Jerome Engel, M.D., Ph.D. is a Professor at the Jane and Terry Semel Institute for Neuroscience and Human Behavior , University of California, Los Angeles (UCLA). He serves as Director of the Epilepsy Telemetry Unit within the Seizure Disorder Center and is a member of the Brain Research Institute and the Neuroscience GPB Home Area. His work spans neurology, psychiatry, and biomedical research. Research Focus: Epilepsy, epileptogenesis, high-frequency oscillations (HFOs), neuroimaging, surgical interventions, and biomarker development. Key Contributions: Pioneering studies on fast ripples as biomarkers, network-based surgical outcome prediction, and advanced HFO detection algorithms. Publications (15 most recent) address topics such as kainic acid models of epileptogenesis, thalamic sleep spindles in pediatric epilepsy, self-supervised HFO analysis, and graph theoretical measures for surgical planning. His work frequently employs medRxiv and Epilepsia as platforms for translational findings. Contact: engel@ucla.edu
Prof. Dr. Johan Robertsson is a Full Professor of Applied Geophysics and Head of the Exploration and Environmental Geophysics (EEG) Group at ETH Zürich's Department of Earth and Planetary Sciences. He holds a MSc from Uppsala University (1991) and a PhD in Geophysics from Rice University (1994). Before joining ETH in 2012, he spent 15 years at Schlumberger in R&D roles, leading projects that revolutionized marine seismic data acquisition. His research focuses on wave propagation physics, seismic data inversion, and applications in exploration and environmental geophysics. He pioneered the use of Distributed Acoustic Sensing (DAS) for landslide monitoring and contributed to Mars seismology via the InSight mission. Education: MSc in Engineering Physics, Uppsala University (1991) PhD in Geophysics, Rice University (1994) Research Interests: Seismic wavefield modeling and inversion Planetary seismology (Mars, Moon) Acoustic metamaterials and wave control Environmental geohazard monitoring Marine seismic acquisition techniques His work on the Martian soil properties using InSight data and lunar exploration instrumentation (ALGEP) reflects his cross-disciplinary approach. He holds 90+ patents and has secured prestigious grants like the ERC Advanced Grant. Awards: EAGE Guido Bonarelli Award (2020) ERC Advanced Grant MATRIX (2017) EAGE Conrad Schlumberger Award (2018) Grants & Advising: Led the MATRIX ERC project advancing seismic imaging algorithms Advised over 20 PhD/MS students (names not listed) Secured Schlumberger's largest R&D project in marine seismic sampling His EEG Group operates cutting-edge labs for immersive wave experimentation and planetary geophysical instrumentation. Current initiatives include lunar subsurface exploration and acoustic invisibility experiments.
Dr. Kanwaljeet S. Anand is a dual-appointed Professor of Pediatrics (Pediatric Critical Care) and Anesthesiology, Perioperative & Pain Medicine at Stanford University School of Medicine. As director of the Pain/Stress Neurobiology Lab and Jackson Vaughan Critical Care Research Fund, he serves as Editor-in-Chief of Pediatric Research and maintains active membership in Bio-X, MCHRI, and Wu Tsai Neurosciences Institute. Rhodes Scholar with D.Phil from University of Oxford Harvard postdoctoral fellowship and Boston Children's Hospital residency Founded Harmony Health Clinic - Arkansas' largest charitable medical-dental facility A translational researcher with 30+ years of impact, Dr. Anand established the first scientific framework for infant pain perception and developed novel pain assessment methodologies. Current research focuses on: Hair biomarker analysis for stress and social affiliation (cortisol/oxytocin) Machine learning systems for objective pain detection in non-verbal infants Biopsychosocial interventions for stress reduction in disadvantaged youth Neurotoxicity mechanisms of sedatives in developing brains Global NICU opioid usage patterns through the NeoOpioid Consortium His work has yielded over 260 publications and significant advances in: Pediatric pain management protocols Neonatal stress biomarker development Critical care neurobiology insights Community health initiatives AI-driven clinical decision support systems Scientific Recognition 9th Annual 'In Praise of Medicine' Public Address, Erasmus University (2014) Nightingale Excellence Award (2016) Honorary Doctorate from University of Örebro (2019) NIH SBIB-H82 Study Section Chair (2018) Multiple IASP and American Pain Society awards Swedish Academy of Medicine's Nils Rosén von Rosenstein Award (2009) St. Jude Endowed Chairholder (2010) As mentor to Med Scholar Anjali Gupta and advisor to numerous professional bodies, Dr. Anand maintains active clinical leadership in Pediatric Intensive Care while advancing computational approaches to pain detection through collaborations with Stanford's AI researchers.
