جست‌وجوی استاد و پژوهشگر

• Surgery
• Medical Informatics
• Artificial Intelligence in Healthcare
• +۵ مورد دیگر

Martin Hylleholt Sillesen serves as a Clinical Associate Professor in the Department of Clinical Medicine within the Faculty of Health and Medical Sciences at the University of Copenhagen. His academic appointment is based at Blegdamsvej 3, 2200 Copenhagen N, Denmark, with primary affiliation through the Capital Region of Denmark (Region Hovedstaden) healthcare system. Dr. Sillesen's research program focuses on the critical intersection of surgical care and artificial intelligence. He pioneers methodologies using deep neural networks and natural language processing to analyze electronic health records for detecting and predicting postoperative complications. His work spans pancreatic surgery outcomes, surgical site infections, opioid therapy impacts, and sarcopenia-related risks, consistently leveraging large-scale clinical datasets to develop validated predictive models. This research directly addresses gaps in current complication surveillance systems by comparing automated coding (ICD-10) against manual curation methods. Analysis of his 36 research outputs (2024-2025) reveals a dominant trend in AI-driven surgical quality improvement. Key thematic clusters include neural network applications for genetic risk prediction (7 Scopus citations), NLP-based complication detection from free-text records (4 citations), and multicenter evaluations of opioid impacts on mortality. His publications in Scandinavian Journal of Surgery , PLoS ONE , and Frontiers in Digital Health demonstrate methodological rigor with significant clinical translation potential. No scientific awards or honors are documented in the current dataset. Information regarding student mentorship, grant funding, laboratory facilities, or collaborative research teams remains unavailable in the provided materials.

Michael Kilgardمشاهده پروفایل

استاد

• Neural Plasticity
• Vagus Nerve Stimulation (VNS)
• Stroke Rehabilitation
• +۶ مورد دیگر

Dr. Michael Kilgard is the Margaret Fonde Jonsson Professor and Interim Executive Director of the Texas Biomedical Device Center at the University of Texas at Dallas (UTD). He holds a Ph.D. in Neuroscience from UCSF (1998) and a B.A. in Molecular and Cellular Biology from UC Berkeley (1993). His primary affiliation is with the School of Behavioral and Brain Sciences, where he leads research on neural plasticity mechanisms for treating neurological and psychiatric conditions. His research focuses on vagus nerve stimulation (VNS) paired with sensory/motor training to enhance recovery from stroke, spinal cord injury, tinnitus, PTSD, and neuropathy. Notable achievements include FDA approval of VNS for stroke rehabilitation and development of implantable VNS devices 50x smaller than existing models. Kilgard has published over 130 peer-reviewed papers in Nature , Science , and Neuron , holds 25 U.S. patents, and his work is funded by DARPA, NINDS, and the W.W. Caruth Jr. Foundation. Key awards include the UT Regents Outstanding Teaching Award (2009), Chancellor’s Council Teaching Award (2008), and NIDCD grants. His lab, Dr. Kilgard's Lab, collaborates on translational projects like RePlay rehabilitation systems and closed-loop neuromodulation algorithms. Current efforts target PTSD treatment via VNS-enhanced exposure therapy and spinal cord injury recovery through closed-loop VNS-motor training pairings. Patents include systems for brain plasticity stimulation (Patent 6,221,908, 1998) and paired VNS-motor therapy for stroke (Patents 8,700,145 and 9,333,355). Recent clinical trials demonstrated safety and efficacy in chronic stroke and spinal cord injury patients.

Francesco Vespignaniمشاهده پروفایل

دانشیار

• Sentence Processing
• ERPs (Event-Related Potentials)
• Language Comprehension
• +۳ مورد دیگر

Francesco Vespignani is an Associate Professor at the University of Padova. His research focuses on the cognitive and neural mechanisms underlying language comprehension, particularly sentence processing, ERPs (event-related potentials), and interactions between syntax and pragmatics. He investigates how language influences visual attention, bilingualism, aging, and deafness. His work integrates psycholinguistic, ERP, and EEG methodologies. Research areas: ERPs and sentence comprehension, syntax-pragmatics interaction, language and visual attention, language in atypical populations (bilingualism, aging, deafness). Publications: Over 40 peer-reviewed articles in journals like Journal of Experimental Psychology , Brain and Language , and Discourse Processes . Recent articles highlight speaker-specific speech prediction, pragmatic leniency toward foreigners, and neural mechanisms of language processing in cochlear implant users. His work bridges cognitive psychology, neuroscience, and linguistics. Key findings include the role of novel linguistic labels in visual attention disengagement and ERP evidence of discourse expectations linked to the question under discussion. Collaborations span Italy, Germany, and the UK, focusing on bilingualism and neurocognitive language processing.

