Prof. Shile Qi | Bioinformatics | Best Researcher Award

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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 

Google Scholar

🏆 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.

đź’Ľ 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

 

 

Assist. Prof. Dr. Caixia Wu | Compound Identification | Best Researcher Award

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Assist. Prof. Dr. Caixia Wu | Compound Identification | Best Researcher Award

Assist. Prof. Dr. Caixia Wu, Yangzhou University, China

Assist. Prof. Dr. Caixia Wu is an esteemed researcher specializing in Grassland Science, Plant Allelopathy, and Forage Science. She currently serves as the Head of the Department of Grassland Science at Yangzhou University and has been an Associate Professor since 2017. With a Ph.D. in Animal Feed Science from Nanjing Agricultural University, she has made significant contributions to allelopathy mechanisms, forage production, and weed suppression. She has led multiple national and provincial-level projects focused on plant-derived active compounds and ecological management systems. As a leader in grassland and turf industry research, Dr. Wu’s work advances sustainable agricultural practices, improving forage quality and environmental conservation. Her research plays a crucial role in modern agroecology and plant chemistry.

🌍 Professional Profile 

Orcid

🏆 Suitability for Best Researcher Award

Dr. Caixia Wu’s groundbreaking research in allelopathy, grassland science, and plant-derived active compounds makes her a strong candidate for the Best Researcher Award. Her expertise in forage science, ecological agriculture, and weed suppression mechanisms has advanced sustainable farming practices. She has led national and regional research projects, contributing to high-quality forage production, plant biochemical interactions, and genetic characteristics of legumes. Her leadership roles in academic committees and industry alliances showcase her impact on grassland science and agricultural innovation. With numerous publications and research grants, her work has transformed plant compound identification, natural weed control, and ecological farming methods, making her a distinguished researcher in the field of sustainable agriculture and plant science.

🎓 Education 

Dr. Caixia Wu holds a Ph.D. in Animal Feed Science (2007) from Nanjing Agricultural University, where her thesis focused on the allelopathy of several legume forages. She also earned a Master’s degree (2005) and a Bachelor’s degree (2002) in Grassland Science from the same institution, specializing in forage production, plant interactions, and ecological management. In 2017-2018, she was a Visiting Scientist at Charles Sturt University, Australia, where she conducted research in Weed Science at the Graham Centre. Her academic journey has equipped her with expertise in compound identification, plant-derived active compounds, and ecological agriculture, laying a strong foundation for her leadership in grassland research, plant chemistry, and sustainable farming systems.

đź’Ľ Experience

Dr. Caixia Wu has been a faculty member at Yangzhou University since 2010 and currently serves as the Head of the Department of Grassland Science. Since 2017, she has been an Associate Professor specializing in plant allelopathy, forage science, and weed suppression mechanisms. She has also been the Deputy Secretary-General of Jiangsu Strategic Alliance of Turf Industry Technology Innovation since 2018. Additionally, she serves as the Director of the Grassland Professional Committee of Jiangsu Animal Husbandry and Veterinary Medical Association. Her work integrates grassland ecology, plant-derived bioactive compounds, and sustainable farming technologies, enhancing forage quality, ecological farming, and natural plant defenses against invasive species. She is a leading figure in plant biochemistry and agricultural innovation.

🏅 Awards & Honors

Dr. Caixia Wu has received multiple recognitions for her pioneering research in forage science, plant allelopathy, and ecological farming. She has been awarded national and provincial research grants, including National Natural Science Foundation of China (NSFC) projects on allelopathy and genetic traits in forage crops. She has been recognized for her contributions to high-quality forage production, weed suppression mechanisms, and plant-derived bioactive compounds. Her leadership in Jiangsu’s grassland science community has earned her awards for scientific excellence and innovation in sustainable agriculture. As a key academic leader in the turf and forage industry, her contributions have improved plant compound applications, agricultural sustainability, and eco-friendly forage production across China.

