Chaima AOUICHE | Mathematics and Bioinformatics | Outstanding Scientist Award

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Dr. Chaima AOUICHE | Mathematics and Bioinformatics | Outstanding Scientist Award

Dr. Chaima Aouiche is a dedicated academic and researcher in computer science with expertise in artificial intelligence, machine learning, cybersecurity, and bioinformatics. Born on October 24, 1990, in Tebessa, Algeria, she began her academic journey at Larbi Tebessi University and pursued her Ph.D. at Northwestern Polytechnical University (NPU), China. With international exposure, Dr. Aouiche has authored impactful publications on cancer gene prediction, data integration, and AI-based energy systems. She has collaborated across disciplines and countries, contributing to international conferences and peer-reviewed journals. Currently serving as a university teacher in Algeria, she is also a multilingual educator with teaching experience in China and Algeria. Dr. Aouiche combines technical knowledge with strong interpersonal skills and a passion for teaching, traveling, and community service, making her a well-rounded and globally competent scholar committed to innovation and education.

Profile

🎓 Education

Dr. Chaima Aouiche holds a strong academic foundation in computer science. She earned her Bachelor’s degree (2008–2011) and Master’s degree (2011–2013) in Computer Science from Larbi Tebessi University, Algeria, where she was recognized with the “Outstanding Student Award” in 2013. She expanded her horizons by studying the Chinese language for a year (2013–2014) at Northwestern Polytechnical University (NPU) in Xi’an, China. She then pursued a Ph.D. in Computer Science and Technology at NPU (2014–2021), focusing on stage-specific gene prediction, big data integration, and artificial intelligence. Throughout her academic journey, she acquired various global certifications, including Artificial Intelligence Foundations, Advanced Machine Learning, and Deep Learning, further enriching her qualifications. With multilingual skills in Arabic, French, English, and Chinese, she integrates global perspectives into her research and teaching. Her academic path reflects both depth and international breadth.

đŸ§Ș Experience

Dr. Chaima Aouiche has a diverse background in academia, industry, and cross-cultural teaching. She began her professional career in project management at MPE-MPI Investments, Tebessa (2011–2013), where she gained hands-on technical and administrative skills. In 2017, she taught English and Arabic in Xi’an, China, enhancing her intercultural communication and educational outreach. Currently, she works as a university teacher in Algeria, engaging in teaching, research supervision, and publication. Her training includes courses in project management, AI, and big data, complemented by technical expertise in programming (Python, Java, R), MATLAB, web technologies, and networking. Her ability to communicate in four languages (Arabic, French, English, Chinese) and her volunteering and mentoring activities reflect her commitment to holistic professional development. Dr. Aouiche’s career is defined by interdisciplinary collaboration, international exposure, and a passion for applied technological solutions, making her an asset in both academia and industry.

🏅 Awards and Honors

Dr. Aouiche’s academic and professional excellence has been recognized through multiple awards and certificates. She was awarded the Outstanding Student Award by Larbi Tebessi University in 2013. Her further accolades include numerous international certifications, such as the HSK 4 Chinese Proficiency Certificate, Artificial Intelligence and Big Data Training (Xi’an Jiaotong University), AI Foundations Masterclass (2023), and Advanced Machine Learning and Deep Learning Certificates (2024). She has also been recognized for her participation in global academic initiatives, such as the International Winter Camp (2017) and the Silk Road Engineering Science Program (2020). In addition to formal honors, her significant co-authorship on high-impact publications in BMC Bioinformatics, Frontiers in Genetics, and IEEE conferences speaks to her professional standing. These accolades collectively highlight her dedication to academic distinction, global engagement, and technological innovation.

