Carmela nappi | Health | Women Researcher Award

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Dr. carmela nappi | Health | Women Researcher Award

Dr. carmela nappi, Federico II University Naples, Italy

Dr. Carmela Nappi is an Assistant Professor at the Department of Advanced Biomedical Sciences, University of Naples Federico II. 🎓 She holds a degree in Medicine and Surgery, a PhD in Biomorphological and Surgical Sciences, and board certifications in Nuclear Medicine. 🏅 Her research focuses on nuclear cardiology, with expertise in PET/CT and SPECT procedures. She has held prestigious fellowships, including at Massachusetts General Hospital and SDN Foundation for Research. 🏥 Dr. Nappi is also involved in academic teaching and has contributed to innovative healthcare projects. 💡 Her work bridges clinical and research domains in nuclear medicine.

 

Publication Profile

Google Scholar

Education & Qualifications 🎓

Dr. Carmela Nappi completed her degree in Medicine and Surgery at the University of Naples Federico II, with a score of 108/110 in 2009. 🏅 She obtained her Medical Board Certification in 2010 (Order of Doctors, Surgeons, and Dentists of Naples). In 2015, she earned Board Certification in Nuclear Medicine with a score of 50/50 cum laude. 🏥 Additionally, Dr. Nappi holds an IELTS Academic English Level B2 certification (2015). She further advanced her studies by obtaining a PhD in Biomorphological and Surgical Sciences from the University of Naples Federico II in 2019. 🎓

 

Achieved Qualifications 🎓

Dr. Carmela Nappi has earned the prestigious Italian National Scientific Qualification for the role of Associate Professor in two key sectors. 🌟 She was recognized in the competition sector 06/I1-Diagnostic Imaging, Radiotherapy, and Neuroradiology, valid from February 7, 2022, to February 7, 2032. Additionally, she achieved the qualification for Associate Professor in Sector 06/N1-Sciences of Health Professions and Applied Medical Technologies, valid from October 5, 2022, to October 5, 2032. These qualifications highlight her academic excellence and dedication to advancing medical and scientific fields. 🏅

 

Academic and International Teaching Experience 📚🌍

Dr. Carmela Nappi has made significant contributions to education, particularly in nuclear medicine and radiology. She has supported teaching activities at the University of Naples Federico II, where she assisted in courses on nuclear medicine, medical imaging, and radiotherapy from 2015 to present. Additionally, she co-tutored Master’s and Specialization theses in Medicine and Nuclear Medicine. Her international teaching experience includes advanced online courses and webinars, such as the “Quantification in Myocardial Perfusion Imaging” and the “Non-ischemic Heart Disease” webinar, collaborating with esteemed institutions like the European School of Multimodality Imaging and Therapy and the Japanese Society of Nuclear Medicine. 🌟🎓

 

Research Focus

Dr. Carmela Nappi’s research predominantly revolves around cardiovascular imaging and nuclear medicine. Her work focuses on myocardial perfusion imaging, including the use of PET/CT and PET/MRI technologies for early detection of coronary artery disease and cardiac involvement in genetic conditions such as Anderson-Fabry disease. She explores quantification techniques in myocardial perfusion and evaluates the diagnostic accuracy of CZT-SPECT. Dr. Nappi is also involved in coronary artery calcium scoring and coronary CT angiography, investigating their prognostic value for assessing atherosclerotic burden and vascular function. Her contributions span both clinical and technological advancements in cardiac care. 🫀📊

 

Publication Top Notes

  • EANM procedural guidelines for PET/CT quantitative myocardial perfusion imaging – R Sciagrà, M Lubberink, F Hyafil, A Saraste, RHJA Slart, D Agostini, … – European Journal of Nuclear Medicine and Molecular Imaging 48, 1040-1069 – Cited by: 124 📊 (2021)

 

  • Biparametric 3T Magnetic Resonance Imaging for prostatic cancer detection in a biopsy-naïve patient population: a further improvement of PI-RADS v2? – A Stanzione, M Imbriaco, S Cocozza, F Fusco, G Rusconi, C Nappi, … – European Journal of Radiology 85(12), 2269-2274 – Cited by: 106 🔬 (2016)

 

  • First experience of simultaneous PET/MRI for the early detection of cardiac involvement in patients with Anderson-Fabry disease – C Nappi, M Altiero, M Imbriaco, E Nicolai, CA Giudice, M Aiello, … – European Journal of Nuclear Medicine and Molecular Imaging 42, 1025-1031 – Cited by: 88 🧑‍⚕️ (2015)

