Shaliza Alwi | Sustainable Development | Best Researcher Award

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Assoc. Prof. Dr. Shaliza Alwi | Sustainable Development | Best Researcher Award

Assoc. Prof. Dr. Shaliza Alwi | Sustainable Development – Lecturer at MARA University of Technology, Malaysia.

Assoc. Prof. Dr. Shaliza Alwi is an experienced academician and finance professional with over two decades of teaching, research, and academic leadership. She currently serves as an Associate Professor at a government-linked company (GLC) and was previously a lecturer at Taylor’s University, where she held several key academic and administrative roles. Dr. Shaliza has been instrumental in curriculum development, program coordination, and student mentorship at the undergraduate and postgraduate levels. Her expertise spans across financial management, investment, multinational finance, Islamic banking, derivatives, and insurance practices. She has supervised numerous postgraduate students and actively contributed to quality assurance processes such as AACSB accreditation and internal audits. With a solid grounding in finance and a commitment to academic excellence, she is also engaged in various community outreach projects, including entrepreneurship training programs targeted at Malaysia’s B40 community. She has collaborated with industry partners such as CIMB Islamic and Daikin Sdn Bhd, fostering academic-industry linkages. As a member of prestigious international professional bodies, Dr. Shaliza continues to contribute to the development of finance education and financial literacy both in academic settings and community platforms. Her contributions are marked by a passion for inclusive growth, innovative pedagogy, and strategic academic development.

Publication Profile

Scopus

Orcid

Google Scholar

Educational Details

Dr. Shaliza Alwi holds a diverse academic background in business and finance. She earned her Bachelor of Business Administration (Hons) with a major in Finance from Universiti Utara Malaysia (UUM) in 1996. She then pursued her postgraduate studies internationally, completing her Master of Business Administration (MBA) at Charles Sturt University, Australia in 2001, with a focus on General Management. Furthering her specialization in finance, she obtained a Ph.D. in Finance from the Arshad Ayub Graduate Business School, Universiti Teknologi MARA (UiTM), Shah Alam. Her doctoral research focused on the theme “Interlocking Directorates and Firm Sustainable Growth within Industry Settings”, contributing valuable insights to corporate governance and strategic financial management. This blend of local and international education has equipped her with a strong theoretical foundation, cross-cultural academic exposure, and critical insights into financial systems and corporate sustainability. Her academic qualifications not only reflect her deep understanding of business and finance but also serve as the basis for her impactful teaching and research contributions in higher education institutions across Malaysia.

Professional Experience

Assoc. Prof. Dr. Shaliza Alwi has over 20 years of professional experience in academia. From August 2014 to June 2023, she served as a Lecturer at Taylor’s University, where she was actively involved in teaching, research supervision, curriculum development, and community engagement. She led various finance modules such as Derivatives, Islamic Banking, Corporate Finance, and Insurance Practices, and developed specialized content for the Actuarial Studies Program. She also mentored first-year students and supervised both undergraduate and postgraduate research projects. In addition to her teaching responsibilities, she served as Stream Coordinator for the Finance Cluster, participated in multiple committees, and supported AACSB accreditation processes. She worked closely on interdisciplinary projects, including consultancy assignments with Daikin Sdn Bhd, and advised student teams in national and international competitions. Currently, she is an Associate Professor at a Government-Linked Company (GLC), where she continues her role in postgraduate teaching, supervision, and academic assessments. Her contributions span administrative leadership, student engagement, academic audits, and institutional quality assurance—underscoring her comprehensive role in shaping finance education in Malaysia.

Research Interest

Dr. Shaliza Alwi’s research interests lie at the intersection of corporate finance, interlocking directorates, firm sustainability, and Islamic finance. Her Ph.D. dissertation explored the dynamics of board interconnectivity and sustainable growth within industry frameworks, offering a strategic perspective on corporate governance. She has a keen interest in analyzing how financial networks and decision-making structures influence long-term organizational performance. Additionally, she engages in applied research in investment analysis, portfolio management, and multinational finance, aligning academic theories with real-world industry challenges. Dr. Shaliza is also interested in financial literacy, entrepreneurial development, and Islamic banking models, particularly in the context of Malaysia’s socio-economic development. Her community-based projects reflect her commitment to enhancing financial education among underserved groups such as the B40 community. Through consultancy projects and academic collaborations, she has expanded her research into market analysis, insurance systems, and business innovation. She also integrates interdisciplinary approaches into finance research, working with partners across business, actuarial science, and policy studies. Her work contributes to academic literature while fostering practical applications that support both public and private sector growth.

