Neda Fayyazi | Bioinformatics | Excellence in Innovation

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Dr. Neda Fayyazi | Bioinformatics | Excellence in Innovation

ogen/ medical iincubator, Iran

ย Author Profile ๐Ÿ“š

Google Scholar

Early Academic Pursuits ๐ŸŽ“

Dr. Neda Fayyazi’s academic journey is marked by significant achievements, beginning with her Ph.D. in Pharmaceutical and Medicinal Chemistry from Isfahan University of Medical Sciences, which she completed between February 2015 and October 2019. Her outstanding academic performance earned her a top rank (A) with a GPA of 3.60. Dr. Fayyazi’s thesis, titled Molecular modeling, synthesis, and biological evaluation of hybrid multi-target iron chelators for anticancer and antimalarial applications, was highly regarded, securing an Excellent Rank (A). Her interdisciplinary focus on combining computational techniques and medicinal chemistry positions her as an emerging leader in the field.

Professional Endeavors ๐Ÿ’ผ

Dr. Fayyazi’s professional experience spans academia and industry, with roles in pharmaceutical companies and research institutions. She has worked with leading organizations, including TPCO Holding, Pars Darou Pharmaceutical Company, and Barkat Holding. Her roles involved product development, technology transfer, and research in the areas of pharmaceutical formulations, softgel production, and drug discovery. As a board member of Ozhan Pharma Group and an active participant in various projects, Dr. Fayyazi continues to bridge the gap between scientific research and commercial application.

Contributions and Research Focus ๐Ÿ”ฌ

Dr. Fayyaziโ€™s research contributions are primarily in the field of Medicinal Chemistry, with a focus on drug design, molecular modeling, and hybrid molecule synthesis. Her work on molecular dynamics simulations and protein-ligand interactions using cutting-edge tools like Gromacs, GOLD, and Discovery Studio has been instrumental in advancing the understanding of drug interactions at the molecular level. Additionally, she is passionate about developing novel drug delivery systems, particularly in sustained-release formulations, and exploring natural products for therapeutic potential. Her research integrates AI and machine learning approaches in drug discovery, a testament to her forward-thinking methodology.

Impact and Influence ๐ŸŒ

Dr. Fayyazi has had a significant impact on the pharmaceutical industry and academia through her work in drug discovery, technology transfer, and industry collaboration. Her expertise in bioassay-guided screening and cancer therapy research has led to breakthroughs in medicinal chemistry, especially in the development of multi-target anticancer agents. As a member of various prestigious associations such as the European Bioinformatics Association and the American Chemical Society, she has contributed to the global advancement of biotechnology and pharmaceutical sciences. Her work has garnered recognition, including a prize for innovative ideas in drug research at the Euro Chemistry Conference in Berlin (2019).

Academic Cites and Research Recognition ๐Ÿ“‘

Dr. Fayyazi’s academic publications and research outputs are highly cited in the fields of molecular modeling and pharmaceutical sciences. Her collaborations with research institutions and pharmaceutical companies have cemented her reputation as a thought leader in medicinal chemistry. Notably, her research on iron chelators and cancer treatment has positioned her as a pivotal figure in advancing drug discovery methodologies. Her work is frequently referenced in studies related to drug design, molecular dynamics, and natural product research.

Technical Skills and Expertise ๐Ÿง‘โ€๐Ÿ”ฌ

Dr. Fayyaziโ€™s technical proficiency extends across a broad spectrum of advanced scientific methods and tools. She is highly skilled in QSAR (Quantitative Structure-Activity Relationship), molecular dynamics simulations, and protein-ligand interactions. Her ability to synthesize hybrid compounds, coupled with her expertise in computational chemistry and machine learning, allows her to investigate complex biochemical interactions and design novel therapeutic agents. Furthermore, her knowledge of Linux environments, scripting, and drug discovery processes in natural products underscores her broad technical competence.

Teaching Experience ๐Ÿ“š

Dr. Fayyaziโ€™s passion for teaching is evident in her role as an educator at Isfahan University of Medical Sciences, where she has taught undergraduate students in organic chemistry, pharmacology, and computational chemistry. She has also supervised and mentored numerous masterโ€™s and Ph.D. students in the fields of medicinal chemistry and bioassays. Her teaching methods emphasize interdisciplinary approaches, integrating cutting-edge technology and scientific research into practical applications.

Legacy and Future Contributions ๐ŸŒŸ

Looking ahead, Dr. Fayyazi’s future contributions are poised to continue shaping the pharmaceutical and biotechnology sectors. Her ongoing projects in controlled release formulations, softgel technology transfer, and sustained-release drug delivery systems demonstrate her commitment to improving patient outcomes through innovative therapeutic solutions. As she works to bridge the gap between academic research and industry applications, Dr. Fayyazi aims to inspire the next generation of scientists and continue her impactful work in drug design and natural product research.

