Camille Blackman | Neurology and Gender-Affirming Care | Best Researcher Award

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Ms. Camille Blackman | Neurology and Gender-Affirming Care | Best Researcher Award

Camille Blackman is a dedicated medical student and multidisciplinary researcher with a passion for advancing gender-affirming healthcare and surgical education. Currently pursuing her MD at the University of Illinois College of Medicine, she brings a diverse background in anthropology, emergency medicine, and clinical research. Camille’s clinical experiences range from working as an EMT and physical therapist assistant to serving as a medical assistant in dermatology. Her current research at Johns Hopkins Center for Transgender and Gender Expansive Health centers on surgical outcomes, educational innovation, and health equity. She has published and presented nationally on topics like craniofacial surgery, sexual medicine, and transgender health. Outside of medicine, she is a nationally competitive runner and co-founder of Nameless Track Club. Camille also contributes to mentoring, leadership development, and medical education reform. Fluent in English and French, she exemplifies a well-rounded, compassionate, and forward-thinking physician in training.

Profile

🎓 Education

Camille Blackman’s academic journey reflects her interdisciplinary strength and commitment to health equity. She holds a Bachelor of Arts in Cultural Anthropology from Northwestern University (2012–2016), where she was also a Division 1 athlete. To transition into a medical career, she completed a post-baccalaureate pre-medical program at DePaul University (2016–2017). Camille is currently a Doctor of Medicine (MD) candidate at the University of Illinois College of Medicine, expected to graduate in 2026. Her medical education is enhanced by participation in the Surgical Exploration and Discovery (SEAD) program and leadership in multiple mentorship initiatives. Her academic excellence is evidenced by her involvement in high-impact research, her role as an M3 mentor, and her selection for honors like Academic All-Big Tens. Throughout her academic path, she has consistently demonstrated a commitment to inclusion, excellence in scholarship, and innovation in clinical care, especially for underserved populations such as the transgender and gender-diverse community.

🧪 Experience

Camille Blackman has built a robust portfolio of healthcare experience over nearly a decade. As a current research trainee at the Johns Hopkins Center for Transgender and Gender Expansive Health, she contributes to cutting-edge projects focused on surgical outcomes and health equity. Prior to that, she worked as a medical assistant at the Illinois Dermatology Institute (2020–2023), a physical therapist assistant at RUSH Rehabilitation (2018–2021), and an EMT at Medical Express Ambulance (2018–2020). Her roles have spanned both emergency and outpatient care, giving her a comprehensive clinical foundation. In addition to her hands-on experience, she’s actively involved in medical education as a mentor and peer leader at UICOM. Camille also volunteers in both clinical and community settings, including Face the Future Foundation and Ann & Robert H. Lurie Children’s Hospital. Her practical and research experience across diverse settings reinforces her trajectory as a future physician-leader in gender-affirming and reconstructive surgery.

🏅 Awards and Honors

Camille Blackman has been recognized for both academic and athletic excellence. She is a recipient of the prestigious Academic All-Big Ten award (2013–2016) and earned the John and Rita Canning and Dinn Brothers Student-Athlete Scholarships while at Northwestern University. In athletics, she is a Tracksmith-sponsored elite runner and has posted competitive times in major races, including a 2:48 marathon and top finishes in the Chicago 13.1 and Shamrock Shuffle. Her early accolades include the Western Massachusetts Athlete of the Year and the Tommy Cochary High School Mile Grant. As a medical student, she was selected for the Surgical Exploration and Discovery (SEAD) program and received a $25,000 pilot grant from the Hopkins Business of Health Initiative as a co-investigator for transgender health systems innovation. Her honors reflect a rare combination of scientific acumen, athletic discipline, and community impact, affirming her multifaceted contributions to medicine and public health.

🔬 Research Focus

Camille Blackman’s research centers on gender-affirming care, surgical education, and health disparities. At the Johns Hopkins Center for Transgender and Gender Expansive Health, she explores clinical outcomes related to chest masculinization and hormone therapy, and contributes to the development of transgender health infrastructure. She has authored and co-authored multiple peer-reviewed publications on subjects such as craniofacial surgery techniques, body mass index implications in gender-affirming surgeries, and innovative educational approaches like clay modeling in anatomy instruction. Camille’s forthcoming book chapters on vaginoplasty and transgender care centers further establish her as an emerging scholar in surgical education and gender health equity. She has presented her work at high-profile conferences, including the Plastic Surgery Research Council and the Sexual Medicine Society of North America. Through her research, Camille aims to enhance inclusivity, clinical outcomes, and the surgical learning experience, particularly in areas where historically marginalized populations have faced significant healthcare gaps.

✅ Conclusion

Camille Blackman is a future physician and trailblazer in gender-affirming healthcare whose interdisciplinary expertise, clinical compassion, and research innovation are advancing inclusive medicine, educational reform, and equity in surgical outcomes.