Research Skills

Dr. Shaliza Alwi possesses a robust set of research skills acquired through years of academic and applied research. She is proficient in using Bloomberg terminals for financial data analysis and has hands-on experience with Stata, EndNote, and Microsoft Office tools for statistical analysis, referencing, and research documentation. Her academic research involves both quantitative and qualitative methodologies, enabling her to conduct empirical studies across diverse financial domains. She is experienced in developing course materials, conducting assessment evaluations, and leading curriculum design for finance-related programs. Dr. Shaliza is skilled in guiding postgraduate research, particularly in corporate finance and governance-related topics. Her role as an AACSB internal auditor and assessor has further strengthened her capabilities in quality assurance and compliance-based research. She has also applied her research skills in consultancy projects, such as a market analysis for Daikin Sdn Bhd, and in evaluating entrepreneurship initiatives under corporate social responsibility programs. Through her training in Train the Trainer (HRDF-certified), she effectively transfers research knowledge to diverse learner groups. These competencies enable her to bridge theory with practice, ensuring her research maintains both academic rigor and practical relevance.

Awards and Honors

Assoc. Prof. Dr. Shaliza Alwi has been recognized for her excellence in teaching and innovation. She was honored in 2018 with the “e-learning Superhero Competition” award by Taylor’s University, highlighting her exceptional contributions to digital learning and curriculum delivery. As a Module Leader, her leadership in e-learning content and online pedagogy significantly improved student engagement and academic outcomes. Beyond institutional recognition, she has actively mentored student teams that have excelled in various national and international competitions, such as the KUIZ Pelaburan PNB Malaysia (Champion, 2015), CIMB Trading Challenge, CIMA-EBLM Business Challenge (2020), CFA Institute Research Challenge (2020/21), and CME Group University World Trading Challenge (2020–2022). These accolades reflect her strong mentorship and commitment to student success. She is also a Certified Train the Trainer professional under HRDF Malaysia since 2006, which underscores her contribution to training and professional development. Her memberships in prestigious bodies such as the Asian Institute of Chartered Bankers and Bursa Malaysia’s Knowledge Centre further affirm her professional stature and recognition in the field of finance.

Author Metrics

  • Total Citations: 409

  • h-index: 9

  • i10-index: 8

These metrics reflect Dr. Alwi’s research influence and academic impact, particularly in the areas of fintech, consumer behavior, and educational workload studies.

Top Noted Publication

  • Title: Time Element in the Construct of Special Education Teacher Workload in Malaysia
    Authors: MNN Irma Shayana Bte Samaden, Irfah Najihah, Shaliza Alwi, Rabiatul Munirah
    Journal: Turkish Journal of Computer and Mathematics Education
    Year: 2021
    Citations: 82

  • Title: An Empirical Study on the Customers’ Satisfaction on Fintech Mobile Payment Services in Malaysia
    Authors: S. Alwi, R.M. Alpandi, M.N.M. Salleh, I. Najihah
    Journal: International Journal of Advanced Science and Technology
    Year: 2019
    Citations: 68

  • Title: Fintech as Financial Inclusion: Factors Affecting Behavioral Intention to Accept Mobile E-Wallet during Covid-19 Outbreak
    Authors: S. Alwi, M.N.M. Salleh, R.M. Alpandi, F.F. Ya’acob, S.M.M. Abdullah
    Journal: Turkish Journal of Computer and Mathematics Education
    Year: 2021
    Citations: 59

  • Title: Factors Affecting Savings Habits within Millennials in Malaysia: Case Study on Students of Taylor’s University
    Authors: S. Alwi, I.Z. Amir Hashim, M.S. Ali
    Journal: Proceedings of the Fourth Asia-Pacific Conference on Global Business, Economics, Finance and Social Sciences
    Year: 2015
    Citations: 59

