QIANG QU | Artificial Intelligence Award | Best Researcher Award

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Prof. QIANG QU | Artificial Intelligence Award | Best Researcher Award

PROFESSOR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Dr. Qiang Qu is a distinguished professor and a leading researcher in blockchain, data intelligence, and decentralized systems. He serves as the Director of the Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Additionally, he holds a professorship at Shenzhen University of Advanced Technology and has previously served as a guest professor at The Chinese University of Hong Kong (Shenzhen). Dr. Qu has also contributed as the Director and Chief Scientist of Huawei Blockchain Lab. With a strong international academic presence, he has held research positions at renowned institutions such as ETH Zurich, Carnegie Mellon University, and Nanyang Technological University. His pioneering work focuses on scalable algorithm design, data sense-making, and blockchain technologies, making significant contributions to AI, data systems, and interdisciplinary studies.

Publication Profile

๐ŸŽ“ Education

Dr. Qiang Qu earned his Ph.D. in Computer Science from Aarhus University, Denmark, under the supervision of Prof. Christian S. Jensen. His doctoral research was supported by the prestigious GEOCrowd project under Marie Skล‚odowska-Curie Actions. He further enriched his academic journey as a Ph.D. exchange student at Carnegie Mellon University, USA. He holds an M.Sc. in Computer Science from Peking University, China, and a B.S. in Management Information Systems from Dalian University of Technology.

๐Ÿ’ผ Experience

Dr. Qu has a diverse professional background, reflecting his global expertise. Since 2016, he has been a professor at SIAT, leading groundbreaking research in blockchain and distributed IoT security. He also served as Vice Director of Hangzhou Institutes of Advanced Technology (SIATโ€™s Hangzhou branch). Prior to this, he was an Assistant Professor and the Director of Dainfos Lab at Innopolis University, Russia. His research journey includes being a visiting scientist at ETH Zurich, a visiting scholar at Nanyang Technological University, and a research fellow at Singapore Management University. He also gained industry experience as an engineer at IBM China Research Lab.

๐Ÿ… Awards and Honors

Dr. Qu has received several national and international research grants, recognizing his impactful contributions to blockchain and AI-driven data intelligence. He is a prominent editorial board member of the Future Internet Journal and serves as a guest editor for multiple high-impact journals. As an active contributor to the research community, he has been a TPC (Technical Program Committee) member for prestigious conferences and regularly reviews top-tier AI and data systems journals.

๐Ÿ”ฌ Research Focus

Dr. Quโ€™s research interests revolve around data intelligence and decentralized systems, with a strong focus on blockchain, scalable algorithm design, and data-driven decision-making. His work has been instrumental in developing efficient data parallel approaches, AI-driven network analysis, and cross-blockchain data migration techniques. His interdisciplinary contributions bridge AI, IoT security, and geospatial analytics, driving innovation in secure and intelligent computing.

๐Ÿ”š Conclusion

Dr. Qiang Qu stands as a thought leader in blockchain and data intelligence, combining academic excellence with real-world impact. His contributions to AI-driven decentralized systems and scalable data solutions continue to shape the fields of computer science and IoT security. His extensive research collaborations, editorial roles, and international experience make him a key figure in advancing secure and intelligent computing technologies. ๐Ÿš€

๐Ÿ“š Publications

SNCA: Semi-supervised Node Classification for Evolving Large Attributed Graphsย โ€“ IEEE Big Data Mining and Analytics (2024). Cited in IEEE ๐Ÿ“–

CIC-SIoT: Clean-Slate Information-Centric Software-Defined Content Discovery and Distribution for IoTย โ€“ IEEE Internet of Things Journal (2024). Cited in IEEE ๐Ÿ“–

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computingย โ€“ IEEE Journal on Selected Areas in Communications (2022). Cited in IEEE ๐Ÿ“–

On Time-Aware Cross-Blockchain Data Migrationโ€“ Tsinghua Science and Technology (2024). Cited in Tsinghua University ๐Ÿ“–

Few-Shot Relation Extraction With Automatically Generated Promptsย โ€“ IEEE Transactions on Neural Networks and Learning Systems (2024). Cited in IEEE ๐Ÿ“–

Opinion Leader Detection: A Methodological Reviewย โ€“ Expert Systems with Applications (2019). Cited in Elsevier ๐Ÿ“–

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classificationโ€“ IEEE Transactions on Affective Computing (2021). Cited in IEEE ๐Ÿ“–

Efficient Online Summarization of Large-Scale Dynamic Networks – ย IEEE Transactions on Knowledge and Data Engineering (2016). Cited in IEEE ๐Ÿ“–

Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

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Dr. Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Postdoc Fellow, Inje University, South Korea

๐ŸŽ“ Sikandar Ali is a passionate AI researcher and educator specializing in Artificial Intelligence applications in healthcare. Currently pursuing a PhD at Inje University, South Korea, he has a strong academic background and extensive research experience in digital pathology, medical imaging, and machine learning. As a team leader of the digital pathology project, he develops innovative AI algorithms for cancer diagnosis while collaborating with a global team of researchers. Sikandar is a recipient of prestigious scholarships, accolades, and recognition for his contributions to AI and healthcare innovation.

