Haranath Ghosh | Computational Methods | Research Excellence Award

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Prof. Dr. Haranath Ghosh | Computational Methods | Research Excellence Award

Professor | Raja Ramanna Centre for Advanced Technology | India

Prof. Dr. Haranath Ghosh is a leading researcher at the Raja Ramanna Centre for Advanced Technology, specializing in condensed matter physics and material science. His interests include superconductivity, electron correlation, and optical properties of advanced materials. He demonstrates expertise in theoretical modeling, computational analysis, and spectroscopy. He has received recognition for impactful scientific contributions. With over 1,593 citations, an h-index of 20, and 43 i10-index (Google Scholar), his work significantly advances understanding of quantum materials and supports innovations in modern physics and technology.

 

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1593

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Featured Publications

 

Ali Anaissi | Data Science | Best Researcher Award

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Dr. Ali Anaissi | Data Science | Best Researcher Award

Senior Lecturer at University of Technology Sydney, Australia

Dr. Ali Anaissi 🎓 is a seasoned data scientist and academic, currently a Senior Lecturer at the University of Technology Sydney 🇦🇺 and Deputy Director of the Master of Data Science and Innovation. With a Ph.D. in Data Science from UTS 🧠 and a background in Computer & Communication Engineering, he brings a rich blend of academic and industry experience. He has worked at prestigious institutions including the University of Sydney and CSIRO-Data61 🔬. His research spans machine learning, anomaly detection, and structural health monitoring, contributing significantly to data-driven innovations 🤖📊.

Professional Profile:

Orcid

Scopus

Google Scholar

Education & Experience🎓

  • 🎓 Ph.D. in Data Science, University of Technology Sydney (2009–2013)

  • 🎓 Bachelor’s in Computer & Communication Engineering, Islamic University of Lebanon (2000–2005)

  • 👨‍🏫 Senior Lecturer, UTS (2022–Present)

  • 👨‍🏫 Lecturer, University of Sydney (2017–2022)

  • 🧪 Research Scientist, CSIRO–Data61 (2015–2017)

  • 👨‍💻 Software Developer, Agile Technologies, Lebanon (2004–2008)

  • 🧑‍🏫 Research Assistant, Tutor & Lecturer, UTS (2010–2016)

Professional Development📚

Dr. Anaissi continually enhances his expertise through active participation in professional and academic development activities 📈. He engages in international conferences, publishes high-impact research articles 📑, and collaborates with industry partners on real-world data science challenges 🌐. His role as a mentor to Ph.D. candidates fosters growth in the next generation of researchers 👨‍🎓👩‍🎓. He is also involved in curriculum development and education innovation at UTS and the University of Sydney 📘💡. These efforts reflect his commitment to bridging theory and practice, ensuring his work remains at the forefront of data science advancement 🚀🔍.

Research Focus🧠

Dr. Anaissi’s research lies at the intersection of data science, machine learning, and real-world application domains 🤖📊. His core focus includes anomaly detection, structural health monitoring, cyber security analytics 🔐, and predictive modeling. By developing intelligent algorithms and data-driven models, he improves the accuracy, safety, and efficiency of engineering and cyber systems ⚙️🛰️. He often works on interdisciplinary teams, blending data science with civil engineering, health informatics, and IT infrastructure. His innovations contribute to smarter decision-making systems, with a strong emphasis on explainability and robust performance across diverse environments 🌍🧪.

Awards & Honors🏅

  • 🏆 Honors Distinction (2005) – Awarded for outstanding dissertation on Face Detection and Recognition 📸🧠

  • 🥇 Academic Achievement Award – Recognized for excellence in academic performance during undergraduate studies 🎓📘

  • 🌟 Best Paper Presentation (various conferences) – For impactful research presentations in data analytics and AI forums 🎤📊

Publication Top Notes

1. A Balanced Iterative Random Forest for Gene Selection from Microarray Data

Authors: A. Anaissi, P.J. Kennedy, M. Goyal, D.R. Catchpoole
Published in: BMC Bioinformatics, 2013, Volume 14, Article 1, Pages 1–10
Citations: 93
DOI: 10.1186/1471-2105-14-261
Summary:
This study presents a Balanced Iterative Random Forest (BIRF) algorithm for gene selection from high-dimensional microarray datasets. The method iteratively refines feature subsets to enhance classification performance while addressing class imbalance. It was evaluated on multiple cancer datasets and showed improvements in both gene selection stability and classification accuracy over existing methods.