 

  • Combined evaluation of regional coronary artery calcium and myocardial perfusion by 82Rb PET/CT in the identification of obstructive coronary artery disease – E Zampella, W Acampa, R Assante, C Nappi, V Gaudieri, CG Mainolfi, … – European Journal of Nuclear Medicine and Molecular Imaging 45, 521-529 – Cited by: 68 ❤️ (2018)

 

  • Quantification of myocardial perfusion reserve by CZT-SPECT: A head to head comparison with 82Rubidium PET imaging – W Acampa, E Zampella, R Assante, A Genova, G De Simini, T Mannarino, … – Journal of Nuclear Cardiology 28(6), 2827-2839 – Cited by: 55 💡 (2021)

 

  • Diagnostic performance of myocardial perfusion imaging with conventional and CZT single-photon emission computed tomography in detecting coronary artery disease: A meta-analysis – V Cantoni, R Green, W Acampa, E Zampella, R Assante, C Nappi, … – Journal of Nuclear Cardiology 28(2), 698-715 – Cited by: 55 🔍 (2021)

 

  • Prognostic value of coronary artery calcium score and coronary CT angiography in patients with intermediate risk of coronary artery disease – M Petretta, S Daniele, W Acampa, M Imbriaco, T Pellegrino, G Messalli, … – The International Journal of Cardiovascular Imaging 28, 1547-1556 – Cited by: 50 📉 (2012)

 

  • Prognostic value of normal stress myocardial perfusion imaging in diabetic patients: A meta-analysis – W Acampa, V Cantoni, R Green, F Maio, S Daniele, C Nappi, V Gaudieri, … – Journal of Nuclear Cardiology 21(5), 893-902 – Cited by: 45 💓 (2014)

 

  • Transient ischemic dilation in SPECT myocardial perfusion imaging for prediction of severe coronary artery disease in diabetic patients – M Petretta, W Acampa, S Daniele, MP Petretta, C Nappi, R Assante, … – Journal of Nuclear Cardiology 20(1), 45-52 – Cited by: 44 🩺 (2013)

 

  • Incremental prognostic value of coronary flow reserve assessed with single-photon emission computed tomography – S Daniele, C Nappi, W Acampa, G Storto, T Pellegrino, F Ricci, E Xhoxhi, … – Journal of Nuclear Cardiology 18(4), 612-619 – Cited by: 44 ⏳ (2011)

 

Venkata Lakshmi Dasari| Deep Learning | Best Researcher Award

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Prof Dr. Venkata Lakshmi Dasari| Deep Learning | Best Researcher Award

Prof Dr. Venkata Lakshmi,VIT-AP University, Andhra Pradesh,India

Prof. Dr. Venkata Lakshmi is a distinguished academician and researcher at VIT-AP University in Andhra Pradesh, India. With a strong background in her field, she has made significant contributions to research and education, mentoring students and collaborating on innovative projects. Her work is recognized both nationally and internationally, making her a prominent figure in her academic community.

Summary:

Prof. Dr. Venkata Lakshmi is a highly qualified candidate with a robust academic background, significant teaching experience, and a strong record of mentoring PhD students. Her dual PhDs in CSE and Mathematics, coupled with over 25 years of academic service, make her a strong contender for the Best Researcher Award. She has demonstrated a commitment to both teaching and research, with recognized achievements in both areas.

Professional Profiles:

Orcid

🎓 Education :

With an impressive academic background, the candidate holds two PhDs—one in Computer Science and Engineering (CSE) from Vellore Institute of Technology University, Chennai Campus (2023), and another in Mathematics from Central University of Hyderabad (2008). Additionally, they completed an MTech in CSE from Acharya Nagarjuna University, Guntur (2010), and an M.Phil. in Mathematics from Central University of Hyderabad (1994-1995). Their educational journey began with a B.Sc. in Mathematics, Physics, and Chemistry (M.P.C) from Acharya Nagarjuna University (1988-1991), followed by an M.Sc. in Mathematics, where they secured the University Second Rank (1991-1993).

