Huifang Wang | Neuroscience | Best Researcher Award

 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

Ganesh Basawaraj Birajadar | EEG Signal Processing | Best Researcher Award

Dr. Ganesh Basawaraj Birajadar | EEG Signal Processing | Best Researcher Award

Associate Professor| Fabtech Technical Campus College of Engineering & Research, Sangola

Dr. Birajadar Ganesh Basawaraj is an Associate Professor with over 12 years of academic and research experience, specializing in Machine Learning, Signal Processing, Biomedical AI, and Computer Vision. Currently serving as the Head of Department at Fabtech Technical Campus, Sangola, he holds a Ph.D. in Electrical & Electronics Engineering from VTU, where his research focused on brain abnormality detection using EEG signal analysis. Dr. Birajadar has published over 10 Scopus/SCI-indexed papers, guided numerous UG/PG projects, and reviewed international conferences (IEEE, Springer). With five patents filed in IoT and robotics and a copyright for a dermatological AI tool, he actively contributes to innovation in biomedical and AI domains. His academic journey and professional excellence reflect in his subject expertise, technical leadership, and student mentorship, making him a valuable contributor to engineering education and applied AI research.

Profile

🎓 Education

Dr. Birajadar earned his Ph.D. in Electrical & Electronics Engineering Sciences from PDACE Kalaburagi (VTU) in March 2024, focusing on AI-based EEG signal analysis for brain abnormality detection. He holds an M.E. in Signal Processing from SKNCOE, Pune University, where he ranked 7th with a CGPA of 7.45 in May 2012. He completed his B.E. in Electronics & Telecommunication from SVERI, Solapur University, with distinction in July 2009. His pre-university education includes H.S.C. (86.50%) from KBP College and S.S.C. (85.20%) from DHK Prashala, both under Pune Board. Dr. Birajadar’s education combines a strong theoretical foundation with hands-on expertise in electronics, signal processing, and AI applications, further strengthened by numerous certifications in Python, MATLAB, IoT, Machine Learning, and Data Science from prestigious platforms like NPTEL, Google, Infosys Springboard, and Coursera, shaping him into a well-rounded academic and research professional.

🧪 Experience

Dr. Birajadar currently serves as Associate Professor and Head of Department at Fabtech Technical Campus College of Engineering & Research, Sangola (from Jan 2025), and previously as Assistant Professor at the same institute (June–Dec 2024). Prior to that, he spent over a decade (2012–2024) as Assistant Professor at Smt. Kashibai Navale SCOE, Pandharpur, where he significantly contributed to teaching and research. He also worked as a Trainee Engineer at ITIE Knowledge Solutions, Bangalore. With over 12 years of academic experience, Dr. Birajadar has taught a wide range of subjects such as Machine Learning, Digital Signal Processing, MATLAB Simulink, Communication Buses, Image and Video Processing, and AI tools. He has delivered expert lectures at various institutions and led curriculum innovation. His strong command of tools like Python, MATLAB, TensorFlow, and LabVIEW complements his hands-on guidance of 18 UG and 2 PG projects in AI and biomedical domains.

🏅 Awards and Honors

Dr. Birajadar has received several prestigious awards and recognitions. He secured the 7th University Rank in M.E. (Signal Processing) from Pune University and received a Silver Medal in the NPTEL course “The Joy of Computing using Python.” He was awarded second prize in an AICTE-sponsored STTP Idea Competition and received a Letter of Appreciation from the Entrepreneurship Development Cell. He served as the Primary Evaluator in Toycathon 2021 and is a member of the National Institute for Technical Training & Skill Development. He is designated as a reviewer for “Inter Journal of Computing and Digital Systems (IJCDS)” (SCOPUS-indexed) and the “International Journal of Biomedical Engineering and Clinical Science” (2024–2027). Additionally, he has worked as a reviewer for several international conferences and contributed significantly to promoting innovation, technical evaluation, and academic excellence in AI, signal processing, and biomedical applications.

🔬 Research Focus

Dr. Birajadar’s research focuses on the intersection of Artificial Intelligence and Biomedical Engineering, particularly EEG signal analysis for brain abnormality detection. His work explores the use of AI/ML algorithms to interpret non-stationary biomedical signals, offering clinical insights into neurological disorders. He is deeply involved in Biomedical Signal and Image Processing, leveraging Machine Learning, Deep Learning, and Computer Vision techniques for healthcare innovation. He also explores the Internet of Things (IoT), Embedded Systems, and Big Data Analytics to develop smart, real-time solutions such as landmine detection robots, COVID care bots, and smart vending machines—many of which are patented. His contributions span across pattern recognition, signal feature extraction, and intelligent classification systems. Dr. Birajadar integrates academic rigor with practical application, aiming to enhance diagnostics, patient monitoring, and AI-based clinical tools. He has guided several projects and published extensively in indexed journals, cementing his role as a leading researcher in biomedical AI.

Conclusion

Dr. Birajadar Ganesh Basawaraj is a dedicated academician, innovative researcher, and inspiring mentor, whose interdisciplinary expertise in AI, biomedical signal processing, and IoT drives impactful solutions in healthcare and engineering education.