  • Title: Consumer Acceptance and Adoption towards Payment-Type Fintech Services from Malaysian Perspective
    Authors: S. Alwi, M.N.M. Salleh, S.E.A. Razak, N.M. Naim
    Journal: International Journal of Advanced Science and Technology
    Year: 2019
    Citations: 22

Conclusion

Assoc. Prof. Dr. Shaliza Alwi embodies the qualities of an accomplished academic leader, finance educator, and community advocate. Her multifaceted career reflects a deep commitment to excellence in teaching, research, mentorship, and social responsibility. With a strong academic foundation in finance, enhanced by international education and interdisciplinary exposure, she has effectively bridged academic theory with practical insights. Her contributions span program development, AACSB accreditation, academic audits, and postgraduate supervision, reinforcing her role as a key driver of educational quality and innovation. Equally committed to societal impact, Dr. Shaliza has initiated and led numerous financial literacy and entrepreneurial programs for Malaysia’s B40 community, creating meaningful engagement beyond the classroom. Through her work with industry partners and professional bodies, she continues to influence both academic and financial landscapes. Whether guiding students, auditing programs, or delivering community training, Dr. Shaliza consistently demonstrates leadership, empathy, and academic rigor. Her vision for inclusive and sustainable growth—through education, research, and outreach—positions her as a respected thought leader in the Malaysian academic and financial sectors.

 

Jinfu Fan | Machine Learning | Best Researcher Award

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Assoc. Prof. Dr. Jinfu Fan | Machine Learning | Best Researcher Award

Professor at Qingdao University, China.

Assoc. Prof. Dr. Jinfu Fan is a dedicated researcher and academic currently serving as an Associate Professor at the College of Computer Science Technology, Qingdao University (QU), China. He earned his Ph.D. in Computer Science from Tongji University, Shanghai, and further expanded his academic horizons as a visiting scholar at the School of Computing (SoC), National University of Singapore (NUS), from January to December 2022. Dr. Fan specializes in the fields of machine learning, data mining, and computer vision, with a notable emphasis on weakly supervised multi-label learning and super-resolution image reconstruction. His recent research project, MDiffSR, explores the application of mutual information and diffusion models in image super-resolution and has been published in the renowned Neurocomputing journal. His scholarly work demonstrates a solid blend of theoretical foundations and practical innovation, offering new directions in data-efficient learning and visual computing. Beyond research, Dr. Fan is actively involved in curriculum development and student mentorship at QU, contributing significantly to academic growth and collaborative learning. His passion for integrating advanced algorithms with real-world applications marks him as a progressive thinker and an impactful contributor in the field of artificial intelligence and computational imaging.

Publication Profile

Scopus

Orcid

Google  Scholar

Educational Details

Dr. Jinfu Fan obtained his Ph.D. degree in Computer Science from Tongji University, Shanghai, one of China’s leading research universities known for its engineering and technological disciplines. During his doctoral training, he focused on advanced learning algorithms and optimization techniques, gaining strong expertise in machine learning and data-driven modeling. His academic foundation combines rigorous computational training with a deep understanding of mathematical modeling, which laid the groundwork for his current research in weakly supervised learning and super-resolution technologies. In addition to his doctoral studies, Dr. Fan further honed his academic skills through a postdoctoral or visiting scholar tenure at the National University of Singapore (NUS), a globally recognized institution in computer science and AI research. This international experience provided him with broader research exposure, particularly in collaborative and cross-disciplinary projects, and allowed him to work alongside some of the field’s top minds. His educational background not only reflects technical depth but also a proactive approach toward lifelong learning and global academic engagement. His training in both national and international settings has helped cultivate a well-rounded understanding of computer science principles and their real-world applications, making him a versatile researcher and educator in the evolving tech landscape.

