Ged Smith | Systems Neuroscience | Outstanding Educator Award

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Dr. Ged Smith | Systems Neuroscience | Outstanding Educator Award

Dr. Ged Smith,  UK AFT, United Kingdom.

Dr. Ged Smith is a highly esteemed Consultant Systemic/Family and Couples Psychotherapist with over 25 years of experience in clinical practice, academic teaching, and international consultation. His early academic journey began with a B.Ed from Liverpool University, followed by advanced degrees culminating in a Professional Doctorate from Birkbeck University and the Institute of Family Therapy. Throughout his career, Dr. Smith has made influential contributions through clinical supervision, research publication, and educational leadership. He is widely published in top-tier journals and is the longstanding Editor of “Context,” the UK’s principal family therapy journal. He also holds senior roles in professional organizations such as the Association for Family Therapy (AFT) and the European Family Therapy Association (EFTA). Dr. Smith’s work bridges therapeutic practice with systemic theory, making significant impact on the field both nationally and internationally.

Profile

Orcid

🎓 Early Academic Pursuits

Dr. Ged Smith began his academic journey at Liverpool University, where he earned his Bachelor of Education (B.Ed) in 1980, laying the foundation for a lifelong dedication to learning and teaching. His growing interest in social care and mental health led him to pursue a Certificate of Qualification in Social Work (CQSW) at the University of Cardiff, which he completed in 1988. Deepening his expertise in systemic practices, Dr. Smith undertook a Master of Science (MSc) in collaboration with the Institute of Family Therapy (IFT) and Birkbeck University, London in 1996. His academic excellence culminated in the attainment of a Professional Doctorate from the same institutions in 2011, solidifying his scholarly contributions to systemic and family therapy.

🧠 Professional Endeavors in Systemic Therapy

Dr. Smith’s career spans over 25 years of clinical experience in both Merseyside and London, where he has provided systemic and family therapy across diverse communities. As a UKCP Registered Systemic Psychotherapist and AFT Accredited Supervisor, he currently supervises more than 30 mental health and social care professionals. His professional influence extends across clinical settings, educational platforms, and governmental agencies, making him a sought-after consultant for Social Services and Care Agencies in the North West of England. His dedication to systemic thinking is evident in his role as a Live Supervisor on the Manchester Family Therapy Qualifying Course, where he brings practical and ethical insight to emerging therapists.

📝 Contributions and Research Focus

Dr. Smith has been an unwavering contributor to the dissemination of systemic knowledge, both as a prolific writer and respected editor. As the long-standing Editor of “Context,” the UK’s leading Family Therapy Journal, he has significantly influenced the field’s intellectual discourse. His research focus centers on transformative and relational practices in systemic therapy, engaging with contemporary themes in mental health. His published work appears in globally respected journals such as the Journal of Family Therapy, Family Process (USA), Human Systems, and the Australian and New Zealand Journal of Family Therapy. Notably, he contributed chapters to Systemic Therapy as Transformative Practice (2017), reflecting his commitment to therapeutic innovation and social justice.

📚 Academic Leadership and Teaching Excellence

Dr. Smith has played a vital role in academic mentorship and systemic education. A revered Visiting Lecturer at the Tavistock Clinic London, and universities including Manchester, Exeter, and Hull, he continues to influence systemic thinking across academic and clinical boundaries. In his role as an External Doctoral Supervisor at the University of Bedfordshire, he nurtures the next generation of systemic scholars. His expertise in integrating theory with practice has made him a preferred speaker and educator at family therapy training courses throughout the UK.

🏆 Accolades and Recognition

Dr. Smith’s long-standing contributions to systemic therapy have earned him national and international recognition. As Chair of AFT Publishing for over 20 years, he has guided the ethical and academic standards of family therapy literature in the UK. He also represents the UK at the European Family Therapy Association (EFTA) meetings, further elevating the UK’s presence on the global systemic stage. His respected status in the field is not only a result of his academic output but also his unwavering dedication to supervision, teaching, and ethical therapeutic practice.

