Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

Mr. Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

Research Scholar at Indian Institute of Technology Kharagpur, India

Raviteja Sista is a dynamic and accomplished researcher specializing in Artificial Intelligence, Deep Learning, and Medical Image Analysis. Currently pursuing his Ph.D. at the Indian Institute of Technology Kharagpur with an outstanding GPA of 9.4, he is a recipient of the prestigious Prime Minister’s Research Fellowship. Raviteja holds an MSc in Signal Processing and Communications from the University of Edinburgh and a Bachelor’s in Electronics and Communication Engineering from Osmania University. His research focuses on developing AI-driven frameworks for surgical planning and outcome prediction, with notable contributions to multimodal graph-based learning and surgical video analysis. He has published extensively in top-tier journals such as Medical Image Analysis and Computers in Biology and Medicine, and has actively contributed to international AI challenges and symposia. His technical expertise, academic excellence, and dedication to solving real-world healthcare problems through AI make him a standout figure in the research community.

Professional Profile 

🎓 Education of Raviteja Sista

Raviteja Sista has pursued a stellar academic path marked by excellence and innovation. He is currently enrolled in a Ph.D. program at the Indian Institute of Technology Kharagpur, specializing in Artificial Intelligence at the Centre of Excellence, where he maintains an impressive GPA of 9.4/10. Prior to this, he earned his Master of Science in Signal Processing and Communications with Distinction from the University of Edinburgh (2019–2020). His foundational engineering training was completed with a Bachelor of Engineering in Electronics and Communication from M.V.S.R. Engineering College, affiliated with Osmania University, where he secured a remarkable 85.34%. Raviteja also boasts an outstanding academic record from his early years, achieving 94.6% in Intermediate studies at Narayana Junior College and a CGPA of 9.8/10 in Class X from Lotus National School, Hyderabad.

💼 Professional Experience of Raviteja Sista

Raviteja Sista has a well-rounded professional background that bridges academia, research, and industry. He is currently a Teaching Assistant at IIT Kharagpur, where he supports academic instruction in AI and deep learning. Over the years, he has held teaching roles at several institutions including SRKR Engineering College, CSI Wesley Institute of Technology, Assam Down Town University, and JNTU Kakinada, demonstrating his commitment to education and knowledge dissemination. Complementing his academic roles, Raviteja also gained valuable industry experience as an Associate Software Developer Intern at Accenture Solutions Pvt. Ltd. and through multiple internships at Defence Research and Development Laboratory (DRDL), Hyderabad. His professional journey reflects a strong blend of research, software development, and teaching expertise, all anchored in the field of artificial intelligence and signal processing.

🔬 Research Interests of Raviteja Sista

Raviteja Sista’s research interests lie at the intersection of artificial intelligence and healthcare, with a strong focus on applying deep learning techniques to complex real-world problems. His core areas of interest include Deep Learning, Medical Image Analysis, Digital Signal Processing, Image Processing, Artificial Intelligence, and Design of Algorithms. He is particularly passionate about developing AI-powered systems for surgical planning and automation, leveraging multimodal data, graph neural networks, and computer vision. His work aims to enhance patient safety, improve clinical outcomes, and drive innovation in intelligent medical systems. Raviteja’s commitment to impactful, interdisciplinary research is evident in his projects and publications, which bridge technical depth with healthcare relevance.

🏅 Awards and Honors of Raviteja Sista

Raviteja Sista has been recognized with several prestigious awards and honors that highlight his academic brilliance and research potential. Most notably, he was awarded the Prime Minister’s Research Fellowship (PMRF) in 2022, one of India’s most esteemed research fellowships supporting exceptional doctoral scholars. He also earned a Certificate of Merit for completing the “Advanced Certification in Artificial Intelligence and Machine Learning” from IIIT Hyderabad in 2019. Additionally, Raviteja demonstrated national-level academic excellence by ranking in the Top 3% among over 1 lakh candidates in GATE 2019, a highly competitive examination for engineering graduates in India. These accolades reflect his consistent pursuit of excellence and his growing reputation as a promising researcher in the field of artificial intelligence.

