Xu Liu | Statistics | Best Researcher Award

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Prof. Xu Liu | Statistics | Best Researcher Award

Prof. Xu Liu, Shanghai University of Finance and Economics, China

Prof. Xu Liu, a tenured Full Professor at the School of Statistics and Management, Shanghai University of Finance and Economics (SUFE), is a distinguished expert in high-dimensional data analysis, statistical genetics, and AI-enabled decision-making. With a Ph.D. in Statistics from Yunnan University and postdoctoral training from Northwestern University and Michigan State University, he brings extensive international research experience. His work focuses on machine learning, transfer learning, and deep generative models, with numerous publications in top-tier journals. Prof. Liu actively contributes to academic leadership as an editor and conference organizer, making him a key figure in the field of modern statistics and data science.

Author Profile

Scopus

🎓 Early Academic Pursuits

Prof. Xu Liu’s academic journey is rooted in a deep passion for mathematics and statistics. His formal education began with a Bachelor of Science in Mathematics from Hengyang Normal University (2000–2004). Demonstrating early excellence and commitment to the field, he pursued a Master of Science in Mathematics at Yunnan University, graduating in 2007. His intellectual drive and mathematical acumen culminated in a Ph.D. in Statistics from the same institution in 2011. These formative years laid a strong theoretical foundation and cultivated his research interests in complex data structures, machine learning, and statistical inference.

During his graduate studies, Prof. Liu delved into challenging problems in mathematical modeling, statistical theory, and early explorations in computational statistics, developing an academic rigor that continues to define his work today.

👨‍🏫 Professional Endeavors

Following the completion of his doctoral studies, Prof. Liu embarked on a postdoctoral research path that would span internationally recognized institutions. From 2011 to 2013, he was a postdoctoral researcher in the Department of Statistics at Northwestern University, followed by another impactful postdoc position in the Department of Statistics and Probability at Michigan State University (2013–2016). These roles enabled him to collaborate with prominent statisticians and immerse himself in cutting-edge research on high-dimensional inference and statistical learning.

In 2016, Prof. Liu joined the School of Statistics and Management at Shanghai University of Finance and Economics (SUFE) as an Assistant Professor. Over the years, his academic progression has been steady and well-earned—rising to Associate Professor (2019), achieving tenure in 2022, and recently being promoted to Full Professor in 2024.

His professional tenure at SUFE reflects both his teaching excellence and research productivity, positioning him as a pillar of the department and a leader in statistical education.

📊 Contributions and Research Focus

Prof. Xu Liu’s research contributions span a diverse range of statistical and data science disciplines:

  • High-dimensional data analysis

  • Machine learning and deep generative models

  • Representation and transfer learning

  • Statistical genetics and gene-environment interactions (G×E and G×G)

  • Advanced variable selection methods

  • Uncertainty quantification and statistical inference

His recent publications appear in prestigious journals like Statistics in Medicine, Journal of Multivariate Analysis, Bioinformatics, Journal of Computational and Graphical Statistics, and Statistics in Biosciences. These works include the development of tools such as the Python-based REGS sampler and the R-package qfabs, demonstrating a strong commitment to open-source statistical computing and reproducible science.

He is also contributing significantly to academic literature with three upcoming books focused on AI decision-making and high-dimensional statistical inference, all scheduled for release in 2025.

🏅 Accolades and Recognition

Prof. Liu’s excellence in research and academic contribution has earned him significant accolades:

  • Outstanding Achievement Award of Philosophy and Social Science, Shanghai (2023)

  • Third Prize, 17th “Challenge Cup” Shanghai Science and Technology Competition (2022)

These awards underscore his relevance not only in theoretical statistics but also in its impactful applications across economics, social science, and public policy.

Moreover, his professional recognition extends to editorial roles. He serves as:

  • Associate Editor, Journal of Statistical Theory and Applications

  • Associate Editor, International Journal of Organizational and Collective Intelligence

  • Guest Editor, Special Issue in Axioms on Mathematical and Statistical Finance

🌐 Impact and Influence

Prof. Liu’s influence transcends national boundaries through his international collaborations, open-source software contributions, and thought leadership in machine learning applications in health, finance, and genomics. His work on gene-environment interactions is particularly impactful in the area of statistical genetics, with real-world implications for personalized medicine and epidemiological modeling.