Honor and Awards ๐Ÿ†

Dr. Fayyazi’s career has been punctuated with numerous accolades, including the Best Score in the National Entrance Exam for Pharmaceutical Projects in 2001, the Productivity Management Prize at Soha Pharmaceutical Company (2010), and the prestigious PhD Scholarship for Medicinal Chemistry at Isfahan University. These awards reflect her unwavering dedication to excellence and innovation in both academic and industrial settings.

 

Top Noted Publications ๐Ÿ“–

Molecular dynamics simulation and 3D-pharmacophore analysis of new quinoline-based analogues with dual potential against EGFR and VEGFR-2
    • Authors: N. Fayyazi, A. Fassihi, S. Esmaeili, S. Taheri, J.B. Ghasemi, L. Saghaie
    • Journal: International Journal of Biological Macromolecules
    • Year: 2020
Pharmacophore modeling, synthesis, scaffold hopping and biological ฮฒ-Hematin inhibition interaction studies for anti-malaria compounds
    • Authors: N. Fayyazi, S. Esmaeili, S. Taheri, F.F. Ribeiro, M.T. Scotti, L. Scotti, …
    • Journal: Current Topics in Medicinal Chemistry
    • Year: 2019
Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19
    • Authors: N. Fayyazi, T. Mostashari-Rad, J.B. Ghasemi, M.M. Ardakani, F. Kobarfard
    • Journal: Journal of Biomolecular Structure and Dynamics
    • Year: 2022
Design, synthesis, in silico studies, and antiproliferative evaluations of novel indolin-2-one derivatives containing 3-hydroxy-4-pyridinone fragment
    • Authors: P. Shirvani, N. Fayyazi, S. Van Belle, Z. Debyser, F. Christ, L. Saghaie, …
    • Journal: Bioorganic & Medicinal Chemistry Letters
    • Year: 2022
Phytochemical analysis and purification of chief components of Ononis spinosa
    • Authors: N. Fayyazi, F. Naghibi, S. Taheri
    • Journal: N/A
    • Year: 2014
A new insight into the mechanism of loading of Granisetron, Naltrexone and Risperidone in poly (ortho ester) and poly (lactic-co-glycolic acid) as the controlled release drug
    • Authors: L. Karami, N. Fayyazi, M. Akbari, A. Molaei-Baghbanan, M. Maaza
    • Journal: Journal of Drug Delivery Science and Technology
    • Year: 2024

Christopher Prashad | Public Health | Best Researcher Award

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Dr. Christopher Prashad | Public Health | Best Researcher Award

York University, Canada

Author Profile ๐Ÿ“š

Orcid

๐Ÿ” Summary

Dr. Christopher Prashad is a PhD candidate in Mathematics and Statistics, specializing in Data Science for Public Health at York University. With a strong foundation in advanced quantitative research, including time series analysis, machine learning, stochastic simulation, and probabilistic programming, Dr. Prashad excels in transforming complex data into actionable insights. He has a proven track record of collaborating with academic and industry teams, effectively communicating technical concepts, and is eager to apply his skills in Data Science to drive meaningful decision-making.

๐ŸŽ“ Education

Dr. Prashad is currently pursuing his PhD in Mathematics and Statistics โ€“ Data Science for Public Health at York University (expected June 2025). He is also completing a MicroMasters in Finance from MIT Sloan School of Management (expected January 2025). He holds a Masterโ€™s degree in Mathematics and Statistics from York University and a Bachelor of Science in Mathematics and Chemistry from the University of Toronto.

๐Ÿ’ผ Professional Experience

Dr. Prashad has worked as a Mathematics for Public Health Researcher at York Universityโ€“Sanofi Pasteur (2022โ€“2024), where he collaborated with pharmaceutical companies and public health organizations to drive evidence-based policies and modeling initiatives. Prior to this, he worked as a Statistician at Western University (2014โ€“2019), conducting longitudinal studies to assess the impact of STEM education programs. He has also served as a Course Director for Statistical Methods in Health Studies at York University (2018โ€“2019), delivering lectures and tutorials while fostering student engagement in applied statistics.

๐Ÿ“š Academic Projects & Presentations

Dr. Prashad has contributed to several notable academic projects, such as Multiperiod Forecasting of Epidemic Prevalence using machine learning to predict the spread of flu-like illnesses, and Stochastic Threshold Analysis for SIRS Model, where he applied advanced mathematical techniques to epidemic modeling. He also analyzed Indexed GIC Investment Products using stochastic volatility models, presenting his findings to industry leaders in finance.

๐Ÿ’ป Technical Skills

Dr. Prashad is proficient in a wide range of software and analytical tools, including Python, R, MATLAB, SQL, SPSS, and C. He specializes in Time Series Analysis, Stochastic Simulation, Machine Learning, Multivariate Statistics, and Probabilistic Programming, with expertise in Bayesian Methods, Optimization, and Data Visualization.

๐Ÿ‘ฉโ€๐Ÿซ Teaching Experience

As a Course Director at York University, Dr. Prashad designed and delivered lectures on statistical methods in health studies, supported diverse student learning needs, and contributed to collaborative course design and student development.

