Hafiz Khan | Machine Learning | Best Researcher Award

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Prof. Dr. Hafiz Khan | Machine Learning | Best Researcher Award

Professor at Texas Tech University Health Sciences Center, United States

Dr. Hafiz M. R. Khan is a Full Professor of Biostatistics at Texas Tech University Health Sciences Center, with an extensive academic and research background. He holds a Ph.D. in Statistics from the University of Western Ontario and has postdoctoral training in Bioinformatics. His career spans multiple institutions, including Florida International University and the University of Medicine & Dentistry of New Jersey. Dr. Khan has held leadership roles such as Associate Chair and Director of Outcome Measures, contributing significantly to academic committees and research initiatives. He has published extensively in peer-reviewed journals, focusing on biostatistics, public health, and cognitive impairment research. His strengths for the Best Researcher Award include a strong publication record, leadership in academia, and interdisciplinary collaboration. Areas for improvement may include further engagement in international research projects. Overall, his contributions to biostatistics and public health research make him a strong candidate for the Best Researcher Award.

Professional ProfileĀ 

Education

Dr. Hafiz M. R. Khan has a strong educational background in statistics and biostatistics. He earned his Ph.D. in Statistics from the University of Western Ontario, Canada, where he specialized in statistical methodologies and their applications in health sciences. To further enhance his expertise, he completed postdoctoral training in Bioinformatics, gaining advanced knowledge in computational biology and data analysis. His academic journey also includes a Masterā€™s and Bachelorā€™s degree in Statistics, which provided him with a solid foundation in quantitative analysis and research methods. Throughout his education, Dr. Khan focused on interdisciplinary applications of statistics, particularly in public health, epidemiology, and biomedical sciences. His strong academic credentials have enabled him to contribute significantly to research, teaching, and mentoring students in biostatistics and public health. His education has played a pivotal role in shaping his career, allowing him to bridge the gap between statistical theory and real-world health applications.

Professional Experience

Dr. Hafiz M. R. Khan has an extensive professional background in statistics, biostatistics, and public health research. He has held various academic and research positions, contributing significantly to statistical methodologies in biomedical and epidemiological studies. As a professor and researcher, he has taught biostatistics, data analysis, and public health courses at reputable institutions, mentoring numerous students and professionals. His expertise extends to consulting for healthcare organizations and research institutions, where he applies statistical models to solve complex health-related problems. Dr. Khan has also collaborated on interdisciplinary projects involving bioinformatics, machine learning, and predictive analytics in healthcare. His professional journey includes publishing high-impact research papers, serving as a peer reviewer for scientific journals, and participating in international conferences. His work has been instrumental in advancing statistical applications in medicine and public health, bridging the gap between theoretical research and practical implementation in real-world health challenges.

Research Interest

Dr. Hafiz M. R. Khan’s research interests lie at the intersection of biostatistics, epidemiology, and public health, with a strong focus on statistical modeling, predictive analytics, and machine learning applications in healthcare. He is particularly interested in developing advanced statistical methodologies to analyze complex biomedical data, improve disease prediction models, and enhance public health decision-making. His work explores the integration of statistical techniques with bioinformatics to study genetic influences on diseases and health outcomes. Additionally, he investigates the application of artificial intelligence in medical research, aiming to optimize diagnostic accuracy and treatment effectiveness. Dr. Khan is also passionate about global health issues, including infectious disease surveillance, health disparities, and aging populations. Through interdisciplinary collaborations, he strives to bridge the gap between statistical theory and real-world healthcare applications, contributing to innovative solutions that enhance patient care, policy-making, and public health interventions worldwide.

Award and Honor

Dr. Hafiz M. R. Khan has received numerous awards and honors in recognition of his outstanding contributions to biostatistics, public health, and epidemiology. He has been honored with prestigious research grants and fellowships from esteemed institutions, highlighting his excellence in statistical modeling and healthcare analytics. His groundbreaking work has earned him accolades such as the Best Researcher Award and Excellence in Public Health Research recognition. Dr. Khan has been invited as a keynote speaker at international conferences and has received distinguished scholar awards for his impactful publications. His dedication to academic excellence has also been acknowledged through teaching awards, mentoring recognitions, and leadership roles in professional organizations. Additionally, he has been recognized for his contributions to global health initiatives, demonstrating his commitment to improving healthcare outcomes. These awards and honors underscore his influence in the field and his continuous efforts to advance research, education, and policy in health sciences.

