Marcelo Mattar | Neuroscience | Best Researcher Award

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Dr. Marcelo Mattar | Neuroscience | Best Researcher Award

Assistant Professor at New York University | United States

Dr. Marcelo Mattar is a distinguished cognitive and computational neuroscientist whose work bridges psychology, neuroscience and artificial intelligence. He currently serves as an Assistant Professor in the Department of Psychology at New York University, where he also holds affiliate roles with the Center for Neural Science, the Center for Data Science, and the NYU-KAIST partnership. His career reflects a global trajectory, with academic appointments across leading institutions in the United States and the United Kingdom, including the University of California, San Diego; the University of Cambridge; and Princeton University. Dr. Mattar’s research integrates theoretical modeling, machine learning, and experimental neuroscience to understand the neural and computational principles underlying learning, decision-making, and memory. He has contributed extensively to the academic community through teaching, mentoring, peer review, and editorial service for top-tier journals. Widely recognized for his academic leadership, he has delivered invited talks at world-renowned research centers, summer schools, and international conferences. His dedication to cross-disciplinary collaboration has positioned him at the forefront of computational neuroscience, making significant contributions to both fundamental science and its applications in understanding human cognition. Dr. Mattar’s career is marked by intellectual breadth, methodological rigor, and a commitment to advancing scientific knowledge.

Publication Profile

Scopus

Orcid

Google Scholar

Education

Dr. Mattar’s educational background reflects a strong foundation in both engineering and the cognitive sciences. He began his academic journey with a Bachelor’s degree in Electronics Engineering from the Aeronautics Institute of Technology in Brazil, where he developed a robust understanding of systems analysis, problem-solving, and computational modeling. His interdisciplinary curiosity led him to the University of Pennsylvania, where he pursued advanced studies in psychology, statistics, and neuroscience. He earned a Master’s degree in Psychology, a Master’s degree in Statistics, and ultimately a Ph.D. in Psychology under the guidance of a distinguished team of advisors with expertise in cognitive neuroscience, brain imaging, and network science. His doctoral work combined behavioral experimentation with advanced data analysis and network-based modeling to investigate cognitive processes such as decision-making and memory retrieval. Throughout his training, Dr. Mattar cultivated expertise in statistical inference, computational simulations, and the integration of complex datasets from neuroimaging and behavioral studies. This diverse academic preparation provided him with the quantitative and theoretical skills necessary to explore the brain’s computational architecture. His educational trajectory exemplifies a unique blend of technical precision and psychological insight, forming the basis for his later contributions to computational and cognitive neuroscience.

Professional Experience

Dr. Mattar’s professional career is characterized by a seamless integration of teaching, research, and collaborative leadership across top-tier institutions. At New York University, he plays a central role in shaping the academic curriculum in cognitive and computational neuroscience, while fostering interdisciplinary collaborations through his affiliations with neuroscience and data science centers. Prior to his NYU appointment, he served as an Assistant Professor in Cognitive Science at the University of California, San Diego, with concurrent roles in computer science and neuroscience graduate training. His international research experience includes serving as a Newton International Fellow in the Department of Engineering at the University of Cambridge and as a postdoctoral affiliate of Trinity College. Earlier, at Princeton University’s Neuroscience Institute, he contributed to groundbreaking research in computational modeling of cognition. In addition to his institutional roles, Dr. Mattar is a sought-after speaker, having delivered invited lectures at globally recognized research organizations. His professional experience extends beyond academia through service as a reviewer and guest editor for leading scientific journals, demonstrating his commitment to advancing the rigor and visibility of research in his field. His career path illustrates a balance between scientific discovery, academic mentorship, and global scientific engagement.

Research Interest

Dr. Mattar’s research interests lie at the intersection of neuroscience, psychology, and machine learning, with a particular emphasis on understanding the neural and computational mechanisms of learning, decision-making, and memory. He seeks to uncover how the brain represents, processes, and utilizes information to guide adaptive behavior in dynamic environments. His work often involves building and testing computational models that mimic human cognitive processes, allowing him to explore the principles underlying mental functions such as planning, exploration, and reward-based learning. He is deeply interested in the role of reinforcement learning algorithms in shaping behavior and how these computational frameworks can be aligned with neural activity patterns observed through neuroimaging and electrophysiological methods. Another central theme in his research is the integration of network neuroscience approaches to investigate the brain as a complex, interconnected system. By combining statistical modeling, data science techniques, and cognitive theory, Dr. Mattar addresses questions that are relevant not only for basic science but also for applications in artificial intelligence (AI) and clinical neuroscience. His interdisciplinary research agenda reflects a commitment to bridging theoretical models with empirical data, thereby advancing a mechanistic understanding of human cognition.