๐Ÿ”ฌ Research Interests

Dr. Prashad’s research interests lie at the intersection of data-driven public health modeling, machine learning in healthcare, and mathematical epidemiology, with a particular focus on applying quantitative analysis to solve public health challenges.

 

๐Ÿ“– Top Noted Publications

Stochastic Threshold Analysis for Mean-Reverting SIRS Model
    • Authors: Dr. Christopher Prashad (and collaborators)
    • Journal: Journal of Theoretical Biology
    • Year: 2024
Problems and Solutions in Modern Finance, Financial Accounting, Mathematical Methods for Quantitative Finance, and Derivatives Markets
    • Authors: Dr. Christopher Prashad (and collaborators)
    • Journal: Independent publication
    • Year: 2024
Assessing the Impact of Community Mobility on COVID-19 Dynamics
    • Authors: Dr. Christopher Prashad (and collaborators)
    • Journal: PLOS Computational Biology
    • Year: 2024
State-Space Modelling for Infectious Disease Surveillance Data: Stochastic Simulation Techniques and Structural Change Detection
    • Authors: Dr. Christopher Prashad (and collaborators)
    • Journal: Journal of Infectious Disease Modelling, Primer Articles
    • Year: 2024
State-Space Modelling for Infectious Disease Surveillance Data: Dynamic Regression and Covariance Analysis
    • Authors: Dr. Christopher Prashad (and collaborators)
    • Journal: Journal of Infectious Disease Modelling, Primer Articles
    • Year: 2024

Endalkachew Etana | Statistical analysis | Editorial Board Member

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Mr. Endalkachew Etana | Statistical analysis | Editorial Board Member

Mattu University, Ethiopia
Author Profile

Orcidย 

๐ŸŽ“ Early Academic Pursuits

Mr. Endalkachew Etanaโ€™s academic journey started at Mizan Tepi University in Ethiopia, where he obtained his BSc in Chemistry (2012-2014) with a CGPA of 3.72. He further advanced his education with an MSc in Analytical Chemistry from Ambo University (2019-2021), achieving a CGPA of 3.90. These foundational years shaped his career in analytical and instrumental chemistry, setting the stage for his future contributions to the field.

๐Ÿ‘จโ€๐Ÿซ Teaching Experience

With extensive teaching experience, Mr. Etana has imparted knowledge across several critical chemistry courses, including Analytical Chemistry, Instrumental Analysis, and Environmental Chemistry. He has also taught practical modules in Instrumental Analysis and Real Sample Analysis. His commitment to high-quality education is evident in his involvement in curriculum development and laboratory manual preparation, ensuring students receive a hands-on and comprehensive learning experience.

๐Ÿ… Professional Endeavors & Committee Involvement

Mr. Etana has actively participated in various professional committees, including serving as the Analytical Chemistry Stream Leader. His roles in staff development, recruitment, laboratory installations, and curriculum reviews highlight his leadership in improving academic standards. These contributions demonstrate his dedication to fostering a thriving academic community within his institution.

๐Ÿ”ฌ Contributions and Research Focus

Mr. Etanaโ€™s research contributions primarily focus on analytical chemistry, environmental issues, and health risks. His work includes the analysis of trace heavy metals in Ethiopian white sugar products, the development of greener microextraction methods for pesticide residue analysis, and the evaluation of potentially toxic elements in agricultural soils. His ongoing projects, such as assessing radionuclides and heavy metals in soil samples, continue to drive innovation in analytical methodologies and environmental safety.

๐ŸŒ Impact and Influence

The impact of Mr. Etanaโ€™s work extends beyond academic circles. His research on heavy metals, environmental pollutants, and food safety influences public health policies in Ethiopia. His published articles in leading journals like the Journal of Agriculture and Food Research and the Chinese Journal of Analytical Chemistry shape the discourse on environmental and health risks, particularly in the context of Ethiopian agriculture. His findings aim to improve the quality of life through safer food products and cleaner environments.

๐Ÿ“š Academic Cites and Publications

Mr. Etanaโ€™s research has been widely published, with notable contributions in peer-reviewed journals. His work includes studies on the human health risk assessments of heavy metals in Ethiopian sugar products, the development of microextraction methods for pesticide residues, and evaluating toxic elements in agricultural soils. His articles have contributed significantly to the body of knowledge on food safety and environmental health.

๐Ÿ› ๏ธ Technical Skills

Mr. Etana is highly skilled in a variety of technical tools essential for research and teaching. These include proficiency in basic computer applications (MS Word, Excel, PowerPoint), Mendeley for reference management, and SPSS for statistical data analysis. His strong laboratory skills further enhance his research capabilities, making him well-equipped to conduct sophisticated scientific investigations.

