Summary

Prof. Haigen Hu is a seasoned academic and researcher with a Ph.D. in Control Theory and Engineering. With extensive experience in deep learning, computer vision, and medical image processing, he is currently serving as a professor at Zhejiang University of Technology. His work bridges academia and industry, having led multiple national and provincial research projects.

 Education

• Ph.D. in Control Theory and Control Engineering, Tongji University, Shanghai, China (2013)

• Postdoctoral Research, LITIS Laboratory, University of Rouen Normandy, France (Completed in 2019)

 Professional Experience

• Professor, College of Computer Science and Technology, Zhejiang University of Technology, China

• Former Postdoctoral Fellow, LITIS Laboratory, University of Rouen Normandy, France

Research Interests

• Deep Learning

• Computer Vision

• Medical Image Processing

 

 Summary

Ms. Beenish Khalid is a seasoned Research Software Engineer with a Ph.D. in Computer Engineering and over 10 years of experience in developing advanced software solutions. At Hecta Solutions, she has spearheaded projects in AI, machine learning, and web development, bringing cutting-edge innovations to life. With deep proficiency in numerous programming languages, database systems, and tools, she is passionate about technological advancement and collaborative innovation.

 Education

• Ph.D. in Computer Engineering – NUST, Islamabad (2025)

• MS in Computer Engineering – UET Taxila (2018)

• B.Sc. in Computer Engineering – UET Taxila (2015)

 Professional Experience

• Research Software Engineer – Hecta Solutions (2022–2024)
  Developed AI/ML-based BCI systems using React, Django, and TensorFlow.

• Junior Research Officer – Air Headquarters (2018–2021)
  Focused on cryptography, secure architectures, and defense-grade applications.

• Freelance Developer – Freelancer Site (2018)
  Delivered high-performance software using Assembly, C++, Python, and MATLAB.

• Software Developer (Intern) – National Development Center (2014)
  Built robust Android applications in Java using Eclipse IDE.

Research Interests

• Brain-Computer Interface (BCI) systems

• Artificial Intelligence & Deep Learning

• Cybersecurity and Secure Systems

• Signal Processing and EEG Compression

• Web Technologies and UI/UX Design

 

Early Academic Pursuits

Ms. Chetna Vaid Kwatra embarked on her academic journey with a strong foundation in engineering and technology. She earned a Diploma in Digital Electronics & Microprocessor System Design from Kasturba Polytechnic for Women, Delhi, in 2002. Building upon this, she pursued a B.Tech. in Information Technology from Maharaja Surajmal Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, in 2005. Her commitment to academic excellence led her to attain an M.Tech. in Computer Science Engineering from Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, in 2014, solidifying her expertise in computer science and engineering.

Professional Endeavors

Ms. Kwatra has amassed extensive experience in academia and industry. She commenced her career as a Software Engineer at L&T Infotech, Mumbai (2005–2007), gaining valuable industry exposure before transitioning into academia. Her teaching career spans multiple institutions, having served as an Assistant Professor in the Department of Computer Science & Engineering at Lovely Professional University in various tenures (2008–2015, 2017–2018, 2022–Present). She also contributed to Guru Nanak Dev University – Regional Campus, Jalandhar, as an Assistant Professor (2015–2016), shaping the next generation of engineers and researchers.

Contributions and Research Focus

Ms. Kwatra’s research is deeply rooted in Artificial Intelligence (AI), Network Security, and Cryptography, with a focus on Intrusion Detection Systems, Cybersecurity, and AI applications in Healthcare. Her contributions to Machine Learning in Cybersecurity and Healthcare have led to multiple high-impact publications. Her works include “Harnessing Ensemble Deep Learning Models for Precise Detection of Gynaecological Cancers” (2025) and “Early Detection of Gynecological Malignancies Using Ensemble Deep Learning Models: ResNet50 and Inception V3” (2025), showcasing her dedication to AI-driven healthcare solutions. Additionally, she has delved into cryptography and security, with significant publications such as “Exploring State-of-the-Art Cryptography: A Systematic Exploration of Advanced Approaches for IoT Device Authentication” (ICAIHC 2023) and “Artificial Intelligence Application for Security Issues and Challenges in IoT” (IC3I 2022).