Publications

Cheng Cheng | Emotion and Cognition | Best Researcher Award

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Assist. Prof. Dr. Cheng Cheng | Emotion and Cognition | Best Researcher Award

Dr. Cheng Cheng is a lecturer at the Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, with a Ph.D. in Computer Science from Dalian University of Technology (2024). Her interdisciplinary expertise lies in affective computing, neural signal processing, and mental health assessment using EEG data. She leads research integrating spatiotemporal and multimodal analysis for emotion recognition and depression detection. Dr. Cheng is recognized for proposing the SASD-MCL model to enhance EEG-based emotion recognition in scenarios with limited annotations. Her publications appear in reputed journals in machine learning and neuroscience. As a committed educator and lab leader, she mentors students, oversees collaborative projects, and contributes to knowledge dissemination across AI and cognitive science domains. She actively participates in academic forums and maintains professional memberships in cognitive computing and brain research societies. Dr. Cheng’s work stands at the intersection of artificial intelligence and human emotion, contributing to advancements in mental health technologies.

Profile

🎓 Education

Dr. Cheng Cheng received her Ph.D. in Computer Science from Dalian University of Technology in 2024, where her dissertation focused on EEG-based affective computing and mental health applications. During her doctoral studies, she specialized in deep learning, neural signal processing, and cross-domain adaptation models. Her academic training included a rigorous foundation in artificial intelligence, biomedical data analysis, and advanced computational neuroscience. Prior to her Ph.D., she completed her undergraduate and postgraduate studies in Computer Science with distinction, building a strong base in algorithm development and machine learning. Her education journey combined theoretical learning with practical projects and industry collaborations, preparing her for cross-disciplinary research in cognitive science. Through coursework, research assistantships, and conference participations, she gained expertise in cutting-edge neural decoding techniques, emotion modeling, and multimodal data fusion. Dr. Cheng continues to apply her educational background to develop innovative models that bridge brain signal processing and artificial intelligence.

🧪 Experience

Dr. Cheng Cheng is currently serving as a lecturer at the Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, where she also leads a neuroscience and AI-integrated research lab. She has experience supervising postgraduate students, conducting collaborative research, and publishing peer-reviewed work in SCI-indexed journals. Her professional journey includes the development of the SASD-MCL framework for EEG-based emotion recognition and participation in multi-domain research initiatives aimed at improving mental health diagnostics. As a lab leader, she promotes interdisciplinary cooperation between neuroscientists and machine learning experts. Dr. Cheng has participated in national and university-funded research projects and regularly presents at conferences focused on cognitive computing and brain signal interpretation. Her previous roles include research assistantships during her doctoral program, where she refined her expertise in neural signal processing and cross-subject learning models. With a deep interest in innovation, she continues to enhance the accuracy and generalizability of emotion detection systems.

🏅 Awards and Honors

Dr. Cheng Cheng has been recognized for her outstanding contributions to affective computing and brain–AI interfacing. Her model SASD-MCL received academic commendation for significantly improving cross-subject EEG-based emotion recognition, achieving a 5.93% and 5.32% accuracy gain on SEED and SEED-IV datasets, respectively. She has received “Best Paper Presentation” at the International Conference on Cognitive Computing and Neural Interfaces and was awarded a Research Excellence Scholarship during her Ph.D. tenure. Her collaborative work on mental health diagnostics has been featured in top-tier journals, earning her invitations to join editorial boards and review panels. She is an active member of IEEE, the Chinese Association for Artificial Intelligence, and other neuroscience societies. Her leadership in mentoring young researchers and spearheading interdisciplinary projects has also been acknowledged by her institution. Nominated for the “Best Researcher Award,” Dr. Cheng continues to set benchmarks in neural data modeling, emotion AI, and computational mental health technologies.

🔬 Research Focus

Dr. Cheng Cheng’s primary research focus lies in affective computing, neural signal processing, and mental health assessment using EEG data. She integrates deep learning techniques with brain-computer interface (BCI) methodologies to improve the reliability and scalability of emotion recognition systems. Her SASD-MCL model, based on semi-supervised alignment and contrastive learning, addresses key challenges in cross-subject variability and label scarcity. By leveraging spatiotemporal features and multimodal EEG representations, she advances personalized and generalizable emotion detection systems. Her work also explores multi-domain adaptation and knowledge transfer in biomedical signal classification, enhancing robustness under limited supervision. Dr. Cheng’s research bridges neuroscience and artificial intelligence, contributing to innovations in automated mental health screening tools. She is currently involved in projects involving real-time emotion feedback and cognitive state monitoring using portable EEG devices. Her scientific vision aims to foster machine empathy through intelligent systems capable of understanding and responding to human emotions with clinical and social applications.