Research Skill

Dr. Hafiz M. R. Khan possesses exceptional research skills in biostatistics, public health, and epidemiology, enabling him to conduct advanced statistical analyses and develop innovative models for healthcare studies. His expertise includes data analysis, predictive modeling, machine learning applications in health research, and designing population-based studies. He has a strong command of statistical software such as R, SPSS, SAS, and STATA, which he utilizes to interpret complex datasets effectively. Dr. Khan excels in systematic reviews, meta-analysis, and quantitative research methodologies, ensuring rigorous scientific inquiry and evidence-based conclusions. His ability to synthesize large datasets and extract meaningful insights has contributed significantly to policy recommendations and healthcare improvements. Additionally, his collaborative approach to interdisciplinary research allows him to work seamlessly with experts from diverse fields. His critical thinking, problem-solving abilities, and meticulous research design skills make him a valuable contributor to advancing public health, epidemiology, and statistical sciences.

Conclusion

Dr. Hafiz M. R. Khan is a highly qualified candidate for the Best Researcher Award due to his extensive contributions to academia, research, and public health. His leadership roles, mentoring, and commitment to advancing Biostatistics make him a strong contender. However, enhancing visibility of research impact, citations, international collaborations, and applied innovations could further strengthen his application.

Publications Top Noted

  • Title: Metabolic syndrome in aboriginal Canadians: prevalence and genetic associations
    Authors: RL Pollex, AJG Hanley, B Zinman, SB Harris, HMR Khan, RA Hegele
    Year: 2006
    Citations: 145

  • Title: Differences between carotid wall morphological phenotypes measured by ultrasound in one, two and three dimensions
    Authors: K Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, …
    Year: 2005
    Citations: 142

  • Title: Genetic Variation in PPARG Encoding Peroxisome Proliferator-Activated Receptor Ī³ Associated With Carotid Atherosclerosis
    Authors: KZ Al-Shali, AA House, AJG Hanley, HMR Khan, SB Harris, B Zinman, …
    Year: 2004
    Citations: 123

  • Title: Guillainā€“BarrĆ© syndrome after Gardasil vaccination: data from vaccine adverse event reporting system 2006ā€“2009
    Authors: N Souayah, PA Michas-Martin, A Nasar, N Krivitskaya, HA Yacoub, …
    Year: 2011
    Citations: 120

  • Title: Type 2 diabetes and its correlates among adults in Bangladesh: a population-based study
    Authors: MAB Chowdhury, MJ Uddin, HMR Khan, MR Haque
    Year: 2015
    Citations: 110

  • Title: Physical therapistsā€™ attitudes, knowledge, and practice approaches regarding people who are obese
    Authors: S Sack, DR Radler, KK Mairella, R Touger-Decker, H Khan
    Year: 2009
    Citations: 78

  • Title: Trends in outcomes and hospitalization costs for traumatic brain injury in adult patients in the United States
    Authors: K Farhad, HMR Khan, AB Ji, HA Yacoub, AI Qureshi, N Souayah
    Year: 2013
    Citations: 56

  • Title: Predictive inference from a two-parameter Rayleigh life model given a doubly censored sample
    Authors: HMR Khan, SB Provost, A Singh
    Year: 2010
    Citations: 49

  • Title: Optimizing RNA extraction yield from whole blood for microarray gene expression analysis
    Authors: J Wang, JF Robinson, HMR Khan, DE Carter, J McKinney, BA Miskie, …
    Year: 2004
    Citations: 48

  • Title: Secondhand smoke exposure reduction intervention in Chinese households of young children: a randomized controlled trial
    Authors: AS Abdullah, F Hua, H Khan, X Xia, Q Bing, K Tarang, JP Winickoff
    Year: 2015
    Citations: 45

  • Title: Statistical machine learning approaches to liver disease prediction
    Authors: F Mostafa, E Hasan, M Williamson, H Khan
    Year: 2021
    Citations: 40

  • Title: The safety profile of home infusion of intravenous immunoglobulin in patients with neuroimmunologic disorders
    Authors: N Souayah, A Hasan, HMR Khan, HA Yacoub, M Jafri
    Year: 2011
    Citations: 34

  • Title: Tumor-infiltrating lymphocytes (TILs) as a biomarker of abscopal effect of cryoablation in breast cancer: A pilot study
    Authors: SY Khan, MW Melkus, F Rasha, M Castro, V Chu, L Brandi, H Khan, …
    Year: 2022
    Citations: 31

  • Title: Vulnerability prioritization, root cause analysis, and mitigation of secure data analytic framework implemented with MongoDB on Singularity Linux containers
    Authors: AM Dissanayaka, S Mengel, L Gittner, H Khan
    Year: 2020
    Citations: 31

  • Title: Colorectal cancer screening use among insured adults: Is out-of-pocket cost a barrier to routine screening?
    Authors: A Perisetti, H Khan, NE George, R Yendala, A Rafiq, S Blakely, …
    Year: 2018
    Citations: 31

Qiao Ke | Deep Learning | Best Researcher Award

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šŸŒŸAssist Prof Dr. Qiao Ke, Deep Learning, Best Researcher AwardšŸ†

Ā  Assistant professor at Northwestern Polytechnical University, China

Qiao Ke is an Assistant Professor at Northwestern Polytechnical University, specializing in Deep Learning, Machine Learning, Statistics Learning, Intelligent Software Engineering, and Internet of Things. Qiao holds a Ph.D. in Mathematics from Xi’an Jiao Tong University and has been actively engaged in research, contributing significantly to various areas of computational mathematics and artificial intelligence.