๐ŸŒ Legacy and Future Contributions

Mr. Etanaโ€™s academic legacy is rooted in his commitment to advancing analytical chemistry and its applications in environmental health. His ongoing research into the optimization of extraction methods for caffeine in Ethiopian products and the study of heavy metal contamination continues to have far-reaching implications. As he continues to lead research projects, his work will contribute significantly to the field of environmental chemistry and the sustainability of agricultural practices in Ethiopia.

๐Ÿ… Awards and Conference Presentations

Mr. Etana has been recognized for his scholarly contributions, including winning the Best Paper Award at the international SIST-SHE 22 conference in India. He also participated in the 6th Annual National Research Conference at Mattu University in Ethiopia, where he presented on the integration of research with indigenous knowledge. These accolades reflect the high regard in which his work is held in the scientific community.

๐Ÿ“œ Short-term Courses & Certifications

Mr. Etana demonstrates a commitment to continuous professional development by completing certifications in various fields. Notably, he has completed courses in Data Analysis Fundamentals and Artificial Intelligence Fundamentals, underscoring his dedication to staying current with the latest technological advancements and integrating them into his research and teaching.

 

๐Ÿ“– Top Noted Publications

Evaluation of some physicochemical parameters and health risks associated with potentially toxic elements (PTEs) in agricultural soils from the southwest region of Ethiopia

Authors: Endalkachew Etana, Redwan Hussein, Abire Huluka
Journal: Journal of Hazardous Materials Advances
Year: 2025

A development in greener microextraction methods for analysis of pesticide residues in environmental water samples: A review

Authors: Endalkachew Etana, Birhanu MEKASSA
Journal: Chinese Journal of Analytical Chemistry
Year: 2024

Proximate analysis, levels of trace heavy metals and associated human health risk assessments of Ethiopian white sugars

Authors: Birhanu Mekassa, Endalkachew Etana, Lemessa B. Merga
Journal: Journal of Agriculture and Food Research
Year: 2024

Yanjie Zhu | Algorithm Development | Best Researcher Award

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Prof. Yanjie Zhu | Algorithm Development | Best Researcher Award

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

๐Ÿ‘จโ€๐ŸŽ“Professional Profiles

๐Ÿ”ฌ Summary

Prof. Yanjie Zhu is a renowned expert in fast magnetic resonance imaging (MRI) and its applications in medical diagnostics. He specializes in developing innovative imaging techniques through machine learning, deep learning, and model-driven methods. As a Professor at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, he leads cutting-edge research projects focused on cardiovascular imaging, brain health, and intelligent diagnostic tools.

๐ŸŽ“ Education

Prof. Zhu earned his Ph.D. in Circuits and Systems from the Shanghai Institute of Technical Physics, Chinese Academy of Sciences, China (2006-2011). He also holds a B.S. in Electronic Engineering and Information Science from the University of Science and Technology of China, Hefei (2002-2006).

๐Ÿซ Professional Experience

Prof. Zhu has a distinguished academic career at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, where he progressed from Assistant Professor (2011-2015) to Associate Professor (2015-2020), and is currently a Professor (2020-present). Additionally, he served as a Visiting Scholar (2017-2018) at Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, under the guidance of Reza Nezafat.

๐Ÿ“š Academic Contributions

Prof. Zhu has published several impactful papers in the field of MRI and medical imaging. Notable works include:

  • “Online reconstruction of fast dynamic MR imaging using deep low-rank plus sparse network,” IEEE CBMS, 2022.
  • “k-space based reconstruction method for wave encoded bSSFP sequence,” CECIT, 2021.
  • “Quantification of pectinate muscles inside left atrial appendage from CT images using fractal analysis,” ICMIPE, 2021.
  • Myocardial Edema Imaging – A Comparison of Three Techniques, ISMRM 2018 (Power Pitch, Magna Cum Laude Merit Award).

๐Ÿ” Research Interests

Prof. Zhuโ€™s research interests revolve around model-driven fast MRI techniques, generative model-based adaptive MRI methods, and diffusion tensor and edema imaging for myocardial and brain tissue. He is also focused on intelligent diagnosis for the early detection of stroke-related plaques and other cardiovascular conditions.

๐Ÿ’ป Technical Skills

Prof. Zhu is highly skilled in MRI imaging techniques, including fast MRI, cardiac MRI, and brain MRI. He also excels in deep learning and AI-based image reconstruction methods, along with proficiency in medical imaging software and model-driven approaches. His expertise in data analysis and computational methods has contributed to advancing medical applications.

๐Ÿ‘จโ€๐Ÿซ Teaching Experience

Throughout his career, Prof. Zhu has mentored and taught graduate students and professionals in the fields of MRI technology, medical imaging, and computational healthcare methods. His research-driven approach has helped develop comprehensive educational materials that support the training of professionals in advanced imaging systems.