🏢Teaching Experience:

The candidate has accumulated over two decades of teaching experience in various academic roles. They began their teaching career as a Lecturer in the Department of Mathematics at Bapatla College for Women (1993-1994) and Bapatla College of Arts and Sciences (1995-1996). They then served at Bapatla Engineering College, first as a Lecturer (1996-2008) and later as an Associate Professor (2008-2009). From 2009 to 2019, they held the position of Vice-Principal at Bapatla Women’s Engineering College, while on deputation from Bapatla Engineering College. The candidate returned to Bapatla Engineering College as an Associate Professor (2019-2021) before transitioning to their current role as a Professor at the School of Computer Science and Engineering, VIT AP University, Andhra Pradesh, since June 2021.

🛠️Skills:

The candidate possesses a diverse skill set, including expertise in Functional Analysis, Harmonic Analysis, Theory of Computation, Machine Learning, and Deep Learning. Their research and teaching proficiency span both Mathematics and Computer Science, with a strong foundation in both theoretical and applied aspects of these fields.

🔬Awards and Achievements:

Throughout their career, the candidate has been recognized for their academic excellence and contributions to research. Notably, they received the Research Award for Publications in Engineering & Advanced Sciences in the academic year 2023-24. Their achievements also include a Silver Medal for outstanding performance in Mathematics during their B.Sc., the Mathematical Sciences Trust Society Prize for securing the top position in M.Sc. (Pure Mathematics) at the University of Hyderabad in 1992, and a Silver Medal for securing the third rank in the Mathematical Olympiad at the postgraduate level, sponsored by the Andhra Pradesh Association of Mathematics Teachers in 1992.

Research Focus:

The candidate’s research focus spans across multiple areas, including Functional Analysis, Harmonic Analysis, Theory of Computation, Machine Learning, and Deep Learning. Their PhD dissertations reflect their deep engagement with these subjects, with their Mathematics dissertation titled “On Vector Valued Amalgam Spaces” and their CSE dissertation titled “Self-replicability of Graphs through Graph Reproduction System.”

Conclusion:

Prof. Dr. Venkata Lakshmi is well-suited for the Best Researcher Award, particularly given her multidisciplinary expertise and extensive teaching and mentorship experience. However, enhancing her publication record with more detailed metrics, expanding interdisciplinary research efforts, and increasing international exposure could further solidify her standing as an outstanding researcher. With these improvements, she would not only be a deserving recipient but also a model researcher with global impact.

Publications :

  • Title: Label-guided Low-rank Approximation for Functional Brain Network Learning in Identifying Subcortical Vascular Cognitive Impairment
    Authors: Jiang, X., Wang, G., Zhang, L., Leone, R.D., Qiao, L.
    Source: Biomedical Signal Processing and Control
    Year: 2024

 

  • Title: Joint Selection of Brain Network Nodes and Edges for MCI Identification
    Authors: Jiang, X., Qiao, L., De Leone, R., Shen, D.
    Source: Computer Methods and Programs in Biomedicine
    Year: 2022

 

  • Title: Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
    Authors: Jiang, X., Zhou, Y., Zhang, Y., Qiao, L., De Leone, R.
    Source: Frontiers in Neuroscience
    Year: 2022

 

  • Title: Extracting BOLD Signals Based on Time-Constrained Multiset Canonical Correlation Analysis for Brain Functional Network Estimation and Classification
    Authors: Wang, H., Jiang, X., De Leone, R., Qiao, L., Zhang, L.
    Source: Brain Research
    Year: 2022

 

  • Title: Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification
    Authors: Zhang, Y., Jiang, X., Qiao, L., Liu, M.
    Source: Frontiers in Neuroscience
    Year: 2021

 

  • Title: Estimating Functional Connectivity Networks via Low-Rank Tensor Approximation with Applications to MCI Identification
    Authors: Jiang, X., Zhang, L., Qiao, L., Shen, D.
    Source: IEEE Transactions on Biomedical Engineering
    Year: 2020

 

  • Title: Completing Missing Exam Scores with Structural Information and Beyond
    Authors: Jiang, X., Zhang, L., Qiao, L.
    Source: Journal of Applied Remote Sensing
    Year: 2019

 

 

 

Xiao Jiang | Medical | Best Researcher Award

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 Dr.Xiao Jiang | Medical | Best Researcher Award

 Dr. Xiao Jiang, Liaocheng University,China

Dr. Xiao Jiang is a distinguished academic at Liaocheng University, China, where he specializes in With a robustDr. Jiang has made significant contributions to [mention notable research, publications, or projects]. His work is recognized for advancing understanding in His research continues to influence

Summary:

Dr. Xiao Jiang is a promising candidate for the Best Researcher Award, with a strong academic background and an impressive portfolio of research achievements. His work in developing innovative methods for brain network analysis and cognitive impairment identification stands out for its technical sophistication and contribution to the field. His leadership in securing and managing significant research projects further demonstrates his capabilities as a researcher.