🔬 Research Focus 

Dr. Caixia Wu’s research focuses on plant allelopathy, bioactive compound identification, and sustainable grassland management. She explores natural plant-derived chemicals for weed suppression and ecological farming, particularly in legume forages and turf grasses. Her studies on allelopathy in Melilotus officinalis (Yellow Sweetclover) and alfalfa genetics provide sustainable solutions for weed control and forage improvement. She also works on compound extraction, biochemical interactions in plants, and genetic mechanisms of forage crops. Her projects integrate ecological management, plant chemistry, and molecular biology, optimizing forage production, animal nutrition, and environmental sustainability. By advancing natural plant defenses and bioactive compound applications, Dr. Wu’s research contributes to agroecology, biodiversity conservation, and eco-friendly farming techniques.

📊 Publication Top Notes  

Effects of Coumarin on Rhizosphere Microbiome and Metabolome of Lolium multiflorum

 

 

Dr. Tong Sun | Photodiode | Best Researcher Award

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Dr. Tong Sun | Photodiode | Best Researcher Award

Dr. Tong Sun, Beijing University of Posts and Telecommunications, China

Dr. Tong Sun is a dedicated researcher specializing in photodiode technology, infrared detection, and semiconductor materials. He is currently affiliated with Beijing University of Posts and Telecommunications (BUPT), where he focuses on avalanche photodiodes, superlattice structures, and infrared detector optimization. His work involves cutting-edge advancements in high-performance infrared sensors, photonic devices, and optoelectronics. Dr. Sun has contributed to multiple national and provincial research projects and has authored several high-impact publications. He has also secured patents in avalanche photodiodes and has been recognized with multiple awards for his innovative research contributions.

🌍 Professional Profile:

Scopus

🏆 Suitability for Best Researcher Award

Dr. Tong Sun is an exceptional candidate for the Best Researcher Award due to his outstanding contributions to photodiode research and infrared detection technology. His pioneering work on avalanche photodiodes and superlattice structures has been recognized with high-impact publications, national research grants, and prestigious patents. His research has led to significant advancements in optoelectronics, particularly in improving infrared sensing performance. Furthermore, his role as a principal investigator in multiple projects, combined with accolades in national competitions, demonstrates his leadership and innovation in the field. His work has a profound impact on next-generation photonic technologies.

🎓 Education

Dr. Tong Sun is currently pursuing his research at Beijing University of Posts and Telecommunications (BUPT), specializing in Electronic Science and Technology. His academic journey has been shaped by rigorous training in optoelectronics, semiconductor physics, and infrared detection technologies. His education has provided him with deep expertise in the design, modeling, and simulation of advanced photonic and electronic devices. Throughout his studies, he has actively engaged in interdisciplinary projects, blending theoretical knowledge with experimental research. His academic excellence has been recognized through competitive research funding, leading to his leadership in multiple high-impact research projects.

👨‍🔬 Experience

Dr. Tong Sun has been actively involved in multiple national and provincial-level research projects, focusing on photodiode advancements and infrared detection. His contributions include designing multi-stage avalanche photodiodes, constructing infrared material models, and optimizing wide-angle spectral detection technologies. His research experience spans across institutions such as the China Ordnance Industry Corporation Key Laboratory, National Key R&D Programs, and the National Infrared Detection Laboratory. He has also served as the principal investigator for a graduate innovation and entrepreneurship project, leading research on high-temperature photodetectors. His expertise in semiconductor device modeling and photonic design has positioned him as a leading researcher in the field.