Professional Experience

Assoc. Prof. Dr. Jinfu Fan is currently affiliated with the College of Computer Science Technology at Qingdao University (QU), where he serves as an Associate Professor. In this role, he is actively involved in both research and teaching, guiding undergraduate and graduate students in areas related to machine learning, computer vision, and artificial intelligence. Prior to joining QU, he completed his Ph.D. at Tongji University and enriched his professional experience as a visiting scholar at the National University of Singapore (NUS) from January to December 2022. During his time at NUS, Dr. Fan collaborated with prominent researchers at the School of Computing, which broadened his research scope and provided him with valuable international experience. At Qingdao University, he has initiated and led several research projects, including the development of image super-resolution frameworks using mutual information and diffusion models. His role as an academic also includes curriculum development, research supervision, and interdisciplinary collaboration. His professional journey reflects a strong commitment to technological advancement and academic excellence, marked by his ability to integrate research with education and translate theory into practical innovations. Dr. Fan’s progressive academic and research track record underscores his dedication to scholarly leadership in AI and computational science.

Research Interest

Dr. Jinfu Fan’s research interests span across several cutting-edge areas within artificial intelligence and computer vision. He is particularly focused on machine learning and data mining, with a growing specialization in weakly supervised multi-label learning, an area that seeks to develop intelligent systems capable of learning from limited or incomplete labeled data. This field is crucial for reducing the cost and effort associated with data annotation, making AI more accessible and scalable. In addition, Dr. Fan is extensively involved in super-resolution image reconstruction, aiming to enhance the resolution of images using deep learning techniques and novel optimization models. His recent work on MDiffSR integrates mutual information and diffusion modeling to boost image quality and reconstruction accuracy, addressing core challenges in medical imaging, remote sensing, and surveillance. He also maintains active interest in areas such as representation learning, information theory in AI, and unsupervised learning strategies. His goal is to bridge the gap between theoretical development and real-world application, and his projects frequently target practical outcomes in health diagnostics, smart cities, and digital imaging. Dr. Fan’s interdisciplinary vision and technical versatility position him at the forefront of research aimed at making AI models more robust, efficient, and interpretable.

Research Skills

Assoc. Prof. Dr. Jinfu Fan possesses a comprehensive suite of research skills in both theoretical and applied computer science. His primary strength lies in machine learning algorithm design, where he has developed and evaluated models for classification, multi-label learning, and super-resolution tasks. He is proficient in deep learning frameworks such as TensorFlow and PyTorch, which he uses to build and test neural networks tailored for image processing and representation learning. Additionally, Dr. Fan has a strong grasp of information theory, particularly mutual information, which he applies to improve learning efficiency and performance in weakly supervised environments. His research methodology is firmly rooted in rigorous mathematical modeling and statistical analysis, supported by tools like Python, MATLAB, and R. He is also skilled in experimental design and evaluation, ensuring that his studies follow reproducible and scalable processes. Moreover, his experience with image reconstruction techniques, especially in the context of the MDiffSR project, demonstrates his ability to integrate domain-specific knowledge with advanced AI models. Dr. Fan’s collaborative experience at the National University of Singapore further highlights his international research exposure and ability to work on multidisciplinary teams, equipping him with both technical and cross-cultural collaboration skills vital for modern scientific research.

Awards and Honors

While specific awards and honors are not detailed in the available records, Assoc. Prof. Dr. Jinfu Fan’s academic and research career reflects commendable achievements that position him as a strong candidate for national and international recognition. His selection as a visiting scholar at the National University of Singapore (NUS)—a prestigious institution ranked among the top in Asia for computer science—demonstrates peer recognition of his academic potential and research capabilities. His publication in Neurocomputing, a well-regarded SCI-indexed journal, adds to his credentials, showcasing his ability to contribute impactful research to the scientific community. Dr. Fan’s research project “MDiffSR: Mutual information and diffusion model in image super-resolution” represents an innovative stride in AI-based imaging solutions and has earned citation attention, indicating its influence in the academic domain. As his body of work continues to expand, it is likely that his contributions will be recognized by leading societies and research funding bodies. He is an ideal candidate for honors such as the Best Researcher Award, Excellence in Research, or Outstanding Scientist Award, given his growing research portfolio, international collaborations, and commitment to academic advancement in machine learning and computer vision.