Professional Profiles:

Scopus

🎓 Education :

Xiao Jiang earned his Ph.D. in Computer Science and Mathematics from the University of Camerino, Italy (October 2020 – June 2024). Prior to that, he completed his Master’s in System Science at Liaocheng University (September 2016 – June 2019) and his Bachelor’s in Mathematics and Applied Mathematics at the same institution (September 2012 – June 2016).

🏢Research Projects:

Brain Function Data Visualization and Classification Learning: Funded by the National Natural Science Foundation of China (Project Approval Number: 62176112, January 2022 – December 2025).,High-Order Regularization Framework for Functional Brain Image Learning and Its Application Research: Funded by the National Natural Science Foundation of China (Project Approval Number: 61976110, January 2020 – December 2023).

🛠️Research Achievements:

Jiang, X., Zhang, L., Qiao, L., Shen, D. (2020). “Estimating Functional Connectivity Networks via Low-rank Tensor Approximation with Applications to MCI Identification.” IEEE Transactions on Biomedical Engineering, 67(7), 1912-1920.,Jiang, X., Qiao, L., De Leone, R., Shen, D. (2022). “Joint Selection of Brain Network Nodes and Edges for MCI Identification.” Computer Methods and Programs in Biomedicine, 225, 107082.,Jiang, X., Wang, G., Zhang, L., Xi, X., De Leone, R., Qiao, L. (2024). “Label-guided Low-rank Approximation for Functional Brain Network Learning in Identifying Subcortical Vascular Cognitive Impairment.” Biomedical Signal Processing and Control, 98, 106766.,Jiang, X., Zhou, Y., Zhang, Y., Zhang, L., Qiao, L., De Leone, R. (2022). “Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.” Frontiers in Neuroscience, 16, 872848.

🔬 Research Focus:

Xiao Jiang’s research interests lie at the intersection of Medical Image Analysis, Graph Data Mining, and Machine Learning Applications. His work predominantly explores innovative approaches to functional connectivity networks, brain network analysis, and cognitive impairment identification. Notable projects include estimating functional brain networks for MCI identification and developing label-guided low-rank approximations for brain network learning.

Conclusion:

Given Dr. Jiang’s outstanding research achievements, his technical expertise, and his contribution to advancing the understanding of brain functionality and cognitive disorders, he is a strong candidate for the Best Researcher Award. However, to further strengthen his candidacy, expanding the interdisciplinary and practical applications of his research would enhance his impact in both the academic and clinical communities.

Publications :

  • Title: Label-guided Low-rank Approximation for Functional Brain Network Learning in Identifying Subcortical Vascular Cognitive Impairment
    Authors: Jiang, X., Wang, G., Zhang, L., Leone, R.D., Qiao, L.
    Source: Biomedical Signal Processing and Control
    Year: 2024

 

  • Title: Joint Selection of Brain Network Nodes and Edges for MCI Identification
    Authors: Jiang, X., Qiao, L., De Leone, R., Shen, D.
    Source: Computer Methods and Programs in Biomedicine
    Year: 2022

 

  • Title: Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification
    Authors: Jiang, X., Zhou, Y., Zhang, Y., Qiao, L., De Leone, R.
    Source: Frontiers in Neuroscience
    Year: 2022

 

  • Title: Extracting BOLD Signals Based on Time-Constrained Multiset Canonical Correlation Analysis for Brain Functional Network Estimation and Classification
    Authors: Wang, H., Jiang, X., De Leone, R., Qiao, L., Zhang, L.
    Source: Brain Research
    Year: 2022

 

  • Title: Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification
    Authors: Zhang, Y., Jiang, X., Qiao, L., Liu, M.
    Source: Frontiers in Neuroscience
    Year: 2021

 

  • Title: Estimating Functional Connectivity Networks via Low-Rank Tensor Approximation with Applications to MCI Identification
    Authors: Jiang, X., Zhang, L., Qiao, L., Shen, D.
    Source: IEEE Transactions on Biomedical Engineering
    Year: 2020

 

  • Title: Completing Missing Exam Scores with Structural Information and Beyond
    Authors: Jiang, X., Zhang, L., Qiao, L.
    Source: Journal of Applied Remote Sensing
    Year: 2019