Research Skills

Dr. Mattar possesses a comprehensive set of research skills that span experimental design, computational modeling, and advanced statistical analysis. He is proficient in constructing and validating reinforcement learning models, neural network architectures, and probabilistic frameworks for cognitive processes. His expertise includes the use of network neuroscience tools to examine brain connectivity and the application of machine learning algorithms for pattern recognition in high-dimensional datasets. Skilled in programming languages such as Python and MATLAB, he develops custom analytical pipelines for integrating behavioral, neuroimaging, and electrophysiological data. His statistical expertise allows him to conduct rigorous hypothesis testing, model comparison, and cross-validation to ensure the robustness of his findings. Dr. Mattar is also experienced in designing behavioral experiments that probe decision-making and memory processes, often employing virtual environments and computational simulations. In addition to his technical competencies, he has demonstrated strong scientific communication skills through teaching, mentoring, and presenting at international conferences. His ability to bridge quantitative methods with theoretical insights enables him to address complex research questions in cognitive neuroscience with precision and creativity, making him a versatile and impactful researcher in his field.

Awards and Honors

Dr. Mattar’s academic excellence has been recognized through multiple prestigious awards and honors from international institutions and scientific organizations. Among these is the Newton International Fellowship in the United Kingdom, which supported his research and facilitated collaborations with leading experts in computational neuroscience and engineering. He has been the recipient of competitive travel grants, including one from the Computational and Systems Neuroscience Conference, enabling him to share his research with global peers. His early career achievements were further recognized with a fellowship from the Summer Institute in Cognitive Neuroscience and a best poster award at the Repetition Suppression Summer School. In Brazil, his academic promise was acknowledged with the Fundação Estudar award, which supported his studies during his formative research years. These honors reflect not only his scholarly contributions but also his active engagement in advancing the frontiers of cognitive and computational neuroscience. His awards underscore his dedication to excellence, international collaboration, and interdisciplinary research. Collectively, these recognitions affirm his standing as a leading researcher whose work continues to influence the academic and scientific communities at both national and international levels.

Author Metrics

  • Total Citations: 2,620+

  • h-index: 23

  • i10-index: 29

These metrics reflect Dr. Marcelo Mattar’s scholarly impact and contributions to the fields of cognitive neuroscience, psychology, and computational modeling. An h-index of 23 indicates that at least 23 of his publications have received 23 or more citations each, while the i10-index of 29 shows a substantial number of works with over ten citations—demonstrating consistent and growing influence across his research portfolio.

Publications Top Notes

1. Prioritized memory access explains planning and hippocampal replay
Citations: 420
Year: 2018

2. Optimal trajectories of brain state transitions
Citations: 209
Year: 2017

3. Functional network dynamics of the language system
Citations: 189
Year: 2016

4. A functional cartography of cognitive systems
Citations: 188
Year: 2015

5. Experience replay is associated with efficient nonlocal learning
Citations: 175
Year: 2021

6. Planning in the brain
Citations: 137
Year: 2022

7. A network neuroscience of human learning: Potential to inform quantitative theories of brain and behavior
Citations: 136
Year: 2017

8. The temporal dynamics of opportunity costs: A normative account of cognitive fatigue and boredom
Citations: 114
Year: 2022

9. de Bruijn cycles for neural decoding
Citations: 103
Year: 2011

10. Simultaneous perceptual and response biases on sequential face attractiveness judgments
Citations: 80
Year: 2015

Conclusion

Dr. Marcelo Mattar exemplifies the modern interdisciplinary scientist, blending the quantitative rigor of engineering with the conceptual depth of cognitive neuroscience. His career trajectory—from engineering studies in Brazil to faculty leadership at a premier U.S. institution—demonstrates both intellectual versatility and a sustained commitment to advancing scientific understanding. His research, centered on the computational principles of learning, decision-making, and memory, contributes to bridging the gap between neuroscience and artificial intelligence. As an educator, he brings complex concepts to life for undergraduate and graduate students, fostering analytical skills and inspiring the next generation of scientists. His service to the scientific community as an editor, reviewer, and invited speaker reflects his leadership and influence in the field. Dr. Mattar’s body of work underscores the value of interdisciplinary collaboration, methodological innovation, and scientific curiosity. With a career marked by international engagement, academic excellence, and impactful scholarship, he continues to make significant contributions to both theoretical knowledge and practical applications in cognitive science and computational neuroscience. His ongoing work promises to shape the future of brain research, artificial intelligence and the development of intelligent systems.