Author Metrics

  • Total Citations: 186

  • h-index: 8
    (8 publications have at least 8 citations each)

  • i10-index: 8
    (8 publications have at least 10 citations each)

Top Noted Publication

  • A dynamic ensemble method for residential short-term load forecasting
    Y. Yang, F. Jinfu, W. Zhongjie, Z. Zheng, X. Yukun
    Alexandria Engineering Journal, 2023, 
    Citations: 25

  • GraphDPI: Partial label disambiguation by graph representation learning via mutual information maximization
    J. Fan, Y. Yu, L. Huang, Z. Wang
    Pattern Recognition, 2023, 
    Citations: 23

  • Spatial-frequency dual-domain feature fusion network for low-light remote sensing image enhancement
    Z. Yao, G. Fan, J. Fan, M. Gan, C.L.P. Chen
    IEEE Transactions on Geoscience and Remote Sensing, 2024
    Citations: 17

  • A new multi-source transfer learning method based on two-stage weighted fusion
    L. Huang, J. Fan, W. Zhao, Y. You
    Knowledge-Based Systems, 2023, 
    Citations: 17

  • Partial label learning based on disambiguation correction net with graph representation
    J. Fan, Y. Yu, Z. Wang, J. Gu
    IEEE Transactions on Circuits and Systems for Video Technology, 2021, 
    Citations: 17

Conclusion

Assoc. Prof. Dr. Jinfu Fan exemplifies a forward-thinking academic who integrates rigorous research with practical innovation in the fields of machine learning and computer vision. With a solid educational foundation from Tongji University and global exposure through his time at the National University of Singapore, he brings a well-rounded and internationally informed perspective to his work. His specialized interests in weakly supervised multi-label learning and image super-resolution position him at the cutting edge of artificial intelligence research. The MDiffSR project, as one of his leading contributions, reflects his ability to blend theory with impactful applications. At Qingdao University, he not only leads significant research initiatives but also plays a vital role in mentoring students and fostering academic excellence. His research skills, encompassing deep learning, data mining, and mathematical modeling, make him a valuable contributor to both academic and industry-oriented projects. As he continues to expand his publication record and research collaborations, Dr. Fan stands as a promising candidate for recognition in scientific innovation. His commitment to knowledge advancement, problem-solving, and global engagement makes him a distinguished figure in the AI research landscape, with continued potential to make meaningful contributions to science and society.

Xiaofeng Dai | Medicine Advances | Best Researcher Award

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Prof. Xiaofeng Dai | Medicine Advances | Best Researcher Award

Professor at The First Affiliated Hospital of Xi’an Jiaotong University, China.

Prof. Dr. Xiaofeng Dai is a distinguished expert in precision oncology and plasma medicine, currently serving as Professor and Research Theme Leader at the First Affiliated Hospital of Xi’an Jiaotong University. With dual PhDs in Systems Biology (Genetics) and Econo-Informatics from Finland, she integrates interdisciplinary knowledge across molecular oncology, bioinformatics, and translational medicine. Prof. Dai has established herself as a global leader in exploring the molecular and clinical applications of cold atmospheric plasma (CAP) therapy, publishing over 114 SCI-indexed articles with a cumulative impact factor of 806.74. She is the first or corresponding author of all these works, including four ESI highly cited papers. Her scientific achievements are further marked by 2 U.S. patents, 4 Chinese invention patents, and 10 software copyrights. She has secured and led national and industrial research funding exceeding 10 million RMB. Recognized for her pioneering work, Prof. Dai has received prestigious honors including Academician of the European Academy of Natural Sciences (2023), Outstanding Engineer Award (2022), and selection in the Jiangsu Province 333 High-level Talents Program (2024). As an active contributor to academic societies and a passionate educator, she is committed to translating advanced research into tangible clinical innovations.

Publication Profile

Scopus

Educational Details

  • Ph.D. in Econo-informatics, Aalto University, Finland (2016)

  • Ph.D. in Systems Biology – Genetics, Tampere University of Technology, Finland (2010)

  • M.Sc. in Bioinformatics, University of Tampere, Finland (2010)

  • M.Sc. in Molecular Biology, Chinese Academy of Agricultural Sciences, China (2006)

  • B.Sc. in Biotechnology, Central South University of Forestry and Technology, China (2003)

Professional Experience

  • 2023/06 – Present: Professor, Theme Leader – Precision Oncology, The First Affiliated Hospital of Xi’an Jiaotong University

  • 2017/06 – 2023/05: Professor, Theme Leader – Precision Medicine, Jiangnan University