🧾 Conclusion 

Sista Raviteja stands out as a highly qualified, technically accomplished, and visionary researcher in AI for healthcare. With strong academic credentials, impactful projects, respected publications, and active involvement in the scientific community, he demonstrates clear potential for leadership in scientific research.Despite minor areas of potential growth in independent authorship and translational work, his contributions already meet and, in some cases, exceed the typical benchmarks for the Best Researcher Award.

📚 Publications Top Noted

  1. Title: Deep neural hashing for content-based medical image retrieval: A survey
    Authors: A. Manna, R. Sista, D. Sheet
    Journal: Computers in Biology and Medicine, Volume 196, Article 110547
    Year: 2025
    Citations:
  2. Title: Artificial Intelligence (AI)–Based Model for Prediction of Adversity Outcome Following Laparoscopic Cholecystectomy—a Preliminary Report
    Authors: R. Agrawal, S. Hossain, H. Bisht, R. Sista, P.P. Chakrabarti, D. Sheet, U. De
    Journal: Indian Journal of Surgery, Volume 87 (1), Pages 52–59
    Year: 2025
    Citations: 1
  3. Title: Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation
    Authors: A. Srivastava, R. Sista, P.P. Chakrabarti, D. Sheet
    Conference: Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP)
    Year: 2024
    Citations:
  4. Title: SimCol3D—3D reconstruction during colonoscopy challenge
    Authors: A. Rau, S. Bano, Y. Jin, P. Azagra, J. Morlana, R. Kader, E. Sanderson, …, R. Sista
    Journal: Medical Image Analysis, Volume 96, Article 103195
    Year: 2024
    Citations: 16
  5. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, K. Yuan, …, R. Sista
    Journal: Medical Image Analysis, Volume 89, Article 102888
    Year: 2023
    Citations: 29
  6. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, T. Xia, F. Jia, …, R. Sista
    Journal: Medical Image Analysis, Volume 86, Article 102803
    Year: 2023
    Citations: 61
  7. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, …, R. Sista
    Repository: arXiv, arXiv:2302.06294
    Year: 2023
    Citations:
  8. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: R. Sista, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP 2022
    Year: 2022
    Citations: 1
  9. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, …, R. Sista
    Repository: arXiv, arXiv:2204.04746
    Year: 2022
    Citations: 1
  10. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: S. Raviteja, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP
    Year: 2022
    Citations:
  11. Title: Challenges of Decomposing Tools in Surgical Scenes Through Disentangling The Latent Representations
    Authors: S.L. Gorantla, R. Sista, A. Srivastava, U. De, P.P. Chakrabarti, D. Sheet
    Workshop: ICLR Workshop on Challenges in Applied Deep Learning (ICBNB)
    Year: 2025 (Accepted)
    Citations:

 

Abdul Aziz | Computer Science and Artificial Intelligence | Best Researcher Award

Abdul Aziz | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Abdul Aziz, Khulna University of Engineering & Technology, Bangladesh

🧑‍🏫 Abdul Aziz is an Assistant Professor at Khulna University of Engineering & Technology (KUET), specializing in computer science and engineering. With a passion for deep learning 🤖, fuzzy logic, and smart city innovations 🌆, he has presented at major conferences like ICCIT and BIM. A recipient of the Vice-Chancellor Award 🏅 and a University Gold Medalist 🥇, Abdul’s research focuses on AI-driven solutions for real-world problems. His notable works include danger detection for women and children and risk evaluation of hazardous chemicals. Dedicated to education and research, he inspires future engineers at KUET. 📚✨

Publication Profile

Scopus

Academic Qualifications

Mr. Abdul Aziz is an accomplished computer science professional with a strong academic background 🎓. He earned his Master of Science in Computer Science & Engineering from Khulna University of Engineering & Technology (KUET) in 2022, achieving a CGPA of 3.75/4.00 💻. Previously, he completed his Bachelor of Science at KUET in 2017 with an outstanding CGPA of 3.92/4.00, securing 1st place among 59 students and topping the EEE Faculty 🏆. His academic journey began at Shahid Syed Nazrul Islam College, where he completed his Higher Secondary Certificate in 2012 📚. Abdul Aziz exemplifies dedication and excellence in his field.

