جست‌وجوی استاد و پژوهشگر

• Cognitive Psychology
• Instructional Psychology
• Educational Technology
• +۱۰ مورد دیگر

Dr. Christian Scharinger is an Associated Scientist and Principal Investigator at the Leibniz-Institut für Wissensmedien (IWM) in Tübingen, Germany, where he has been actively engaged in research since 2010. He is a member of the Multimodal Interaction Lab and leads a DFG-funded project on neurophysiological measures in instructional design. His research integrates cognitive psychology with educational technology, focusing on how digital learning environments affect cognitive processes. Christian Scharinger earned his M.A. in Linguistics, Informatics, and Media Sciences from the University of Trier and the University of Konstanz in 2007. He completed his doctoral thesis in Cognitive Science at the University of Tübingen in 2015. From February to September 2017, he served as a postdoctoral researcher at the University of Tübingen’s Chair of Applied Cognitive Psychology and Media Psychology. His research interests center on the cognitive and neurophysiological mechanisms underlying learning in digital environments. He employs advanced methodologies such as combined EEG and eye-tracking to investigate cognitive load, working memory, and the effects of multimedia elements like decorative pictures and virtual reality. He is particularly interested in how seductive details influence learning and how user-friendly digital interfaces can be designed based on neurocognitive data. The recent publications of Christian Scharinger reflect a strong trend in using neurophysiological indicators—especially EEG frequency band power and pupil dilation—to assess cognitive load across various learning contexts, including text reading, VR, and gamified tasks. His work bridges cognitive theory with practical applications in educational design, often evaluating the validity of multimedia learning principles through empirical neurocognitive data. Task-irrelevant decorative pictures increase cognitive load (2024) Effects of emotional decorative pictures on cognitive load (2023) Gamification of n-back tasks (2023) EEG and eye-tracking in text-picture learning (2020) Cross-subject cognitive load classification (2018) Christian Scharinger has been involved in multiple DFG and interdisciplinary research projects, including those on virtual reality, working memory load, and perception of historical sites. He has taught seminars at Hochschule Tuttlingen and Hochschule Fresenius Heidelberg, contributing to academic training in psychology and technology. He is also a member of the IWM postdoc network 'Cognitive Conflicts During Media Use', indicating active engagement in collaborative research. While no formal advisees are listed, his role as a PI and lab member suggests mentorship responsibilities. He has contributed to setting up and operating a combined EEG-eye-tracking laboratory at IWM, demonstrating technical leadership. His work spans both basic and applied research, with implications for instructional design, human-computer interaction, and educational technology. He regularly presents at major conferences such as EARLI, TeaP, and ETRA, and has organized symposia and workshops, reflecting strong academic leadership.

Michaela Müller-McNicollمشاهده پروفایل

استاد

• RNA Regulation
• Alternative Splicing
• Gene Expression
• +۵ مورد دیگر

Professor Michaela Müller-McNicoll is a full Professor at the Institute of Molecular Biosciences at Goethe University Frankfurt am Main and a Max Planck Fellow at the Max Planck Institute of Biophysics since March 2024. She leads a research group investigating RNA regulation and its role in cellular processes. Müller-McNicoll is also an elected director of the international RNA Society since 2022 and participates in several research consortia including the SCALE consortium and the International Max Planck Research School on Cellular Biophysics (IMPRS-CBP). Her research focuses on: RNA regulation and alternative splicing mechanisms The role of RNA-binding proteins (particularly SR proteins) in gene expression mRNP assembly and remodeling throughout the mRNA lifecycle Connections between splicing and post-splicing functions How RNA regulation relates to diseases including cancer and inflammation Professor Müller-McNicoll's recent publications demonstrate a strong focus on RNA regulation mechanisms, with particular attention to alternative splicing, RNA-binding proteins, and their roles in cellular processes. Her work spans both fundamental molecular mechanisms and potential applications in understanding disease processes. The publications show an interdisciplinary approach combining molecular biology, biophysics, and computational methods. Scientific awards and recognition: Prestigious EMBO Fellowship Elected director of the international RNA Society (since 2022) Professor Müller-McNicoll actively mentors students and researchers. She has successfully supervised PhD students including Hannah and Clara who recently defended their theses, and currently advises IMPRS PhD students Ritaja and Harsh, as well as Master's students Olivia, Alex, Luisa, and Laura. Her research is supported through the Max Planck Fellow program, which provides funding for five years to lead a research group at the MPI of Biophysics, and through the SCALE consortium which received additional funding for 2024. She leads the 'RNA Regulation in Higher Eukaryotes' working group at the Institute of Molecular Biosciences, located at the Biologicum on the Riedberg Campus. Her lab investigates how RNA-binding proteins regulate gene expression from transcription to translation, with particular focus on SR proteins and their multifunctional roles in mRNA processing.