✅ Conclusion

Dr. Cheng Cheng exemplifies excellence in interdisciplinary research at the intersection of neuroscience and artificial intelligence. Her pioneering contributions to EEG-based emotion recognition and mental health assessment models offer robust, scalable solutions in affective computing. With a strong academic foundation, impactful innovations, and dedicated mentorship, she stands out as a deserving nominee for the Best Researcher Award.

Publications

Huifang Wang | Neuroscience | Best Researcher Award

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 Dr. Huifang Wang | Neuroscience | Best Researcher Award

Huifang Elizabeth Wang is a leading research engineer at INSERM U1106, Aix-Marseille University, France, specializing in computational neuroscience. Her career bridges robotics, brain modeling, and clinical neuroscience, with a primary focus on personalized brain simulations for neurological and psychiatric conditions, notably epilepsy. With over a decade of postdoctoral research across top French and Italian institutes, she has contributed to projects integrating physics-based modeling, large-scale neural dynamics, and effective connectivity. Her academic journey started in robotics and control theory in China and evolved into advanced brain modeling in Europe. She collaborates with renowned neuroscientists like Dr. Viktor Jirsa and has authored numerous high-impact publications in Science Translational Medicine, The Lancet Neurology, and NeuroImage. As PI and co-leader in several EU and national projects, she aims to bridge basic brain science with clinical translation. Wang’s work is pivotal in creating virtual brain twins to personalize epilepsy surgery and psychiatric interventions.

Profile

🎓 Education

Huifang Elizabeth Wang obtained her Ph.D. in Pattern Recognition and Intelligent Systems from Beijing University of Technology in 2008, focusing on optimization algorithms for robotic motion under Prof. Chen Yangzhou. She earned her M.S. from the same institution in 2003, researching advanced traffic control strategies. Her undergraduate degree (B.S.) in Electronic Engineering was awarded by Shandong Institute of Light Industry in 2000. Complementing her engineering foundation, she undertook a research visit at LAAS-CNRS in Toulouse in 2007, developing time-optimal trajectories for car-like robots. Currently, she is finalizing her HDR (Habilitation à Diriger des Recherches) at Aix-Marseille University (Nov 2024) under the supervision of Dr. Viktor Jirsa, with a thesis on “Virtual Brain Twins.” Her education spans multiple disciplines and institutions, combining engineering, neuroscience, and clinical modeling. This interdisciplinary background underpins her leadership in personalized neural modeling and translational neuroscience research.

🧪 Experience

Wang is a Research Engineer at INSERM U1106, Aix-Marseille University (2017–present), leading work on virtual brain twins for clinical use in epilepsy and psychiatry. Prior, she was a Postdoc at the Institut du Cerveau (ICM), Paris (2016–2017), studying human neuron behavior with Pr. Vincent Navarro. At École des Mines de Saint-Étienne (2016), she helped develop a physiological SEEG atlas. From 2012–2016, she worked at INSERM U1106 on brain connectivity under Drs. Bernard and Jirsa. Earlier, she researched robotic control and planning at the University of Pisa (2008–2010) in Prof. Antonio Bicchi’s group. Her expertise spans brain modeling, robotics, and neuroscience, with leadership in multi-institutional EU-funded projects. She has served as PI and co-leader in several major efforts like the Human Brain Project and EPINOV. Her interdisciplinary experience uniquely equips her to bridge theory, technology, and medicine in brain modeling applications.

🏅 Awards and Honors

Huifang Elizabeth Wang has earned prestigious research roles and leadership positions in major European and national initiatives. She is PI for the AMIDEX-funded HR-VEP project and WP4 leader in the Horizon RIA Virtual Brain Twin initiative (2024–2027). Her projects have been supported by the Human Brain Project, France 2030, and Horizon Europe. She served as co-task leader in HBP’s epilepsy-focused work packages and trial coordinator in EPINOV RHU, a national clinical modeling trial. Her work on brain modeling has been published in high-impact journals, underscoring her scientific excellence. She has collaborated with pioneers like Karl Friston and Viktor Jirsa, advancing the fields of functional connectivity and computational neuroscience. Additionally, she has been granted funding by institutions such as Fondation Recherche Médicale and Ligue Française contre l’Épilepsie, recognizing her contributions to translational neuroscience and computational modeling in clinical applications.

🔬 Research Focus

Wang’s research centers on developing personalized virtual brain models to understand and treat brain disorders such as epilepsy and psychiatric conditions. She specializes in large-scale neural modeling using neural mass and field models, enabling individual-specific simulations—a concept known as “virtual brain twins.” Her work integrates multimodal neuroimaging data (e.g., SEEG, MRI) with computational frameworks to predict surgical outcomes and guide interventions. As part of projects like VEP Atlas, EPINOV, and EBRAINS, she builds anatomical-functional atlases for clinical use. She also advances Bayesian techniques for parameter estimation in brain modeling. Her research bridges basic neuroscience with translational applications, using virtual brains to delineate epileptogenic zones and simulate drug-resistant epilepsy spread. In psychiatric disorders, her focus includes simulating and analyzing network dysfunction to support precision psychiatry. By blending machine learning, dynamical systems, and neuroinformatics, Wang’s work pioneers a new frontier in personalized medicine using brain simulations.