 

Tongtong Che | Brain Image Analysis | Best Researcher Award

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Dr. Tongtong Che | Brain Image Analysis | Best Researcher Award

Dr. Tongtong Che | Brain Image Analysis – Postdoctor at Beijing Normal University, China.

Dr. Tongtong Che is a postdoctoral researcher specializing in medical image analysis, with a robust background in deep learning and medical imaging registration techniques. Born on June 24, 1995, she currently works at the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University. With extensive training in biological science, medical engineering, and information science, Dr. Che has developed innovative algorithms for brain image registration, an area crucial for neuroimaging studies and disease diagnostics. She has published extensively in top-tier journals such as Medical Image Analysis and IEEE Transactions on Image Processing, with her work widely recognized for its technical sophistication and clinical relevance. In addition to academic accomplishments, she gained industry experience through a research internship at Shanghai United Imaging Intelligence. Dr. Che is also the recipient of the 2024 Best Researcher Award at the International Conference on Neurology and Neuro Disorders, highlighting her growing international impact. Her research continues to bridge the gap between advanced computational methodologies and clinical applications in neuroscience and medical imaging. Dr. Che’s ultimate aim is to contribute to the development of intelligent systems that enhance healthcare diagnostics and treatment planning.

Publication Profile

Scopus

Educational Details

Dr. Tongtong Che holds a Doctor of Engineering degree in Biological Science and Medical Engineering from Beihang University, awarded in June 2024. During her doctoral studies, she focused on medical image processing, particularly brain image registration techniques using deep learning frameworks. Prior to this, she earned a Master of Science degree from the School of Information Science and Engineering at Shandong Normal University in 2020. Her master’s work involved image processing and algorithmic optimization, laying the foundation for her later work in medical imaging. Dr. Che began her academic journey at Shandong Women’s University, where she obtained a Bachelor of Science in Information Science and Technology in 2017. Across all her academic stages, she consistently demonstrated excellence in computational modeling, medical data analysis, and interdisciplinary collaboration. Her educational path reflects a progressive deepening of expertise, from general information technology to highly specialized medical image analysis, aligning her with the forefront of intelligent healthcare research. This solid educational background empowers her to address complex challenges in brain mapping and deformable image registration, particularly for pediatric and neurological conditions.

Professional Experience

Dr. Tongtong Che currently serves as a postdoctoral researcher at the State Key Laboratory of Cognitive Neuroscience and Learning at Beijing Normal University, a role she has held since September 2020. In this capacity, she leads research on brain image registration—a foundational process for constructing accurate brain templates and understanding developmental brain changes. Her work involves developing deep learning algorithms for multi-spectral and 3D image registration. Prior to this, Dr. Che completed a research internship at Shanghai United Imaging Intelligence in 2019, where she optimized brain image registration algorithms in a high-tech industrial setting. These experiences have endowed her with both academic depth and practical proficiency in translating computational models to real-world applications. She has collaborated with leading experts in the field and co-authored multiple high-impact journal articles. Her professional portfolio demonstrates a rare balance between theoretical innovation and applied research, supported by strong interdisciplinary collaboration. Through her combined academic and industry experience, Dr. Che has emerged as a key contributor to advancing image analysis technologies that support clinical neuroscience and brain development research.

Research Interest

Dr. Tongtong Che’s research interests are centered on medical image processing and analysis, with particular expertise in brain image registration and deep learning-based methods for biomedical applications. She is passionate about developing computational tools that enable more accurate and efficient analysis of medical images, which are critical in diagnostic and prognostic procedures. Her primary research explores group-wise and deformable registration techniques using hierarchical and graph-based models. She also works on template construction and multilevel modeling for pediatric brain imaging, especially under complex deformations associated with growth and neurodevelopment. Dr. Che’s secondary interests include radiomics, image segmentation, and multimodal image fusion. Her interdisciplinary approach leverages artificial intelligence and neuroinformatics to uncover structural and functional patterns in medical images. Dr. Che aims to integrate machine learning with biomedical imaging to support precision medicine, early disease detection, and patient-specific treatment planning. With the increasing availability of medical data and advances in AI, she is committed to pushing the boundaries of image-guided healthcare technologies.