As an educator, Prof. Liu has taught a wide spectrum of courses—from foundational subjects like Mathematical Statistics to advanced topics such as Empirical Process Theory and Computer Programming in C/C++. His mentoring of students and junior researchers helps foster the next generation of statisticians and data scientists.

🌟 Legacy and Future Contributions

With a growing legacy built on innovation, scholarship, and mentorship, Prof. Xu Liu stands at the forefront of modern statistical science in China. His upcoming books will serve as valuable references in both academic and applied settings. As a conference organizer and invited speaker, he continues to shape conversations around statistical learning, AI in economics, and computational statistics.

Looking ahead, his work promises deeper integration of AI-enabled modeling, data-driven decision-making, and ethical data science, especially in public health, policy, and business analytics.

✍️Publication Top Notes

📘Subgroup testing in the change-plane Cox model.

Author: Zhang, X., Ren, P., Shi, X. Ma, S. and Liu, X

Journal: Statistics in Medicine

Year: 2025

📘Random projection-based response best-subset selector for ultra-high dimensional multivariate data

Author: Hu, J., Li, T., Liu, X. and Liu, X

Journal: Multivariate Analysis

Year: 2025

📘Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

Author: Tan, X., Zhang, X., Cui, Y. and Liu, X.

Journal: Bioinformatics

Year: 2024

 

Mehran Pourvahab | Data Science | Best Researcher Award | 13254

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Dr. Mehran Pourvahab | Data Science | Best Researcher Award

Dr. Mehran Pourvahab, University of Beira Interior, Portugal

Dr. Mehran Pourvahab is a researcher, developer, educator, and network engineer affiliated with the University of Beira Interior (UBI), Portugal. An IEEE Senior Member, he specializes in Medical Informatics, Data Science, Machine Learning, Cloud Security, Software-defined Networking, and Blockchain Technology. He holds a Ph.D. in Computer Engineering from Azad University, Iran, and has completed one postdoctoral position at UBI’s C4 – Cloud Computing Competences Centre. He is currently pursuing a second postdoc at ALLab, focusing on AI-driven predictive algorithms in Medical Informatics. As Data and Project Manager at HULTIG for the Phara-On project, he contributes to ICT solutions for ageing populations. Dr. Pourvahab has extensive teaching, consulting, and research experience, with numerous publications and contributions to academia and industry.

Profile

Orcid

Scopus

🎓 Early Academic Pursuits

Dr. Mehran Pourvahab’s journey into the world of computer engineering began with a solid foundation in academia. His early education equipped him with a deep understanding of computing principles, networking, and software systems. Driven by a passion for technological innovation, he pursued higher education in computer engineering, culminating in a Ph.D. from Azad University, Iran. His doctoral research, which focused on collecting reliable legal evidence in cloud network forensics, was groundbreaking and earned him top honors with a perfect thesis evaluation of 20/20.

After earning his doctorate, Dr. Pourvahab sought to deepen his expertise further through postdoctoral research. His first postdoctoral position at the University of Beira Interior (UBI) in Portugal, under the C4 – Cloud Computing Competences Centre, allowed him to explore the intersection of machine learning, data science, and medical informatics. His research aimed to develop a predictive and monitoring platform for cardiology and neurodegenerative diseases, leveraging AI and data analytics to improve healthcare outcomes.

Currently, he is pursuing a second postdoctoral research position at UBI’s Assisted Living Computing and Telecommunications Laboratory (ALLab). His work here continues to push the boundaries of medical informatics, AI-driven predictive algorithms, and knowledge-based systems that enhance healthcare solutions.

💼 Professional Endeavors

Dr. Pourvahab’s professional journey is a testament to his commitment to both academic excellence and industry innovation. His expertise spans multiple domains, including:

✅ Medical Informatics
✅ Data Science & Machine Learning
✅ Cloud Security & Forensics
✅ Software-Defined Networking (SDN)
✅ Blockchain Technology

Beyond academia, he has made significant contributions to the networking and cybersecurity industry. As a senior network engineer, he holds multiple professional certifications, including:

🔹 MikroTik Certified Routing Engineer
🔹 MikroTik Certified Wireless Engineer
🔹 Cyberoam Certified Network & Security Professional

Dr. Pourvahab also played a key role in establishing MehranNet, an Internet Service Provider (ISP) founded in 2001, where he continues to serve as a board member. His entrepreneurial vision and leadership have helped shape the company into a recognized name in the networking sector.