🌍 Global Engagement and Influence

A distinguished conference speaker and workshop presenter, Dr. Smith has shared his insights on systemic and psychological approaches to mental health at international platforms. His presentations emphasize both clinical depth and sociocultural relevance, addressing topics like family systems, relational ethics, and collaborative practices in therapy. By integrating global perspectives into his work, Dr. Smith continues to expand the reach and relevance of systemic psychotherapy.

🧬 Legacy and Future Contributions

Dr. Ged Smith’s career represents a profound legacy of relational practice, scholarly excellence, and ethical leadership. As systemic therapy continues to evolve in response to modern challenges, his work sets a benchmark for future generations. With his continued supervision of doctoral candidates, editorial leadership, and international teaching, he remains at the forefront of shaping the future of family therapy. His vision is clear: to maintain systemic practice as not only a clinical method but a transformative social discourse that can empower families, communities, and practitioners alike.

Publication

  • Title: So, You’re Doing a Family Therapy Course……
    Author: Ged Smith
    Year: 2025

 

  • Title: A 1.5‐Order Therapy: Between Knowing and Not‐Knowing
    Author: Ged Smith
    Year: 2023

 

✅ Conclusion

Dr. Ged Smith exemplifies excellence in systemic and family psychotherapy through a unique blend of scholarly depth, clinical wisdom, and passionate teaching. His enduring influence on the development of family therapy—through publications, supervision, and organizational leadership—makes him a key figure in shaping contemporary mental health practices. As a researcher, educator, and clinician, he has created a meaningful legacy grounded in relational ethics and transformative therapeutic approaches. Dr. Smith’s continued contributions will undoubtedly inspire future practitioners and scholars committed to holistic, systemic care.

Aiying Zhang | Computational Neuroscience | Best Researcher Award

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Assist. Prof. Dr. Aiying Zhang | Computational Neuroscience | Best Researcher Award

Assist. Prof. Dr. Aiying Zhang, University of Virginia, United States.

Dr. Aiying Zhang is a rising scholar in the field of mental health data science, currently serving as an Assistant Professor at the University of Virginia and a Faculty Member at the UVA Brain Institute. Her academic foundation spans statistics, biomedical engineering, and clinical biostatistics, acquired from esteemed institutions including USTC, Tulane University, and Columbia University. Her research focuses on developing advanced computational and statistical tools—such as graphical models and multimodal fusion—to decode complex brain data from imaging and genetics. She applies these innovations to better understand and predict psychiatric conditions such as schizophrenia and Alzheimer’s disease. Her work is distinguished by its interdisciplinary nature, translational relevance, and potential to reshape clinical approaches to mental health.

Profile

Google Scholar

 

🎓 Early Academic Pursuits

Aiying Zhang’s journey into the realm of data science and mental health research began with a strong foundation in quantitative sciences. She earned her Bachelor of Science degree in Statistics from the prestigious School for the Gifted Young at the University of Science and Technology of China (USTC) in 2014. Driven by a passion for biomedical innovation and its intersection with human health, she pursued a Ph.D. in Biomedical Engineering from Tulane University, which she completed in 2021. Her graduate years were marked by deep inquiry into statistical modeling and neuroimaging, laying the groundwork for her later interdisciplinary research. She further honed her expertise through postdoctoral training in Clinical Biostatistics and Psychiatry at Columbia University Irving Medical Center, where she blended statistical rigor with clinical insight.

💼 Professional Endeavors

Dr. Zhang is currently an Assistant Professor of Data Science at the University of Virginia, where she has been on the tenure-track faculty since August 2023. She also holds a concurrent position as a Faculty Member at the UVA Brain Institute, underscoring her active role in advancing brain research across institutional boundaries. Prior to her academic appointment at UVA, she served as a Research Scientist II at the New York State Psychiatric Institute, contributing to high-impact psychiatric research. Her professional journey also includes research assistantships at Tulane University and the University of Florida, roles in which she cultivated strong collaborative and translational research skills.