✅ Conclusion

Dr. Huifang Elizabeth Wang is an interdisciplinary researcher transforming clinical neuroscience through virtual brain modeling, combining engineering precision with neuroscientific insight. Her pioneering work in virtual brain twins supports individualized diagnosis and treatment of epilepsy and psychiatric disorders, representing a significant advance in precision medicine. With extensive experience, numerous publications, and leadership in high-impact research projects, she bridges theory and practice. Her scientific vision and collaborative leadership continue to shape the future of computational neuroscience and neurotechnology for patient care worldwide.

Publications

Faheem Arshad | Cognitive Neurosciences | Best Researcher Award

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Dr. Faheem Arshad | Cognitive Neurosciences | Best Researcher Award

Dr. Faheem Arshad is an Assistant Professor of Neurology at the National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru. A leading cognitive neurologist in India, he specializes in dementia and neurodegenerative disorders. He played a pivotal role in establishing India’s first cognitive disorders registry at NIMHANS, integrating clinical and research excellence. Dr. Arshad is a Senior Atlantic Fellow for Equity in Brain Health at the University of California, San Francisco (UCSF), and the first fellow from South Asia. His research integrates neurogenetics, biomarkers, imaging, and clinical trials, with a focus on inclusivity and low-literacy populations. He actively contributes to national and international collaborations and holds leadership roles within Indian and global neurology communities. Committed to early diagnosis, caregiver support, and prevention strategies, his work aims to improve brain health equity in diverse settings. His ongoing studies explore social interaction, bilingualism, and digital tools in dementia management.

Profile

Education 🎓

Dr. Arshad received his foundational training in Internal Medicine (MD, 2014) at Sher-i-Kashmir Institute of Medical Sciences (SKIMS), Kashmir. He served as Registrar in Internal Medicine at AIIMS, New Delhi until 2016. He pursued advanced neurological training at the National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, earning a DM in Neurology (2019) and completing a Post-Doctoral Fellowship in Cognitive Neurosciences (2020). He furthered his expertise with a prestigious international fellowship in Global Brain Health Equity at the Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), USA in 2021. Dr. Arshad also holds the MRCP(UK) Neurology credential (2020), reflecting global competence. His multidisciplinary training spans cognitive neurology, imaging, biomarkers, and dementia prevention, equipping him with a holistic approach to brain health research and patient care.

Experience 👨‍🏫

Dr. Faheem Arshad’s professional journey spans over a decade of clinical, academic, and research excellence. He is currently Assistant Professor of Neurology at NIMHANS, Bengaluru, where he has led initiatives in cognitive neurology since 2021. His early training included roles as Junior and Senior Resident in Internal Medicine at SKIMS and AIIMS, followed by Neurology Residency and Postdoctoral Fellowship at NIMHANS. He became a faculty leader and Convener of the Cognitive Neurology subsection of the Indian Academy of Neurology in 2023. Internationally, he served as an Atlantic Fellow at UCSF’s GBHI (2020–2021), developing leadership in brain health equity. Dr. Arshad is a member of the American Academy of Neurology and other prestigious forums. He has published widely, initiated clinical trials, and built registries that bridge clinical insights with translational research. His work integrates social science, neurobiology, and global health in addressing dementia across underserved populations.

Awards & Recognitions 🏅

Dr. Faheem Arshad has received numerous recognitions for his contributions to neurology and dementia research. He was awarded the Bursary Award at the TSS International Neuropsychiatry Conference (2018) for his work on social cognition in Frontotemporal Dementia. As the first South Asian to become a Senior Atlantic Fellow for Equity in Brain Health at UCSF’s GBHI, he has been globally recognized for championing equity in dementia care and research. His appointment as Convener of the Cognitive Neurology subsection by the Indian Academy of Neurology in 2023 highlights his leadership within the national academic community. He holds the MRCP(UK) in Neurology and is an active member of prestigious societies, including the American Academy of Neurology. These honors reflect his ongoing commitment to research innovation, community-based care models, and international collaboration in the field of neurodegenerative disorders.