🔬 Research Focus

Dr. Aouiche’s research intersects bioinformatics, artificial intelligence, machine learning, and cybersecurity. Her work has emphasized integrating multiple datasets to predict stage-specific cancer-related genes, mapping copy number variations, and modeling aberrant genomic events. She co-authored key studies published in BMC Bioinformatics, Frontiers in Genetics, and Quantitative Biology, which propose dynamic gene modules and data-driven cancer diagnostics. Recent work explores ensemble learning and AI approaches to detect cyberattacks using integrated datasets, showing a pivot toward cybersecurity and smart systems. Additionally, her research extends into renewable energy, specifically applying AI models to optimize photovoltaic systems and MPPT (Maximum Power Point Tracking) control. Her interdisciplinary approach bridges computational biology and engineering, reflecting her adaptability and innovative vision. Dr. Aouiche is particularly interested in applied AI that addresses real-world challenges in medicine, energy, and security, with a growing focus on industry 4.0 applications.

✅ Conclusion

Dr. Chaima Aouiche is an innovative computer scientist and academic whose international education, multidisciplinary research in AI and bioinformatics, commitment to teaching, and dynamic professional experiences make her a valuable contributor to global science and technology.

Publications

Mustaqeem Khan | Deep learning | Best Researcher Award

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Assist. Prof. Dr. Mustaqeem Khan | Deep learning | Best Researcher Award

Dr. Mustaqeem Khan is an accomplished researcher and educator specializing in speech and video signal processing, with a keen focus on emotion recognition using deep learning. Recognized among the Top 2% Scientists globally (2023–2024), he currently serves as an Assistant Professor at the United Arab Emirates University (UAEU). He earned his Ph.D. in Software Convergence from Sejong University, South Korea, and has authored over 40 high-impact publications in IEEE, Elsevier, Springer, and ACM. His contributions span multimodal systems, computer vision, and intelligent surveillance. With extensive experience in academia and research labs, Dr. Khan has also served as a lab coordinator, team leader, and guest editor. He actively collaborates internationally and mentors graduate students. His technical expertise includes TensorFlow, PyTorch, MATLAB, and computer vision frameworks, making him a key contributor to projects involving emotion detection, UAV surveillance, and medical imaging. He brings innovation, leadership, and academic excellence to his roles.

Profile

🎓 Education

Dr. Mustaqeem Khan holds a Ph.D. in Software Convergence (2022) from Sejong University, Seoul, South Korea, where he achieved an outstanding CGPA of 4.44/4.5 (98%) and earned the Outstanding Research Award. His doctoral dissertation focused on advanced studies in speech-based emotion recognition using deep learning. He completed his MS in Computer Science (2018) at Islamia College Peshawar with a Gold Medal, securing a CGPA of 3.94/4.00, and specialized in video-based human action recognition. His undergraduate degree (BSCS, 2015) was from the Institute of Business and Management Sciences, AUP Peshawar, where he developed a web-based design project. His academic background laid the foundation for his research in multimodal deep learning, AI, and signal processing. Throughout his education, Dr. Khan combined rigorous coursework with impactful research, leading to numerous publications and international recognition.

đŸ§Ș Experience

Dr. Mustaqeem Khan is currently serving as an Assistant Professor at UAEU (2025–Present), focusing on teaching, research, and student supervision. From 2022 to 2024, he was a Postdoctoral Fellow and Lab Coordinator at MBZUAI, where he led AI projects like drone surveillance and collaborated with the Technical Innovation Institute. At Sejong University (2019–2022), he worked as a Research Assistant and IT Lab Coordinator, guiding projects and mentoring graduate students in speech processing and energy informatics. Prior to this, he was a Lecturer (2018–2019) and Research Assistant (2016–2018) at Islamia College Peshawar, where he taught courses in programming, image processing, and AI. He also led computer vision and speech analytics projects. His international collaborations span institutes in South Korea, France, Saudi Arabia, and India, highlighting his global academic footprint. Dr. Khan is deeply involved in editorial roles and research supervision, embodying academic excellence and research leadership.