🧠 Contributions and Research Focus

Dr. Zhang’s research lies at the intersection of data science, neuroscience, and mental health. She specializes in developing advanced statistical and computational methodologies to investigate the biological underpinnings of psychiatric and neurodevelopmental disorders. Her work prominently features the use of graphical models—both directed and undirected—and machine learning techniques to analyze complex datasets, such as MRI, DTI, fMRI, MEG, and various genomic modalities including SNP and DNA methylation. Her research has contributed to a deeper understanding of conditions like schizophrenia, Alzheimer’s disease, obsessive-compulsive disorder, and anxiety disorders, through the lens of multimodal data fusion and integrative neurogenetics.

🧪 Innovation in Mental Health Data Science

A distinctive hallmark of Dr. Zhang’s scholarship is her innovative application of multimodal fusion techniques to disentangle the complexities of typical and atypical brain development. Her work leverages high-dimensional neuroimaging and genetic data to draw meaningful inferences about mental health trajectories. She is particularly focused on building interpretable models that bridge the gap between data and clinical insight, thereby enabling earlier and more precise diagnostics. By combining machine learning with biomedical expertise, her contributions pave the way for next-generation tools in psychiatry and neuroscience.

🏅 Accolades and Recognition

Throughout her academic and professional trajectory, Dr. Zhang has earned widespread respect for her analytical acumen and interdisciplinary collaborations. Her postdoctoral role at Columbia, a hub for clinical psychiatry and biostatistics, positioned her among leaders in the field and enriched her research portfolio with translational applications. Her selection as faculty at a leading institution like UVA further reflects recognition of her scholarly excellence and her potential to drive future innovations in mental health data science.

🌍 Impact and Influence

Dr. Zhang’s work has significant implications for both the scientific community and clinical practice. Her methods empower researchers and clinicians alike to draw meaningful patterns from multimodal datasets, thereby advancing precision psychiatry. Moreover, her collaborative efforts across biomedical engineering, statistics, and clinical disciplines have fostered integrative frameworks that extend beyond academic settings into real-world applications. Her contributions are helping to shape a more data-driven and personalized future in mental health care.

🔮 Legacy and Future Contributions

As she continues her academic journey, Dr. Zhang aims to expand her research frontiers by exploring dynamic brain-behavior associations and improving the interpretability of AI models in clinical contexts. With a commitment to mentorship and open science, she is building a legacy rooted in intellectual rigor, innovation, and societal relevance. Her future contributions are expected to not only deepen our understanding of mental health disorders but also inspire a new generation of data scientists dedicated to neuroscience and human well-being.

Publication

  • Leverage multimodal neuro-imaging and genetics to identify causal relationship between structural and functional connectivity and ADHD with Mendelian randomization
    C Ji, S Lee, S Sequeira, J Jin, A Zhang — 2025

 

  • Integrated brain connectivity analysis with fmri, dti, and smri powered by interpretable graph neural networks
    G Qu, Z Zhou, VD Calhoun, A Zhang, YP Wang — 2025

 

  • Altered hierarchical rank in intrinsic neural time-scales in autism spectrum disorder
    A Solomon, W Yu, J Rasero, A Zhang — 2025

 

  • A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
    Y Zhang, L Wang, KJ Su, A Zhang, H Zhu, X Liu, H Shen, VD Calhoun, … — 2025

 

  • A Novel GNN Framework Integrating Neuroimaging and Behavioral Information to Understand Adolescent Psychiatric Disorders
    W Yu, G Qu, Y Kim, L Xu, A Zhang — 2025

 

  • A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
    A Zhang, G Zhang, B Cai, TW Wilson, JM Stephen, VD Calhoun, YP Wang — 2024

 

  • Exploring hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer’s disease
    A Zhang, K Wengler, X Zhu, G Horga, TE Goldberg, S Lee — 2024

 

  • Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing
    L Sun, A Zhang, F Liang — 2024

 

  • Altered hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer’s disease
    A Zhang, K Wengler, X Zhu, G Horga, TE Goldberg, S Lee, … — 2024

 

  • Associations Between Brain Connectivity and Psychiatric Symptoms in Children: Insights into Adolescent Mental Health
    D Mutu, K Ji, X He, S Lee, S Sequeira, A Zhang — 2024

 

🧾 Conclusion

Dr. Zhang’s journey exemplifies a seamless integration of data science and neuroscience to address pressing mental health challenges. Her innovative use of multimodal data and machine learning not only contributes to scientific advancement but also enhances real-world clinical decision-making. As she continues to pioneer research at the intersection of computation and psychiatry, her influence is poised to grow, shaping the future of precision mental health care and empowering both academia and clinical practice through data-driven insights.