• Neuroscience
• Neural Activity
• Cortical Rhythms
• +۳ مورد دیگر

Quentin Perrenoud is an Associate Research Scientist in the Department of Neuroscience at Yale School of Medicine. His research focuses on cortical interneurons, neural rhythms, and seizure mechanisms, with significant work on VIP interneurons, cholinergic arousal, and neurovascular coupling. Research Interests include: Cortical Rhythmic Activity Interneuron Functional Diversity Seizure-Induced Consciousness Impairment Neurovascular Response Mechanisms Frequency Spectrum Analysis of Neural Dynamics Laminar Organization of Cortical Circuits Recent Publications demonstrate expertise in: Mouse models of focal seizures Gamma oscillations in visual processing Molecular profiling of parvalbumin and nNOS interneurons Perineuronal net composition 5-HT3 receptor-mediated vascular responses Comparative analysis of cortical and hippocampal GABAergic neurons

Bennett Novitchمشاهده پروفایل

استاد

• Neural Development
• Brain Organoids
• Motor Neuron Differentiation
• +۷ مورد دیگر

Bennett Novitch is a Professor of Neurobiology in the School of Medicine at the University of California, Los Angeles (UCLA). His research focuses on neural development, stem cell biology, and neurodevelopmental disorders, with particular expertise in brain organoid modeling, spinal cord development, and cortical neurogenesis. Dr. Novitch completed his BS in Biological Sciences at the University of Vermont (1991), followed by an MMSC in Medical Sciences from Harvard Medical School (1993), a PhD in Biological Chemistry from Harvard University (1998), and postdoctoral training in Biochemistry and Neurobiology at Columbia University (2002). His early work centered on molecular mechanisms of neural development, particularly the role of transcription factors like Olig2 in motor neuron specification. Novitch's research has evolved significantly toward advanced in vitro modeling of human neural development and disease. His laboratory has pioneered the use of brain organoids to model neurodevelopmental disorders including epilepsy, intellectual disability, and Zika virus-related microcephaly. His team has made important contributions to understanding how transcription factors like Foxp1 regulate cortical development and neural progenitor maintenance, and how signaling pathways such as TGFβ and Notch modulate neural fate determination. Recent work has focused on the molecular mechanisms underlying cortical organization, spinal cord patterning, and the development of neural circuits in human brain assembloids. Analysis of Novitch's publication record reveals a clear progression from fundamental studies of neural tube patterning and motor neuron development in model organisms toward sophisticated human stem cell-based models of brain development and disease. His work consistently bridges developmental neurobiology with translational applications, particularly in understanding and modeling neurodevelopmental disorders. The increasing focus on human brain organoids since 2015 represents a strategic shift toward clinically relevant models that can capture human-specific aspects of neural development. Dr. Novitch maintains an active research program with multiple NIH-funded projects, including the UCLA High-Throughput Neuropsychiatric Disorder Phenotyping Center (as Co-PI) and investigations into the molecular mechanisms of neurodevelopmental disorders using brain organoids (as PI). His laboratory is part of UCLA's broader neuroscience and developmental biology community, collaborating extensively with researchers studying stem cell biology, neurogenetics, and computational neuroscience. The Novitch Lab at UCLA operates at the intersection of developmental neurobiology and translational medicine, with a growing emphasis on using human stem cell-derived models to understand and potentially treat neurodevelopmental conditions. The lab's work has significant implications for understanding the cellular and molecular basis of conditions like autism spectrum disorders, intellectual disability, and epilepsy.

