Prof. Shile Qi | Bioinformatics | Best Researcher Award

Posted on 05/04/202505/04/2025 by ScienceFather

Prof. Shile Qi | Bioinformatics | Best Researcher Award

Prof. Shile Qi, Nanjing University of Aeronautics and Astronautics, China

Prof. Shile Qi is a distinguished expert in computational psychiatry, brain imaging, and data science, currently serving as a Professor of Artificial Intelligence at Nanjing University of Aeronautics and Astronautics. With postdoctoral experience at TReNDS (Georgia State, Georgia Tech, Emory) and The Mind Research Network (USA), Prof. Qi specializes in multimodal neuroimaging, machine learning, and individualized mental health prediction. His research has been recognized globally through prestigious presentations and awards at IEEE ISBI, ICASSP, and OHBM. His work bridges AI, neuroscience, and psychiatry, advancing early diagnosis and personalized treatment of disorders like schizophrenia and depression. Prof. Qi is a rising leader in neuroinformatics, integrating computational innovation with medical science for impactful mental health solutions.

🌍 Professional Profile

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🏆 Suitability for Best Researcher Award

Prof. Shile Qi is an exceptional candidate for the Best Researcher Award due to his high-impact contributions in computational neuroscience and AI-powered psychiatry. His groundbreaking work in individualized mental health prediction, multimodal brain data fusion, and bioinformatics has earned him multiple international honors and oral presentations at top-tier conferences like IEEE ISBI, ICASSP, and OHBM (top 1–3%). With a proven record of excellence in brain imaging and mental health research, he offers innovative tools for diagnosing complex psychiatric disorders. His international training, interdisciplinary skills, and leadership in neuroimaging AI make him a transformative figure driving precision psychiatry forward. Prof. Qi exemplifies the ideal balance of academic rigor, innovation, and societal impact.

🎓 Education

Prof. Shile Qi earned his Ph.D. in Pattern Recognition (2018) from the Institute of Automation, Chinese Academy of Sciences, focusing on computational neuroscience and AI algorithms. He holds a Master’s degree in Mathematics (2014) from Fuzhou University, and a Bachelor’s degree in Mathematics (2011) from Zhoukou Normal University. His solid mathematics background supports his innovations in neuroimaging data analysis, multimodal integration, and personalized prediction models. From 2018 to 2021, he completed prestigious postdoctoral fellowships in the U.S. at The Mind Research Network and the TReNDS Center, working alongside leading experts in neuroimaging, psychiatry, and AI. His education reflects a unique blend of mathematics, pattern recognition, and brain science, forming the foundation of his cutting-edge research.

💼 Experience

Prof. Shile Qi is currently an Artificial Intelligence Professor at Nanjing University of Aeronautics and Astronautics (2021–present), focusing on brain image analysis, computational psychiatry, and AI-driven mental health research. From 2019–2021, he was a Postdoctoral Researcher at TReNDS (a collaborative center of Georgia State, Georgia Tech, and Emory University), where he worked on multimodal data fusion and individualized prediction of psychiatric disorders. Earlier, he completed a postdoc at The Mind Research Network (2018–2019), contributing to high-level brain imaging studies. With extensive experience in interdisciplinary collaboration, Prof. Qi has published and presented work at global platforms and continues to pioneer AI-based diagnostics in neuroscience, combining machine learning, data science, and clinical insights.

🏅 Awards & Honors

Prof. Shile Qi’s research excellence has earned multiple prestigious awards and presentations. These include:

✨ 2020 OHBM Merit Abstract Award (Top 1%)
✨ 2020 & 2021 OHBM Oral Presentations (Top 3%)
✨ 2017 OHBM Merit Abstract Award (Top 1%)
🎤 2025 IEEE ICASSP & ISBI Oral Presentations
🎤 2021 IEEE ISBI Oral Presentation

His work on multiple psychiatric disorders and ECT treatment studies was consistently ranked among the top abstracts internationally. These honors highlight his cutting-edge contributions to neuroimaging, AI-based psychiatry, and multimodal data fusion. Recognized for technical depth, innovation, and clinical relevance, Prof. Qi has emerged as a thought leader in computational neuroscience, driving AI-enhanced healthcare forward globally.