🏅 Awards and Honors

Dr. Mustaqeem Khan has been recognized as one of the Top 2% Scientists in the world (2023–2024), a testament to his research impact. He received the Outstanding Research Award from Sejong University in 2022 and was a Gold Medalist during his MS in Computer Science at Islamia College Peshawar (2016–2018). His work has earned multiple Best Paper Awards, including from the Korea Information Processing Society (2021) and Mathematics Journal (2020). He was also granted a fully funded Ph.D. scholarship at Sejong University. Dr. Khan has reviewed for over 35 reputed international journals and serves as an editor and guest editor for several leading publications, including MDPI, IEEE, and Springer journals. His patents in speech-based emotion recognition further validate his innovation. These accolades underscore his academic rigor, global recognition, and leadership in signal processing, AI, and intelligent systems.

🔬 Research Focus

Dr. Mustaqeem Khan’s research lies at the intersection of speech signal processing, multimodal emotion recognition, and computer vision. His Ph.D. work established a foundation for deep learning-based systems capable of understanding human emotions through speech. He has since expanded his research to include age/gender detection, action recognition, violence detection, and medical image analysis using AI. His deep learning models—ranging from CNNs to transformers—have been applied across audio, video, text, and sensor-based data. Dr. Khan is particularly interested in cross-modal transformer-based architectures, edge-AI surveillance systems, and emotion recognition for smart cities. He is also exploring medical AI for fetal, retinal, and Parkinson’s disease diagnostics. His work is published in top-tier venues like IEEE Transactions, Nature Scientific Reports, and ACM. Ongoing collaborations with MBZUAI, TII, and Korean institutions focus on real-time AI applications in UAV systems, smart healthcare, and metaverse content generation.

✅ Conclusion

Dr. Mustaqeem Khan is a globally recognized AI researcher and educator specializing in multimodal emotion recognition and computer vision, whose impactful contributions, international collaborations, and innovative deep learning applications continue to shape the fields of signal processing, smart surveillance, and healthcare technologies.

Publications

Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

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Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Hong Wang | Memristors | Best Researcher Award

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Prof. Dr. Hong Wang | Memristors | Best Researcher Award

Dr. Hong Wang is an accomplished Associate Professor at Hebei University, China, specializing in the field of neuromorphic electronics and low-dimensional ferroelectric materials. With a strong academic foundation in Physics, Integrated Circuits, and Optical Engineering, she has rapidly advanced in her field since earning her doctorate in 2021. Her research has led to 15 SCI-indexed publications as a first author, 8 patents, and over 1300 citations, underscoring her scientific impact. Dr. Wang actively collaborates with leading researchers from institutions such as the National University of Singapore, the Chinese Academy of Sciences, and Jilin University, achieving multiple experimental firsts in ferroelectricity and memristor behavior. Her innovative work bridges material science and cognitive computing, making significant contributions to optoelectronic sensing and neuromorphic systems. She is a member of several prestigious scientific societies, including the Chinese Optical Society. Dr. Wang’s dedication and research excellence make her a standout in cognitive science innovations.

Profile

🎓 Education

Dr. Hong Wang’s academic journey began with a Bachelor’s degree in Physics from Beihua University in 2016, which laid the foundation for her interdisciplinary approach to electronic materials. She then earned her Master’s degree in Integrated Circuits from Hebei University in 2018, further refining her expertise in semiconductor and electronic system design. Driven by a passion for optical and neuromorphic technologies, she pursued a PhD in Optical Engineering at Hebei University, completing it in 2021. Her doctoral research focused on the application of low-dimensional ferroelectric materials, contributing valuable insight into the behavior of memristive systems and their implications for artificial neural networks. This strong educational background has enabled her to explore innovative technologies in cognitive sensing and computing, bridging physics, materials science, and neural engineering. Her academic training not only exemplifies depth and rigor but also reflects a unique ability to translate theoretical research into applied cognitive systems.

đŸ§Ș Experience

Since 2021, Dr. Hong Wang has served as an Associate Professor at the School of Electronic Information and Engineering, Hebei University. In this role, she has taken on responsibilities spanning research leadership, mentoring graduate students, and leading interdisciplinary projects at the frontier of neuromorphic computing. She has directed five major research projects and collaborated internationally with scholars from Singapore, the Chinese Academy of Sciences, and Jilin University. Her work has provided novel insights into ferroelectricity in materials like SnSe and ReSe₂, and its application in memristive devices. In addition to her academic duties, Dr. Wang has contributed to two industry consultancy projects, aligning academic innovation with technological advancement. Her ability to bridge material innovation with neural system architecture distinguishes her as a versatile and future-oriented cognitive scientist. Her professional experience is marked by innovation, collaboration, and a commitment to enhancing cognitive systems through novel material applications.