2. Ensemble Feature Learning of Genomic Data Using Support Vector Machine

Authors: A. Anaissi, M. Goyal, D.R. Catchpoole, A. Braytee, P.J. Kennedy
Published in: PLOS ONE, 2016, Volume 11, Issue 6, e0157330
Citations: 52
DOI: 10.1371/journal.pone.0157330
Summary:
The authors propose an ensemble feature learning approach that integrates SVMs and gene ranking techniques to extract key genomic features from cancer data. The approach is validated using pediatric acute lymphoblastic leukemia (ALL) data, enhancing classification accuracy and interpretability for biomarker discovery.

3. Smart Pothole Detection System Using Vehicle-Mounted Sensors and Machine Learning

Authors: A. Anaissi, N.L.D. Khoa, T. Rakotoarivelo, M.M. Alamdari, Y. Wang
Published in: Journal of Civil Structural Health Monitoring, 2019, Volume 9, Pages 91–102
Citations: 45
DOI: 10.1007/s13349-019-00313-5
Summary:
This work introduces a machine learning-based system for real-time pothole detection using vehicle-mounted accelerometers and gyroscopes. A supervised learning model is trained to classify road conditions, providing a low-cost, scalable solution for road infrastructure monitoring.

4. A Tensor-Based Structural Damage Identification and Severity Assessment

Authors: A. Anaissi, M. Makki Alamdari, T. Rakotoarivelo, N.L.D. Khoa
Published in: Sensors, 2018, Volume 18, Issue 1, Article 111
Citations: 42
DOI: 10.3390/s18010111
Summary:
The authors propose a tensor decomposition-based method to assess structural damage using vibration data. The approach not only detects damage but also estimates severity, offering a robust solution for structural health monitoring (SHM) systems.

5. hsa-miR-29c and hsa-miR-135b Differential Expression as Potential Biomarker of Gastric Carcinogenesis

Authors: A.F. Vidal, A.M.P. Cruz, L. Magalhães, A.L. Pereira, A.K.M. Anaissi, N.C.F. Alves, et al.
Published in: World Journal of Gastroenterology, 2016, Volume 22, Issue 6, Pages 2060–2070
Citations: 42
DOI: 10.3748/wjg.v22.i6.2060
Summary:
This biomedical study investigates the differential expression of microRNAs hsa-miR-29c and hsa-miR-135b as biomarkers for gastric cancer. The research combines molecular profiling with clinical data, suggesting these miRNAs as promising early diagnostic markers for gastric carcinogenesis.

Conclusion

Dr. Ali Anaissi embodies the ideal candidate for a Best Researcher Award. His multi-dimensional research profile, impactful publications, dedicated supervision, and leadership roles make him a standout in the field of data science and applied AI.

He not only advances scientific knowledge but also translates it into real-world solutions, addressing global challenges in smart cities, healthcare, cybersecurity, and infrastructure resilience.

Xiao-Yong Zhang | AI-Driven | Best Researcher Award

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Prof. Xiao-Yong Zhang | AI-Driven | Best Researcher Award

Prof. Xiao-Yong Zhang, Shanghai Jiao Tong University School of Medicine, China

Dr. Xiao-Yong Zhang is a leading expert in medical imaging, with a special focus on using MRI and AI to advance diagnostic technology for brain health. He has held esteemed positions across top institutions in China and the U.S., contributing to neuroimaging through his roles as an editor, committee member, and principal investigator of high-impact research projects. Dr. Zhang’s innovative work in the quantitative detection of neuroinflammation and Alzheimer’s biomarkers underscores his commitment to advancing neurological diagnostics and personalized medicine.