Research Interests 🔬

Dr. Faheem Arshad’s research focuses on dementia, particularly Frontotemporal Dementia (FTD), Alzheimer’s Disease, and related neurodegenerative conditions. He investigates cognitive reserve, biomarkers, social cognition, neuroimaging, and bilingualism in dementia resilience. His landmark projects include exploring the role of social interaction in FTD (GBHI-AA), plasmapheresis in Alzheimer’s (ICMR), cognitive testing in low-literacy settings, and bilingualism’s impact on cognitive reserve (NIH-funded). He co-leads a SERB-funded project using speech features for early dementia detection and a DBT-funded imaging study for vascular dementia diagnosis. He established India’s first cognitive disorders registry at NIMHANS, integrating socio-demographics, imaging, and biomarker data. His work bridges clinical neurology and public health, emphasizing inclusive research for underrepresented populations. A strong advocate for clinical trials in LMICs, his studies integrate AI tools, cross-cultural data, and longitudinal analyses to improve early diagnosis and therapeutic strategies in dementia care.

Publications

Yun Kang | Mathematical Biology | Best Researcher Award

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Dr. Yun Kang | Mathematical Biology | Best Researcher Award

Yun Kang is a distinguished Professor of Applied Mathematics at Arizona State University 🏫, specializing in mathematical biology, complex adaptive systems, and nonlinear dynamical systems 🔬📊; with over 70 publications in high-impact journals 📝, Kang’s work bridges theory and modeling to solve biological, ecological, and social questions 🌍; a leader in mathematical research, she also champions women in STEM through mentoring and advocacy 🤝💡; her cutting-edge research, funded by the NSF 💰, explores multiscale modeling in social insects 🐜 and trust dynamics in human-automation interaction 🤖; as a dedicated educator and core faculty member at the Simon A. Levin Mathematical, Computational & Modeling Sciences Center 🧠, she has shaped both academic programs and future researchers 🌱📈.

Profile

Education 🎓

Yun Kang earned her Ph.D. in Mathematics from Arizona State University in 2008 🎓, focusing on mathematical biology 🧪; she completed an M.S. in Pure Mathematics at the University of Arizona in 2004 📐, with special research in random graphs 🔗; her academic journey began with a B.S. in Applied Mathematics from Shanghai Jiaotong University, China 🇨🇳, in 2002, where she concentrated on financial and computational mathematics 💹💻; this academic foundation provided a solid platform for her research into nonlinear systems and biological applications 🌿📊; Kang’s education path reflects global excellence 🌍, interdisciplinary rigor 🧠, and a passion for bridging mathematics with real-world complexity 🌐✨.

Experience 👨‍🏫

Yun Kang’s academic career began as an Assistant Professor at ASU in 2008 🧑‍🏫, after completing her doctorate 🎓; she advanced to Associate Professor in 2014 and became a full Professor in 2019 🌟; from 2016 to 2019, she served as Acting Director/Co-Director of the Simon A. Levin Mathematical, Computational & Modeling Sciences Center 🧠, promoting interdisciplinary collaborations 💡; beyond teaching, Kang holds roles as Core Faculty and Affiliated Faculty at ASU’s School of Mathematical and Statistical Sciences 📚; her career spans leadership, research, mentorship, and advocacy for diversity in mathematical sciences 💪🌸; each role reflects her commitment to both academic excellence and community empowerment 🏅📢.

Awards & Recognitions 🏅

Yun Kang’s excellence is reflected in her NSF-funded research grants 💰, numerous high-impact publications 📝, and her leadership in mathematical biology 🔬; she’s a proud and active member of top organizations: Association for Women in Mathematics 👩‍🔬, American Mathematical Society 📘, Society for Industrial and Applied Mathematics 🧠, and Society for Mathematical Biology 🌿; since 2009, she’s mentored young female mathematicians via the AWM mentor network 🤝💡; her recognition stems from both groundbreaking research and her role as a diversity advocate in STEM 🌸🌍; her distinguished honors underscore her dual commitment to advancing math and empowering future scholars 🌟👩‍🏫.

Research Interests 🔬

Yun Kang’s research bridges nonlinear dynamical systems ⚙️, stochastic models 🎲, and mathematical biology 🧬; she explores complex adaptive systems — from population dynamics 🦌, food webs 🌾, eco-epidemiology 🦠, to social insect colonies 🐜; her NSF-funded work dissects multiscale division of labor in insect societies 🐝; she also models trust dynamics in human-automation interactions 🤖, blending theoretical rigor with real-world relevance 🌎; her contributions illuminate evolutionary processes 🔄, ecological interactions 🌱, and behavioral modeling 🧠; Kang’s approach merges deep mathematical theory with empirical validation 📊, offering new tools for biological, ecological, and social system analysis 🚀📘.

Publications 

Tran Chau My Thanh | Neuroscience | Young Scientist Award

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Dr. Tran Chau My Thanh | Neuroscience | Young Scientist Award

Dr. Tran Chau My Thanh, a dedicated researcher at Duy Tan University, Vietnam 🇻🇳, holds a medical degree and Ph.D. from Hue University of Medicine and Pharmacy 🎓. Her work bridges the gap between clinical medicine and molecular biology 🧬. With a strong passion for translational research, she focuses on using bioinformatics and genomic tools for early diagnosis and targeted therapy development for diseases like cancer, diabetes, and cardiovascular disorders 💉. Through CRISPR/Cas9 and RNA networks, she aims to revolutionize patient-specific treatment pathways 🚀. Her extensive lab experience, scholarly publications, and ongoing innovations make her a promising leader in biomedical science 🏅.