🏅 Awards and Honors

While specific awards are not explicitly listed, Dr. Hong Wang’s impressive research metrics and collaborations signify her recognition within the global scientific community. With 15 SCI-indexed publications as first author and over 1365 citations, her work has garnered significant academic attention. Her successful collaborations with leading institutions like the National University of Singapore and the Chinese Academy of Sciences validate her contributions through groundbreaking experimental confirmations in ferroelectric behavior. Additionally, she holds 8 patents, reflecting the originality and applied potential of her research in neuromorphic computing. Her memberships in the Chinese Optical Society, the Chinese Institute of Electronics, and the Chinese Society for Optical Engineering indicate peer recognition and professional trust. These accomplishments, coupled with her high-impact research output, suggest that Dr. Wang is a strong contender for prestigious awards in cognitive science and materials research, and she is an exemplary nominee for the Best Researcher Award in Cognitive Science.

🔬 Research Focus

Dr. Hong Wang’s research centers on the design and application of neuromorphic memristors using low-dimensional ferroelectric materials. She explores how novel quantum dots and two-dimensional semiconductors, such as SnSe and ReSe₂, can mimic synaptic behavior for brain-like computing. A notable achievement includes her demonstration of robust dual-mode optical sensing using ferroelectric quantum dots, enabling both short-range and remote synapse-like responses, leading to high-accuracy image recognition systems. Her experimental work debunks traditional notions in electronics, such as the inertness of Pd electrodes, and provides novel insights into conductive filament formation. Her research has practical implications in artificial vision systems, optoelectronic sensing, and cognitive learning circuits. She is pioneering the application of ferroelectric polarization for neuromorphic behavior, with implications for smart sensing and adaptive cognitive devices. Through multidisciplinary collaborations and material innovations, Dr. Wang is shaping the future of neuromorphic computing, advancing cognitive technologies toward higher efficiency and closer brain mimicry.

✅ Conclusion

Dr. Hong Wang is an emerging leader in neuromorphic computing, merging ferroelectric material innovation with cognitive system design, making her a strong candidate for the Best Researcher Award.

Publications

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.

đŸ§Ș 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

Abdeldjalil Ouahabi | Face recognition | Best Paper Award

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Prof. Abdeldjalil Ouahabi | Face recognition | Best Paper Award

Dr. Abdeldjalil Ouahabi is a distinguished Full Professor and leading researcher at the iBrain INSERM laboratory, University of Tours, France. His pioneering work spans signal and image processing, biomedical engineering, and artificial intelligence. With over 170 peer-reviewed publications, his research has significantly advanced multiple scientific domains. Dr. Ouahabi holds key editorial roles in top-tier journals, including associate editorships at the Journal of King Saud University – Computer and Information Sciences and the International Journal of Imaging Systems and Technology. He is internationally recognized for his academic contributions, collaborative AI research with Fields Medalist Prof. CĂ©dric Villani, and numerous visiting positions, notably at Bucknell University and Qatar University. In addition to scholarly engagement, he frequently appears on media platforms and organizes high-impact workshops and conferences. A respected scientific leader in Algeria and France, he is also an active member of governmental and civic committees focused on research policy and regional development.

Profile

🎓 Education

Although specific degree information is not detailed in the provided content, it is evident that Dr. Abdeldjalil Ouahabi has received extensive academic and research training, leading to his current role as a Full Professor and researcher at the iBrain INSERM laboratory, University of Tours. His educational background must include advanced degrees (PhD or equivalent) in electrical engineering, biomedical engineering, or related fields, enabling him to contribute profoundly to signal processing and AI. His international exposure—highlighted by academic appointments in the United States (Bucknell University) and the Middle East (Qatar University)—reflects a strong foundation in interdisciplinary learning and cross-cultural academic collaboration. His continuous involvement in editorial boards and scientific committees further implies rigorous scholarly preparation and a sustained commitment to academic excellence and innovation throughout his educational and professional journey.