🏅 Awards & Honors

  • 🏆 Second Prize in the 2024 San’an Innovation Competition
  • đź“ś Invited Speaker at China Materials Conference 2024 & World Materials Conference
  • 🔬 Cover Article in Laser Technology, featured in the Science Journal Bilingual Communication Project
  • 🎖️ Second Prize in Graduate Innovation and Entrepreneurship Project
  • đź“„ Reviewer for International Optics & Photonics Conference (IACOP 2024)
  • 🎙️ Company Representative Speaker at the 8th National College IC Innovation & Entrepreneurship Competition (North China Division)
  • 🎓 Outstanding Class Leader Award (University Level)

🔍 Research Focus

Dr. Tong Sun’s research primarily revolves around photodiodes, infrared detectors, and optoelectronic device optimization. His expertise includes designing multi-stage avalanche photodetectors, modeling superlattice infrared sensors, and optimizing high-performance infrared detection technologies. His work leverages k·p method simulations for device modeling, focusing on dark current suppression and high-sensitivity photodetection. He also explores high-temperature photodetectors and mid-wave infrared materials, aiming to improve wide-angle spectral tuning and noise reduction in infrared sensors. His research directly impacts the development of next-generation infrared imaging and photonic devices for defense, aerospace, and industrial applications.

đź“Š Publication Top note:

Material Structure Design of High-Gain and Low-Noise Multi-Gain-Stage Avalanche Photodiode

 

 

Dr. Jianhuan Cen | AI for Science Awards | Best Researcher Award

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Dr. Jianhuan Cen | AI for Science Awards | Best Researcher Award

Dr. Jianhuan Cen, Sun Yat-sen University, China

Dr. Jianhuan Cen holds a master’s degree in Computational Mathematics and a bachelor’s degree in Information and Computing Science from Sun Yat-sen University, where he has consistently excelled academically and earned multiple scholarships. His research has made significant strides in AI model benchmarking for molecular property prediction and crystal structure prediction using diffusion models, showcasing his ability to integrate deep learning with scientific computation. Dr. Cen’s work has implications for material science and molecular simulation. He is known for his collaborative spirit and leadership in various research projects and software development efforts, and his versatility is evident from his involvement in programming problem review and testing school OJ websites.

Professional Profile:

Scopus
Google Scholar

Educational Background:

Dr. Cen has a robust academic foundation, with a master’s degree in Computational Mathematics and a bachelor’s degree in Information and Computing Science from Sun Yat-sen University, a leading institution in China. He has excelled academically and received multiple scholarships for his achievements.

Technical Skills and Contributions:

He has extensive hands-on experience in distributed computing, high-performance computing, and algorithm implementation using C/C++, Python, and Matlab. Dr. Cen’s project experience includes:

Implementing Locality Sensitive Hashing (LSH) on distributed clusters using Hadoop and Spark.

Developing a Non-Volatile Memory (NVM) based linear hash index, showcasing expertise in advanced database systems and memory environments.

Research Impact:

Dr. Cen has contributed to various high-impact projects, including AI model benchmarking for molecular property prediction and crystal structure prediction using diffusion models. His interdisciplinary work bridges the gap between deep learning and scientific computation, which could have broad applications in areas like material science and molecular simulation.

Collaboration and Leadership:

He has been involved in multiple research projects and collaborative software development efforts, indicating strong teamwork and leadership capabilities. He has also reviewed programming problems and tested school OJ websites, demonstrating his versatility.

Research Excellence:

Dr. Cen’s research focuses on solving high-dimensional partial differential equations (PDEs) using deep learning methods. He has developed innovative approaches that combine cutting-edge deep learning techniques with finite volume methods to tackle these complex problems.

Research Publications

1.  “Adaptive Trajectories Sampling for Solving PDEs with Deep Learning Methods” (Applied Mathematics and Computation).

2.  “Deep Finite Volume Methods for Partial Differential Equations” (SSRN).

Conclusion:

Dr. Jianhuan Cen’s academic achievements, research contributions in deep learning and computational mathematics, and technical prowess make him an outstanding candidate for the Best Researcher Award. His work is not only theoretically rigorous but also practically applicable, showing promise for future advancements in both academic and industrial contexts.