In the academic sphere, he has served as a faculty member at Azad University, Langarud Branch, where he mentored students in various fields of computer science. His courses have covered topics such as algorithm design, database systems, networking, and programming languages. He has also supervised Ph.D. and master’s students, providing guidance on cutting-edge research topics.

🔬 Contributions and Research Focus

Dr. Pourvahab has been actively engaged in cutting-edge research, particularly at the intersection of AI, cloud computing, cybersecurity, and medical informatics. Some of his notable research contributions include:

📊 Data Science & AI in Healthcare

At UBI’s ALLab, Dr. Pourvahab is pioneering AI-driven predictive models that assist in early diagnosis and monitoring of medical conditions. His research is crucial in areas such as:
✔ Cardiology – Predicting heart disease risks using AI-based modeling
✔ Neurodegenerative Disorders – Developing smart monitoring systems for Alzheimer’s and Parkinson’s disease
✔ Behavioral Health – Enhancing mental health assessments using data analytics

🛡️ Cloud Forensics & Cybersecurity

His Ph.D. research in cloud forensics provided a framework for collecting reliable legal evidence in cloud networks. This work is essential for:
✔ Strengthening digital forensics methodologies
✔ Enhancing legal evidence collection in cybercrime cases
✔ Improving security measures in cloud computing environments

🔗 Blockchain and SDN Technologies

Dr. Pourvahab has also contributed significantly to software-defined networking (SDN) and blockchain technology by:
✔ Developing secure cloud architectures
✔ Enhancing network automation and security
✔ Implementing blockchain-based authentication mechanisms

🏆 Accolades and Recognition

Dr. Mehran Pourvahab’s contributions have not gone unnoticed. Over the years, he has received numerous honors and accolades for his work, including:

🥇 IEEE Senior Membership – A prestigious recognition of his contributions to computer engineering and networking.

📜 Research Publications – He has authored and co-authored numerous peer-reviewed journal papers and conference articles in high-impact venues.

📝 Editorial and Peer-Review Contributions – Dr. Pourvahab has reviewed research articles for various esteemed journals and conferences, further cementing his role as an authority in his field.

🏅 Leading R&D Projects – As a Data Manager and Project Manager for the Unified Data Model in the Phara-On project, he played a crucial role in one of Europe’s biggest R&D initiatives focused on ICT solutions for aging populations.

📡 Leadership in Networking and Security – His involvement in the Iranian ICT Guild Organization, where he served as head of the security and network commission, highlights his leadership and impact on national technology policies.

Publication Top Notes

Enhancing Neural Network Generalisation with Improved Differential Evolution

Contributors: Seyed Jalaleddin Mousavirad; Seyyed Mohammad Tabatabaei; Davood Zabihzadeh; Mahshid Helali Moghadam; Mehran Pourvahab; Diego Oliva
Journal: Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions
Year: 2025

Evolutionary Self-adjusting Masi Entropy Thresholding

Contributors: Mousavirad, Seyed Jalaleddin; Diego Oliva; Seyyed Mohammad Tabatabaei; Davood Zabihzadeh; Pourvahab, Mehran; Autor correspondente: Mousavirad, Seyed Jalaleddin.
Journal: Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions
Year: 2025

 

 

 

Dr. KALPANA Singh | Data Science and Analytics | Best Researcher Award

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Dr. KALPANA Singh | Data Science and Analytics | Best Researcher Award

Dr. KALPANA Singh, Hamad Medical Corporation, Qatar

Dr. Kalpana Singh is a seasoned biostatistician with a PhD in Statistics from Amity University, specializing in survival analysis for cardiovascular disease. With over 16 years in public and clinical health research, she excels in both quantitative and qualitative research designs. Currently a Senior Epidemiologist at Hamad Medical Corporation in Qatar, she leads statistical analyses and mentors researchers. She holds lifetime membership in the Indian Society of Medical Statistics and has received numerous awards and grants. Proficient in R, STATA, and various statistical tools, Dr. Singh is dedicated to advancing health research. 🧬📊🌍