PROFILE

Orcid  Profile

Educational Details

Ph.D. in Medical Imaging (2007), Fourth Military Medical University, Xi’an, China

Master of Medicine (M.Med.) in Radiological Sciences (2004), Fourth Military Medical University, Xi’an, China

Bachelor of Science (B.S.) in Biomedical Engineering (1998), Fourth Military Medical University, Xi’an, China

Professional Experience

Professor (2024–Present), Shanghai Jiao Tong University School of Medicine

Associate Professor (2017–2024), Fudan University

Research Associate (2014–2016), Vanderbilt University, under Dr. John C. Gore

Postdoctoral Fellow (2009–2014), Georgia Institute of Technology and Emory University, under Dr. Xiaoping P. Hu

Engineer/Lecturer (2007–2009), Fourth Military Medical University

Research Interests

Magnetic Resonance Imaging (MRI): Developing visualization techniques for brain microenvironments.

Deep Learning Algorithms: Creating AI-driven diagnostic tools to improve the detection and understanding of neurological diseases.

Grants

Quantitative Detection of Neuroinflammation using Hydroxyl Proton Transfer MRI
(2022–2025) | National Natural Science Foundation of China (NSFC) | Principal Investigator

Imaging Biomarkers for Alzheimer’s Disease
(2020–2021) | Fudan-Cambridge collaboration | Principal Investigator

Glioma Genotyping using CEST-NOE MRI
(2020–2023) | Shanghai Science and Technology Committee (STCSM) | Principal Investigator

Label-Free NOE MR Imaging of Choline Phospholipids
(2019–2022) | National Natural Science Foundation of China (NSFC) | Principal Investigator

Global Analysis of Brain Functional and Metabolic Networks
(2018–2022) | Subproject of Shanghai Municipal Science and Technology Major Project | Principal Investigator

Memberships

Organization for Human Brain Mapping (OHBM) (since 2022)

Institute of Electrical and Electronics Engineers (IEEE) (since 2021)

American Association for the Advancement of Science (AAAS) (since 2016)

International Society for Magnetic Resonance (ISMRM) (since 2010)

Top Notable Publications

Zhang, Xiao-Yong et al. “HiFi-Syn: Hierarchical granularity discrimination for high-fidelity synthesis of MR images with structure preservation.” Medical Image Analysis, November 2024. DOI: 10.1016/j.media.2024.103390

Zhang, Xiao-Yong et al. “Resting State Brain Networks under Inverse Agonist versus Complete Knockout of the Cannabinoid Receptor 1.” ACS Chemical Neuroscience, April 17, 2024. DOI: 10.1021/acschemneuro.3c00804

Authors. “Benchmarking spatial clustering methods with spatially resolved transcriptomics data.” Nature Methods, April 2024. DOI: 10.1038/s41592-024-02215-8

Authors. “A neural signature for the subjective experience of threat anticipation under uncertainty.” Nature Communications, February 20, 2024. DOI: 10.1038/s41467-024-45433-6

Authors. “A Convolutional Neural Network Model for Distinguishing Hemangioblastomas From Other Cerebellar-and-Brainstem Tumors Using Contrast-Enhanced MRI.” Journal of Magnetic Resonance Imaging, 2024. DOI: 10.1002/jmri.29230

Zhang, Xiao-Yong et al. “CQformer: Learning Dynamics Across Slices in Medical Image Segmentation.” IEEE Transactions on Medical Imaging, 2024. DOI: 10.1109/TMI.2024.3477555

Authors. “A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders.” Medical Image Analysis, December 2023. DOI: 10.1016/j.media.2023.102932

Authors. “Downregulation of mGluR1-mediated signaling underlying autistic-like core symptoms in Shank1 P1812L-knock-in mice.” Translational Psychiatry, October 25, 2023. DOI: 10.1038/s41398-023-02626-9

Authors. “A neural signature for the subjective experience of threat anticipation under uncertainty.” Preprint, September 22, 2023. DOI: 10.1101/2023.09.20.558716

Conclusion

Professor Xiao-Yong Zhang’s extensive research in MRI, deep learning diagnostics, and successful collaborations place him as an exemplary candidate for the Research for Best Researcher Award. His contributions to medical imaging innovation, demonstrated research leadership, and commitment to interdisciplinary collaborations reflect the award’s values and criteria.