đŸ§Ș Experience

Dr. Ouahabi’s professional experience spans academia, research, science communication, and governmental advisory roles. As a Full Professor at the University of Tours, he conducts research at the renowned iBrain INSERM laboratory. He has served as a Visiting Professor at Bucknell University (USA, 2010) and as a Visiting Scholar at Qatar University (2016–2017), conducting pioneering AI research. He is an Associate Editor for leading journals (Elsevier and Wiley) and organizes high-level scientific events, including the EEA Club Congress (2009) and national seminars in Algeria (2023–2024). His expertise has earned him invitations to collaborate with top researchers, including Prof. CĂ©dric Villani. In 2022, he was appointed to Algeria’s DGRSDT Standing Sector Committee and, in 2023, joined a citizens’ panel in France for long-term policy planning. He frequently shares scientific insights on international TV, underlining his role as both a scholar and a public intellectual.

🏅 Awards and Honors

Dr. Abdeldjalil Ouahabi has received numerous honors recognizing his outstanding contributions to research and academia. He was named Outstanding Reviewer by Knowledge-Based Systems (Elsevier, Q1) in 2018 and by Measurement (Elsevier, Q1) in 2016. Early in his career, he earned the Best Paper Award from the IEEE Instrumentation and Measurement Society in 1999. In 2010, he was honored as Outstanding Visiting Professor at Bucknell University in the U.S. His scientific impact also led to collaborative work with Nobel-level minds, such as Fields Medalist Prof. CĂ©dric Villani. He has received national recognition in Algeria, being appointed to the DGRSDT Standing Sector Committee in 2022 and organizing government-endorsed scientific seminars. These honors not only reflect the scholarly quality of his work but also his leadership in advancing scientific policy, mentoring, and international collaboration.

🔬 Research Focus

Dr. Ouahabi’s research integrates image and signal processing, biomedical engineering, and artificial intelligence with a strong focus on interdisciplinary applications. At the iBrain INSERM laboratory, he explores the intersection of computational modeling and medical diagnostics, contributing significantly to healthcare technologies. His work extends into intelligent systems and machine learning, particularly for enhancing neuroimaging, medical signal analysis, and real-time processing frameworks. He actively collaborates with international researchers on projects aimed at societal impact, including an ambitious AI initiative with Prof. CĂ©dric Villani to advance Algeria’s scientific ecosystem. He emphasizes reproducibility, algorithmic transparency, and journal quality, often training PhD candidates and researchers in scientific writing and journal selection. Dr. Ouahabi’s 170+ publications in top-tier journals reflect a consistent output in high-impact research areas. His work not only advances theory but also promotes real-world applications, especially in smart healthcare systems and regional innovation planning.

✅ Conclusion

Dr. Abdeldjalil Ouahabi is a globally recognized professor and researcher whose interdisciplinary contributions to artificial intelligence, biomedical engineering, and signal processing have profoundly impacted both academia and public policy, earning him international awards, editorial positions in top journals, and leadership roles in science communication and national research development.

 

Publications

  • Neonatal EEG classification using a compact support separable kernel time–frequency distribution and attention-based CNN

    Biomedical Signal Processing and Control
    2025-12 | Journal article
    DOI: 10.1016/j.bspc.2025.108072
    CONTRIBUTORS: Arezki Larbi; Mansour Abed; Jaime S. Cardoso; Abdeljalil Ouahabi

  • Advanced genetic image encryption algorithms for intelligent transport systems

    Computers and Electrical Engineering
    2025-04 | Journal article
    DOI: 10.1016/j.compeleceng.2025.110162
    CONTRIBUTORS: Ismahane Souici; Meriama Mahamdioua; Sébastien Jacques; Abdeldjalil Ouahabi