🔬 Research Focus

Prof. Shile Qi’s research lies at the intersection of AI, neuroscience, and psychiatry. He specializes in computational psychiatry, using brain imaging and bioinformatics to model mental disorders like schizophrenia and depression. His expertise spans multimodal data fusion, individualized prediction models, and deep learning techniques for detecting subtle brain abnormalities. Prof. Qi develops novel methods to integrate MRI, fMRI, EEG, and other neuroimaging data, providing personalized insights for early diagnosis and treatment planning. He also contributes to bioinformatics and mental health AI, creating predictive models that are both clinically relevant and technically robust. His work aims to transform how psychiatric conditions are understood, detected, and managed through neuroinformatics innovation.

📊 Publication Top Notes

Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion
- Citations: 159
- Year: 2018
Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships
- Citations: 151
- Year: 2020
Aberrant dynamic functional network connectivity and graph properties in major depressive disorder
- Citations: 148
- Year: 2018
Gender differences in connectome-based predictions of individualized intelligence quotient and sub-domain scores
- Citations: 137
- Year: 2020
Connectome-based individualized prediction of temperament trait scores
- Citations: 87
- Year: 2018

Prof. Konstantinos Moustris | Meteorology Awards | Excellence in Research

Posted on 14/10/2024 by ScienceFather

Prof. Konstantinos Moustris | Meteorology Awards | Excellence in Research

Prof. Konstantinos Moustris, University of West Attica, Athens, Greece

Dr. Konstantinos Moustris is a Professor at the Mechanical Engineering Department, School of Engineering, University of West Attica, Greece, where he leads the Air Pollution Laboratory and the Artificial Neural Networks (ANNs) & Machine Learning Lab. He holds a BSc in Physics, an MSc in Environmental Physics and Meteorology, and a PhD in Environmental Fluid Mechanics, specializing in artificial neural networks modeling. Dr. Moustris has published over 50 original scientific papers in international peer-reviewed journals, focusing on ANN modeling and forecasting, and has presented more than 80 works at international conferences. His research interests include fluid mechanics, renewable energy sources modeling using ANNs, stochastic energy management and forecasting, and environmental fluid mechanics forecasting. His notable contributions to various European projects showcase his commitment to innovation in hybrid materials and nanomaterials. Dr. Moustris has a significant impact in the scientific community, with an h-index of 22 on Google Scholar and extensive recognition for his research work.

Professional Profile:

Scopus

Orcid

Google Scholar

Suitability Summary for Dr. Konstantinos Moustris for Excellence in Research Award

In conclusion, Dr. Konstantinos Moustris stands out as a leading researcher whose contributions significantly impact environmental engineering and AI applications. His combination of academic excellence, leadership in research, prolific publication record, and innovative projects make him a highly suitable candidate for the Research for Excellence in Research Award. Recognizing his efforts through this award would not only honor his achievements but also encourage continued excellence in research that benefits society at large.

🎓Education:

Dr. Konstantinos Moustris holds a Ph.D. in Environmental Fluid Mechanics from the National Technical University of Athens (N.T.U.A.), Greece, completed in 2009, with a thesis focused on “Air quality forecasting with the use of artificial neural networks in the greater Athens area.” He also earned an MSc in Environmental Physics and Meteorology from the National and Kapodestrian University of Athens in 1992, following his BSc in Physics from the same institution in 1989.

🏢Work Experience:

Dr. Konstantinos Moustris is a Professor at the Mechanical Engineering Department, School of Engineering, University of West Attica, Greece, where he also serves as the Head of the Air Pollution Laboratory and the Artificial Neural Networks (ANNs) & Machine Learning Lab. His recent research contributions highlight his expertise in developing innovative solutions using artificial intelligence. From 2024 onwards, he has been working on the PROMATAI-HORIZON-MSCA-2022-SE-0 project, focusing on hybrid materials and nanomaterials. Between 2020 and 2023, Dr. Moustris contributed to the development of ANN forecasting models for the DarWEN project, aimed at managing the water and energy nexus in island regions. From 2015 to 2019, he played a key role in the TILOS project, developing ANN models for integrating battery energy storage, and in 2014-2015, he worked on the PHAROS project, creating an integrated design tool to address energy and water needs on Aegean Sea islands using renewable energy-based hybrid systems.