Publication Profile

Google Scholar

Education

๐Ÿ“˜ Sikandar Ali holds a PhD in Artificial Intelligence in Healthcare (CGPA: 4.46/4.5) from Inje University, South Korea, where his thesis focuses on integrating pathology foundation models with weakly supervised learning for gastric and breast cancer diagnosis. He earned an MS in Computer Science from Chungbuk National University, South Korea (GPA: 4.35/4.5), with research on AI-based clinical decision support systems for cardiovascular diseases. His undergraduate degree is a Bachelor of Engineering in Computer Systems Engineering from Mehran University of Engineering and Technology, Pakistan, with a CGPA of 3.5/4.0.

Experience

๐Ÿ’ป Sikandar is an experienced researcher and AI specialist. Currently working as an AI Research Assistant at Inje University, he focuses on cutting-edge projects in digital pathology, cancer detection, and medical imaging. Previously, he worked as a Research Assistant at Chungbuk National University, focusing on cardiovascular disease diagnosis using AI. His industry experience includes roles such as Search Expert at PROGOS Tech Company and Software Developer Intern at Hidaya Institute of Science and Technology.

Awards and Honors

๐Ÿ† Sikandar has received multiple awards, including the Brain Korean Scholarship, European Accreditation Council for Continuing Medical Education (EACCME) Certificate, and recognition as an outstanding Teaching Assistant at Inje University. He has also earned full travel grants for international conferences, extra allowances for R&D industry projects, and certificates for reviewing research papers in leading journals. Additionally, he is a Guest Editor at Frontiers in Digital Health.

Research Focus

๐Ÿ”ฌ Sikandarโ€™s research focuses on developing AI algorithms for medical imaging, with expertise in weakly supervised learning, self-supervised learning, and digital pathology. His projects include designing AI systems for cancer detection, COVID-19 prediction, and IPF severity classification. He also works on object detection applications using YOLO models and wearable sensor-based activity detection for pets. His commitment to explainability and interpretability in AI models ensures their practical utility in healthcare.

Conclusion

๐ŸŒŸ Sikandar Ali is a dedicated AI researcher driving innovation in healthcare through artificial intelligence. With his strong educational foundation, diverse research experience, and impactful contributions, he aims to bridge the gap between AI and medicine, making healthcare more efficient and accessible.

Publications

Detection of COVID-19 in X-ray Images Using DCSCNN
Sensors 2022, IF: 3.4

A Soft Voting Ensemble-Based Model for IPF Severity Prediction
Life 2021, IF: 3.2

Metaverse in Healthcare Integrated with Explainable AI and Blockchain
Sensors 2023, IF: 3.4

Weakly Supervised Learning for Gastric Cancer Classification Using WSIs
Springer 2023

Classifying Gastric Cancer Stages with Deep Semantic and Texture Features
ICACT 2024

Computer Vision-Based Military Tank Recognition Using YOLO Framework
ICAISC 2023

Activity Detection for Dog Well-being Using Wearable Sensors
IEEE Access 2022

Cat Activity Monitoring Using Wearable Sensors
IEEE Sensors Journal 2023, IF: 4.3

Deep Learning for Algae Species Detection Using Microscopic Images
Water 2022, IF: 2.9

Comprehensive Review on Multiple Instance Learning
Electronics 2023

Hybrid Model for Face Shape Classification Using Ensemble Methods
Springer 2021

Cervical Spine Fracture Detection Using Two-Stage Deep Learning
IEEE Access 2024

 

Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

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Ms. Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

LIMS Junior Developer, ALS Group USA, Corp., United States

Deekshitha Kosaraju is an accomplished Computer Science graduate from The University of Texas at Dallas, with a strong academic foundation and technical expertise in a variety of programming languages, frameworks, and cloud technologies. Her expertise spans Java, Python, JavaScript, and R, among others. Deekshitha is currently working as a Junior Developer at ALS Group USA, where she focuses on improving data integration and system efficiency. She is passionate about cloud computing, machine learning, and AI, and has published several papers on cutting-edge AI techniques, including explainable AI and quantum computing integration. ๐ŸŽ“๐Ÿ‘ฉโ€๐Ÿ’ป๐Ÿ“š

Publication Profile

Google Scholar

Education

Deekshitha Kosaraju graduated with a Bachelor of Science in Computer Science from The University of Texas at Dallas, maintaining a GPA of 3.6/4.0. During her time at university, she was honored with the Academic Excellence Scholarship. Her coursework included a wide range of subjects such as Data Structures, Machine Learning, Software Engineering, and Operating Systems. ๐ŸŽ“๐Ÿ†

Experience

Deekshitha has gained invaluable professional experience through internships and full-time roles. Currently, she works as a Junior Developer at ALS Group USA, where she contributes to streamlining workflows, automating processes, and improving data transfer efficiency. She has previously interned at Radiant Digital, where she worked on low-code platforms and developed mobile applications that enhanced field coordination. In addition, her experience at Pearson as a Software Engineer Intern allowed her to improve user engagement and business outcomes through AI-driven applications. ๐Ÿ’ผ๐Ÿ’ป