  • 2014/02 – 2017/05: Associate Professor, Precision Medicine, Jiangnan University

  • 2016/12 – 2017/02: Visiting Scholar, Queensland University of Technology (QUT), Australia

  • 2013/10 – 2014/01: Principal Investigator, Chinese National Human Genome Center

  • 2011/02 – 2013/09: Senior Research Fellow, Helsinki University Central Hospital, Finland

  • 2010/02 – 2011/01: Senior Research Fellow, Institute for Molecular Medicine Finland

  • 2007/03 – 2011/01: Research Fellow, Tampere University of Technology, Finland

Research Interest

Prof. Dai’s research centers on cold atmospheric plasma (CAP)-based therapies in oncology and beyond. Her interests lie in exploring the molecular underpinnings of CAP’s selective cytotoxicity against malignant cells, investigating its influence on cancer stemness, metastasis, and tumor microenvironment. Expanding from cancer, her current work includes the use of CAP in immunomodulation, metabolic disorders, viral prevention, and vaccine enhancement. She also focuses on developing innovative CAP delivery systems such as ingestible CAP capsules and CAP-mediated photodynamic therapy, aiming to enhance its versatility and applicability. Prof. Dai is also addressing key translational barriers—such as CAP’s short-lived reactive components—by designing storage-stable and sustained-release solutions. Her research integrates bioinformatics, molecular biology, and plasma physics to decipher therapeutic pathways and identify CAP-mimicking biomolecules. Through computational modeling and synthetic chemistry, she is developing molecules with CAP-like efficacy, pushing forward the concept of molecular plasma medicine. Her goal is to create clinically adaptable, precision-targeted, and minimally invasive treatment solutions across a wide spectrum of diseases.

Research Skills

Prof. Dai is proficient in a broad array of interdisciplinary research techniques, spanning molecular biology, plasma physics, bioinformatics, and translational medicine. Her core expertise includes the design and execution of high-throughput omics experiments, real-time plasma-matter interaction studies, and in vitro/in vivo cancer models. She is skilled in CRISPR-based functional genomics, computational modeling of plasma-induced cellular responses, and RNA-seq data interpretation. Her strong background in systems biology enables her to construct multi-scale models that integrate molecular, cellular, and clinical data. She has extensive experience in developing biomedical devices and software tools tailored to plasma applications, backed by 10 software copyrights. Her ability to secure industrial partnerships and government funding demonstrates her leadership in translational research. Additionally, Prof. Dai has led cross-disciplinary teams to develop CAP-based interventions for autoimmune diseases, metabolic disorders, and infectious disease prevention. She is also adept at scientific communication, grant writing, project management, and mentoring junior researchers and students.

Awards and Honors

Prof. Dai’s research excellence and leadership have been widely recognized. In 2024, she was named a Level 3 recipient in the prestigious “333 High-Level Talent Program” of Jiangsu Province. In 2023, she was elected an Academician of the European Academy of Natural Sciences, highlighting her international stature. Her innovative research earned her the Outstanding Engineer Young Award in 2022 from the International Scientific Exchange Foundation of China. Earlier honors include being a member of the Dual Innovative Team of Jiangsu Province (2017), receiving the Six Talent Peaks Award (2016), and being named a Leading Talent for Social Undertakings in Wuxi (2015). These accolades underscore her sustained contributions to scientific innovation, particularly in the interdisciplinary domains of bioengineering, oncology, and plasma medicine. They also reflect recognition by both national and international academic bodies, industry collaborators, and government agencies. Her ability to integrate science, education, and clinical translation has made her a respected figure in China’s high-impact research ecosystem.

Author Metrics

  • Total Citations: 4,522

  • Citations by Documents: 4,063

  • Total Publications (Documents): 125

  • h-index: 29

Top Noted Publication

1. Advanced Science (2020)

Title: Innovative precision gene-editing tools in personalized cancer medicine
Citation: Xiaofeng Dai#*, Blancafort Pilar, Peiyu Wang, Agustin Sgro, Erik W Thompson, Kostya (Ken) Ostrikov
Journal: Advanced Science

  • IF (2023): ~17.5

  • JCR Rank: Q1 (Multidisciplinary Sciences)

  • Reason: Highly impactful, interdisciplinary, cutting-edge gene-editing research published in a top-tier journal.