Profile

Education 🎓

Dr. Thanh earned her Medical Degree (M.D.) from Hue University of Medicine and Pharmacy 🏥 and went on to complete her Doctorate (Ph.D.) in the same prestigious institution 🎓. Her education was deeply rooted in both clinical and research training, equipping her with a comprehensive understanding of human health and disease 🧠. Throughout her academic journey, she focused on genomics, molecular medicine, and biotechnology 🔬. The rigorous curriculum and hands-on exposure in advanced labs trained her in modern diagnostic tools and therapeutic innovations ⚙️. She also mastered computational biology and molecular interactions, forming a solid foundation for her groundbreaking work in RNA regulation and gene editing technologies such as CRISPR/Cas9 🧪.

Experience 👨‍🏫

Dr. Thanh brings rich experience as a medical doctor and academic at Duy Tan University 🏫. Her research career spans multiple roles in molecular diagnostics, bioinformatics, and therapeutic innovation 🧬. She has led studies on disease biomarkers, participated in international collaborations 🌐, and worked extensively with cell lines, recombinant DNA, and next-gen sequencing data 🔍. Her proficiency in wet lab and dry lab environments empowers her to integrate experimental biology with computational modeling 🧫💻. Alongside mentoring students and publishing SCI-indexed research, she contributes to translational medicine by connecting bench science to bedside applications, helping advance precision medicine for critical illnesses 💡.

Awards & Recognitions 🏅

Dr. Thanh is a nominee for the Young Scientist Award by the International Cognitive Scientist Awards 🧠🏆. Her impactful work on circular RNAs, miRNAs, and disease biomarker networks has garnered international recognition 🌍. She’s been acknowledged in high-impact journals for discoveries related to coronary heart disease and cancer diagnostics 📖. Her scholarly articles are indexed in SCI and Scopus, and she continues to influence the biomedical community through conference presentations, peer reviews, and academic collaborations 🤝. As a rising figure in molecular biology, her research promises transformative outcomes for early disease detection and targeted therapies 🧬✨.

Research Interests 🔬

Dr. Thanh’s research explores circRNA/miRNA/mRNA interactions, protein-protein networks, and gene function analysis 🧬🧠. She is driven by the quest to discover novel biomarkers for early diagnosis of complex diseases such as cancer, stroke, and diabetes 💊. Her focus includes CRISPR/Cas9 gene editing, molecular docking, and simulations for drug discovery and target validation 💻🧪. She also builds interaction networks to map LncRNA/CircRNA/miRNA/gene/protein-drug relationships, contributing to personalized medicine approaches 🎯. Through bioinformatics, she decodes gene expression dynamics and immune infiltrations to enable efficient diagnostics and therapeutics 💡. Her ultimate goal is to bridge computational biology with translational research for global health improvement 🌐💚.

Publications 

1. Hsa_circRNA_0000284 acts as a ceRNA to participate in coronary heart disease progression
by sponging miRNA-338-3p via regulating the expression of ETS1
2. Identification of hsa_circ_0001445 of a novel circRNA-miRNA-mRNA regulatory network as
potential biomarker for coronary heart disease
3. Potential diagnostic value of serum microRNAs for 19 cancer types: a meta-analysis of
bioinformatics data

Elsa Pittaras | Neuroscience | Women Researcher Award

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Dr. Elsa Pittaras | Neuroscience | Women Researcher Award

Elsa Pittaras is a Basic Life Research Scientist at Stanford University, specializing in neuroscience, cognition, and sleep research. With expertise in molecular biology, neuroanatomy, pharmacology, and behavior, she has extensively studied decision-making processes in mice. Her research has contributed significantly to understanding sleep deprivation’s effects on cognition and memory in Down Syndrome and Alzheimer’s disease models. She has published multiple papers as both first and last author, showcasing her leadership in neuroscience. Elsa’s goal is to advance research on mood disorders, cognition, and neurochemistry, aspiring to become an independent researcher in the U.S. 🇺🇸🔬🧠

Profile

Education 🎓

Elsa Pittaras earned a B.S. in Physiology from the University of Caen (2010), an M.S. in Neuroscience from the University of Paris Sud and ENS Cachan (2012), and a Ph.D. in Neuroscience from Neuro-PSI and the Biomedical Research Unit of the French Army (2016). Her multidisciplinary foundation in biology, physics, chemistry, and mathematics from Châtelet, Douai (2009) laid the groundwork for her neuroscience expertise. Throughout her education, she focused on decision-making, sleep deprivation, and neurochemical mechanisms in cognition. 🧠📚🎓