 

Jiwei Nie | Emerging Areas in Neuroscience | Best Researcher Award

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Dr. Jiwei Nie | Emerging Areas in Neuroscience | Best Researcher Award

Dr. Jiwei Nie, Haier Group, China.

Jiwei Nie is an accomplished Chinese researcher specializing in Artificial Intelligence-based Pattern Recognition and Intelligent Detection, with a strong focus on AI large models. His academic journey began with a Bachelor’s in Mechanical Design and Automation and evolved into a deeply integrated path through a Master’s and Ph.D. in Control Science and Engineering at Northeastern University. Throughout his doctoral research, he has made notable contributions to the field of Visual Place Recognition (VPR) for autonomous systems, publishing in prestigious journals such as IEEE Transactions on Intelligent Transportation Systems and IEEE Robotics and Automation Letters. Jiwei’s innovations—especially in lightweight, training-free image descriptors and adaptive texture fusion—have positioned him at the forefront of applied AI in robotics and automation. He has also presented at major international conferences and holds multiple patents.

Profile

Google Scholar

🎓 Early Academic Pursuits

 Jiwei Nie displayed a deep interest in engineering and innovation from an early age. His academic journey began at Hebei University of Science and Technology, where he pursued a Bachelor’s degree in Mechanical Design, Manufacturing, and Automation. His strong academic performance earned him first-class honors, and he graduated in July 2018. Motivated to delve deeper into the fusion of machinery and intelligence, he advanced to Northeastern University, completing his Master’s degree in Mechanical and Electronic Engineering by July 2020. Driven by a vision to integrate control systems with intelligent technologies, he enrolled in a PhD program in Control Science and Engineering under a prestigious Integrated Master-PhD track, further solidifying his expertise in the intelligent automation domain.

💼 Professional Endeavors

Jiwei’s professional development has been tightly interwoven with his academic path, where he has continuously applied theoretical insights to practical problems in Artificial Intelligence and Control Systems. As a member of the Communist Party of China, he approaches his work with a strong sense of discipline and public responsibility. His fluency in English, proven by his CET-6 certification, has enabled him to actively contribute to the global research community, engaging in international collaborations and conferences. Alongside his research, Jiwei has contributed to academic circles through mentorship roles and cross-institutional projects, making a significant impact both inside and outside his university.

🤖 Contributions and Research Focus

Jiwei Nie’s research is at the forefront of Artificial Intelligence-based Pattern Recognition and Intelligent Detection, with a special emphasis on AI Large Models. His work focuses on developing lightweight, efficient algorithms for Visual Place Recognition (VPR)—a critical capability for autonomous vehicles and robotic systems. He has pioneered new methods in saliency encoding, feature mixing, and texture fusion, leading to more robust and adaptive AI systems. Through these contributions, he has addressed real-world challenges in long-term navigation and intelligent perception, pushing the boundaries of control science and machine intelligence.

🏆 Accolades and Recognition

During his PhD, Jiwei published multiple high-impact articles in leading SCI-indexed journals. His paper in the IEEE Transactions on Intelligent Transportation Systems, titled “A Training-Free, Lightweight Global Image Descriptor for Long-Term Visual Place Recognition Toward Autonomous Vehicles”, has been particularly well-received and is ranked in Q1. Additional works in IEEE Robotics and Automation Letters have been ranked in Q2, highlighting his innovations such as MixVPR++ and Efficient Saliency Encoding. Furthermore, Jiwei’s presence has been notable at world-class conferences like ICPR, ICRA, and IROS, where he presented his work to a global audience of peers and experts. He also holds several patents, including an invention patent, and continues to submit further manuscripts to top-tier venues.