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Arash Mohammadi is an Assistant Professor in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Canada. He holds a PhD from the University of Toronto (2015) and was formerly affiliated with Amirkabir University of Technology, Iran. His research bridges signal processing, artificial intelligence, and biomedical applications. Research Interests: Signal and image processing for healthcare (e.g., lung cancer detection, ECG analysis) Machine learning for smart grids and cyber-physical systems AI in mobile edge computing and 6G networks Transformer and diffusion models for medical and motion data Federated and efficient deep learning for edge devices His recent publications (2021–2025) in top venues like IEEE TSP, ICASSP, and AAAI demonstrate a strong focus on applying cutting-edge AI—especially vision transformers, Mamba architectures, and diffusion models—to critical domains such as medical diagnostics, gesture recognition, and network security. Trends include multimodal fusion, uncertainty quantification, and efficient model design. Scientific Contributions: Developed novel frameworks like NYCTALE and MIXCAPS for lung nodule malignancy prediction Introduced CacheMamba and TEDGE-Caching for edge network optimization Advanced EMG-based gesture recognition using hybrid and transformer models Contributed to cybersecurity in smart grids via attack detection models He actively advises students and collaborates with researchers such as Konstantinos N. Plataniotis and Jamshid Abouei. He has contributed to special issues on neurorehabilitation and AI for COVID-19 diagnosis. His work often involves interdisciplinary teams and real-world applications in healthcare and smart infrastructure.

• Medical Imaging
• PET/CT Imaging
• Quantitative Image Analysis
• +۵ مورد دیگر

Drew A. Torigian is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania, with extensive contributions to medical imaging research. His work focuses on developing advanced methodologies for PET/CT image analysis, disease quantification, and anatomical segmentation through innovative applications of deep learning and computer vision techniques. Dr. Torigian's research interests span multiple critical areas in medical imaging including quantitative image analysis, PET/CT applications, deep learning in medicine, radiation therapy planning, and anatomical segmentation. His work has significantly advanced the field of medical image analysis through the development of novel algorithms for disease quantification without explicit object delineation, standardized anatomic space frameworks, and attention-based neural networks for medical image interpretation. His recent publications demonstrate a strong trend toward integrating artificial intelligence with medical imaging, particularly in developing gaze-guided neural networks, geographical attention mechanisms, and hybrid transformer-convolutional architectures for improved medical image analysis. These works consistently address critical challenges in radiology including disease quantification, anatomical segmentation, and the development of clinically interpretable AI models. Dr. Torigian has received recognition through his substantial publication record in top-tier medical imaging venues including Medical Image Analysis, IEEE Transactions on Biomedical Engineering, and leading medical imaging conferences. His research has been consistently funded through various mechanisms supporting innovation in medical imaging technology. Through his mentorship and collaborative research, Dr. Torigian has contributed to advancing the field of medical imaging with practical applications in radiation therapy planning, disease quantification, and the development of standardized methodologies for image analysis. His work bridges the gap between computer science innovation and clinical radiology applications.

• Computer Vision
• Machine Learning
• Biometrics
• +۳ مورد دیگر

David Menotti is a prominent researcher in computer vision and machine learning, with a focus on biometrics, license plate recognition, and video analysis. He has collaborated extensively with institutions and researchers globally, contributing to over 171 publications between 2003-2025. Key research areas include face recognition, synthetic data generation, and zero-shot learning Major contributions in license plate super-resolution, sign language translation, and ocular biometrics Active in organizing competitions like FRCSyn and OCFR to advance synthetic data applications His work often combines diffusion models, CNN architectures, and multimodal approaches to solve real-world problems in unconstrained environments. Notable recent projects involve enhancing face recognition with synthetic data, vehicle color recognition under adverse conditions, and Libras-to-Portuguese translation. Menotti's publications appear in journals like Information Fusion , IEEE Access , and conferences including CVPR, SIBGRAPI, and IJCNN. He frequently collaborates with researchers such as Rayson Laroca, William Robson Schwartz, and Pedro Vidal.

• Microelectronics
• Circuit Design
• Analog-to-Digital Converters
• +۳ مورد دیگر

Zhangming Zhu is a Professor at Xidian University in the School of Microelectronics . He specializes in Microelectronics and Circuit Design , with a focus on Analog-to-Digital Converters (ADCs) , CMOS Technology , and Low-Power Electronics . His work addresses challenges in high-speed, high-precision, and energy-efficient circuit design. Research Interests: His publications highlight expertise in ADCs, PLLs, energy harvesting, biomedical sensors, and RF systems. Recent Publications: 2025 papers include a 12-bit 1.5-GS/s ADC , a 5-18-GHz Quadrature Receiver , and 20-bit SAR ADC with thermal error suppression. Collaborations: Frequently co-authors with Shubin Liu, Yi Shen, Ruixue Ding, and others. Applications: Work spans consumer electronics, IoT, biomedical devices, and energy-efficient systems.