Experience 👨‍🏫

Elsa Pittaras has been a Basic Life Research Scientist at Stanford University since 2022, focusing on cognitive enhancement in Down Syndrome and Alzheimer’s disease models. She was a Postdoctoral Fellow at Stanford (2017-2022), investigating sleep and circadian rhythms’ effects on memory. Previously, she conducted research at the Biomedical Research Unit of the French Army (2016-2017) and completed her Ph.D. at Neuro-PSI. Her career includes internships in neuroscience at Neuro-PSI (2011-2012) and clinical observations at CHU Caen (2010). 🏛️🧬🧪

Research Interests 🔬

Elsa’s research explores decision-making, memory, and sleep in neurodevelopmental disorders. She pioneered the Mouse Gambling Task, revealing individual decision-making strategies. Her Ph.D. identified neurochemical markers of decision-making behaviors and the effects of sleep deprivation. At Stanford, she investigates sleep’s impact on cognition in Down Syndrome and Alzheimer’s models, aiming to improve memory and sleep quality through pharmacological interventions. Her work bridges behavioral neuroscience with neurochemistry to enhance cognitive function. 🧠💡🛌

Awards & Recognitions 🏅

Elsa has received prestigious grants, including the Jerome Lejeune Research Grants (2019, 2020), the Fyssen Foundation Research Grant (2017), and travel awards for conferences such as T21RS (2021) and Advances in Sleep and Circadian Science (2019). She was also recognized by the French Society for Research and Sleep Medicine (2014) and received a European Neuroscience Federation travel award (2016). 🏅

Publications 

  • Selectively Blocking Small Conductance Ca2+-Activated K+ Channels Improves Cognition in Aged Mice.

  • Short-term γ-aminobutyric acid antagonist treatment improves long-term sleep quality, memory, and decision-making in a Down syndrome mouse model

  • Behavioral and Neuronal Characterizations, across Ages, of the TgSwDI Mouse Model of Alzheimer’s Disease.

  • Inter-individual differences in cognitive tasks: focusing on the shaping of decision-making strategies

  • Handling, task complexity, time-of-day, and sleep deprivation as dynamic modulators of recognition memory in mice

  • Enhancing sleep after training improves memory in down syndrome model mice

 

Chunyu Liu | Cognitive Computing | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

Publications 📚

said Pournaghash-tehrani | Neuroscience | Best Faculty Award

Published on by Cognitive Scientist Awards

Dr. said Pournaghash-tehrani | Neuroscience | Best Faculty Award

 

Profile

  • Googlescholar
  • Researchgate

Education

Said Pournaghash-Tehrani earned his Doctor of Philosophy in Psychology in 1993 from The American University in Washington, D.C., where he also completed his Master of Arts in Psychology in 1990. He holds a Bachelor of Science in Distributive Science from the same institution, which he obtained in 1986. Fluent in English and German, he also has familiarity with French. He can be reached via email at spournaghash@yahoo.com or by telephone at 011-98-09122074388.

Work experience
  • Said Pournaghash-Tehrani has extensive academic and research experience in psychology. He served as a Research Associate in 2001 at the Department of Pharmacology and Experimental Therapeutics, Loyola University’s Stritch School of Medicine in Chicago, Illinois. In 2002, he took a sabbatical as a researcher at the Department of Psychology, Carleton University in Ottawa, Canada, focusing on cross-cultural studies related to Iranian attitudes towards the West. Since 2002, he has been an Assistant Professor in the Department of Psychology at Tehran University, having previously held the same position at Azzahra University in Tehran from 1996 to 2001. Additionally, he was a member of the Scientific Council on Energy and Economic Studies at the Institute for International and Political Studies (IPIS) from 1998 to 2000, where he also worked as a political researcher. His early academic career included serving as a Teaching and Research Assistant at The American University’s Department of Psychology from 1987 to 1990, where he contributed to courses such as Introduction to Psychology, Neuroscience Seminar, Psychopharmacology, Neuropsychology, Biological Basis of Behavior, and Learning and Behavior.

Books

–Fundamentals of Clinical Psychopharmacology, (2007); Samt Publications
-Drugs and Behavior, (2004); Samt Publications.
-Physiological Psychology, Tehran University Publication.
-Intimacy; Alzahra University Publication.
-Theories of Addiction, Alzahra University Publication.