🌍 Impact and Influence

Jiwei’s research has had a significant influence on the future of intelligent transportation and autonomous systems. His development of training-free VPR models has contributed to making autonomous navigation more scalable and cost-effective, especially in dynamic environments where traditional AI systems fail. His proposed methods are not only academically rigorous but are also computationally efficient, paving the way for real-world deployment. Through his innovation and academic collaborations, he has helped bridge the gap between theoretical AI models and practical engineering applications, which is vital for industries moving toward Industry 4.0 and smart mobility solutions.

🧠 Legacy and Future Contributions

Looking ahead, Jiwei Nie aspires to deepen his research in generalized large AI models, expanding the scalability and generalization abilities of pattern recognition systems across domains beyond transportation—such as smart surveillance, industrial robotics, and medical imaging. His planned future publications and continued patent filings reflect a strong ambition to lead the next generation of intelligent systems research. Jiwei is committed to fostering innovation that aligns with both academic excellence and societal needs, aiming to establish himself as a pioneering researcher and mentor in the evolving field of intelligent detection and AI integration.

🔬 Vision in AI and Control Engineering

Jiwei Nie stands as a rising expert in the convergence of Artificial Intelligence, Control Science, and Robotic Vision, a field essential for the future of smart systems and automation. His deep technical knowledge, coupled with a strategic vision, positions him to contribute not only as a researcher but also as a thought leader in AI-driven engineering. With a career rooted in innovation and societal benefit, his trajectory points toward a legacy of breakthroughs that will influence smart cities, autonomous systems, and global AI research landscapes for years to come.

Publication

  • Title: A survey of extrinsic parameters calibration techniques for autonomous devices
    Authors: J Nie, F Pan, D Xue, L Luo
    Year: 2021

 

  • Title: A training-free, lightweight global image descriptor for long-term visual place recognition toward autonomous vehicles
    Authors: J Nie, JM Feng, D Xue, F Pan, W Liu, J Hu, S Cheng
    Year: 2023

 

  • Title: Forest: A lightweight semantic image descriptor for robust visual place recognition
    Authors: P Hou, J Chen, J Nie, Y Liu, J Zhao
    Year: 2022

 

  • Title: A novel image descriptor with aggregated semantic skeleton representation for long-term visual place recognition
    Authors: J Nie, JM Feng, D Xue, F Pan, W Liu, J Hu, S Cheng
    Year: 2022

 

  • Title: Efficient saliency encoding for visual place recognition: Introducing the lightweight pooling-centric saliency-aware VPR method
    Authors: J Nie, D Xue, F Pan, Z Ning, W Liu, J Hu, S Cheng
    Year: 2024

 

  • Title: 3D semantic scene completion and occupancy prediction for autonomous driving: A survey
    Authors: G Xu, W Liu, Z Ning, Q Zhao, S Cheng, J Nie
    Year: 2023

 

  • Title: A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition
    Authors: N Jiwei, F Joe-Mei, X Dingyu, P Feng, L Wei, H Jun, C Shuai
    Year: 2022

 

  • Title: Optic Disc and Fovea Localization based on Anatomical Constraints and Heatmaps Regression
    Authors: L Luo, F Pan, D Xue, X Feng, J Nie
    Year: 2021

 

  • Title: A Novel Fractional-Order Discrete Grey Model with Initial Condition Optimization and Its Application
    Authors: Y Liu, F Pan, D Xue, J Nie
    Year: 2021

 

  • Title: EPSA-VPR: A lightweight visual place recognition method with an Efficient Patch Saliency-weighted Aggregator
    Authors: J Nie, Q Zhào, D Xue, F Pan, W Liu
    Year: 2025

 

🔚 Conclusion

With a solid foundation in engineering and control systems and an innovative mindset in artificial intelligence, Jiwei Nie is poised to become a key figure in the evolution of intelligent automation technologies. His work contributes not only to academic theory but also to practical applications that influence the development of autonomous vehicles, intelligent detection systems, and large AI model architectures. As he approaches the completion of his Ph.D. in early 2025, Jiwei is expected to continue pushing technological boundaries, inspiring future advancements in AI research and real-world intelligent systems deployment.

Shumao Xu | Neurotechnology | Best Researcher Award

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Assoc. Prof. Dr. Shumao Xu | Neurotechnology | Best Researcher Award

Assoc. Prof. Dr.  Shumao Xu, Fudan University, China.