Publication Profile

Education

Dr. KALPANA Singh earned a Ph.D. in Statistics from Amity University, Noida, India (2014-2018), specializing in the application of survival analysis on cardiovascular disease. 📊❤️ They completed a Post Graduate Diploma in Biostatistics & Data Management from the Indian Institute of Public Health, Hyderabad, in 2008-2009. 🎓📈 Dr. [Name] also holds an M.Sc. in Statistics from Dr. B.R. Ambedkar University, Agra (2005-2007), and a B.Sc. in Physics, Chemistry, and Mathematics from the same university (2002-2005). 🔬📚 Their academic journey reflects a strong foundation in statistics and a keen interest in public health and data management. 💡🌟

Experience

With over 16 years of experience in public and clinical health research, I excel in quantitative and qualitative research designs. My skills include excellent management and coordination, and I am efficient in independently managing the analytical aspects of projects. My expertise covers Randomized Control Trials, complex quasi-experimental designs, mixed-method research, and advanced analyses such as survival analysis, longitudinal analysis, and multi-level modeling. I actively participate in new grant proposals and co-author peer-reviewed publications, aligning with the research project’s aims. 🌐📊🧪📈📉📚💡📑

Awards

🩺💡 Awarded the Fellowship for PGDBM by the Public Health Foundation of India in August 2009. 🏅 Completed a Master’s in Data Science from Emory University & CCDC in May 2022. 📊 As Principal Investigator, leading a Nurse-led medication self-management intervention study to improve medication adherence in adults with multimorbidity, funded by HMC MRC Qatar. 🤝 Co-Investigator for a Digital Health-enabled intervention in India funded by MRC/Wellcome Trust. 📚 Serving as co-PI on 15 ongoing studies at HMC, mentoring 3 Ph.D. students and 5 Master’s students. 🎓

Research focus

K Singh’s research focuses on public health, specifically targeting cardiovascular health, hypertension, multimorbidity, and chronic diseases in South Asian populations. Key areas include the prevalence and risk factors of hypertension and obesity, the effectiveness of yoga-based cardiac rehabilitation, and the impact of vegetarian diets on cardiometabolic risks. Singh’s studies often compare health outcomes between urban and rural regions, as well as between South Asian and US populations, to identify trends and inform health interventions. Additionally, Singh investigates the prevalence of depression in diabetic patients and chronic kidney disease progression.

Publication Top Notes

Multimorbidity in South Asian adults: prevalence, risk factors and mortality

Changes in hypertension prevalence, awareness, treatment and control rates over 20 years in National Capital Region of India: results from a repeat cross-sectional study

Yoga-based cardiac rehabilitation after acute myocardial infarction: a randomized trial

Vegetarianism and cardiometabolic disease risk factors: differences between South Asian and US adults

20-Year trend of CVD risk factors: urban and rural national capital region of India

Prevalence of sustained hypertension and obesity among urban and rural adolescents: a school-based, cross-sectional study in North India

Prevalence of depression in patients with type 2 diabetes attending an outpatient clinic in India

Effectiveness and cost-effectiveness of a Yoga-based Cardiac Rehabilitation (Yoga-CaRe) program following acute myocardial infarction: Study rationale and design of a multi …

Prevalence of chronic kidney disease and risk factors for its progression: a cross-sectional comparison of Indians living in Indian versus US cities

Determinants of birth intervals in Tamil Nadu in India: developing Cox hazard models with validations and predictions

 

 

Sunil Kumar | Machine Learning | Best Researcher Award

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Dr. Sunil Kumar | Machine Learning | Best Researcher Award

Assistant Professor at MS Ramaiah University of Applied Sciences, India.

Dr. Sunil Kumar is a dedicated professional with a rich academic background and diverse expertise. With a Ph.D. from Birla Institute Technology (BIT), Mesra Ranchi, and master’s and bachelor’s degrees from YMCA University of Science and Technology and Anna University, respectively, Dr. Kumar brings a wealth of knowledge to his work. His passion for academia is evident in his objective to develop young minds and contribute to their overall growth. With nearly a decade of experience in teaching and industry, including roles at prestigious institutions like Ramaiah University of Applied Sciences and Madanapalle Institute of Technology & Science, Dr. Kumar has demonstrated his commitment to education and research. He is an active member of various professional societies, including IEEE and ISTE, and has contributed significantly to workshops, seminars, and short-term courses aimed at enhancing technical education. Dr. Kumar’s research interests span signal processing, machine learning, and microwave applications, areas in which he has published and reviewed extensively. His dedication to his field, coupled with his diverse skill set and academic achievements, makes him a valuable asset to the academic and research community.