Professional Experiences

Mr. Abdul Aziz is an accomplished academic in computer science, currently serving as an Assistant Professor at Khulna University of Engineering & Technology (KUET) since December 2020 🎓💻. He began his journey at KUET as an Adjunct Faculty (Lecturer) in August 2017 and later became a Lecturer from January 2018 to December 2020 📚. Prior to this, he contributed to Northern University of Business and Technology Khulna (NUBTK) as a Lecturer from July to December 2017 🏫. With a strong dedication to education and research, Mr. Aziz continues to shape future engineers and drive innovation in computer science 🚀🔍.

Achievements, Awards, and Certifications

Mr. Abdul Aziz is a distinguished academic and researcher recognized for his outstanding achievements🏅. In 2024, he received the Vice-Chancellor Award for High Impact Research Journal Publication📚. He was the University Gold Medalist in 2018 for securing 1st position in his graduating class🥇. From 2013 to 2016, Abdul earned the University Vocational Scholarship and the Dean’s Award for ranking among the top 10% of students for four consecutive years🏆. His programming skills were highlighted in 2014 when he secured 3rd place in one intra-batch contest and 1st place in another💻🥇.

Membership

Mr. Abdul Aziz is a passionate coach, mentor, and trainer in programming and technology 💻. Since 2018, he has coached 25+ teams for ICPC regionals, National Girls Programming Contests, and university competitions. He led the KUET_Effervescent team to the 48th ICPC World Finals in Astana, Kazakhstan (2024) 🏆. Aziz serves as an Associate Member of the Institution of Engineers, Bangladesh ⚙️ and reviews international conferences. As a trainer for BDSET and ITEE programs, he uplifts digital skills 📊. He also organized major events like BitFest 2019 and NHSPC, mentoring future innovators. His journey began as a debate champion 🎤.

Academic Projects

Mr. Abdul Aziz has undertaken diverse academic projects during his undergraduate studies at KUET. In his 3rd semester (2014), he developed a Java-based smart home automation desktop app 🏠💻. In the 4th semester (2014-2015), he created a medical center automation website using PHP, HTML, and MySQL for doctor-patient communication 🏥🌐. His 5th semester (2015) featured hospital DBMS design with PL/SQL and Oracle 📊. By the 6th semester (2015-2016), he built an Android app for real-time object tracking 📱🗺️ and a keypad/Bluetooth-controlled LCD display project using Arduino 📟🔷. In his final semester, he developed a 3D car racing game with C++ and OpenGL 🚗🎮.

Research Focus

Abdul Aziz’s research focuses on applying deep learning 🤖, signal processing 🎵, and fuzzy logic 🔢 to develop innovative solutions in safety, smart cities 🌆, and mobile applications 📱. His work spans danger detection for women and children 🚨, city service task distribution 🏙️, and chemical risk evaluation 🧪. Additionally, Aziz explores advanced error detection and correction in computing 💻. His contributions aim to enhance public safety, improve urban services, and boost system reliability. With publications in top-tier journals 🏆, his research bridges technology and real-world applications, fostering smarter and safer environments.

Publication Top Notes

DangerDet: A mobile application-based danger detection platform for women and children using deep learning

ShopiRound: An Android application-based e-commerce system to find products nearby using travelling salesman problem

A fuzzy logic-based risk evaluation and precaution level estimation of explosive, flammable, and toxic chemicals for preventing damages

Multi-bit error detection and correction technique using HVDK (Horizontal-Vertical-Diagonal-Knight) parity

CitySolution: A complaining task distributive mobile application for smart city corporation using deep learning

 