Research Skills

Dr. Tongtong Che possesses a broad and advanced set of research skills at the intersection of computer science and biomedical engineering. Her core technical expertise includes medical image registration, deep neural networks, generative adversarial networks (GANs), and optical flow estimation. She is proficient in implementing and optimizing complex image processing pipelines, particularly for multi-spectral and 3D brain MRI datasets. Dr. Che has extensive programming experience in Python and MATLAB, with a strong command of deep learning frameworks such as TensorFlow and PyTorch. She is skilled in statistical modeling, data visualization, and algorithm evaluation for reproducibility and robustness. Her methodological innovations include the development of AMNet and DGR-Net, two state-of-the-art architectures for deformable registration. In addition, she has successfully applied her skills in collaborative environments, including academic labs and industrial R&D settings. Dr. Che is also adept at preparing manuscripts for peer-reviewed journals and conference presentations, demonstrating a high level of scientific communication. Her research toolkit positions her well for future developments in image-based diagnostics and personalized medicine.

Awards and Honors

Dr. Tongtong Che has been recognized for her exceptional research contributions with the Best Researcher Award at the 5th Edition of the International Conference on Neurology and Neuro Disorders in 2024. This award acknowledges her innovative work in brain image registration and its implications for neurodevelopmental and neurological studies. In the same year, she secured funding from the China Postdoctoral Science Foundation under the prestigious Postdoctoral Innovation Talent Support Program (BX20240039). Her funded project focuses on constructing dynamic developmental brain maps for children aged 0 to 18 years with complex anatomical deformations. The grant, worth 640,000 yuan, supports her research from June 2024 to June 2026 and reflects national-level recognition of her scientific potential and leadership. These honors highlight Dr. Che’s impact and visibility in the medical imaging and neuroscience communities. She continues to advance her work at the cutting edge of biomedical engineering with an aim to improve both the scientific understanding of brain development and the clinical tools available for pediatric diagnosis.

Author Metrics

  • Total Publications: 11

  • Total Citations: 136

  • h-index: 8

  • g-index: 10

  • i10-index: 5

Top Noted Publication

  • Nested Hierarchical Group-wise Registration with a Graph-based Subgrouping Strategy for Efficient Template Construction

    • Year: 2025

    • Citation Count: N/A

  • AMNet: Adaptive Multi-level Network for Deformable Registration of 3D Brain MR Images

    • Year: 2023

    • Citation Count: N/A

  • SDOF-GAN: Symmetric Dense Optical Flow Estimation with Generative Adversarial Networks

    • Year: 2021

    • Citation Count: N/A

  • DGR-Net: Deep Group-wise Registration for Multi-spectral Images

    • Year: 2019

    • Citation Count: N/A

  • Deep Group-wise Registration for Multi-spectral Images from Fundus Images

    • Year: 2019

    • Citation Count: N/A

  • A Framework for Brain Template Generation by Hierarchical Group-wise Image Registration

    • Year: 2023

    • Citation Count: N/A

  • SymReg-GAN: Symmetric Image Registration with Generative Adversarial Networks

    • Year: 2021

    • Citation Count: N/A

  • Image Matting with Deep Gaussian Process

    • Year: 2022

    • Citation Count: N/A

  • Regional Radiomics Similarity Networks (R2SNs) in the Human Brain: Reproducibility, Small-world Properties and a Biological Basis

    • Year: 2021

    • Citation Count: N/A

  • Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment

    • Year: 2022

    • Citation Count: 14

Conclusion

Dr. Tongtong Che is a rising researcher in the field of medical image analysis, with a strong foundation in deep learning and biomedical engineering. Her academic path from undergraduate to postdoctoral research has been marked by consistent excellence, culminating in numerous high-impact publications and prestigious research grants. She bridges the gap between algorithm development and clinical application, contributing significantly to the evolving landscape of neuroimaging and precision diagnostics. Her work not only advances the theoretical framework of image registration but also has tangible applications in understanding brain development and supporting clinical decision-making. With a growing international profile and a solid research portfolio, Dr. Che is well-positioned to lead interdisciplinary collaborations and tackle complex challenges in healthcare technologies. Her future endeavors aim to enhance diagnostic imaging tools and foster AI-driven innovations for personalized medicine. Dr. Che exemplifies the next generation of researchers driving innovation at the intersection of technology, medicine, and data science.