๐Ÿ“–Top Noted Publications

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping

Authors: Chiyi Huang, Longwei Sun, Dong Liang, Haifeng Liang, Hongwu Zeng, Yanjie Zhu
Journal: Computers in Biology and Medicine
Year: 2024

High-Frequency Space Diffusion Model for Accelerated MRI

Authors: Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: IEEE Transactions on Medical Imaging
Year: 2024

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

Authors: Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: IEEE Transactions on Medical Imaging
Year: 2024

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Authors: Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu
Journal: IEEE Journal of Biomedical and Health Informatics
Year: 2024

High efficiency free-breathing 3D thoracic aorta vessel wall imaging using self-gating image reconstruction

Authors: Caiyun Shi, Congcong Liu, Shi Su, Haifeng Wang, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: Magnetic Resonance Imaging
Year: 2024

Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging

Authors: Yanjie Zhu, Jing Cheng, Zhuo-Xu Cui, et.al.
Journal: IEEE Signal Processing Magazine
Year: 2023

Jing An | Artificial Intelligence | Best Researcher Award

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Dr. Jing An | Artificial Intelligence | Best Researcher Award

Yancheng Institute of Technology, China

๐Ÿ‘จโ€๐ŸŽ“Professional Profile

Scopus Profile

๐Ÿ‘จโ€๐Ÿซ Summary

Dr. Jing An is a distinguished professor and master tutor at Yancheng Institute of Technology, China. He specializes in intelligent manufacturing engineering, industrial big data fault diagnosis, and residual life prediction. With numerous patents and software copyrights, Dr. An has published over 20 SCI/EI indexed papers and contributed to key academic texts. His pioneering research in artificial intelligence-based fault diagnosis has earned him prestigious awards, including first-place recognition in the China Commerce Federation Science and Technology Award .

๐ŸŽ“ Education

Dr. An holds a Ph.D. in Computer Science from Hohai University (2021) and a Masterโ€™s degree in Computer Science from Harbin University of Science and Technology (2006). His academic foundation has strongly influenced his research in AI and fault diagnosis.

๐Ÿ’ผ Professional Experience

Having joined Yancheng Institute of Technology in 2006, Dr. An is also the Vice President of Science and Technology for Jiangsu Province’s “Double Innovation Plan.” He has led numerous provincial-level projects, and his expertise has extended to more than 10 industry partnerships ๐Ÿš€.

๐Ÿ“š Academic Citations

Dr. An has authored over 20 peer-reviewed papers in prestigious journals such as IEEE Access and Mathematical Problems in Engineering. His impactful research on AI-based fault diagnosis methods is frequently cited within the academic community .

๐Ÿ”ง Technical Skills

Dr. An is skilled in AI-based fault diagnosis, deep learning, machine learning, and industrial big data analytics. He has expertise in Convolutional Neural Networks (CNNs) and intelligent manufacturing systems, focusing on improving machinery reliability and efficiency .

๐Ÿง‘โ€๐Ÿซ Teaching Experience

Dr. An has been recognized as an outstanding teacher twice at Yancheng Institute of Technology. He teaches courses in intelligent manufacturing engineering and industrial big data fault diagnosis, preparing students to advance in the field of AI and data science .

๐Ÿ” Research Interests

Dr. Anโ€™s research is focused on developing intelligent systems for fault diagnosis in rotating machinery, predictive maintenance, and the application of deep learning techniques in industrial big data. His work aims to enhance manufacturing processes and equipment reliability .

๐Ÿ“–Top Noted Publications

Hybrid Mechanism and Data-Driven Approach for Predicting Fatigue Life of MEMS Devices by Physics-Informed Neural Networks

Authors: Cheng, J., Lu, J., Liu, B., An, J., Shen, A.

Journal: Fatigue and Fracture of Engineering Materials and Structures

Year: 2024

Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

Authors: An, J., An, P.

Journal: International Journal of Circuits, Systems and Signal Processing

Year: 2022

Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

Authors: An, J., Ai, P., Liu, C., Xu, S., Liu, D.

Journal: IEEE Access

Year: 2021

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Riemann Metric Correlation Alignment

Authors: An, J., Ai, P.

Journal: Mathematical Problems in Engineering

Year: 2020

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Authors: An, J., Ai, P., Liu, D.

Journal: Shock and Vibration

Year: 2020

Haixia Mao | Data Science | Best Researcher Award

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Dr. Haixia Mao | Data Science | Best Researcher Award

Shenzhen Polytechnic University, China

๐Ÿ‘จโ€๐ŸŽ“Professional Profile

Scopus Profile

๐Ÿ‘ฉโ€๐Ÿซ Summary

Dr. Haixia Mao is an Associate Professor at the School of Automobile and Transportation at Shenzhen Polytechnic University, China. She holds a Ph.D. in Geographic Information System (GIS) and Remote Sensing from the Hong Kong Polytechnic University. Her research focuses on GIS-T, big data analytics, and analyzing urban mobility patterns, using spatial-temporal data from transportation systems to improve urban planning and human behavior modeling.

๐ŸŽ“ Education

Dr. Mao earned her Ph.D. in GIS and Remote Sensing from the Hong Kong Polytechnic University in 2010. She also completed her M.S. in GIS at Wuhan University in 2004 and her B.S. in Photogrammetry and Remote Sensing from Wuhan University.