Yunqiang Sun | Artificial Intelligence | Best Researcher Award

Yunqiang Sun | Artificial Intelligence | Best Researcher Award

Prof. Dr Yunqiang Sun, 中北大学, China

Prof. Dr. Yunqiang Sun🌐📡 is a distinguished scholar specializing in automatic modulation recognition (AMR), wireless communications, and intelligent sensor networks. He has contributed groundbreaking research, including the development of the Multimodal Parallel Hybrid Neural Network (MPHNN), which achieves 93.1% recognition accuracy with reduced complexity. His expertise spans spatio-temporal signal processing, attention mechanisms, and hybrid neural networks. Prof. Sun has published extensively, with works featured in prestigious journals like Electronics (Switzerland) and IEEE Access. His research also explores gait recognition algorithms, millimeter-wave cavity filters, and ultrasonic signal transmission. A dedicated innovator, Prof. Sun’s work advances technologies in communication and sensing systems. 📊📖✨

Publication Profile

Scopus

Proposed Solution 🤖✨

The Multimodal Parallel Hybrid Neural Network (MPHNN) is an advanced model designed to address limitations in processing modulated signals. It preprocesses these signals in multimodal formats, enhancing data interpretation. By combining Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Gated Recurrent Units (Bi-GRU) for temporal feature processing, MPHNN efficiently captures both spatial and temporal dependencies. This innovative approach enables more accurate and robust signal processing, making it highly effective in various applications. Prof. Dr. Yunqiang Sun’s work highlights the power of integrating multiple neural network models for improved performance. 🧠🔧📡📊

Attention Mechanisms 🎯🔗

Prof. Dr. Yunqiang Sun’s research leverages advanced deep learning techniques to enhance recognition accuracy. By integrating the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA), his work in the Multi-Path Hierarchical Neural Network (MPHNN) effectively combines both temporal and spatial features. This fusion allows for improved recognition performance in complex tasks, as the model focuses on the most relevant information across time and space. Prof. Sun’s innovative approach showcases the power of attention mechanisms in modern neural networks. 🤖📊🧠🔍

Results 📊✅

Prof. Dr. Yunqiang Sun, MPHNN, has achieved an impressive 93.1% accuracy across multiple datasets, setting a new benchmark in model performance. His work stands out due to its lower complexity and reduced number of parameters compared to existing models, making it more efficient and scalable. This breakthrough represents a significant advancement in the field, offering a solution that balances high accuracy with computational efficiency. Prof. Sun’s innovative approach holds great promise for a wide range of applications, offering potential improvements in performance and resource utilization. 🔬📊💡📈

Diverse Publication Record

Prof. Dr. Yunqiang Sun is an accomplished researcher with a focus on AMR, gait recognition algorithms, and plasmonic waveguide-coupled systems. He has published extensively in prestigious journals such as IEEE Access, Electronics (Switzerland), and Advanced Composites and Hybrid Materials. Notable works include impactful publications like CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network and Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism. Prof. Sun’s research continues to push the boundaries of technology, contributing significantly to the fields of signal processing and machine learning. 📚🔬📈💡

Citations and Recognition

Prof. Dr. Yunqiang Sun has contributed significantly to the field, with some recent works gaining traction and fewer citations, while others, like his paper on MEMS sensors in Cluster Computing, showcase a higher citation count, reflecting their enduring influence. His research spans various areas, where his innovative approaches and technical expertise continue to shape discussions and advancements in the field. Despite the varying citation impact, Prof. Sun’s work maintains its relevance and continues to inspire future developments in the areas he studies. 🌟📚🔬🧠📈

Research Focus

Prof. Dr. Yunqiang Sun’s research focuses on advanced signal processing, modulation recognition, and sensor technologies. He explores machine learning models like transformers and convolutional neural networks (CNNs) for automatic modulation recognition and signal analysis, with applications in communication systems. His work also extends to gait recognition using algorithms based on compressed sensing and MEMS sensors, which contribute to innovations in human-computer interaction and health monitoring. Prof. Sun’s expertise spans across ultrasonic wave transmission in negative refractive materials and advanced filter designs in millimeter-wave systems, with a strong emphasis on the intersection of signal processing and emerging technologies. 📡🤖📊

Publication Top Notes

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Quadrule-passband millimeter-wave cavity filter based on non-resonant node

Transmission characteristics of ultrasonic longitudinal wave signals in negative refractive index materials