Publications

Lucas Muñoz-Lopez | Neuroscience | Best Academic Researcher Award

Dr. Lucas Muñoz-Lopez | Neuroscience | Best Academic Researcher Award

El Dr. Lucas Muñoz-López 🎓 es logopeda neurológico especializado en rehabilitación logopédica en daño neurológico 🧠, con amplia experiencia clínica y académica 👨‍🏫. Tras graduarse en Logopedia por la Universidad de Granada en 2015, combinó práctica profesional y formación de posgrado, incluyendo dos másteres y un doctorado con mención Cum Laude 🏅. Ha ejercido como docente universitario en Melilla y Ceuta, y colabora en investigación con publicaciones en revistas como Frontiers in Psychology 📖. Además, ha tutorizado numerosos TFG y TFM 🎓. Su trabajo integra logopedia clínica, investigación sobre alteraciones de la comunicación y neurociencias, aportando al avance académico y a la rehabilitación de pacientes. Es autor de un libro publicado y miembro activo en proyectos de evaluación y tratamiento en contextos educativos y sanitarios 🏥. Su pasión por la docencia y la terapia lo posiciona como referente en logopedia clínica e investigación 📈.

Profile

Education 🎓

Dr. Muñoz-López 🧠 inició su carrera académica con el Grado en Logopedia (2015) en la Universidad de Granada 🎓, complementándolo ese mismo año con un Máster en Logopedia Clínica en Daño Cerebral (Isep Clinic, Madrid) 💡. En 2017 completó su segundo máster en Neurociencias Básicas y Aplicadas y Dolor en la Facultad de Medicina de Granada 🔬. Culminó su formación con un Doctorado en Psicología y Logopedia en la Universidad de Granada (2021) con mención Cum Laude 🏅, convirtiéndose en experto en alteraciones de la comunicación y rehabilitación neurológica 🧬. Su formación académica combina sólidos conocimientos clínicos, teóricos y metodológicos para abordar con éxito la intervención logopédica y la investigación científica 🏛️. Además, su trabajo refleja una constante actualización y compromiso con la excelencia educativa y terapéutica 💯.

Experience 👨‍🏫

Desde 2015, el Dr. Muñoz-López 🩺 ha desarrollado una sólida trayectoria profesional como logopeda clínico, combinando práctica asistencial con docencia universitaria 👨‍🏫. Ha ejercido en el Centro de Valoración y Orientación de Granada como Auxiliar Técnico en Logopedia 🏥, además de impartir clases en los campus de Ceuta y Melilla de la Universidad de Granada 📚. Ha sido tutor de TFG y TFM en varias universidades, incluyendo la Universidad de Granada y la Universidad Internacional de La Rioja ✍️. Su experiencia docente abarca asignaturas de logopedia, neuropsicología y psicopedagogía en grados y másteres universitarios 🎓. Además, colabora activamente en proyectos de intervención educativa, rehabilitación y formación de futuros profesionales. Su enfoque multidisciplinar y experiencia en contextos clínicos y penitenciarios 🏛️ le permiten aplicar soluciones eficaces e innovadoras en su área.

Awards & Recognitions 🏅

Dr. Muñoz-López ha recibido mención Cum Laude 🎖️ por su tesis doctoral en Psicología y Logopedia en la Universidad de Granada (2021) 🏅. Su tesis fue publicada como libro 📗, reafirmando su valor académico. Ha logrado publicar en la revista Frontiers in Psychology (Q2, JCR 2.99) 🧠, destacándose en el área de psicología y neurociencias. También ha publicado en la revista Journal for Educators, Teachers and Trainers, indexada en bases relevantes como Scopus y Dialnet 📖. Su libro fue editado por la Editorial Académica Española, con índice ICEE de 0.299 📘. Estos reconocimientos consolidan su reputación científica, reflejando la calidad y relevancia de su producción académica, así como su compromiso con la excelencia investigadora y docente 🧑‍🔬✨.

Research Interests 🔬

El Dr. Muñoz-López centra su investigación en la rehabilitación logopédica de alteraciones de la comunicación 🗣️, disfagia 🥄 y terapia miofuncional 💪, con especial atención a contextos neurológicos y penitenciarios 🏛️. Su trabajo profundiza en la intersección entre neurociencias, psicología y logopedia 🧠, explorando tanto enfoques clínicos como educativos. Ha contribuido al avance del conocimiento en alteraciones del lenguaje, evaluación cognitiva y técnicas de intervención para pacientes con daño cerebral adquirido 🧬. Además, participa en estudios sobre integración social, intervención educativa en contextos de riesgo y psicología forense ⚖️. Su enfoque combina metodología cuantitativa, intervención basada en evidencia y aplicación clínica, orientado siempre a mejorar la calidad de vida de las personas con trastornos de la comunicación 💬💡.

