 Education

🎓 Ph.D. in Computer Engineering (GPA: 4.00/4.00)Virginia Tech, USA (2009-2012)
Dissertation: “Architectural Enhancements to Increase Trust in Cyber-Physical Systems Containing Untrusted Software and Hardware”

🎓 M.Sc. in Electrical Engineering (GPA: 4.00/4.00)Alexandria University, Egypt (2003-2007)
Thesis: “Hardware Implementation of The Advanced Encryption Standard on Field Programmable Gate Arrays”

🎓 B.Sc. in Electrical Engineering, Distinction with Honor (GPA: 3.89/4.00)Alexandria University, Egypt (1998-2003)
Project: “VLSI Design of Cryptographic Algorithms”

📚 Research Interests

🔍 Machine Learning for Signal Processing & Edge Computing
🔐 Cybersecurity and Hardware Security
💾 VLSI Design and Embedded Systems
🤖 AI Applications in Electrical Engineering
🌐 Cyber-Physical Systems

🏆 Key Achievements

📝 Citations: 411 | h-index: 11 | i10-index: 11 (As of October 2024)
📖 Published in IEEE Access, Sensors, and top-tier journals.
💰 Secured multiple research grants from King Faisal University, totaling over 100,000 SAR.

💻 Technical Expertise

💡 Programming: Python, C++, MATLAB
🖥️ Hardware Design: VHDL, Verilog
📊 Machine Learning: TensorFlow, PyTorch, Keras
🔧 CAD Tools: Synopsys, Cadence, Xilinx

🎓 Teaching Experience

🎓 Electrical Circuits, Signal Processing, Digital Logic, VLSI Design, Embedded Systems, and more!
🎯 Special focus on fostering practical skills in Semiconductor Devices and Cybersecurity.

Publication Top Notes

Wearable sensors based on artificial intelligence models for human activity recognition

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge

Design and Analysis of Convolutional Neural Layers: A Signal Processing Perspective

Matched Filter Interpretation of CNN Classifiers with Application to HAR

A Self-Contained STFT CNN for ECG Classification and Arrhythmia Detection at the Edge

Aggregated CDMA Crossbar With Hybrid ARQ for NoCs

Overloaded CDMA crossbar for network-on-chip

Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

Ms. Rachel Stephen Mollel, University of Strathclyde, United Kingdom

Rachel Stephen Mollel is a Ph.D. student in Electrical and Electronic Engineering at the University of Strathclyde, UK. Her research focuses on machine learning, explainable AI, energy demand-side management, smart metering, and non-intrusive load monitoring (NILM). She holds a Master of Engineering from Arkansas Tech University, USA, and a Bachelor’s degree in Telecommunication Engineering from Visvesvaraya Technological University, India. Rachel has contributed significantly to the energy sector, exploring the role of smart meters in reducing energy costs and enhancing communication between energy providers and consumers. Her recent work, which investigates the potential of NILM to reveal hidden demand flexibility in residential energy consumption, has been published in various peer-reviewed journals and conferences. Additionally, she is actively involved in improving the interpretability of NILM models to enhance algorithm performance. Her contributions have been recognized with a Commonwealth Scholarship in 2020.

Professional Profile:

ORCID

Summary of Suitability for the Best Scholar Award:

Rachel Stephen Mollel is a highly suitable candidate for the Best Research Scholar Award based on her significant contributions to the fields of machine learning, explainable AI, and energy demand-side management. As a PhD student at the University of Strathclyde, her research aims to address critical energy issues through innovative approaches like Non-Intrusive Load Monitoring (NILM), which helps uncover hidden demand flexibility in residential energy consumption.

Education:

  • 2021 – Present: PhD in Electrical and Electronic Engineering, University of Strathclyde, UK
  • 2010 – 2012: Master of Engineering, Arkansas Tech University, USA (GPA: 3.75/4.0)
  • 2006 – 2010: Bachelor’s degree in Telecommunication Engineering, Visvesvaraya Technological University, India (First Class)

Work Experience:

  • 2011 – 2012: Graduate Assistant, Arkansas Tech University, USA
    Assisted in the Digital Logic and Robotics Course & Lab; delivered tutorials, graded lab reports and exams, and supported the development of course materials under faculty supervision.
  • 2014 – 2020: Assistant Lecturer, University of Dar es Salaam, Tanzania
    Delivered lectures, prepared and graded exams in Control Systems Engineering and Fundamentals of Electrical Engineering. Supervised undergraduate student projects, practical training, and fieldwork. Managed various administrative duties, such as student registration and coordination of departmental examinations.

Publication top Notes:

Explainability-Informed Feature Selection and Performance Prediction for Nonintrusive Load Monitoring

Using explainability tools to inform non-intrusive load monitoring algorithm performance

Using explainability tools to inform NILM algorithm performance