Numerical calculus solution of gait recognition algorithm based on compressed sensing

Application and research of MEMS sensor in gait recognition algorithm

 

 

ABDULKADIR DAUDA | Computer Science and Artificial Intelligence | Best Researcher Award

ABDULKADIR DAUDA | Computer Science and Artificial Intelligence | Best Researcher Award

ABDULKADIR DAUDA, University of Reims Champagne-Ardenne, France

Based on the information provided, Mr. Abdulkadir Dauda appears to be a strong candidate for the Best Researcher Award. His academic background, professional experience, and research contributions highlight his qualifications and impact in the field of computer science. Below is an evaluation of his suitability for the award:

Publication profile

Orcid

Academic and Professional Qualifications

Mr. Dauda has a robust academic background, including a Master of Science Degree in Computer Science with Distinction and ongoing doctoral studies at Universite De Reims Champagne-Ardenne, France. His academic achievements, particularly his distinction at the Master’s level, underscore his dedication and capability in his field.

Work Experience and Contributions

Mr. Dauda’s professional experience as a Lecturer II in the Department of Computer Science at the Federal University of Lafia (2014-2022) demonstrates his commitment to education and research. He has taken on significant roles, such as Departmental Examination Officer and Programme Coordinator, which show his leadership and involvement in academic administration. His work in system and network administration during his tenure at the Federal Capital Territory Judiciary further highlights his practical expertise in computer science.

Research Contributions

Mr. Dauda has an impressive portfolio of research publications that focus on critical areas such as IoT Security, High-Performance Computing, and Distributed and Parallel Architectures. His publications in reputed journals and conferences, including the 2023 International Conference on Wireless Networks and Mobile Communications (WINCOM), demonstrate his active engagement in advancing knowledge in these fields. His collaborative work with international scholars further reflects the quality and impact of his research.

Research Interests and Impact

Mr. Dauda’s research interests in emerging and high-impact areas like IoT Security and Big Data are particularly relevant in today’s technological landscape. His contributions to these fields, through both his research and practical work, suggest a deep understanding and innovative approach to solving complex problems in computer science.

Conclusion

Mr. Abdulkadir Dauda’s academic excellence, professional experience, and significant research contributions make him a suitable candidate for the Best Researcher Award. His work not only advances the field of computer science but also demonstrates a commitment to teaching, mentoring, and community service, further solidifying his qualification for this honor.

Publication top notes

A Survey on IoT Application Architectures

 

Emmanuel Mutabazi | Computer Science and Artificial Intelligence | Best Researcher Award

Emmanuel Mutabazi | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Emmanuel Mutabazi, Hohai University, China

Based on the information provided, Mr. Emmanuel Mutabazi appears to be a strong candidate for the Best Researcher Award.

Publication profile

google scholar

Education

Mr. Mutabazi is currently pursuing a Ph.D. in Information and Communication Engineering at Hohai University, China, where he has been enrolled since September 2019. He also holds a Master of Engineering in the same field from Hohai University (2016-2019) and a Bachelor of Science in Business Information Technology from the University of Rwanda (2010-2013). His solid educational background has laid a strong foundation for his research endeavors.

Research Interests

Mr. Mutabazi’s research focuses on cutting-edge areas like Natural Language Processing, Machine Learning, Deep Learning, and Computer Vision. His passion for building intelligent systems using AI and ML technologies is evident in his academic and professional work, making him a valuable contributor to these fields.

Skills

He possesses advanced coding skills in multiple programming languages, including Python, MATLAB, C++, Java, and R, among others. His expertise extends to website design, software development, image and video processing, and developing complex systems like Question Answering Systems and Recommender Systems. He is also proficient in using referencing and paper formatting tools such as EndNote, Mendeley, Zotero, and LaTeX.

Experience

Before embarking on his current academic path, Mr. Mutabazi worked as a secondary school teacher at Kiyanza Secondary School (2014-2016), teaching a wide range of subjects. His multilingual abilities (English, French, Swahili, Chinese, and Kinyarwanda) further enhance his capability to engage in global research collaborations.