๐Ÿ’ผ Professional Experience

Dr. Mao currently serves as an Associate Professor at Shenzhen Polytechnic University (since 2012), where she has contributed to the development of transportation-related GIS courses. Previously, she worked as a Postdoctoral Researcher at the Hong Kong Polytechnic University from 2014 to 2016, furthering her expertise in urban computing and GIS.

๐Ÿ“š Research Interests

Her research encompasses GIS-T (Geographic Information Systems for Transportation), big data analytics, remote sensing, deep learning, and urban planning. She investigates the mobility patterns of citizens through traffic data (including taxis, buses, and metro) and leverages spatial-temporal data to enhance urban infrastructure and predict human behavior.

๐Ÿ… Academic Contributions

Dr. Mao has authored over 20 peer-reviewed journal articles and conference papers. She has also been awarded a patent for a caching system with a new cache placement method. She has received research grants for spatial-temporal data analytics in transportation and has actively contributed to major projects in urban computing.

๐Ÿ’ป Technical Skills

Dr. Maoโ€™s technical expertise spans GIS, remote sensing, big data analytics, deep learning, and spatial-temporal data analysis. She integrates these advanced techniques to solve complex transportation and urban planning challenges.

๐Ÿ“š Teaching Experience

Dr. Mao has taught several courses since 2012 at Shenzhen Polytechnic University, including GIS for Transportation, Traffic Engineering Cartography, and Traffic Information Processing. She has a long-standing dedication to educating future professionals in the field of urban planning and transportation technology.

 

๐Ÿ“–Top Noted Publications

Multiple sclerosis lesions segmentation based on 3D voxel enhancement and 3D alpha matting

Authors: Sun, Y., Zhang, Y., Wang, X., Zeng, Z., Mao, H.
Journal: Chinese Journal of Medical Physics
Year: 2022

Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence

Authors: Mao, H., Fan, X., Guan, J., Wang, Y., Xu, C.
Journal: Computers in Human Behavior
Year: 2019

ย When Taxi Meets Bus: Night bus stop planning over large-scale traffic data

Authors: Xiao, L., Fan, X., Mao, H., Lu, P., Luo, S.
Conference: Proceedings – 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Year: 2017

Characterizing On-Bus WiFi passenger behaviors by approximate search and cluster analysis

Authors: Jiang, M., Fan, X., Zhang, F., Mao, H., Liu, R.
Conference: Proceedings – 2016 7th International Conference on Cloud Computing and Big Data, CCBD 2016
Year: 2017

Efficient visibility analysis for massive observers

Authors: Wang, W., Tang, B., Fan, X., Yang, H., Zhu, M.
Conference: Procedia Computer Science
Year: 2017

Web access patterns enhancing data access performance of cooperative caching in IMANETs

Authors: Fan, X., Cao, J., Mao, H., Zhao, Y., Xu, C.
Conference: Proceedings – IEEE International Conference on Mobile Data Management
Year: 2016

Theresa Kรผhle | Data Analysis | Best Researcher Award

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Mrs. Theresa Kรผhle | Data Analysis | Best Researcher Award

Mrs. Theresa Kรผhle at Universitรคt Mรผnster, Germany

๐Ÿ‘จโ€๐ŸŽ“Professional Profile

๐ŸŒŸ Summary

Theresa Kรผhle is a dedicated medical student at the University of Mรผnster, Germany, with a strong focus on pediatric cardiology, prenatal medicine, and psychosomatic disorders. Passionate about research and clinical practice, she has presented at several medical congresses and published research in the field of fetal heart failure. As a Cusanuswerk scholarship recipient, she is committed to advancing in her medical career with international experience.

๐ŸŽ“ Education

Since 2019, Theresa has been studying medicine at the University of Mรผnster, Germany. She also gained international medical experience at the Universidad de Valladolid, Spain, in 2023. Theresa completed her Abitur at Gymnasium Schloss Neuhaus, Paderborn, in 2018, with an outstanding grade of 1.0.

๐Ÿ’ผ Professional Experience

Theresa has gained extensive practical experience in hospitals and medical settings. Since 2024, she has been working as an OP student in the Department of Gynaecology and Obstetrics at St. Franziskus-Hospital in Mรผnster. She also gained clinical experience in pediatrics, cardiology, neurology, urology, and psychosomatic medicine at the University Hospital Mรผnster. In addition, she has been a tutor for a sonography course at her university since 2024.

๐Ÿ“‘ Academic Citations

In 2024, Theresa presented posters at the 65th DGGG Congress in Berlin and the 47th Tri-Nation Meeting in Salzburg. She has also published a research paper on “Dyssynchronous fetal heart failure in maternal diabetes” with Prof. Dr. Ralf Schmitz, highlighting her active involvement in medical research.