Impact and Influence

Ms. Kwatra’s contributions to cybersecurity, AI, and healthcare research have made a profound impact on academic and professional communities. She has received notable recognitions and awards, including the Best Paper Award in Cloud Computing at ICRITO’14, Amity University, Noida. Her role as a committee member at the 7th International Joint Conference on Computing Sciences (ICCS-2023) reflects her influence in the research domain. She has also been acknowledged for conducting guest lectures, such as the Cyber Security session at Government ITI, Talwandi Choudrian, Kapurthala (2023), further extending her expertise beyond academia.

Academic Citations and Book Chapters

Her scholarly works have gained recognition in prestigious international journals and conferences. She has also contributed book chapters, such as “IoMT—Applications, Benefits, and Future Challenges in the Healthcare Domain” in Advances in Fuzzy-Based Internet of Medical Things (IoMT), 2024. With multiple papers published in IEEE and other reputed platforms, her research has significantly contributed to AI applications in cybersecurity and healthcare.

Technical Skills and Certifications

. Kwatra has continuously honed her technical proficiency through certifications and faculty development programs. She holds expertise in cryptography, machine learning, deep learning models, and cybersecurity. Her credentials include:

• Certifications in Cryptography (Asymmetric, Symmetric, Hash & Integrity) from the University of Colorado (2024)

• Faculty Development Programs in AI for IoT, Data Analysis, and AI Security Workshops (2022–2025)

• Completion of Deep Learning workshops and AI-based curriculum design programs

Teaching and Mentorship

Ms. Kwatra has been actively involved in teaching a variety of undergraduate and postgraduate courses, including Java Programming, C++, Computer Networks, Cybersecurity, Network Security & Cryptography, and Intrusion Detection Systems. With a passion for mentoring, she has successfully guided three dissertation students, fostering innovation and critical thinking among aspiring researchers.

Legacy and Future Contributions

With an impressive career spanning academia, research, and industry, Ms. Kwatra continues to drive innovation in AI, cybersecurity, and healthcare applications. Her commitment to research, teaching, and professional training sets a benchmark for future scholars and researchers. As she advances in her academic pursuits, her contributions to AI-driven disease detection, cryptography, and security systems are poised to make lasting impacts in both research and real-world applications.

 

Early Academic Pursuits

Dr. Meng Wang embarked on his academic journey at Xidian University, earning a Bachelor of Science in Communication Engineering in 2004. He later pursued a Master of Engineering in Software Engineering from Xi’an Jiaotong University under the guidance of Prof. Xiaojun Wu in 2012. His passion for artificial intelligence and data science led him to complete his PhD in Computer Science at Xidian University in 2018, where he was mentored by Prof. Jiangtao Cui and Prof. Hui Li.

Professional Endeavors

Dr. Wang has had a progressive academic career at Xi’an Polytechnic University. He joined as an Assistant Professor in 2019 and quickly advanced to Associate Professor in 2023. His leadership capabilities were recognized, and he was appointed Deputy Dean of the School of Computer Science in 2024. Additionally, since 2020, he has been actively supervising Master’s students, guiding them in cutting-edge research areas.

Contributions and Research Focus

Dr. Wang’s research spans artificial intelligence, deep learning, time series analysis, and spatio-temporal big data. His work focuses on optimizing data mining and management techniques to enhance computational efficiency in large-scale systems. His contributions to intelligent scheduling, power load forecasting, and competitive location selection have led to significant advancements in AI-driven decision-making processes.

Impact and Influence

His research excellence has been acknowledged with numerous prestigious awards. Notably, he received the “Sa Shixuan” Best Paper Award at the 40th CCF National Database Conference in 2023, the Rising Star Award by ACM China in 2020, and the Best Paper Award Runner-Up at IEEE MDM 2019. He also earned the Natural Science Award of Shaanxi Province (Second Prize) in 2020 for his impactful contributions to AI-driven data analysis.