Author Metrics:

Ke, Qiao – Scopus Profile

Orcid Profile

Qiao Ke is affiliated with Northwestern Polytechnical University in Xi’an, China. The Scopus Author Identifier 56465532300 provides valuable metrics regarding their academic contributions.

  • Citations: Qiao Ke has received a total of 481 citations across 420 documents, indicating the impact of their research on the academic community.
  • Documents: The author has contributed to 16 documents, showcasing a consistent and substantive scholarly output.
  • h-index: With an h-index of 8, Qiao Ke has demonstrated a noteworthy level of influence in their field. The h-index is a metric that considers both the number of publications and the number of citations they receive.

These metrics reflect the academic impact and productivity of Qiao Ke, highlighting their contributions to the scholarly landscape. The provided information encourages further exploration into the specific content and context of their publications for a comprehensive understanding of their research achievements.

Education:

Qiao Ke pursued a B.S. in Mathematics from Shaanxi Normal University, an M.S. in Mathematics, and a Ph.D. in Mathematics from Xi’an Jiao Tong University. Additionally, they completed postdoctoral research in the Department of Computer Science at Northwestern Polytechnical University.

Research Focus:

Qiao Ke’s research interests span Deep Learning, Machine Learning, Statistics Learning, Intelligent Software Engineering, and the Internet of Things. Notably, their work includes innovative contributions to neural frameworks for software models, hierarchical search-based code generation, and adaptive disentangled representation learning.

Professional Journey:

Qiao Ke’s professional journey involves serving as an Assistant Professor at the School of Mathematics and Statistics, Northwestern Polytechnical University. They have also actively participated as a reviewer for several reputed journals and conferences, demonstrating their commitment to scholarly peer review.

Publications Top Noted & Contributions:

Qiao Ke has made significant contributions to the field, with publications in respected journals and conferences. Notable works include research on modular neural frameworks for software model connections, deep hierarchical search-based code generation, and adaptive disentangled representation learning.

A research paper titled “RRGcode: Deep hierarchical search-based code generation.” The paper addresses the challenges of retrieval-augmented code generation, where a retrieval model is used to select relevant code snippets from a code corpus to strengthen the generation model. The primary concern is that if the retrieval corpus contains errors or sub-optimal examples, the generation model might replicate these mistakes in the generated code.

To overcome these challenges, the authors propose RRGcode, a deep hierarchical search-based code generation framework. The key components of RRGcode are outlined as follows:

  1. Retrieval: The framework first retrieves relevant code candidates from a large code corpus. This initial retrieval step aims to gather a set of potential code snippets based on the given query.
  2. Re-ranking: A re-ranking model is introduced to fine-tune the initial retrieved code rankings. This involves a detailed semantic comparison between the code candidates and the query, ensuring that only the most relevant and accurate candidates are considered. The re-ranking process aims to mitigate the risk of replicating errors from the retrieval corpus.
  3. Generation: The re-ranked top-K codes, along with the query, serve as input for the code generation model. This final step focuses on generating high-quality and reliable code based on the refined set of code candidates.

The authors claim that RRGcode demonstrates state-of-the-art performance in code generation tasks through extensive experiments. The deep hierarchical search-based approach aims to improve the quality of generated code by addressing the limitations associated with erroneous or sub-optimal code examples present in the retrieval corpus.

1. Title: Spline Interpolation and Deep Neural Networks as Feature Extractors for Signature Verification Purposes

2. Title: Intelligent Internet of Things System for Smart Home Optimal Convection

  • Publication Date: June 2021
  • Journal: IEEE Transactions on Industrial Informatics
  • DOI: 10.1109/tii.2020.3009094
  • ISSN: 1551-3203, 1941-0050

3. Title: High-Resolution SAR Image Despeckling Based on Nonlocal Means Filter and Modified AA Model

  • Publication Date: November 28, 2020
  • Journal: Security and Communication Networks
  • DOI: 10.1155/2020/8889317
  • ISSN: 1939-0122, 1939-0114

4. Title: Accurate and Fast URL Phishing Detector: A Convolutional Neural Network Approach

5. Title: Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Research Timeline:

Qiao Ke’s research journey spans from their Bachelor’s degree at Shaanxi Normal University in 2012 to their current role as an Assistant Professor at Northwestern Polytechnical University. Notable milestones include completing a Ph.D., engaging in postdoctoral research, and actively contributing to various research projects, including leadership roles in national and provincial-level foundations.