• Electrical Engineering
• Computer Engineering
• Smart Grids
• +۵ مورد دیگر

Muhammad Jawad is an Assistant Professor at COMSATS University Islamabad in the Department of Electrical and Computer Engineering. He previously completed his PhD at North Dakota State University in 2015 and has also been affiliated with the University of Manchester's Department of Electrical and Electronics Engineering. His academic career spans over a decade with continuous research contributions from 2013 to the present. Dr. Jawad's research focuses on the intersection of electrical engineering and computer science, particularly in smart grid technologies, energy management systems, machine learning applications for power systems, and IoT security. His work demonstrates strong interdisciplinary collaboration, with numerous publications in high-impact journals including IEEE Access, Future Internet, and Expert Systems with Applications. His research addresses critical challenges in modern power systems, renewable energy integration, and computational methods for electrical engineering applications. Analysis of his recent publications reveals a clear research trajectory focusing on applying advanced computational techniques to solve practical problems in power systems and energy management. His work spans machine learning for intrusion detection in IoT devices, optimization techniques for battery sizing and energy management, thermal-aware scheduling algorithms, and blockchain applications for distributed systems. The interdisciplinary nature of his research connects electrical engineering fundamentals with cutting-edge computer science methodologies. Dr. Jawad has established a strong collaborative network, frequently working with researchers from COMSATS University Islamabad, North Dakota State University, and international institutions. His collaborations span across multiple research areas, indicating his ability to bridge different technical domains. While specific grant information isn't detailed in the publication records, his consistent output suggests successful funding support for his research activities. Though specific laboratory affiliations aren't explicitly mentioned in the publication metadata, his research areas suggest involvement with power systems laboratories, smart grid research facilities, and possibly computer architecture labs focused on energy-efficient computing. His work on memristor modeling and thermal-aware scheduling indicates potential connections to hardware and VLSI research facilities as well.

Egor Rumiantsevمشاهده پروفایل

پژوهشگر

• Computational Science
• Materials Science and Engineering
• Quantum Chemistry
• +۳ مورد دیگر

Egor Rumiantsev is a Doctoral Assistant at the Laboratory of Computational Science and Modelling (COSMO) within the Institute of Mechanical Engineering (IMX) at École Polytechnique Fédérale de Lausanne (EPFL) . He is concurrently enrolled in the Doctoral Program in Materials Science and Engineering under the École Doctorale de Mécanique (EDMX). His research focuses on advancing computational methods for materials science through machine learning integration, quantum chemistry, and atomistic simulations. Research Interests: Egor’s work emphasizes developing neural network models for molecular property prediction, optimizing long-range atomistic simulations, and exploring doping effects in materials like FeRh alloys. His projects bridge machine learning innovations with traditional computational chemistry techniques to enhance predictive accuracy and scalability. Recent Contributions: Key works include LAGNet (2025) for electron density prediction, advancements in reinforcement learning for molecule generation (FREED++), and studies on FeRh alloy doping mechanisms. These reflect his commitment to interdisciplinary approaches in computational materials science. Labs/Teams: COSMO Lab at EPFL, collaborating on cutting-edge computational modeling projects within the IMX Institute.

Tameem Albashمشاهده پروفایل

عضو هیئت علمی

• Quantum Information Science
• Quantum Computing
• Quantum Annealing
• +۷ مورد دیگر

Tameem Albash is a Research Associate Professor at the University of New Mexico , affiliated with the Department of Physics and Astronomy in the College of Arts and Sciences . His research focuses on quantum information science, quantum computing, and quantum annealing. He holds a PhD from the University of Southern California (2010). Education: PhD in Physics, University of Southern California, 2010 Research interests include quantum annealing optimization, quantum simulation, noise mitigation in quantum systems, and the interplay between quantum error correction and classical computational methods. His work explores topics like non-stoquastic Hamiltonians, many-body localization, and the application of machine learning to quantum systems. Recent studies investigate the efficiency of quantum annealing hardware for combinatorial problems, decoherence effects in quantum simulations, and the use of neural networks for state approximation. His articles highlight advancements in classical simulation techniques for quantum phenomena, such as analyzing macrostate vs. microstate dynamics in 1D Ising models. He also examines hybrid approaches combining quantum and classical methods to address limitations in current quantum hardware. Despite no listed awards, his contributions to quantum computing benchmarks and hardware validation are notable. Labs/Teams: Albash's research aligns with UNM's Quantum Information Science initiatives, though specific lab affiliations are not detailed in the provided text.

• Particle Physics
• Astrophysics
• Dark Matter Detection
• +۷ مورد دیگر

Anastassopoulos Vassilis is a Professor at the University of Patras, affiliated with the Department of Electronics and Computers within the School of Natural Sciences. His research spans particle physics, astrophysics, and applied technologies such as remote sensing and medical imaging. He leads experimental efforts in axion detection via the CAST helioscope and explores dark matter signatures in geophysical and astrophysical contexts. His work integrates cutting-edge techniques like neural networks for image processing and decision fusion algorithms for data analysis. Research interests focus on dark matter axions, solar physics, and interdisciplinary applications including land cover classification using SAR imagery, thermal intoxication detection, and super-resolution imaging. He contributes to collaborations such as the Proton EDM experiment and the pEDM project, aiming to uncover new physics beyond the Standard Model. His expertise bridges theoretical models with experimental validation, addressing questions in fundamental physics and applied engineering. Key areas include: particle physics experiments (axion searches, proton EDM), environmental monitoring via remote sensing (coastal change detection, ship identification), biomedical imaging (OCT analysis, thermal facial features), and algorithm development (neural networks, inverse problems). His work frequently employs advanced computational methods and interdisciplinary approaches.