Shumao Xu’s career embodies a fusion of material science, biomedical engineering, and neurotechnology, leading to remarkable advancements in neural interfaces and brain-computer interaction. His extensive research, industry collaborations, and prestigious funding awards highlight his influence in the field. With over 60 high-impact publications and thousands of citations, his work has significantly contributed to neuroengineering, setting the foundation for future innovations.

Profile

Orcid

✨ Early Academic Pursuits

Shumao Xu’s journey in academia began with a passion for innovation and exploration in neural interfaces and biomedical engineering. He pursued his Ph.D. at Shanghai Jiao Tong University (SJTU), where he laid the foundation for his research in neural engineering. His early academic years were marked by rigorous studies in material science, bioelectronics, and neurotechnology, setting the stage for his groundbreaking work in neural interfaces. His commitment to excellence led him to postdoctoral training at the prestigious Max Planck Institute for Solid-State Research as an Alexander von Humboldt scholar, followed by further research at Pennsylvania State University and UCLA.

👨‍🎓 Professional Endeavors

Currently an Associate Professor and Principal Investigator at Fudan University’s Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI), Shumao Xu is recognized as a National Overseas Young Talent (2024). His professional trajectory has been defined by his commitment to advancing brain-computer interfaces and neurotechnology. Securing funding from prestigious organizations such as the National Natural Science Foundation of China (NSFC), China Postdoctoral Science Foundation (Innovative Program), and the Shanghai Super Postdoctoral Program, he has spearheaded research that pushes the boundaries of neural engineering.

🧠 Contributions and Research Focus

Shumao Xu has dedicated his research to developing state-of-the-art neural interfaces that revolutionize neurostimulation and brain-computer interactions. His pioneering work includes implantable neural electrodes, non-invasive deep brain stimulation, calcium imaging, and non-genetic optoelectronic neural interfaces. His research extends to the development of soft magnetoelastic energy harvesters, injectable fluorescent neural probes, and triboelectric neurostimulators for self-powered neural systems. His work is crucial in creating biocompatible and energy-efficient neurotechnologies that have the potential to treat neurodegenerative diseases and enhance brain function.

🏆 Accolades and Recognition

With over 60 high-impact publications in renowned journals such as Advanced Materials, Nature Communications, Nano Letters, Matter, and Chem, Shumao Xu has established himself as a leading researcher in neurotechnology. His impressive h-index of 28 and more than 3,300 citations stand as a testament to the significance of his contributions. He has been honored with funding from the NSFC Oversea Young Talent program for his work on injectable fluorescent neural probes and received the Humboldt Foundation’s support for optoelectronic neural modulation. His research has gained international recognition, earning him industry collaborations and consultancy projects.

⚛️ Impact and Influence

Beyond academia, Shumao Xu’s work has practical applications in the medical and technological sectors. His collaborations with leading industry giants, such as Showa Denko and Teijin in Tokyo, Japan, have translated his academic innovations into real-world applications. His research in neural interfaces and brain-computer technologies has the potential to revolutionize treatments for neurological disorders, offering new hope to patients with neurodegenerative diseases. His advancements in self-powered neural stimulation systems have paved the way for sustainable and long-lasting neurotechnologies.

💡 Legacy and Future Contributions

As a visionary in neuroengineering, Shumao Xu continues to shape the future of brain-computer interfaces and neural modulation. His work is not only contributing to academic advancements but also influencing the next generation of researchers and engineers in neurotechnology. His ongoing research projects, including biocompatible neural electrodes and optoelectronic neural modulation, promise to drive innovation in the field. Through his relentless pursuit of scientific breakthroughs, he aims to bridge the gap between neuroscience and technology, ultimately transforming the landscape of brain-computer interaction and neurotherapy.