  • Human Cutaneous Leishmaniasis in North Africa and Its Threats to Public Health: A Statistical Study Focused on Djelfa (Algeria)

    Microorganisms
    2023-10-22 | Journal article
    DOI: 10.3390/microorganisms11102608
    CONTRIBUTORS: Fatma Messaoudene; Slimane Boukraa; Said Chaouki Boubidi; Ahlem Guerzou; Abdeldjalil Ouahabi

  • Post-COVID-19 Education for a Sustainable Future: Challenges, Emerging Technologies and Trends

    Sustainability
    2023-04-11 | Journal article
    DOI: 10.3390/su15086487
    CONTRIBUTORS: Sébastien Jacques; Abdeldjalil Ouahabi; Zoe Kanetaki

  • Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

    Cancers
    2022-09 | Journal article | Author
    DOI: 10.3390/cancers14184399
    CONTRIBUTORS: Naoual Atia; Amir Benzaoui; SĂ©bastien Jacques; Madina Hamiane; Kaouther El Kourd; Ayache Bouakaz; Abdeldjalil ouahab

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.

đŸ§Ș 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.

đŸ§Ș 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 MĂ©dicale 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

Milena Ćœivković | Artificial Intelligence in Medicine | Best Researcher Award

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Ms. Milena Ćœivković | Artificial Intelligence in Medicine | Best Researcher Award

Research Associate| University of Kragujevac, Faculty of Science, Serbia

Milena Ćœivković is a Research Associate at the University of Kragujevac, Faculty of Science, Serbia, with a background in physics and a research focus on the integration of artificial intelligence into medical physics and science education. Her expertise lies in AI-supported educational systems, Monte Carlo simulations in radiotherapy, and environmental radioactivity. With over 38 published papers, her work bridges physics, machine learning, and curriculum innovation. Milena is recognized for her mentorship of gifted students, contribution to interdisciplinary AI-based learning models, and international collaborations with researchers in Europe and the Middle East. She has co-authored dosimetric simulation software for cancer treatment optimization and earned accolades such as Best Oral Presentation Awards at international conferences. As an active member of the Serbian and German Physical Societies, she fosters science communication through national outreach projects and educational initiatives. Her contributions span both academic excellence and impactful community-based science promotion efforts.

Profile

🎓 Education

Milena Ćœivković earned her formal education in physics, culminating in specialized research focused on medical physics, radiation dosimetry, and educational technology. She has completed advanced academic training in English for Academic Communication and Python programming, including Stanford’s “Code in Place.” She holds a Cambridge English Certificate and multiple certificates from the University of Kragujevac in academic writing and pedagogy. Her achievements during her student years include receiving the Annual Award for Best Student from 2015 to 2019, reflecting both academic excellence and extracurricular engagement. Additionally, she has participated in numerous interdisciplinary workshops, competitions, and science communication events, contributing to both her intellectual and pedagogical growth. With a strong foundation in applied physics, her academic journey has been characterized by the seamless integration of theoretical knowledge and practical research, which she continues to expand through post-academic training, conference participation, and interdisciplinary research collaboration with clinical and educational institutions.

đŸ§Ș Experience

Milena Ćœivković has significant experience as a Research Associate at the University of Kragujevac, where she combines artificial intelligence with physics education and medical applications. Her research includes machine learning models for radiation dosimetry, classification systems in physics education, and anomaly detection in environmental radioactivity. She serves as a section editor and reviewer for journals like Imaging and Radiation Research and Radiation Science and Technology. Milena is also a contributor to national gifted education programs, curriculum development initiatives, and AI-assisted learning models. She has collaborated with international institutions, including projects with the Clinical Center Kragujevac and partners from Iraq, enhancing the practical application of her research. She has guided STEM projects for youth and mentored students in high school competitions. Her book on Monte Carlo simulations is used in academic and clinical contexts. Her scientific outreach projects further amplify her impact across the academic, educational, and public spheres.