๐Ÿ› ๏ธ Technical Skills

Theresa possesses advanced skills in sonography, as evidenced by her role as a tutor in the ultrasound course at the University of Mรผnster. She has a deep understanding of prenatal diagnostics, particularly in echocardiography and M-mode software. In addition, she is fluent in English (C1/C2), Spanish (B2.2), and French (B2), with proficiency in Latin.

๐Ÿ‘ฉโ€๐Ÿซ Teaching Experience

Theresa has enriched her teaching portfolio by serving as a tutor for the sonography course at the University of Mรผnster since 2024. She has also gained hands-on teaching experience in psychosomatic treatments, pediatrics, and psychotherapeutic practices during her medical training.

๐Ÿ”ฌ Research Interests

Her research focuses on pediatric cardiology, particularly congenital heart defects, and prenatal medicine, with a specific interest in fetal heart failure caused by maternal diabetes. Additionally, Theresa is passionate about psychosomatic medicine and psychological health treatments for children and adolescents.

๐Ÿ“–Top Noted Publication

Dyssynchronous Fetal Heart Failure in Maternal Diabetes: Evaluation with Speckle Tracking Echocardiography and Novel M-Mode Software

Authors: Theresa M. Kรผhle, Angela Burgmair, Georg Schummers, Mareike Mรถllers, Kathrin Oelmeier, Chiara De Santis, Helen Ann Kรถster, Ute Mรถllmann, Daniela Willy, Janina Braun, et al.

Journal: Ultrasound in Medicine & Biology

Year: 2024

Sogand Dehghan | social network analysis | Best Innovation Award

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Ms. Sogand Dehghan | social network analysis | Best Innovation Award

Ms. Sogand Dehghan at K. N. Toosi University of Technology, Iran

๐Ÿ‘จโ€๐ŸŽ“Professional Profiles

Orcid Profile

Google Scholarย  Profile

๐Ÿ‘ฉโ€๐Ÿ’ป Summary

Ms. Sogand Dehghan an Information Technology specialist with expertise in data analysis and software development. My work primarily focuses on collecting, cleaning, and analyzing data from various sources to create actionable reports and dashboards for organizational decision-making. I am passionate about using data-driven strategies to help businesses achieve their goals, with a particular interest in social media analysis and its alignment with organizational objectives. Additionally, I enjoy developing data-driven software solutions that enhance operational efficiency.

๐ŸŽ“ Education

Ms. Sogand Dehghan earned my Master’s Degree in Information Technology from K.N. Toosi University of Technology (2019-2022) and my Bachelor’s Degree in Information Technology with a grade of 97% from Payame Noor University (2012-2016). My thesis, titled “Provision of an Efficient Model for Evaluation of Social Media Users Using Machine Learning Techniques,” focused on leveraging machine learning to assess social media engagement and user behavior.

๐Ÿ’ผ Professional Experience

Ms. Sogand Dehghan currently hold two key roles: as an Instructor at National Skills University, where I teach courses on Advanced Programming and Data Analysis (since Sep 2024), and as a Data Analyst at GAM Arak Industry, where I focus on data mining, machine learning, and visualizing data insights using tools like Power BI, Python, SQL Server, and SSRS (since Jan 2024). Additionally, I serve as a Software Developer at GAM Arak Industry, working with C#, ASP.NET Core, and SQL Server to build scalable software solutions (since Apr 2023). In my previous role at Kherad Sanat Arvand (Apr 2022 – Jun 2023), I honed my skills in data analysis and dashboard development.

๐Ÿ“š Academic Citations

My research has been published in prestigious journals. My paper, “The Credibility Assessment of Twitter Users Based on Organizational Objectives,” was published in Computers in Human Behavior (Sep 2024), where I developed a model for assessing the credibility of Twitter users by integrating profile data with academic sources like Google Scholar. Another notable publication, “The Evaluation of Social Media Usersโ€™ Credibility in Big Data Life Cycle,” appeared in the Journal of Information and Communication Technology (Sep 2023), in which I reviewed existing frameworks for evaluating social media user credibility.

๐Ÿ”ง Technical Skills

My technical skill set includes expertise in C#, ASP.NET, Python, and SQL Server for software development. I specialize in Data Visualization with Power BI, Data Mining, Machine Learning, and Social Network Analysis. Additionally, I have a solid foundation in Text Mining and Data Modeling, which enables me to provide comprehensive solutions for data-driven challenges in various organizational contexts.

๐Ÿ‘จโ€๐Ÿซ Teaching Experience

As an Instructor at National Skills University, I teach Advanced Programming and Data Analysis. I focus on equipping students with the skills needed to thrive in the data-driven world of technology. My approach integrates real-world data problems with theoretical knowledge to help students apply their learning in practical scenarios.

๐Ÿ” Research Interests

My research interests lie in the intersection of Machine Learning and Social Network Analysis. I am particularly interested in developing models to evaluate social media user credibility, integrating heterogeneous data sources for organizational decision-making, and exploring the broader implications of big data in evaluating trust and influence within social networks.