Publications

Mr. Mutabazi has several peer-reviewed publications, including journal articles and conference papers, showcasing his active participation in research. Notably, his publications include a review on medical textual question-answering systems, a study on SLAM methods, a review of the Marine Predators algorithm, and an improved model for medical forum question classification. His research has been published in reputable journals such as Applied Sciences, Computational Intelligence and Neuroscience, and Machine Learning with Applications.

Conclusion

Considering Mr. Mutabazi’s strong academic background, diverse skill set, significant teaching experience, and impactful research contributions, he is well-suited for the Best Researcher Award. His dedication to advancing knowledge in Information and Communication Engineering, coupled with his proven ability to publish high-quality research, makes him a deserving candidate for this recognition.

Research focus

This researcher focuses on developing advanced deep learning models and algorithms for various applications, particularly in the medical field and computational intelligence. Their work includes creating and improving medical textual question-answering systems and classification models for medical forums using CNN and BiLSTM. Additionally, they explore innovative techniques in marine predator algorithms and direct SLAM methods based on semantic information, highlighting a strong emphasis on machine learning and artificial intelligence in solving complex problems. This research bridges the gap between AI and practical applications in healthcare and robotics. 🤖💡🩺📊

Publication top notes

A review on medical textual question answering systems based on deep learning approaches

Marine predators algorithm: A comprehensive review

An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM

A variable radius side window direct slam method based on semantic information

 

Yasin Fatemi | Computer Science and Artificial Intelligence | Best Researcher Award

Yasin Fatemi | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Yasin Fatemi, Auburn University, United States

Based on the details provided, Mr. Yasin Fatemi is a highly suitable candidate for a Researcher of the Year Award.

Publication profile

google scholar

Educational Background 📚

Mr. Fatemi has a robust academic foundation with a Ph.D. in Industrial and Systems Engineering from Auburn University, where he has maintained a perfect GPA of 4.0. His ongoing M.Sc. in Data Science further complements his expertise, and he also holds an M.Sc. and B.Sc. in Industrial and Systems Engineering from Tarbiat Modares University and the University of Kurdistan, respectively. This diverse and interdisciplinary educational background supports his innovative research in healthcare and systems optimization.

Research Experience and Contributions 🔬

Mr. Fatemi’s research is both extensive and impactful. His recent work involves using machine learning and network analysis to address critical healthcare issues such as low birth weight prediction, racial disparities in maternal outcomes, and cardiovascular death among liver transplant recipients. These projects showcase his ability to apply advanced analytical methods to real-world problems, significantly contributing to the fields of healthcare and data science. His studies have utilized cutting-edge techniques such as Recursive Feature Elimination, SHapley Additive exPlanations (SHAP), and network feature analysis, highlighting his technical prowess and innovation.

Publications and Academic Output 📝

Mr. Fatemi has authored several peer-reviewed articles, contributing to reputable journals like Frontiers in Public Health and Journal of Multidisciplinary Healthcare. His research on the stress and compensation perceptions of frontline nurses during the COVID-19 pandemic, as well as his work on hospital smart notification systems, demonstrates his commitment to improving healthcare environments and outcomes. His publications reflect his ability to tackle diverse and pressing issues, making him a significant contributor to the academic community.

Technical and Academic Skills 🛠️

Mr. Fatemi’s technical skills are impressive, encompassing data analysis tools like Python, R, and SQL, and specialized software for simulation and optimization. His expertise in machine learning, statistical learning, and network analysis is evident in his research outputs, further establishing his credibility as an innovative researcher.

Conclusion

Mr. Yasin Fatemi’s strong educational background, extensive research experience, and impactful contributions to healthcare and data science make him an excellent candidate for a Best Researcher Award. His ability to apply complex analytical techniques to critical issues in healthcare and his consistent academic excellence underscore his suitability for this recognition.