2. Trends in Biotechnology (2018)

Title: The emerging role of gas plasma in oncotherapy
Citation: Xiaofeng Dai#*, Kateryna Bazaka, Derek J. Richard, Erik W. Thompson, Kostya (Ken) Ostrikov
Journal: Trends in Biotechnology

  • IF (2023): ~20.0

  • JCR Rank: Q1 (Biotechnology & Applied Microbiology)

  • Reason: Published in a Cell Press journal, which is highly selective. Recognized for trend-setting reviews.

3. Signal Transduction and Targeted Therapy (2025)

Title: Systemic lupus erythematosus: updated insights on the pathogenesis, diagnosis, prevention and therapeutics
Citation: Xiaofeng Dai#*, Yuting Fan#, Xing Zhao*
Journal: Signal Transduction and Targeted Therapy

  • IF (2023): ~39.3

  • JCR Rank: Q1 (Immunology/Oncology)

  • Reason: Nature Publishing Group journal with very high impact. Although recent, its publication in a Nature journal signifies top-tier status.

4. Trends in Genetics (2022)

Title: Histone lactylation: epigenetic mark of glycolytic switch
Citation: Xiaofeng Dai#*, Xinyu Lv, Erik W. Thompson, Kostya (Ken) Ostrikov
Journal: Trends in Genetics

  • IF (2023): ~12.0

  • JCR Rank: Q1 (Genetics & Heredity)

  • Reason: Cell Press review journal with major influence in the field of epigenetics and gene regulation.

5. Biotechnology Advances (2019)

Title: Standardizing CAR-T therapy: getting it scaled up
Citation: Xiaofeng Dai#*, Yi Mei, Dongyan Cai, Weidong Han*
Journal: Biotechnology Advances

  • IF (2023): ~14.2

  • JCR Rank: Q1 (Biotechnology)

  • Reason: Authoritative journal in biotechnology with strong clinical and commercial relevance, especially in CAR-T research.

Conclusion

Prof. Dr. Xiaofeng Dai is a leading interdisciplinary scientist advancing cold atmospheric plasma (CAP) therapy in precision oncology and regenerative medicine. With dual Ph.D. degrees, over 100 SCI-indexed publications, and multiple patents, she bridges molecular research with clinical application. Her leadership in securing major research funding and fostering industry collaboration highlights her impact across science and innovation. As an educator and mentor, she is shaping future experts in cancer biology and bioinformatics. Through national and international partnerships, Prof. Dai remains at the forefront of translating high-impact research into real-world medical solutions.

 

Bilel Selmi | Mathematics | Best Researcher Award

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Assist. Prof. Dr. Bilel Selmi | Mathematics | Best Researcher Award

Assistant Professor at University of Monastir, Tunisia.

Assist. Prof. Dr. Bilel Selmi is a Tunisian mathematician specializing in multifractal analysis, fractal geometry and geometric measure theory. He received his Ph.D. in Pure Mathematics from the Faculty of Sciences of Monastir, where his work contributed to the understanding of multifractal dimensions and their behavior under projection. He further advanced his research in the field through postdoctoral studies under Profs. Imen Bhouri and Fathi Ben Nasr. Currently, he teaches mathematics and pursues research at ISET Gabes. Dr. Selmi is also a co-author of the French-language textbook Linear Algebra: Course reminders and corrected exercises.

Publication Profile

Scopus

Orcid

Google Scholar

Educational Details

  • 2020 – Postdoctoral Research
    Title: The relative multifractal analysis, review, and examples
    Supervisors: Prof. Imen Bhouri and Prof. Fathi Ben Nasr
    Laboratory: Analysis, Probability and Fractals Laboratory (LR18ES17)
    Institution: Faculty of Sciences of Monastir, Tunisia

  • 2019 – Ph.D. in Mathematics (Pure Mathematics)
    Title: Multifractal analysis and dimensions
    Advisor: Prof. Imen Bhouri
    Institution: Faculty of Sciences of Monastir, Tunisia
    Lab: Analysis, Probability and Fractals Laboratory (LR18ES17)
    Thesis Description: Investigated multifractal Hausdorff and packing dimensions of Borel measures, their orthogonal projections, and developed theoretical foundations for relative multifractal analysis including decomposition theorems and multifractal spectra.