Academic Cites

Dr. Wang has an extensive publication record in top-tier journals and conferences, including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Intelligent Systems and Technology (TIST), and Bioinformatics. His research has been widely cited, underscoring the influence of his work in AI, data science, and computational intelligence. His recent contributions include innovative methods for electricity theft detection, time series forecasting, and multi-view graph neural networks for biomedical applications.

Technical Skills and Expertise

With a strong technical foundation, Dr. Wang specializes in AI model development, deep learning frameworks, spatio-temporal data processing, and intelligent scheduling algorithms. His expertise also extends to data-driven optimization techniques, predictive analytics, and big data management, making his research highly applicable to real-world AI challenges.

Teaching and Mentorship

Dr. Wang is committed to academic excellence, having received multiple teaching awards, including the “Light of Textile” Higher Education and Teaching Achievement Award (First Prize) in 2023. He also won the Excellence Award at the Shaanxi Undergraduate Colleges’ Classroom Teaching Innovation Competition in 2022. His ability to integrate research with education has made a profound impact on his students and the broader academic community.

Legacy and Future Contributions

As a leading AI researcher, Dr. Wang continues to push the boundaries of artificial intelligence, big data, and predictive analytics. His ongoing research in power load forecasting, tariff prediction, and intelligent scheduling is poised to revolutionize energy and data management industries. Through his leadership at Xi’an Polytechnic University, he aims to cultivate the next generation of AI experts, driving technological advancements for a smarter and more data-driven future.

 

Early Academic Pursuits

Dr. Sageengrana is currently pursuing a Ph.D. in Information and Communication Engineering part-time at Anna University, Chennai, with his thesis already submitted. He completed his Master of Engineering (M.E.) in Multimedia Technology from Anna University, where he secured a CGPA of 9.0, earning the distinction of University Second Rank. His journey in academia began with a Bachelor of Engineering (B.E.) in Computer Science and Engineering from the Institute of Road and Transport Technology, Erode, graduating with a First Class CGPA of 8.28. Dr. Sageengrana’s academic foundation was built upon solid early education, with a Higher Secondary School percentage of 83% and Secondary School Leaving percentage of 84.2%.

Professional Endeavors

With over 10 years of professional experience, Dr. Sageengrana has made significant contributions to the field of Computer Science and Engineering. He has served as an Assistant Professor in various esteemed institutions, including VelTech High Tech Dr. Rangarajan Dr. Sankuthala Engineering College, Adhi College of Engineering and Technology, Sri Sairam Institute of Technology, and currently, Sathyabama Institute of Science and Technology. Throughout his career, he has taught diverse subjects like Python Programming, Deep Learning, Compiler Design, Computer Networks, Operating Systems, and more, shaping the minds of the next generation of engineers.

Contributions and Research Focus

Dr. Sageengrana’s research interests are deeply rooted in Data Structures, Object-Oriented Programming, Cloud Computing, Big Data, Deep Learning, and Augmented Reality. His contributions include several publications and patents, particularly in the fields of machine learning, deep learning, and AI applications. Notably, his work on “Optimized RB-RNN: Development of Hybrid Deep Learning for Analyzing Student’s Behaviors in Online Learning Using Brain Waves and Chatbots” was featured in the Expert Systems with Applications journal in 2024, with an impact factor of 8.67. Furthermore, he has contributed to several book chapters on AI and machine learning topics, focusing on applications in finance, cyber security, and environmental intelligence.

Impact and Influence

Dr. Sageengrana has made a profound impact through his research and collaborations. His funded project on “Smart Medication Management and Dispensing System” received ₹55,000 in seed funding from Sathyabama University. Additionally, he has worked closely with the Metropolitan Transport Corporation to develop a purchase order website, showcasing his practical expertise. His innovative patents, such as the design of an unmanned aerial vehicle for emergency medication delivery and systems for water quality management, address pressing global issues and demonstrate his potential for transformative contributions to society.