Publication top notes

Investigating frontline nurse stress: perceptions of job demands, organizational support, and social support during the current COVID-19 pandemic

Listening to the Voice of the hospitalized child: comparing children’s experiences to their parents

The Cost of Frontline Nursing: Investigating Perception of Compensation Inadequacy During the COVID-19 Pandemic

ChatGPT in Teaching and Learning: A Systematic Review

Machine Learning Approach for Cardiovascular Death Prediction among Nonalcoholic Steatohepatitis (NASH) Liver Transplant Recipients

Evaluating a Hospital Smart Notification System in a Simulated Environment: The Method

Machine Learning Approaches for Cardiovascular Death Prediction Among Nash Liver Transplant Recipients

 

 

Simon Wong | Computer Science and Artificial Intelligence | Best Researcher Award

Simon Wong | Computer Science and Artificial Intelligence | Best Researcher Award

Dr Simon Wong, College of Professional and Continuing Education, the Hong Kong Polytechnic University, Hong Kong

Dr. Simon Wong is a distinguished educator with a Doctor of Education from the University of Leicester, UK. His extensive academic background includes an M.Phil. from PolyU and a Bachelor’s in Computer Science from the University of Minnesota, USA. Dr. Wong serves as a lecturer at CPCE, PolyU, and holds professional certifications in financial technology and Oracle. His industrial experience spans roles as a senior consultant and software engineer. Dr. Wong has led numerous academic programs and research initiatives, specializing in subjects like database systems, e-commerce, and cloud computing. He is a committed member of professional organizations and has significantly contributed to academic management and leadership. 🌟🎓💼

Publication profile

Orcid

Academic Qualifications

Dr.  holds a Doctor of Education from the University of Leicester, UK (2012), where they researched effective online learning in Hong Kong higher education institutions, supervised by Prof. Paul Cooper 🎓📚. They also earned a Master of Philosophy from PolyU (1997), focusing on designing and analyzing a bypass construction algorithm for self-healing asynchronous transfer mode networks under the guidance of Dr. K. C. Chang and Prof. Keith Chan 📘💡. Additionally, they graduated with distinction in Computer Science from the University of Minnesota, Twin Cities, USA (1993) 🎓💻.

Experience

With extensive experience in the tech industry, the individual served as a Senior Consultant at Oracle Systems Hong Kong Ltd (Aug 2000 – Sep 2003) 🏢, a Software Engineer at Skyworld Technology Ltd (Jun 1993 – May 1994) 💻, and a Consultant at the Microcomputer Laboratory, University of Minnesota (Sep 1991 – Mar 1993) 📊. Since Sep 2003, they have been a Lecturer at CPCE, PolyU 📚, and previously held roles as a Lecturer (Sep 1998 – Aug 2000) 👨‍🏫, Demonstrator (Sep 1996 – Aug 1998) 🔬, and Research Student (Jun 1994 – Jun 1996) 🎓 in the Department of Computing at PolyU.

Awards

With an illustrious career marked by numerous accolades and significant research contributions, I have received the Best Paper Awards in 2018, 2019, and 2023 🎉📚. I have successfully led and contributed to various high-impact projects, including those funded by the Quality Education Fund and CPCE 🏆💡. My roles have ranged from Associate Academic Director to Co-Investigator and Consultant, focusing on innovative technologies like AI, blockchain, and machine learning 🤖🔗. My work has significantly advanced educational technology and pedagogy, earning over HK$2 million in funding for projects aimed at improving learning experiences and outcomes 🎓💼.

Research focus

Simon Wong’s research focus is on the integration of blockchain technology in supply chain management, emphasizing sustainability. His work includes examining the adoption of blockchain integrated with cloud-based systems and machine learning to enhance sustainable practices in supply chains. Through critical literature reviews and case studies, Wong investigates the technical sustainability and implications of blockchain technology. His research aims to provide insights into the practical applications and benefits of blockchain for improving transparency, efficiency, and sustainability in supply chain operations. 🌐📦🔗📊🌿

Publication top notes

A Critical Literature Review on Blockchain Technology Adoption in Supply Chains

A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices

Technical Sustainability of Cloud-Based Blockchain Integrated with Machine Learning for Supply Chain Management

Sustainability of Blockchain Technology in Supply Chains: Implications from a Critical Literature Review