• Computer Vision
• Deep Learning
• Remote Sensing
• +۵ مورد دیگر

Lin Xiong is a Professor at Xidian University's School of Artificial Intelligence, Department of Computer Science, with an extensive publication record spanning from 2008 to 2025. His research primarily focuses on computer vision, deep learning, and remote sensing applications, with significant contributions to SAR image processing, domain adaptation techniques, and environmental monitoring systems. Dr. Xiong's educational background includes advanced studies in computer science and artificial intelligence, though specific details of his degrees are not readily available in the publication record. His research interests span multiple domains including computer vision (particularly facial recognition and person re-identification), deep learning architectures, domain adaptation methods, and remote sensing applications for environmental monitoring. His publication trends reveal a strong focus on practical applications of deep learning, with recent work emphasizing class-incremental learning, SAR image registration, and environmental monitoring using lidar technology. The research spans both theoretical advancements in neural network architectures and practical implementations in fields ranging from renewable energy forecasting to coastal ecosystem monitoring. Knowledge distillation techniques for continual learning Advanced SAR image processing algorithms Domain adaptation methods for computer vision tasks Environmental monitoring using remote sensing technology Dr. Xiong has established significant collaborations with researchers from NASA (notably on mangrove ecosystem monitoring projects) and maintains strong connections within China's academic community, particularly with Xidian University colleagues. His work demonstrates both theoretical depth and practical applicability across multiple domains requiring advanced computer vision and machine learning solutions.

Shishir Gaurمشاهده پروفایل

دانشیار

• Numerical Modelling
• Optimization Techniques
• Machine Learning
• +۶ مورد دیگر

Dr. Shishir Gaur is an Associate Professor in the Department of Civil Engineering at the Indian Institute of Technology (Banaras Hindu University). He holds an affiliated professorship at the Environnement, Ville et Société (EVS) laboratory at the École des Mines de Saint Étienne, France (2023-2025). His research focuses on numerical modeling, optimization techniques, machine learning, and remote sensing applications in water resources management. Key projects include the CNRS International Research Project (IRP) on river-aquifer exchanges in the Ganga and Rhône basins, and collaborations with institutions like CNRS, ANR, and ISRO. Education: PhD from IIT Delhi (with École des Mines collaboration), Postdoc at EMSE, France. He has led over 40 publications and multiple research projects funded by EU, DST, and MHRD. Current advising includes 4 PhD and 3 M.Tech. students. His work emphasizes Franco-Indian partnerships, such as the MoU between ENS de Lyon and IIT Varanasi. Research interests span groundwater management, river quality monitoring (hyperspectral data), and sustainable solutions for water resources. Notable publications include studies on river-aquifer exchanges, sediment load prediction, and thermal pattern analysis of the Ganges. Collaborations extend to projects like H2O'Lyon Summer School and international workshops on river rehabilitation. Grants include Danish Embassy funding for clean river solutions, ISRO grants for water quality sensors, and DBT-EU initiatives for biotechnical treatments. Technical skills include MATLAB, GMS, and GIS software. He has organized conferences like RHAR-2019 and serves on national committees like AICTE’s Induction Programme.

• Computational Mathematics
• Cardiac Electrophysiology
• Numerical Analysis
• +۳ مورد دیگر

Simone Scacchi is a Researcher at the Department of Mathematics, University of Milan, and a member of the Scientific Computing Group focusing on numerical methods and applications. His work emphasizes cardiac electrophysiology modeling, parallel computing, and mathematical epidemiology. He develops advanced numerical techniques like algebraic multigrid solvers, BDDC preconditioners, and virtual element methods for complex biomedical and environmental systems. Research interests include computational cardiology (e.g., Bidomain models, arrhythmias), epidemic control strategies for agricultural diseases (e.g., Xylella fastidiosa), and high-performance computing for fluid-structure interaction problems. His contributions span parallel algorithms, domain decomposition methods, and operator learning applications in medical imaging. Key Projects: Cardiac electro-mechanical modeling, regional control of vector-borne epidemics, and scalable preconditioners for cardiac simulations. Publications highlight interdisciplinary approaches combining numerical analysis with biomedical and ecological applications. Advances in his work address challenges in simulating cardiac dynamics, optimizing epidemic containment, and enhancing computational efficiency for large-scale systems.