Academic Cites

Dr. Sageengrana’s scholarly work has garnered significant attention in the academic community, with publications indexed in reputable databases like Scopus. His papers, such as the one on “Automatic Face Recognition System to Mark Student Attendance Using Convolutional Neural Networks” and “Smart Phone-Based Bidirectional Communication System for Hearing & Speaking Impaired Community,” have been presented at international conferences and published in high-impact journals, further elevating his profile in the research world.

Technical Skills

• Programming Languages: Python, C, C++, Java, JavaScript, HTML, CSS

• Operating Systems: Windows, UNIX, Linux

• Database Management: MySQL

• Research Tools & Techniques: Machine Learning, Deep Learning, Augmented Reality, Virtual Reality, Cloud Computing, Big Data

Teaching Experience

Dr. Sageengrana’s teaching career spans a wide range of subjects, from foundational topics like Programming in C to advanced fields such as Deep Learning and Augmented Reality. His commitment to high-quality education is reflected in his participation in various faculty development programs (FDPs) on cutting-edge topics such as Quantum Computing, Machine Learning, and Design Thinking. He has delivered guest lectures on emerging technologies like Deep Learning and Neural Networks, fostering deeper understanding among students and peers alike.

Legacy and Future Contributions

Dr. Sageengrana’s dedication to education, research, and innovation positions him as a key figure in the fields of Computer Science and Engineering. His legacy will likely continue through his mentorship, his influential publications, and his future contributions to AI, deep learning, and smart technologies. As he progresses in his Ph.D. and furthers his research on intelligent systems, his work will likely shape the next generation of tech innovations, especially in online learning, healthcare, and environmental management.

Professional Memberships

• Life Member of ISTE (Indian Society for Technical Education)

• Life Member of CSTA (Computer Science Teachers Association)

• Life Member of CSI (Computer Society of India)

• IEEE Member (Institute of Electrical and Electronics Engineers)

 

 Early Academic Pursuits

Mr. Hailong Ning embarked on his academic journey with a strong foundation in signal and information processing. He earned his Ph.D. from the University of Chinese Academy of Sciences (2016–2021), where he focused on advanced artificial intelligence techniques for remote sensing image analysis. His research during this period laid the groundwork for innovative AI-driven methodologies in multimodal data processing.

 Professional Endeavors

Currently serving as an Associate Professor and Director of the Software Engineering Department at Xi’an University of Posts and Telecommunications, Mr. Hailong Ning leads the Intelligent Security Innovation Team. He has been instrumental in pioneering AI-based remote sensing applications, supervising master’s students, and collaborating with prestigious institutions like Northwestern Polytechnical University and the German Aerospace Center (DLR). His leadership extends to national projects funded by entities such as the National Natural Science Foundation of China (NSFC) and industry collaborations in defense, energy, and aerospace.

 Contributions and Research Focus

Mr. Hailong Ning’s research spans artificial intelligence, federated learning, and multimodal data processing, with a particular emphasis on remote sensing image analysis. His innovative hierarchical modeling techniques have addressed critical challenges in aerial image interpretation, cross-domain distribution shifts, and federated domain generalization. His work has significant implications for privacy-preserving AI applications and large-scale image recognition tasks in security and defense.

 Impact and Influence

Mr. Hailong Ning’s contributions extend beyond academia, with his patented AI technologies being deployed in military surveillance, including border security operations in Tibet, and smart city systems. His research outcomes have been adopted for intelligent UAV monitoring and real-time security analysis, processing over one million images. His impact is also evident in his role as a mentor, having guided 26 graduate students toward academic and industry success.