Dawei Zhang | Computer Vision and Deep Learning | Best Researcher Award

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šŸŒŸDr. Dawei Zhang, Zhejiang Normal University, China:Ā  Computer Vision and Deep LearningšŸ†
Professional Profiles:

Bio Summary:

Dawei Zhang is a Ph.D. and Assistant Professor in the Department of Computer Science and Technology at Zhejiang Normal University, located in Jinhua, China. He holds expertise in computer vision, deep learning, and multimedia computing, with a focus on areas such as visual object tracking, video object segmentation, lightweight neural networks, adversarial attacks, and multi-modal information fusion.

Research Focus:

  1. Visual Object Tracking and Video Object Segmentation
  2. Light-weight Neural Networks for Mobile or Edge Computing Devices
  3. Research on Adversarial Attacks and Interpretability in Deep Learning
  4. Applications of Multi-modal Information Fusion in Vision and Language

Professional Journey:

  • Ph.D. (2017.09-2022.06) – Zhejiang Normal University, supervised by Prof. Zhonglong Zheng & Xiaoqin Zhang
  • Visiting Intern (2021.05-2021.09) – ISTBI, Fudan University, supervised by Prof. Yanwei Fu
  • B.E. (2013.09-2017.06) – Huaiyin Institute of Technology, supervised by Prof. Sen Xia

Honors & Awards:

  • 2023: 2nd “Chengtai Gonghao” Qihang Teaching Scholarship of Zhejiang Normal University
  • 2022: Talent Ambassador of Wucheng District, Jinhua City, Zhejiang Province
  • 2022: Outstanding Doctoral Dissertation Award of Zhejiang Normal University
  • 2022: Outstanding Graduate Students of Zhejiang Province
  • 2022: “Top-10 Students” of GREENTOWN Group in Zhejiang Normal University
  • 2021: National Scholarship for Postgraduate Students
  • 2018-2021: First class Academic Scholarship of Zhejiang Normal University
  • 2021: “Top-10 Academic Stars” for Graduate Students of Zhejiang Normal University
  • 2020: Academic Innovation Scholarship of Zhejiang Normal University
  • 2020: Outstanding Paper Award of National Conference of Computer Application of CCF

Publications Top Noted & Contributions:

  • Journals: Several papers in prominent journals including International Journal of Machine Learning and Cybernetics, Neurocomputing, IEEE Access, and Sensors.
  • Conferences: Contributions to conferences such as ICML, AAAI, ACM MM, and more, with papers accepted in CCF-A, CCF-B, and CCF-C category conferences.

Title: “Cross Channel Aggregation Similarity Network for Salient Object Detection”

  • Journal: International Journal of Machine Learning and Cybernetics
  • Year: 2022
  • Citations: 8

Title: “UAST: Uncertainty-Aware Siamese Tracking”

  • Conference: International Conference on Machine Learning (ICML), 2022
  • Year: 2022
  • Citations: 11

Title: “Deep Regression Tracking with Graph Attention”

  • Conference: International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), 2022
  • Year: 2022
  • Citations: 0

Title: “CSART: Channel and Spatial Attention-Guided Residual Learning for Real-Time Object Tracking”

  • Journal: Neurocomputing
  • Year: 2021
  • Citations: 19

Title: “Global Perception Attention Network for Fine-Grained Visual Classification”

  • Conference: International Conference on Computer Communication and Artificial Intelligence (CCAI), 2021
  • Year: 2021
  • Citations: 0

Author Metrics:

  • Total Citations: 170
  • h-index: 8
  • i10-index: 6
  • Documents: 16

Research Timeline:

  • Ongoing: Conducting research on Lightweight Siamese Networks for Efficient UAV Target Tracking (2023-2025).
  • Ongoing: Leading research on Key Algorithms of Intelligent Video Surveillance System in Smart Campus (2023-2025).
  • Ongoing: Participating in Information Asynchronous Propagation Traceability for Temporal Networks (2023-2025).
  • Ongoing: Contributing to Research on Trusted Target Tracking Based on Deep Learning in Intelligent Video Analysis (2023-2026).
  • Ongoing: Involved in Research on Visual Object Tracking Algorithms in Complex Scenarios (2022-2024).