• Computer Science
• Machine Learning
• IoT
• +۷ مورد دیگر

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Prof. Dr. Mario Fritz is a leading academic at the CISPA Helmholtz Center for Information Security and Saarland University , with a focus on Trustworthy Information Processing . His work sits at the intersection of AI, Machine Learning, Security, and Privacy, addressing challenges in foundation models, health data, and adversarial robustness. Key Projects : ELLIOT (Multi-Modal Foundation Models), ELSA (Secure AI), PriSyn (Synthetic Health Data), AIgency (Generative AI in Cybersecurity), HMSP (Medical Security) Research Themes : AI/ML security, privacy-preserving techniques, causal modeling, healthcare applications, and ethical AI Recent Publications : Focus on LLM sampling, causal inference, model stealing, and privacy-aware document analysis Collaborations : European Laboratory for Learning and Intelligent Systems (ELLIS), BMBF-funded initiatives, GHGA (Human Genome Archive) Academic Leadership : As a Professor , he coordinates large-scale EU projects and mentors emerging researchers in AI ethics and security.

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +۱۲ مورد دیگر

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.

Joergen Kornfeldمشاهده پروفایل

استاد پژوهشی

• Connectomics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Joergen Kornfeld is a Research Group Leader at the MRC Laboratory of Molecular Biology in Cambridge, UK, where he leads the 'Connectomics of learned behaviour' group. He also maintains a connection with the Max Planck Institute for Biological Intelligence as a Guest Scientist. Previously, he was a Group Leader at the Max Planck Institute of Neurobiology in Martinsried, Germany (2021-2024). Dr. Kornfeld received his PhD in Biology (summa cum laude) from the Max Planck Institutes of Medical Research and Neurobiology, with research stays at NYU. He completed his Master's in Computational Biology and Bioinformatics at ETH Zurich and his Bachelor's in Molecular Cell Biology at Heidelberg University. Dr. Kornfeld's research focuses on understanding how animals store learned behaviors in their neuronal networks. His work centers on the zebra finch songbird as a model system, investigating how song memories are stored and retrieved from underlying brain circuits. His laboratory employs high-throughput 3D electron microscopy to map brain circuits at synaptic resolution, generating massive datasets that require advanced deep learning techniques for analysis. His team develops and applies state-of-the-art machine learning algorithms to reconstruct neural circuits and infer connectivity patterns. His recent publications demonstrate a clear trajectory toward increasingly sophisticated methods for neural circuit reconstruction and analysis, combining volume electron microscopy with deep learning approaches. The research spans multiple model organisms including zebra finches, mice, and zebrafish, with applications to understanding memory formation, behavioral control, and neural computation. Dr. Kornfeld leads a research team including Caitlin Gillespie, Nelson Medina, and Anastasia Sorokina. His laboratory is at the forefront of developing new methodologies for connectomics that bridge neuroscience, computer science, and engineering. He maintains active collaborations with multiple institutions including MIT, NYU, and the Max Planck Society.

Thomas Geyerمشاهده پروفایل

مدرس ارشد

• Behavioral Neuroscience
• Cognitive Neuroscience
• Biomedical Neuroscience
• +۶ مورد دیگر

Thomas Geyer is a Senior Lecturer in Experimental Psychology and Academic Director at LMU Munich's Department of Psychology , specializing in the interplay between cognitive and neurobiological mechanisms of statistical learning. His research focuses on how humans manage sensory overload through automatic learning processes that enhance object recognition and search efficiency. Primary Research Focus: Behavioral & Cognitive Neuroscience Secondary Focus: Biomedical Neuroscience Tertiary Focus: Theoretical Neuroscience & Technical Applications Geyer employs behavioral methods (reaction times, eye tracking) alongside neuroimaging (EEG, fMRI) to explore attentional capture, scene memory, and cross-modal interactions in visual search. His work bridges theoretical neuroscience with practical applications in human perception and memory systems. Selected Research Trends: Statistical learning in complex environments, neurobiological underpinnings of selective attention, and integration of multi-sensory contextual cues. Notable Contributions: Pioneering studies on cross-trial priming effects, N1pc polarity reversal mechanisms, and cross-modal attentional guidance through contextual memory. Collaborations with renowned researchers like Hermann J. Müller and Joseph Töllner highlight his impact in experimental psychology. Former Students: Dr. Efsun Annac Abhilash Dwarakanath Dr. Bernhard Schlagbauer Contact: geyer@lmu.de | Phone: +49 (0) 89 / 2180 5216