 Academic Citations and Publications

• “Semantics-Consistent Representation Learning for Remote Sensing Image-Voice Retrieval” – IEEE Transactions on Geoscience and Remote Sensing (IF: 7.5, Citations: 39)
• “Disentangled Representation Learning for Cross-modal Biometric Matching” – IEEE Transactions on Multimedia (IF: 8.4, Citations: 33)
• “Difference Enhancement and Spatial-Spectral Non-Local Network for Change Detection in VHR Remote Sensing Images” – IEEE Transactions on Geoscience and Remote Sensing (IF: 7.5, Citations: 76)

 Technical Skills

• Programming: Python, MATLAB, C++
• AI & ML Frameworks: TensorFlow, PyTorch
• Remote Sensing Software: ENVI, ERDAS, Google Earth Engine
• Security & Surveillance Systems: Federated Learning, AI-based anomaly detection

 Teaching and Mentorship

As a dedicated educator, Mr. Hailong Ning supervises graduate students in AI and software engineering, emphasizing real-world problem-solving through research-driven learning. He has designed and taught courses on Deep Learning for Remote Sensing, AI-Driven Image Processing, and Multimodal Data Fusion. His mentorship has empowered students to secure research grants and high-impact publications.

 Legacy and Future Contributions

Mr. Hailong Ning’s ongoing projects focus on intelligent UAV monitoring, privacy-enhanced federated learning, and AI-driven security systems. His commitment to AI innovation is reflected in his role as an editorial board member for Remote Sensing Technology and as a reviewer for IEEE TCYB, IEEE TNNLS, and other leading journals. Moving forward, he aims to bridge theoretical AI advancements with large-scale industrial applications, fostering AI solutions for smart surveillance, environmental monitoring, and national security.

 

Summary

 

Education

Dr. Lei Xiang holds a Ph.D. in Biomedical Engineering from Shanghai Jiao Tong University (2019). He earned his Master’s degree in System Science from Nanjing University of Information Science & Technology (2015) and a Bachelor’s degree in Systems Engineering from the same university (2012).

Professional Experience

Dr. Xiang is currently the Chief Technology Officer (CTO) at Subtle Medical, Shanghai, a leading company in AI-driven medical imaging solutions. Previously, he worked as a Senior Deep Learning Scientist at Subtle Medical, Menlo Park, USA, where he developed advanced deep learning models for medical image enhancement, focusing on PET, MRI, CT, and SPECT imaging.

He has also gained research experience as an Intern at SenseTime (Shanghai), working on automated pelvis segmentation models, and as a Visiting Scholar at the University of North Carolina at Chapel Hill and Shenzhen Institutes of Advanced Technology.

Research & Publications

Dr. Xiang has an extensive research background in deep learning for medical imaging, with over 13 journal papers and 9 conference papers published in top-tier venues, including Medical Image Analysis, IEEE Transactions on Medical Imaging, Neurocomputing, and Pattern Recognition. His research focuses on multi-modal medical image reconstruction, deep learning-based PET/MRI enhancement, and AI-driven medical diagnostics.

Patents, Honors & Awards

Dr. Xiang holds 10+ international patents, including technologies for SubtlePET, SubtleMR, SubtleSPECT, SubtleCT, and SubtleSpine. He has won numerous prestigious awards, including:

• First Place in the Ultra-low Dose PET Imaging Challenge (UDPET, 2022)
• 1st Rank in the FastMRI Challenge Public Leaderboard (2020)
• 3rd Prize in Spark: “Digital Body” AI Challenge (2020)
• 2nd Rank in the Robotic Scene Segmentation Challenge (2018, MICCAI)
• 1st Prize in the National Postgraduate Mathematic Contest in Modeling (2016)
• Outstanding Graduate Award for both Undergraduate and Graduate studies

Professional Services

Dr. Xiang actively contributes to the AI and medical imaging community as a conference reviewer for major AI and medical imaging conferences, including CVPR, ICCV, MICCAI, MIDL, ECCV, and WACV.

Research Interests

• AI-powered medical image enhancement
• Deep learning for multi-modal fusion in medical imaging
• PET/MRI/CT reconstruction and noise-aware image processing
• Automated diagnostics and segmentation in medical imaging