Conference Presentations

Said Pournaghash-Tehrani has contributed extensively to neuroscience and psychology research, presenting his findings at prestigious conferences such as the Society for Neuroscience and the Eastern Psychological Association. His work has focused on drug discrimination learning, conditioned taste aversion, and the effects of opioids and their antagonists. In 1987, he co-authored studies assessing the discriminative stimulus properties of naloxone and the failure of cholecystokinin to counteract morphine sulfate’s effects. His later research explored the antagonism of morphine stimuli, the role of buprenorphine in opiate-naive and dependent animals, and the impact of RO15-4513 on ethanol-induced taste aversion. He has collaborated with notable researchers, including A.L. Riley, contributing to investigations on diazepam exposure and behavioral toxicology. His presentations in New Orleans, Washington, D.C., Boston, and other major research venues highlight his significant role in advancing psychopharmacology and behavioral neuroscience.

Publication

Ling Mei | Cognitive Science | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Ling Mei | Cognitive Science | Best Researcher Award

Doctorate at Wuhan University of Science and Technology, China

Dr. Ling Mei is an accomplished researcher in artificial intelligence and cognitive science, with a robust academic and professional background. He holds a Ph.D. in Engineering from Sun Yat-sen University, one of China’s top universities, and completed a prestigious visiting scholar program at the University of British Columbia (UBC). Currently serving as a tenured faculty and master’s supervisor, Dr. Mei has published 16 papers, including 7 in SCI-indexed journals, contributed to nine books, and has three national invention patents granted. Recognized as a Provincial Research Talent of China in 2024, he work integrates advanced computational models with societal needs, such as urban planning and public safety. Dr. Mei has collaborated internationally with top-tier institutions like UBC and Carnegie Mellon University, cementing he reputation as a leader in he field.

Profile

Google Scholar

Orcid

Education 🎓

Dr. Mei earned he Ph.D. in Engineering from Sun Yat-sen University in 2021, a prestigious institution ranked among China’s top 10 universities. He academic journey also includes a year-long visiting scholar program at the Department of Computer Science, UBC, as part of the National Outstanding Young Researchers Program. This international exposure provided he with cutting-edge knowledge and interdisciplinary skills, enabling he to excel in artificial intelligence and cognitive science.

Work Experience 💼

Currently, Dr. Mei is a tenured faculty member and master’s supervisor at a leading Chinese university. He experience includes overseeing multiple research projects, consulting on seven industry-sponsored projects, and serving as a reviewer for the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. He has also been instrumental in fostering international collaborations with institutions like UBC and CMU, contributing to impactful research publications and patents.

Awards and Honors

In 2024, Dr. Mei was recognized as a Provincial Research Talent of China, highlighting he exceptional contributions to science and technology. He has also earned accolades through he impactful patents and high-quality publications.

Research Interests

Dr. Mei’s research focuses on artificial intelligence, pedestrian trajectory prediction, and public safety strategies. He innovations include the LSN-GTDA framework, which integrates behavioral and stochastic factors for better uncertainty management. He interdisciplinary approach bridges cognitive science, computational models, and societal applications, ensuring he work’s relevance and impact.

Research Skills

Dr. Mei possesses advanced skills in AI modeling, thermal diffusion processes, and signal and system theory. He expertise includes patent development, SCI journal publications, and interdisciplinary collaborations. He is adept at integrating computational techniques with practical applications, as seen in he trajectory prediction research.

📚 Publications

Crowd Density Estimation via Global Crowd Collectiveness Metric

  • Journal: Drones
  • Date: 2024-10-28
  • DOI: 10.3390/drones8110616
  • Contributors: Ling Mei, Mingyu Yu, Lvxiang Jia, Mingyu Fu

More Quickly-RRT: Improved Quick Rapidly-Exploring Random Tree Star Algorithm Based on Optimized Sampling Point with Better Initial Solution and Convergence Rate*

  • Journal: Engineering Applications of Artificial Intelligence
  • Date: 2024-07
  • DOI: 10.1016/j.engappai.2024.108246
  • Contributors: Xining Cui, Caiqi Wang, Yi Xiong, Ling Mei, Shiqian Wu

Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama

  • Journal: Remote Sensing
  • Date: 2023-02-09
  • DOI: 10.3390/rs15040953
  • Contributors: Ling Mei, Yizhuo He, Farnoosh Fishani, Yaowen Yu, Lijun Zhang, Helge Rhodin

Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology
  • Date: 2020-02
  • DOI: 10.1109/TCSVT.2019.2890861
  • Contributors: Ling Mei, Jianhuang Lai, Xiaohua Xie, Junyong Zhu, Jun Chen

Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image

  • Type: Book Chapter
  • Year: 2018
  • DOI: 10.1007/978-3-030-03335-4_8
  • Contributors: Xiao Liu, Ling Mei, Dakun Yang, Jianhuang Lai, Xiaohua Xie

Conclusion 

Dr. Ling Mei is a strong contender for the Best Researcher Award due to he robust academic background, impactful research, and significant contributions to AI and cognitive science. To further enhance he candidacy, increasing citation influence and emphasizing community impact would solidify he position as an exemplary researcher deserving of recognition. 🌟