George R. Mangun is a Distinguished Professor of Psychology and Neurology at the University of California, Davis and Co-Director of the Center for Mind and Brain. He founded the UC Davis Center for Mind and Brain in 2002 and served as Dean of Social Sciences (2008-2015). Education: Ph.D. in Neurosciences (UC San Diego, 1987), B.S. in Chemistry and Life Sciences (Northern Arizona University, 1981) His research focuses on the neuroscience of attention , combining EEG and fMRI to explore how the brain selects and processes sensory stimuli. Key areas include attentional control, brain networks, and neural oscillations. Recent studies investigate hierarchical attention control, decoding spatial attention, and the role of theta/alpha oscillations in cognitive tasks. His work has implications for understanding neurological disorders like ADHD. Scientific Awards : Fulbright U.S. Distinguished Scholar (2025), Society for Neuroscience Education Award (2024), AAAS Fellow (2010), APS Fellow (2007) He has led the Neural Mechanisms of Attention Lab , funded by NSF, NIH, and international organizations, and co-authored the leading textbook Cognitive Neuroscience: The Biology of the Mind (6th ed., 2025).
Rajendra Acharya is a Professor (Artificial Intelligence in Health) at the University of Southern Queensland's School of Mathematics, Physics and Computing. He holds qualifications including BEng, MTech, two PhDs, and a DSc. His research focuses on AI applications in healthcare, pattern recognition, and medical diagnostics, with notable contributions to EEG analysis, deep learning, and disease detection. Awards include multiple Research.com Leader Awards in Computer Science for Australia and Singapore (2022–2025). His work spans over 650 publications, with high-impact studies on automated disease diagnosis via AI, including COVID-19 detection using X-rays and EEG-based seizure detection. His research interests integrate machine learning, signal processing, and healthcare technologies. He collaborates internationally and advises on AI-driven health solutions. No student list provided; however, his extensive supervision is implied through his research output.
Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.
John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.
Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.
Marta Molinas is a Professor at the Department of Engineering Cybernetics within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). Her research spans multiple interdisciplinary domains with a focus on EEG technology and brain-computer interfaces. She actively supervises numerous Master's projects and maintains extensive international collaborations with institutions including Kavli Institute for Systems Neuroscience, RIKEN Center for Brain Science, University of Tsukuba, Juntendo University, and several European universities. Professor Molinas' research interests center on developing innovative EEG technologies, particularly her FlexEEG concept for reduced-channel EEG systems with brain imaging capabilities. Her work integrates signal processing, artificial intelligence, and neuroscience to create practical applications in mental health, sleep research, neurorehabilitation, and human-computer interaction. She specializes in EEG source imaging, machine learning for brain signal analysis, and the development of brain-computer interfaces for various applications including locked-in syndrome communication, ADHD treatment, and driver monitoring systems. Her publication portfolio demonstrates strong trends in interdisciplinary research combining neuroscience with electrical engineering and artificial intelligence. The work shows particular emphasis on developing practical EEG-based systems that minimize invasiveness while maintaining analytical power, with applications spanning healthcare, rehabilitation, and human augmentation. Her research bridges theoretical signal processing with real-world implementations through numerous student projects and international collaborations. Professor Molinas actively supervises a large team of Master's and PhD students across multiple projects, with each project typically requiring two students working collaboratively. Her research is supported through numerous international collaborations with institutions in Japan, India, and Europe, indicating substantial research funding and project leadership. She has developed a pipeline of student projects that build upon previous work, creating a cumulative knowledge base within her research group. She leads the EEG ITK research team at NTNU, which focuses on developing the FlexEEG headset prototype featuring flexible, wireless, dry electrodes designed to move across the scalp. This team works at the intersection of neuroscience, electrical engineering, and computer science, developing applications for sleep research, mental health monitoring, neurorehabilitation, and brain-computer interfaces. The team collaborates extensively with international partners including the Kavli Institute for Systems Neuroscience, the International Institute of Integrative Sleep Medicine at University of Tsukuba, and several engineering departments across Europe and Asia.
Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.
Gunnar Blohm is an Assistant Professor in the Department of Biomedical and Molecular Sciences at Queen's University, affiliated with the School of Medicine and Faculty of Health Sciences. His research focuses on sensorimotor neuroscience, particularly 3D sensorimotor control, eye-hand coordination, and computational modeling of neural processes. He holds a Ph.D. from Université Catholique de Louvain and has held postdoctoral positions at York University and his alma mater. Cross-appointed to the School of Computing, Department of Psychology, and Department of Mathematics and Statistics, he is also Vice-Director of the Connected Minds initiative. His research integrates behavioral experiments, brain imaging (MEG/EEG), and patient studies to understand how sensory information is transformed into goal-directed actions. Key areas include visuomotor transformations, multisensory integration, and Bayesian processes in neural computations. Blohm leads the Computational Sensorimotor Neuroscience Lab, emphasizing collaborative projects like Neuromatch Academy and contributions to open science initiatives. Affiliated with Queen's Centre for Neuroscience Studies and Ingenuity Labs, his work bridges computational approaches with clinical applications, aiming to develop frameworks for understanding brain dysfunction and clinical tools. His recent articles explore topics like saccade dynamics, pupil responses, and generative adversarial collaborations in scientific discourse.