Publication

  • Artificial intelligence assisted nanogenerator applications

    • Authors: Shumao Xu, Farid Manshaii, Xiao Xiao, Jun Chen

    • Year: 2025

 

  • Advances in 2D materials for wearable biomonitoring

    • Authors: Songyue Chen, Shumao Xu, Xiujun Fan, Xiao Xiao, Zhaoqi Duan, Xun Zhao, Guorui Chen, Yihao Zhou, Jun Chen

    • Year: 2025

 

  • A comprehensive review on the mechanism of contact electrification

    • Authors: J Tian, Y He, F Li, W Peng, Y He, Shumao Xu, F Manshaii, X Xiao, Jun Chen

    • Year: 2025

 

  • Advances in Brain Computer Interface for Amyotrophic Lateral Sclerosis Communication

    • Authors: Yuchun Wang, Yurui Tang, Qianfeng Wang, Minyan Ge, Jinling Wang, Xinyi Cui, Nianhong Wang, Zhijun Bao, Shugeng Chen, Jing Wang et al.

    • Year: 2025

 

  • Tailored Terminal Groups in MXenes for Fast-Charging and Safe Energy Storage

    • Authors: Shumao Xu, Minyan Ge, Weiqiang Zhang, Yuchun Wang, Yurui Tang

    • Year: 2025

 

  • Heart-brain connection: How can heartbeats shape our minds?

    • Authors: Xu Shumao, Scott Kamryn, Manshaii Farid, Chen Jun

    • Year: 2024

  • Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging

    • Authors: Xu Shumao, Xiao Xiao, Manshaii Farid, Chen Jun

    • Year: 2024

 

  • Multiphasic interfaces enabled aero-elastic capacitive pressure sensors

    • Authors: Xu Shumao, Manshaii Farid, Chen Jun

    • Year: 2024

 

  • Reversible metal-ligand coordination for photocontrolled metallopolymer adhesives

    • Authors: Xu Shumao, Manshaii Farid, Chen Guorui, Chen Jun

    • Year: 2024

 

  • Self-Thermal Management in Filtered Selenium-Terminated MXene Films for Flexible Safe Batteries

    • Authors: Pang Xin, Lee Hyunjin, Rong Jingzhi, Zhu Qiaoyu, Xu Shumao

    • Year: 2024

 

🌟 Conclusion

Shumao Xu’s pioneering research and dedication to neural engineering continue to push the boundaries of brain-inspired intelligence and medical advancements. His visionary contributions have paved the way for next-generation neurotechnologies that hold the potential to transform neurological treatments and human-computer interactions. As he continues his groundbreaking research, his legacy will inspire future scientists and engineers, driving forward the possibilities of neurotechnology for years to come.

Saba Hesaraki | Neurotechnology | Best Researcher Award

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Ms. Saba Hesaraki | Neurotechnology | Best Researcher Award

Ms. Saba Hesaraki,  Islamic Azad University science and research branch, Iran.

Saba Hesaraki is a computer engineer specializing in artificial intelligence (AI), particularly in medical imaging and generative AI. She holds a Master’s degree in Computer Engineering from Islamic Azad University, Science and Research Branch, Tehran, where her thesis focused on breast cancer image segmentation using an improved 3D U-Net++ model. She has a strong academic background with high GPAs in both her bachelor’s and master’s programs.

Profile

Google Scholar

🌱 Early Academic Pursuits

Saba Hesaraki embarked on her academic journey with a deep passion for computer engineering, earning her Bachelor of Science in Software Engineering from Islamic Azad University, West Tehran Branch. With an outstanding GPA of 17.22 out of 20.0, she demonstrated an early inclination toward problem-solving and artificial intelligence. Her intellectual curiosity and commitment to innovation led her to pursue a Master’s degree in the same domain at Islamic Azad University, Science and Research Branch, Tehran. Her thesis, titled “Segmentation of Breast Cancer Images Using Improved 3D U-Net++ Model,” under the supervision of Dr. Maryam Rastgarpour, showcases her dedication to advancing medical imaging technologies through AI-driven solutions. With an exceptional GPA of 18.12 out of 20.0, her academic excellence laid the foundation for a remarkable research career.