• Cognitive Neuroscience
• Psychophysiology
• Cerebral Electrophysiology
• +۳ مورد دیگر

Francesco Russo is a Full Professor in the Department of Human Motor Sciences and Health at the University of Rome 'Foro Italico'. His research focuses on cognitive neuroscience, psychophysiology, and cerebral electrophysiology, particularly exploring neural mechanisms underlying cognitive processes and brain plasticity induced by motor activity and sport. He leads the Laboratory of Cognitive and Action Neuroscience and contributes to the University Quality Committee. His research interests include the neural basis of action anticipation, sensory-motor integration, and the effects of cognitive-motor training on brain function. Notable projects include studies on anxiety reduction through dual-task exercises and the electrophysiological correlates of decision-making in athletes. Key Research Themes: Anticipatory brain activity, motor-cognitive interactions, neuroplasticity in sport Lab Affiliation: Laboratory of Cognitive and Action Neuroscience Recent Focus: ERP analysis of sensory-motor tasks, aging-related cognitive interventions His work spans experimental psychology, clinical neuroscience, and sports science, with publications analyzing brain dynamics in athletes, aging populations, and neurological patients. Current trends emphasize translational research bridging neural mechanisms and practical applications in rehabilitation and sports performance.

Veit Stuphornمشاهده پروفایل

دانشیار

• Systems Neuroscience
• Cognitive Neuroscience
• Computational Neuroscience
• +۱۲ مورد دیگر

Veit Stuphorn is an Associate Professor of Neuroscience at The Johns Hopkins University, affiliated with the Zanvyl Krieger Mind/Brain Institute and the Department of Psychological and Brain Sciences. His research bridges systems, cognitive, and computational neuroscience, focusing on the neural basis of decision-making and self-control. His research interests lie in understanding the neurophysiological mechanisms of higher executive functions, particularly decision-making and self-control. Using neurophysiological recordings in awake primates during cognitive tasks such as the countermanding paradigm, he investigates how neurons in the frontal cortex—specifically the frontal eye field (FEF), supplementary eye field (SEF), and anterior cingulate cortex (ACC)—encode signals related to action initiation, suppression, error detection, and reward evaluation. He also extends these studies to human subjects using fMRI, enabling cross-species comparisons. His work suggests that medial frontal areas like ACC and SEF form a secondary executive control system that monitors performance and adjusts behavior based on outcomes. The 15 most recent publications reflect a consistent focus on executive control, inhibitory processing, and neural correlates of decision-making. These works span primate neurophysiology, human behavioral neuroscience, and theoretical neuroeconomics, with recurring themes in performance monitoring, error signaling, and the role of frontal-basal ganglia networks in cognitive control. Key techniques include single-neuron recording, local field potential analysis, and fMRI, applied across species to validate findings. Dr. Stuphorn has not been mentioned with any scientific awards in the provided text. He advises graduate students through the Neuroscience Training Program and Psychological and Brain Sciences at Johns Hopkins. While specific grants are not listed, his research is supported by long-term investigations into executive function, suggesting sustained funding. His lab conducts parallel animal and human studies, indicating interdisciplinary collaboration and resource investment. His lab is based at the Zanvyl Krieger Mind/Brain Institute at Johns Hopkins, where he leads a research team focused on neural circuits underlying decision-making and self-control. The lab combines electrophysiological recordings in primates with human neuroimaging to explore executive functions across species.

Michael Tangermannمشاهده پروفایل

دانشیار

• Machine Learning
• Neurotechnology
• Brain-Computer Interfaces (BCI)
• +۵ مورد دیگر

Dr. Michael Tangermann is an Associate Professor at Radboud University and Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour. He leads the Data-Driven NeuroTechnology Lab, focusing on developing machine learning algorithms for neural and behavioral data analysis. His work integrates brain-computer interfaces (BCI), closed-loop neurotechnologies, and deep brain stimulation (DBS) to address clinical and non-clinical challenges. Education: PhD in Computer Science (2007, University of Tübingen) and Diplom in Computer Science (2000, University of Tübingen). Former positions include Head of the Brain State Decoding Lab (University of Freiburg, 2013–2020) and Substitute Professor for Autonomous Intelligent Systems (2019–2020). Research interests span machine learning for time-series analysis, neurotechnological applications (e.g., BCI for communication/rehabilitation, adaptive DBS), and translational neuroscience. His lab collaborates with institutions like the University Medical Center Freiburg and MindAffect. Publications (H-index: 40) emphasize decoding brain states, optimizing BCI paradigms, and developing open-source tools like the Dareplane platform. Teaching includes advanced BCI courses and digital signal processing at the graduate level.