🏅Awards and Recognitions:

Dr. Konstantinos Moustris has made significant contributions to the scientific community, having published over 50 original research papers in peer-reviewed journals. He has also presented more than 80 works at international conferences, showcasing his expertise and commitment to advancing knowledge in his field. His impactful research has earned him a Google Scholar h-index of 22, with a total of 2,014 citations, reflecting the recognition and influence of his work within the academic community.

🔬Research Focus:

Dr. Konstantinos Moustris’s research primarily focuses on fluid mechanics, renewable energy sources modeling utilizing artificial neural networks (ANNs), and stochastic energy management and forecasting. His work emphasizes the applications and modeling of ANNs, particularly in the context of environmental fluid mechanics forecasting. Through his innovative research, he aims to enhance the understanding and predictive capabilities related to these critical areas.

Publication Top Notes:

Title: Evidence for interaction between air pollution and high temperature in the causation of excess mortality
Cited by: 405
Title: 3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece
Title: Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece
Cited by: 119
Title: Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece
Cited by: 114
Title: Rain intensity forecast using artificial neural networks in Athens, Greece
Cited by: 94

Dr. Roseline Ogundokun | Intrusion Detection System | Best Researcher Award

Posted on 04/10/202404/10/2024 by ScienceFather

Dr. Roseline Ogundokun | Intrusion Detection System | Best Researcher Award

Dr. Roseline Ogundokun, Landmark University Omu-Aran, Nigeria

Roseline Oluwaseun Ogundokun is a distinguished academic and researcher in computer science, born in Zaria, Nigeria. Currently serving as a lecturer and researcher at Landmark University, she specializes in machine learning, artificial intelligence, and computer vision. With a strong commitment to education and innovative research, Roseline has made significant contributions to advancing sustainable development goals through technology. She is also involved in mentoring students in STEM fields and has a passion for fostering future generations of scientists.

Professional Profile

Google Scholar

Researcher Suitability Summary for the Best Researcher Award: Roseline Oluwaseun Ogundokun

Based on her extensive research output, significant contributions to academia, and commitment to mentoring and inclusive practices, Dr. Roseline Oluwaseun Ogundokun is an exemplary candidate for the Best Researcher Award. Her work not only advances the field of Computer Science but also positively impacts society through innovative solutions. Recognizing her achievements with this award would honor her contributions and inspire further excellence in research and education.

Education

Roseline’s academic journey began with a Bachelor’s degree in Management Information Systems from Covenant University, followed by a Master’s in Computer Science from the University of Ilorin. She is currently pursuing dual PhDs in Computer Science and Multimedia Engineering, expected to be completed in 2022 and 2025, respectively. Her diverse educational background has equipped her with a strong foundation in both theoretical and practical aspects of technology, enabling her to contribute effectively to her field.

Experience

Roseline has extensive experience in academia, having worked at Landmark University since 2015 as a researcher, lecturer, and administrator. She has taught various courses, including Computer Programming and Software Engineering, while also supervising numerous undergraduate and postgraduate students in innovative research projects. Additionally, she has served as a visiting lecturer at Thomas Adewumi University and the Nigerian Army College of Education, contributing to the development of future tech leaders through her teaching and mentorship.

Awards and Honors

Roseline’s commitment to research and education has earned her multiple accolades. She has been recognized for her contributions to machine learning and sustainable development, receiving awards from various educational institutions. Her research publications have garnered significant attention, leading to an impressive citation record, reflecting her influence in the academic community. She is also actively involved in mentorship programs, advocating for women’s participation in STEM fields.

Research Focus

Roseline’s research interests are centered on artificial intelligence, computer vision, and deep learning. She is particularly focused on employing machine learning models to solve real-world problems across various sectors, including healthcare and telecommunications. Her work aims to advance the integration of technology in achieving sustainable development goals, particularly those related to industry, innovation, and infrastructure.