💼 Professional Endeavors

Saba’s professional journey reflects her deep expertise in artificial intelligence, particularly in the realms of generative AI and medical imaging. She has worked remotely in various esteemed organizations, contributing her skills to groundbreaking AI projects. Her role as a Generative AI Engineer at Care Vox in Mountain View, California, and Nexus in San Jose, California, enabled her to develop innovative AI-driven solutions. Prior to this, she made significant contributions as a Computer Vision Engineer at Koga Studio and the Quantitative MR Imaging and Spectroscopy Group in Tehran. Her engagement as an NLP Researcher at Asr Gooyesh Pardaz further showcases her versatility in the field of AI. Through these roles, she has gained profound experience in AI-based medical diagnostics, image segmentation, and sustainable AI development, paving the way for impactful innovations.

📚 Contributions and Research Focus

As a dedicated researcher, Saba’s work has revolved around the intersection of AI and healthcare, particularly medical image segmentation and generative AI applications. Her research interests extend to AI-driven personalized medicine and sustainable AI solutions. She has co-authored multiple research papers, including “Capsule Fusion for Extracting Psychiatric Stressors for Suicide from Twitter” and “UNet++ and LSTM Combined Approach for Breast Ultrasound Image Segmentation.” Her work reflects a keen interest in leveraging AI to solve complex medical challenges, from cancer detection to mental health analysis. Her research on classifying 3D point cloud objects using hybrid neural networks also highlights her multidisciplinary expertise.

🏆 Accolades and Recognition

Saba’s dedication to AI research has been recognized through her academic achievements and professional contributions. Her IELTS score of 7.5 and GRE score of 332 underscore her strong analytical and communication skills, essential for global collaboration in AI research. Her research papers have been under review and submission in reputable scientific journals, further solidifying her presence in the AI and medical imaging research community. The recognition she has garnered through collaborations and innovative contributions establishes her as an influential figure in AI-driven healthcare solutions.

🌍 Impact and Influence

Saba’s work extends beyond research, as she actively contributes to the global AI community by developing cutting-edge AI applications for real-world problems. Her role in AI for sustainable development and AI-driven personalized medicine signifies her commitment to leveraging technology for societal benefit. Her experience in deep learning frameworks like PyTorch and Keras, along with her expertise in machine learning algorithms, has allowed her to shape AI-driven healthcare innovations that have the potential to save lives and enhance medical diagnostics. Through collaborations and mentorship, she inspires the next generation of AI researchers to push the boundaries of technological advancements.

🚀 Legacy and Future Contributions

As an AI researcher and engineer, Saba continues to drive innovation in medical imaging and generative AI. Her aspirations include advancing AI methodologies for early disease detection, improving healthcare accessibility through AI-driven solutions, and fostering AI applications in sustainable development. Her ability to blend technical expertise with a deep understanding of healthcare challenges positions her as a leader in the field. With a promising future ahead, she remains dedicated to exploring new AI frontiers that will revolutionize medical imaging, AI ethics, and beyond.

Publication

Title: A Comprehensive Analysis on Machine Learning based Methods for Lung Cancer Level Classification
Authors: S. Farshchiha, S. Asoudeh, M.S. Kuhshuri, M. Eisaeid, M. Azadie, S. Hesaraki
Year: 2025

Title: Breast Cancer Ultrasound Image Segmentation Using Improved 3D Unet++
Authors: S. Hesaraki, A.S. Mohammed, M. Eisaei, R. Mousa
Year: 2025

Title: BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
Authors: M. Memari, S.M. Nejad, A.P. Rabiei, M. Eisaei, S. Hesaraki
Year: 2024

Title: UNet++ and LSTM Combined Approach for Breast Ultrasound Image Segmentation
Authors: S. Hesaraki, M. Akbari, R. Mousa
Year: 2024

Title: Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach
Authors: R. Mousa, M. Khezli, M. Azadi, V. Nikoofard, S. Hesaraki
Year: 2024

Title: CapsF: Capsule Fusion for Extracting Psychiatric Stressors for Suicide from Twitter
Authors: M.A. Dadgostarnia, R. Mousa, S. Hesaraki
Year: 2024

Conclusion

Saba Hesaraki is a highly skilled and motivated AI engineer with a strong academic and research background in medical imaging and generative AI. Her experience across various AI-driven projects, coupled with technical expertise in deep learning and computer vision, positions her as a valuable contributor to the field. With multiple publications and collaborations in AI and machine learning, she continues to make significant advancements in healthcare applications using AI.