Awards and Honors

Deekshitha was awarded the Academic Excellence Scholarship during her time at The University of Texas at Dallas. Her achievements in academic and professional arenas reflect her dedication to excellence and innovation in the field of computer science. ๐ŸŒŸ๐Ÿ…

Research Focus

Deekshithaโ€™s research primarily focuses on Artificial Intelligence, with specific attention to explainable AI, zero-shot learning, meta-learning, reinforcement learning, and AI’s integration with cloud computing and quantum technologies. She is also interested in exploring the applications of AI in various domains, such as healthcare and data analytics. Her research contributions include exploring how AI can enhance big data analytics and cloud computing innovations. ๐Ÿค–๐Ÿ“Š

Conclusion

With a diverse set of technical skills and a passion for advancing AI and cloud technologies, Deekshitha Kosaraju continues to make impactful contributions to the field of Computer Science. She remains committed to expanding her knowledge in AI and exploring innovative solutions to real-world problems. ๐ŸŒ๐Ÿš€

Publications :

Shedding light on AI: exploring explainable AI techniques
International Journal of Research and Review, 2020
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Zero-Shot learning: teaching AI to understand the unknown
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20211161

How meta learning enhances reinforcement learning in AI
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20210706

Crossing domains: the role of transfer learning in rapid AI prototyping and deployment
International Journal of Science & Healthcare Research, 2021
DOI: 10.52403/ijshr.20210464

Artificial intelligence in cloud computing: enhancements and innovations
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20211010

Quantum computing and artificial intelligence: a fusion poised to transform technology
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20210974

The role of artificial intelligence in enhancing big data analytics
Galore International Journal of Applied Sciences and Humanities, 2021

sajjad qureshi | Artificial Intelligence Award | Computer Vision Contribution Award

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Dr. sajjad qureshi | artificial intelligence award | Computer Vision Contribution Award

deputy Director (IT), multan electric power company, Pakistan

๐Ÿ“˜ Dr. Sajjad Hussain Qureshi is a seasoned professional with over 21 years of experience in Information Technology. Currently serving as the Deputy Director (IT) at Multan Electric Power Company for the past two decades, Dr. Qureshi has established expertise in system analysis and design, machine learning, cybersecurity, IT auditing, and project management. His multidisciplinary educational background, including a Ph.D. in Computer Science, Ph.D. in Agriculture, and an MBA in Human Resource Management, has enabled him to excel in IT-based quality assurance, data analysis, and human resource management. He is passionate about utilizing cutting-edge technologies to create impactful solutions in diverse domains.

Publication Profile

ORCID

Education

๐ŸŽ“ Dr. Qureshi holds a Ph.D. in Computer Science, a Ph.D. in Agriculture, and an MBA in Human Resource Management. His diverse academic achievements reflect his commitment to integrating IT with interdisciplinary knowledge for impactful research and practical solutions.

Experience

๐Ÿ’ผ Dr. Qureshi has an extensive career spanning over 21 years in the IT field. For the last 20 years, he has been a cornerstone of the Multan Electric Power Company, where he serves as the Deputy Director (IT). His experience covers IT infrastructure development, LAN networks, client/server environments, cybersecurity, and customer billing systems. He also excels in IT-based quality assurance and control, project management, and data management.

Research Interests

๐Ÿ” Dr. Qureshiโ€™s research interests lie at the intersection of machine learning, deep learning, cybersecurity, data management, and agriculture technology. He is particularly interested in applying advanced computational techniques to solve real-world problems, such as disease detection in crops and classification of linguistic data.

Awards

๐Ÿ† Dr. Qureshi has been recognized for his contributions to IT and interdisciplinary research, achieving notable academic and professional milestones. His work continues to inspire innovation and collaboration across diverse fields.

Publications

Classification of English Words into Grammatical Notations Using Deep Learning Technique (2024)
Imran, M., Qureshi, S. H., Qureshi, A. H., & Almusharraf, N.
Published in Information, 15(12), 801.
DOI: 10.3390/info15120801

Rational Study Of The Use Of Computer-Aided Softwares By The Composers (2024)
Qureshi, S. H., Javaid, S., & Javaid, M. A.
Published in Pakistan Journal of Society, Education and Language, 10(2), 213โ€“219.

Comparison of Conventional and Computer-Based Detection of Severity Scales of Stalk Rot Disease in Maize (2024)
Qureshi, S.H., Khan, D.M., Razzaq, A., Baig, M.M., & Bukhari, S.Z.A.
Published in SABRAO Journal of Breeding and Genetics, 56(1), 292โ€“301.
DOI: 10.54910/sabrao2024.56.1.26

Intelligent Resistant Source Detection Against Stalk Rot Disease of Maize Using Deep Learning Technique (2023)
Qureshi, S.H., Khan, D.M., & Bukhari, S.Z.
Published in SABRAO Journal of Breeding and Genetics, 55(6), 1972โ€“1983.