Jana Kainerstorfer is a Professor of Biomedical Engineering at Carnegie Mellon University (CMU), with courtesy appointments in the Neuroscience Institute and Electrical & Computer Engineering. She serves as Associate Department Head for Faculty and Graduate Affairs within the College of Engineering. Her research focuses on developing non-invasive optical imaging methods for disease detection and treatment monitoring, particularly in diffuse optical imaging. Key areas include cerebral hemodynamic monitoring in traumatic brain injury and handheld devices for breast cancer imaging. Dr. Kainerstorfer holds senior membership in the Optical Society of America and has received prestigious awards such as the NIH Trailblazer Award and AHA Scientist Development Grant. She leads the Biophotonics Lab, which bridges engineering and clinical applications, emphasizing translational research. Education: PhD from University of Vienna/NIH (2010), Postdoc at Tufts University Research Interests Her work revolves around biomedical optics , neurophotonics , and medical device innovation . Current projects include: Non-invasive cerebral hemodynamic monitoring Transabdominal fetal pulse oximetry Optical imaging in extreme environments (e.g., freediving physiology) Her lab develops tools like wearable NIRS for marine mammals and self-calibrating pulse oximetry algorithms. Research spans clinical translation and physiological mechanism discovery , with emphasis on microvascular imaging. Awards & Recognition NIH Trailblazer Award (2020) AHA Scientist Development Grant SPIE Fellow (2022) George Tallman Ladd Award (CMU) Lab & Collaborations The Biophotonics Lab collaborates with neurosurgery, oncology, and marine biology teams. Projects address clinical needs in neurocritical care and fetal monitoring, leveraging optical technologies for real-time diagnostics. Ongoing work includes: Optical assessment of cerebral metabolic rates Non-invasive intracranial pressure estimation Multi-modal EEG-NIRS fusion for neural source localization
Saleh A. Alshebeili is a Professor in the Department of Electrical Engineering at King Saud University's College of Engineering, Riyadh, Saudi Arabia. With over 139 publications spanning from 1991 to 2024, his research demonstrates significant contributions across multiple engineering disciplines. His academic profile shows consistent collaboration with Saudi research institutions and international partners, particularly in communications and signal processing fields. Dr. Alshebeili's research interests span wireless communications, optical networks, radar systems, and biomedical signal processing. His work bridges theoretical signal processing with practical applications in 5G/6G communications, IoT security systems, and healthcare monitoring. The interdisciplinary nature of his research connects electrical engineering with computer science, particularly through machine learning applications for signal analysis and system optimization. His publications demonstrate expertise in both traditional signal processing techniques and emerging AI-driven approaches to engineering problems. Analysis of his recent publications (2021-2024) reveals a strong focus on next-generation communication technologies including 6G systems, optical wavelength conversion, and OAM-SDM communication. Simultaneously, he maintains active research in biomedical applications, particularly EEG signal processing for seizure detection and biometric authentication using physiological signals. His work consistently appears in top IEEE journals including IEEE Access, IEEE Transactions on Wireless Communications, and IEEE Journal of Biomedical and Health Informatics, reflecting the high quality and relevance of his research. Dr. Alshebeili has established extensive collaborations with researchers across King Saud University, particularly with Fathi E. Abd El-Samie (29 co-authored papers), Turky N. Alotaiby (22 papers), and Amr Ragheb (21 papers). These long-term collaborations suggest leadership in research groups focusing on communications systems and biomedical signal processing. His work spans theoretical development, simulation, and experimental validation, as evidenced by publications with 'Experimental Investigation' and 'Experimental Demonstration' in their titles.
Soosan Beheshti is a Professor and Program Director in the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. She holds a B.S. from Isfahan University of Technology and M.S./Ph.D. from MIT. Her research focuses on signal processing, statistical learning, and information theory, with applications in biomedical systems, data denoising, and system modeling. She has received awards such as the Dean's Teaching Award (2010) and the EECS Carlton E. Tucker Award (1998). Education: B.S., Electrical Engineering, Isfahan University of Technology (1996) M.S. & Ph.D., Electrical Engineering, MIT (2002) Research Interests: Statistical Signal Processing Information Theory Data Denoising & Compression System Modeling & Control Machine Learning Applications Awards: Dean's Teaching Award (2010) Gold Paper Award (PacRim 2009) Best Paper Award (Remote Sensing 2008) MIT Teaching Excellence Award (1998) Teaching: Courses include Signals and Systems, Control Systems, and Statistical Inference. She has supervised numerous graduate students and postdocs in her Signal and Information Processing (SIP) Lab. Labs/Teams: Director of the SIP Lab, conducting research in signal processing, information theory, and biomedical applications. Collaborates with industry partners like Myant Inc. and Huawei Technologies.
Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.