  • 2014 – M.Sc. in Mathematics (Pure Mathematics)
    Title: Multifractal analysis of Birkhoff averages on symbolic spaces
    Advisor: Prof. Imen Bhouri
    Institution: Faculty of Sciences of Monastir, Tunisia
    Thesis Description: Explored thermodynamic formalism in symbolic dynamics, focusing on pressure functions and their link to Hausdorff and packing dimensions of level sets in symbolic spaces.

  • 2012 – B.Sc. in Mathematics (First-class honors)
    Specialty: Pure Mathematics
    Institution: Faculty of Sciences of Monastir, Tunisia

Professional Experience

Dr. Bilel Selmi began his teaching career in 2016 as a contractual assistant at the Higher Institute of Informatics and Mathematics of Monastir, where he served until 2019. During this period, he held various teaching positions, including temporary assistant roles. Since 2020, he has been serving as an Assistant Professor of Mathematics at the Higher Institute of Technological Studies (ISET) in Gabes, Tunisia. His academic duties include teaching advanced mathematics and conducting research in fractal geometry and dynamical systems.

Research Interest

  • Multifractal Analysis

  • Fractal Geometry

  • Dynamical Systems

  • Geometric Measure Theory

  • Quantization

Author Metrics

  • Total Citations: 1,202

  • h-index: 20

  • i10-index: 45

These metrics reflect Dr. Selmi’s strong research impact in the fields of multifractal analysis, fractal geometry and dynamical systems, showcasing a consistent publication record and growing academic influence.

Top Noted Publication

1. A multifractal formalism for Hewitt–Stromberg measurements

Authors: N. Attia, B. Selmi
Journal: The Journal of Geometric Analysis, 31, 825–862 (2021)
Citations: 55
Description: This paper introduces a multifractal formalism adapted to Hewitt–Stromberg measures, which extend classical measure-theoretic tools in multifractal geometry. The authors establish key relationships between multifractal dimensions and the regularity of measures in this non-traditional framework, contributing new theoretical foundations for geometric measure theory.

2. Multifractal variation for projections of measurements

Authors: Z. Douzi, B. Selmi
Journal: Chaos, Solitons & Fractals, 91, 414–420 (2016)
Citations: 50
Description: This work studies how multifractal properties of measures behave under orthogonal projections. It provides significant results on the preservation and variation of multifractal spectra when measures are projected in Euclidean space, with implications for applications in image analysis and signal processing.

3. Regularities of multifractal Hewitt–Stromberg measurements

Authors: N. Attia, B. Selmi
Journal: Communications of the Korean Mathematical Society, 34(1), 213–230 (2019)
Citations: 44
Description: This study explores the regularity properties of measures in the context of Hewitt–Stromberg analysis. The authors provide necessary and sufficient conditions for regular multifractal behavior, extending the understanding of generalized multifractal formalisms beyond classical settings.

4. The relative multifractal analysis, review and examples

Author: B. Selmi
Journal: Acta Scientiarum Mathematicarum (Szeged), 86(3–4), 635–666 (2020)
Citations: 43
Description: A comprehensive review and extension of the theory of relative multifractal analysis. Selmi introduces new definitions and illustrative examples, and connects relative spectra with classical concepts in fractal geometry. The work is notable for its pedagogical value and depth in foundational theory.

5. Some density results of relative multifractal analysis

Authors: N. Attia, B. Selmi, C. Souissi
Journal: Chaos, Solitons & Fractals, 103, 1–11 (2017)
Citations: 43
Description: This article investigates the density of specific classes of measures satisfying relative multifractal formalism. It discusses convergence properties and limit distributions, providing critical insight into the statistical structure of fractal measures.

Conclusion

Assist. Prof. Dr. Bilel Selmi is a highly deserving candidate for the Best Researcher Award. His extensive contribution to the advancement of multifractal theory and geometric measure analysis, combined with a consistent record of quality publications and citation impact, showcases his excellence in pure mathematics. While there is room to broaden his research outreach and leadership profile, his academic trajectory, originality, and scholarly depth make him a strong contender.