Professional Profiles:

Education

Dr. Sunil Kumar has pursued a robust educational journey, culminating in a Ph.D. from Birla Institute Technology (BIT), Mesra Ranchi. Prior to his doctoral studies, he obtained an M.Tech in Electronics from YMCA University of Science and Technology, Faridabad, and a B.E. in Electronics and Communication Engineering (ECE) from Anna University, Chennai. His educational background reflects a strong foundation in both theoretical knowledge and practical application, preparing him for a successful career in academia and industry. 🎓📚

Professional Experience

Dr. Sunil Kumar has accumulated 9.5 years of rich and diverse experience encompassing both teaching and industry roles. He currently serves as an Assistant Professor at Ramaiah University of Applied Sciences, where he imparts knowledge and mentors students. Previously, he held similar positions at Madanapalle Institute of Technology & Science, CFA Academy in Noida, and Dronacharya College of Engineering. Additionally, he contributed as a Guest Faculty at YMCA UST, Faridabad. Beyond academia, Dr. Kumar also ventured into the industry as a Production Manager at RAJ APEX CONSTRUCTIONS (P) Ltd. His extensive professional journey reflects a dedication to both education and practical application in the field. 👨‍🏫🏭

Research Interest

Dr. Sunil Kumar’s research interests span several domains, reflecting his multidisciplinary approach and expertise. He is particularly inclined towards exploring areas such as signal processing, machine learning, and their applications in diverse fields. Additionally, his research pursuits extend to bioinformatics, microwave applications, and VLSI design. Through his scholarly endeavors, Dr. Kumar aims to contribute significantly to these fields, leveraging his expertise in data analysis, algorithm development, and technological innovation. His diverse research interests underscore a commitment to advancing knowledge and addressing contemporary challenges through innovative research methodologies. 🧪🔬📊

Award and Honors

Dr. Sunil Kumar has accumulated a wealth of experience and expertise in academia and research throughout his career. With a Ph.D. from Birla Institute Technology (BIT), Mesra Ranchi, an M.Tech in Electronics from YMCA University of Science and Technology, and a B.E. in Electronics and Communication Engineering from Anna University, Chennai, Dr. Kumar possesses a strong educational foundation. He has garnered over 9.5 years of combined teaching and industry experience, serving in various roles such as Assistant Professor at Ramaiah University of Applied Sciences and Madanapalle Institute of Technology & Science. Dr. Kumar has also made significant contributions to workshops, seminars, and short-term courses, demonstrating his commitment to continuous learning and professional development. Additionally, he has been honored with certifications in areas such as research, signal processing techniques, and various programming languages. Dr. Kumar’s dedication to academic excellence and his passion for research underscore his significant contributions to his field.

Research Skills

Dr. Sunil Kumar possesses a diverse set of research skills honed through his extensive academic and professional journey. With expertise in areas such as signal processing, machine learning, and microwave applications, Dr. Kumar has demonstrated a proficiency in both theoretical knowledge and practical application. His research acumen extends to the utilization of programming languages like Matlab and Python, enabling him to conduct in-depth analyses and develop innovative solutions. Furthermore, Dr. Kumar’s ability to review and contribute to esteemed journals such as IEEE Sensor Journal and Elsevier’s Infrared Physics and Technology showcases his proficiency in academic writing and critical evaluation. Overall, his research skills reflect a comprehensive understanding of contemporary techniques and methodologies essential for advancing knowledge in his field.

Publications

  1. Title: Detection of peak wavelength of multi-FBG using higher-order derivative of wavelets multiresolution analysis and maximum likelihood estimation
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2023
    • Citations: 1
  2. Title: Machine learning based algorithm for multi-FBG peak detection using generative adversarial network
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2023
  3. Title: Efficient detection of multiple FBG wavelength peaks using matched filtering technique
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022
    • Citations: 7
  4. Title: Adaptive and precise peak detection algorithm for fibre Bragg grating using generative adversarial network
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022
    • Citations: 2
  5. Title: FBG Peak Wavelength Detection Using Transfer Learning-Based Machine Learning Method
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022
  6. Title: Multi Peak Detection Algorithm of Fiber Bragg Grating using Mexican Hat Wavelets and Hilbert Transform
    • Authors: Kumar, S.; Sengupta, S.
    • Year: 2022