 

Yun Xing | Computer Vision | Best Researcher Award

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Mr. Yun Xing | Computer Vision | Best Researcher Award

Mr. Yun Xing at The First Affiliated Hospital of Xi’an Jiaotong University, China

๐Ÿ‘จโ€๐ŸŽ“Professional Profile

๐ŸŽ“ Education and Academic Background

I am currently a second-year Ph.D. student at VI-Lab, Nanyang Technological University (NTU), under the supervision of Prof. Shijian Lu. I received my B.S. degree in Biomedical Engineering and Instrument Science from Zhejiang University in 2021, where I worked under the guidance of Prof. Hong Zhou. Prior to my Ph.D., I completed my Masterโ€™s in Artificial Intelligence at NTU (2021โ€“2022). My research interests primarily focus on vision-language pre-training and foundation model adaptation.

๐Ÿ”ฌ Research Interests

My research revolves around cutting-edge topics in vision-language pre-training and the adaptation of foundation models. I am particularly interested in methods that enable models to transfer knowledge effectively across different domains, such as few-shot learning, cross-domain adaptation, and improving model robustness in complex vision-language tasks.

๐Ÿ† Achievements and News

In recent years, I have been fortunate to have several papers accepted at top-tier conferences. These include NeurIPS 2024, ECCV 2024, CVPR 2024, and NeurIPS 2023, marking significant milestones in my academic journey. Notably, my work on object hallucination mitigation and segmentation adaptation has received considerable attention in the community.

๐Ÿ… Awards and Honors

Throughout my academic journey, I have received various recognitions for my contributions to research. These include the Outstanding Graduate Award from Zhejiang University in 2021, as well as the Academic Excellence Award (2018, 2019) and Academic Progress Award (2019) from the same institution.

๐Ÿ’ป Service and Teaching

As an active member of the academic community, I contribute as a conference reviewer for prominent venues such as CVPR, ICML, ECCV, and NeurIPS 2024, where I was honored to be selected as one of the Top Reviewers at NeurIPS. Additionally, I am involved in teaching and mentoring at NTU. In Spring 2024, I will be assisting with the SC1015: Introduction to Data Science and Artificial Intelligence course.

Jianhong Hao | Predictive Analytics | Best Researcher Award

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Dr. Jianhong Hao | Predictive Analytics | Best Researcher Award

Doctorate at Honghui Hosipital, Xi’an Jiaotong University, China

๐Ÿ‘จโ€๐ŸŽ“Professional Profiles

๐Ÿ‘จโ€โš•๏ธ Summary

Dr. Jianhong Hao is a Master of Anesthesia and a prominent researcher in perioperative care at Honghui Hospital, Xi’an Jiaotong University, China. His expertise lies in analyzing risk factors for perioperative complications and applying ultrasound technology for airway management. With over 9 SCI papers published in the last five years, his research focuses on improving patient outcomes, particularly in trauma and post-surgical settings.

๐ŸŽ“ Education

Dr. Hao completed his Master of Anesthesia at Xi’an Jiaotong University, where he developed a strong foundation in clinical anesthesia and perioperative medicine.

๐Ÿฅ Professional Experience

Currently, Dr. Hao serves as a clinical specialist and researcher at Honghui Hospital. He specializes in risk factor analysis for perioperative complications and has made significant contributions to optimizing care for trauma and surgical patients.

๐Ÿ“š Academic Citations

Dr. Hao’s research is widely recognized in the scientific community. His publications in journals like Journal of Clinical Anesthesia and BMC Anesthesiology cover diverse topics such as postoperative pulmonary complications (PPCs) and the use of ultrasound in predicting respiratory issues in trauma patients.

๐Ÿ’ป Technical Skills

Dr. Hao is skilled in utilizing ultrasound technology for airway management and perioperative care. He also specializes in statistical modeling, data analysis, and risk factor identification for various surgical complications, with an emphasis on predictive tools and clinical trials.

๐Ÿ‘ฉโ€๐Ÿซ Teaching Experience

In addition to his clinical and research work, Dr. Hao is an educator at Xi’an Jiaotong University, where he mentors medical students and residents in anesthesia and perioperative management.

๐Ÿ”ฌ Research Interests

Dr. Haoโ€™s research interests center around the identification of risk factors for perioperative complications, especially those involving trauma patients. His ongoing projects include developing risk stratification models based on lung ultrasound scores for traumatic lung injuries and optimizing care for patients with postoperative pulmonary complications.

๐Ÿ… Key Contributions

Dr. Hao has made pioneering contributions in the use of postoperative lung ultrasound scores to predict pulmonary complications in patients with blunt thoracic trauma. His work also includes developing models for early identification of risks in trauma patients and optimizing blood use in surgery

๐Ÿค Collaborations

Dr. Hao collaborates with multidisciplinary teams at Honghui Hospital and is an active member of the Shaanxi Clinical Trial Research Society, contributing to ongoing clinical studies and evidence-based practices in anesthesia.