Publications
  • Validation and Spanish Adaptation of the Resilience Scale ER-23 in a University Population

    Healthcare
    2025-04-12 | Journal article
    CONTRIBUTORS: Isabel Ramírez-Uclés; Julia Otero; F. Pablo Holgado-Tello; Lucas Muñoz-López; María B. Sánchez-Barrer
  • Analysis of writing in personality disorders in prison population

    Frontiers in Psychiatry
    2024 | Journal article
    EID:
    Part ofISSN: 16640640
    CONTRIBUTORS: Muñoz-López, L.; Fernández-García-Valdecasas, B.; López-Rodríguez, S.; Sánchez-Barrera, M.B.
  • Transformation of Higher Education: Discussion of the Dimensions, Trends and Scenarios of Change in Ibero-America

    Education Sciences
    2024 | Journal article
    Part ofISSN: 22277102
    CONTRIBUTORS: Fernández Cruz, M.; Fernández García Valdecasas, B.; Muñoz López, L.; López Rodríguez, S
  • Associations of Reversal Learning Performance With Personality Disorder Profile and Drug Abuse History in a Sample of Prison Inmates

    2024-09-18 | Journal article
    CONTRIBUTORS: Raquel Martín-Ríos; José C. Perales; Francisca López-Torrecillas; Lucas Muñoz Lóp

Alaa Abd-Elsayed | Neuromidulation | Best Researcher Award

Dr. Alaa Abd-Elsayed | Neuromidulation | Best Researcher Award

Dr. Alaa Abd-Elsayed 🇺🇸 is an American board-certified anesthesiologist and pain medicine specialist at the University of Wisconsin-Madison 🏥, recognized for his leadership, groundbreaking research 🔬, and compassionate patient care 💉, with a prolific academic career as a professor, director, and global speaker 🎤, blending clinical excellence, innovation, and education 📚 in pain management, with over two decades of medical service and leadership roles across Egypt 🇪🇬 and the U.S. 🇺🇸, while holding numerous prestigious certifications 🏅, published research, and leadership awards 🏆, he stands as a dedicated pioneer in improving chronic pain therapy 🔥 and anesthesiology practice worldwide 🌍.

Profile

Education 🎓

Dr. Alaa’s academic journey began at Assuit University 🇪🇬, earning his MBBCh 🩺 in 2000 & MPH 🎓 in 2006; postgrad, he trained extensively in the U.S. 🇺🇸, completing internships, anesthesiology residency, and a pain medicine fellowship 🏥 at the University of Cincinnati 🎯, and a Clinical Research Fellowship at Cleveland Clinic 🧪; board-certified in anesthesiology & chronic pain medicine 💊, and a Certified Physician Executive (CPE) 🏆, he capped his academic prowess with an Executive MBA 🎓 in 2023, mastering both medicine & healthcare leadership 🧠, and attending diverse leadership programs 💼 from AAPL, UW Health, and Faulkner University, cementing a strong foundation in clinical care and strategic innovation ⚡.

Experience 👨‍🏫

With over 20 years in medicine 🩺, Dr. Alaa has held roles from intern 👨‍⚕️ in Egypt 🇪🇬 to Associate Professor 📖, First Division Chief, and Medical Director at UW-Madison 🇺🇸; he’s led UW Health Pain Services 🔥, pioneering chronic pain medicine management 💊; his journey spanned positions at Assuit University, Cleveland Clinic, and University of Cincinnati 🏥; he’s served as chief fellow, staff anesthesiologist, researcher 🔬, educator 📚, and leader, combining advanced clinical practice 🏆 with administrative excellence 💼, mentoring future physicians while driving cutting-edge research 🚀 and pain medicine innovations 🌟.

Awards & Recognitions 🏅

Dr. Alaa’s distinguished career is crowned with awards 🌟 like the Raj/Racz Excellence Award 🥇, Physician of the Year 🏅, America’s Top Doctors 👏, Fellow of ASA 🧠, and recognition as a World Expert 🌍 in pain by Expertscape; multiple top research, poster 🖼️, and abstract prizes 🧾 from ASIPP, MARC, ASPN, ASA, INS & WSA 🏆 highlight his prolific contributions, while his books 📚 were ranked among the best in anesthesiology and pain medicine 💊; his research has shaped clinical practices 🌡️ and his leadership has been applauded across national and global stages 🎤, underlining his impact as a clinician, educator, and thought leader 💡.

Research Interests 🔬

Dr. Alaa’s research explores pain management innovation 🔥, neuromodulation ⚡, spinal cord stimulation 🧠, dorsal root ganglion therapies 💉, and anesthesiology outcomes 🧾; he’s passionate about translating bench-to-bedside discoveries 🏥, optimizing patient-centered chronic pain therapies 💊, and advancing perioperative safety 🌡️; his peer-reviewed publications 📚, clinical trials 🧪, and systematic reviews ⚗️ have influenced global practices 🌍, securing his place among top 0.05% scholars worldwide 🏆; his scientific vision combines clinical evidence, bioethics, and real-world health solutions for pain relief and anesthetic care 🧠💡.

Publications 

Chunyu Liu | Cognitive Computing | Best Researcher Award

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

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