🏅 Awards and Honors

Milena Ćœivković has been the recipient of numerous awards recognizing both academic and community contributions. She received the Best Researcher Award at the University of Kragujevac in 2023 and multiple Best Oral Presentation Awards at international conferences in gynecology, women’s health, and ophthalmology. She also won the Bridge of Mathematics First Place Projects (2023, 2024), highlighting innovative physics education. From 2015 to 2019, she was honored with the Annual Best Student Award and continues to receive high praise for promoting science through projects funded by Serbia’s Center for the Promotion of Science. These include thematic campaigns like Brian May and the Queen of Physics, Our Air = Our Health, and Work + Active = Radioactive. Additionally, she holds advanced training certifications in pedagogy, communication, academic writing, and programming. Her dedication to science communication, youth mentorship, and educational innovation has made her a strong contender for the Young Scientist or Best Researcher Award.

🔬 Research Focus

Milena Ćœivković’s research sits at the intersection of artificial intelligence, medical physics, and education technology. She focuses on developing machine learning-based models for radiation dose analysis, anomaly detection in environmental radioactivity, and AI-assisted problem classification in physics education. Her contributions to the FOTELP-VOX Monte Carlo platform enable precision 3D dose distribution modeling, now applied in clinical settings. She also investigates the ecological effects of radionuclide transfer and food safety. Milena’s interdisciplinary work includes collaborations with philosophers, clinicians, educators, and AI developers to improve curriculum delivery and treatment outcomes. She actively integrates AI into educational strategies to support gifted students and has co-authored software tools used in radiotherapy optimization. Her studies are not only technical but are aimed at real-world impact—ensuring safer radiation practices, informed public health strategies, and accessible science education. Her research promotes knowledge translation, making complex physics applicable to both education and healthcare.

✅ Conclusion

Milena Ćœivković exemplifies a new generation of researchers merging artificial intelligence with applied physics to transform education, healthcare, and science communication. Through interdisciplinary projects, academic excellence, and outreach initiatives, she continues to redefine how science serves society while mentoring future innovators and advancing clinical safety and educational equity.

Publications

  • FOTELP-VOX-OA: Enhancing radiotherapy planning precision with particle transport simulations and Optimization Algorithms

    Computer Methods and Programs in Biomedicine
    2025-08 | Journal article
    DOI: 10.1016/j.cmpb.2025.108838
    CONTRIBUTORS: Milena Zivkovic; Filip Andric; Marina Svicevic; Dragana Krstic; Lazar Krstic; Bogdan Pirkovic; Tatjana Miladinovic; Mohamed El Amin Aichouche

  • FOTELP-VOX 2024: Comprehensive overview of its capabilities and applications

    Nuclear Technology and Radiation Protection
    2024 | Journal article
    DOI: 10.2298/NTRP2403212Z
    CONTRIBUTORS: Milena Zivkovic, P.; Tatjana Miladinovic, B.; Zeljko Cimbaljevic, M.; Mohamed Aichouche, E.A.; Bogdan Pirkovic, A.; Dragana Krstic, Z.

  • Radionuclide contamination in agricultural and urban ecosystems: A study of soil, plant, and milk samples

    Kragujevac Journal of Science
    2024 | Journal article
    DOI: 10.5937/KgJSci2400011A
    CONTRIBUTORS: Mohamed Aichouche, E.A.; Mihajlo Petrović, V.; Milena Ćœivković, P.; Dragana Krstić, Ćœ.; SneĆŸana Branković, R.

  • Development of DynamicMC for PHITS Monte Carlo package

    Radiation Protection Dosimetry
    2023-11-13 | Journal article
    DOI: 10.1093/rpd/ncad278
    Part of ISSN: 0144-8420
    Part of ISSN: 1742-3406
    CONTRIBUTORS: Hiroshi Watabe; Tatsuhiko Sato; Kwan Ngok Yu; Milena Zivkovic; Dragana Krstic; Dragoslav Nikezic; Kyeong Min Kim; Taiga Yamaya; Naoki Kawachi; Hiroki Tanaka et al.

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 đŸ‘šâ€đŸ«

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