Andreas Herzمشاهده پروفایل

استاد

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Coding
• +۱۱ مورد دیگر

Andreas Herz is a Professor and Senior Scientist at the Division of Neurobiology, Faculty of Biology, Ludwig-Maximilians-Universität München (LMU). He is a Board Member and Speaker of the Bernstein Center Munich, a leading institution in computational neuroscience. His research integrates theoretical modeling with experimental data to explore neural dynamics, information processing, and spatial navigation. Institution: Ludwig-Maximilians-Universität München School: Faculty of Biology Department: Division of Neurobiology Leadership: Speaker, Bernstein Center Munich Email: herz@bio.lmu.de Herz’s research focuses on computational and theoretical neuroscience, particularly the neural basis of spatial navigation, population coding in grid cells, dendritic spine biophysics, and sensory information processing. He employs methods from dynamical systems, probability theory, and information theory to model single neurons and large-scale networks. His work spans model systems from grasshoppers to rodents and zebrafish, aiming to uncover general principles of neural computation. The recent publications highlight a strong trend in understanding grid cell dynamics, spatial memory, and dendritic computation. His work frequently involves decoding neural population activity, analyzing in vivo electrophysiological data, and developing theoretical frameworks for neural coding. Themes include burst firing, phase precession, remapping, and energy-efficient neural coding. He has mentored numerous PhD and MSc students through the Graduate School of Systemic Neurosciences (GSN), including Alexander Mathis, Johannes Nagele, and Michaela Poth. His group actively collaborates with experimental labs, contributing to interdisciplinary advances in neuroscience.

• UAV trajectory planning
• Autonomous systems
• Medical imaging
• +۵ مورد دیگر

Xinwei Wang is a Professor at Nanjing University of Aeronautics and Astronautics with an extensive publication record spanning over two decades. Their research primarily focuses on autonomous systems, UAV trajectory planning, medical imaging, and computer vision applications. Wang has established significant collaborative networks with researchers including Yan Zhou, Liang Sun, Xichao Su, and Lei Wang across multiple Chinese institutions. Wang's research interests center on the intersection of robotics, artificial intelligence, and practical engineering applications. Their work demonstrates particular expertise in UAV coordination systems, medical diagnostic technologies, and underwater imaging solutions. Recent publications reveal a growing emphasis on explainable AI systems for medical applications and safety-critical trajectory planning for autonomous vehicles. The publication trends show a consistent output of high-impact research, with recent work increasingly focusing on practical implementations of AI systems in medical diagnostics, autonomous vehicle navigation, and aerospace applications. Wang's research bridges theoretical control systems with real-world engineering challenges, particularly in safety-critical domains requiring precise motion planning and reliable decision-making. Wang has contributed significantly to both theoretical frameworks in optimal control and practical implementations in medical imaging and autonomous systems. Their work on UAV cooperative task assignment and flight deck operations demonstrates strong connections to aerospace engineering applications, while medical imaging research shows interdisciplinary collaboration with healthcare professionals.

• Medical Ultrasound
• Neurosurgical Applications
• Biomedical Engineering
• +۷ مورد دیگر

Amir Manbachi is an Associate Professor at Johns Hopkins University with dual appointments in the Johns Hopkins School of Medicine and the Whiting School of Engineering . He co-directs the HEPIUS Innovation Lab , focusing on wearable and implantable medical ultrasound technologies for spinal cord injury and brain tumor monitoring. He is the engineering co-PI for a $13.48M DoD grant and has mentored over 170 students. Academic Appointments: Departments of Neurosurgery, Anesthesiology and Critical Care Medicine, Biomedical Engineering Education: PhD (2015) and MSc (2010) from University of Toronto, BSc (2008) Postdoctoral Training: Harvard-MIT Program in Health Sciences and Technology Dr. Manbachi's research spans the physics of medical ultrasound, therapeutic applications (tumor ablation, neuromodulation), and clinical translation of engineering innovations. His work bridges acoustic physics with real-world deployment in trauma care and neurological disorders. Recent publications highlight advancements in ultrasound elastography for spinal cord tension monitoring, non-contrast blood flow analysis , and neuromodulation for spinal cord injury. His team's implantable ultrasound sensors and wearable devices have achieved FDA breakthrough designation . Awards: AIUM Peter Arger Excellence Award (2024), Hisako Terasaki Young Innovator (2024), Baltimore Business Journal's 40 under 40 (2022-2023), FDA Breakthrough Device (2023), teaching awards Grants: $13.48M DoD award, Johns Hopkins ICTR Career Development Award Patents: 20+ inventions including ultrasonic bone imaging systems and flexible focused ultrasound transducers As co-founder of Spinesonics Medical , he has commercialized ultrasound-guided spine surgery technologies. He teaches courses in engineering design, medical imaging, and clinical translation at the Whiting School of Engineering .

• Geoscience Data Management
• Research Data Infrastructure
• Open Science
• +۳ مورد دیگر

Melanie Lorenz is a Researcher at the Deutsches GeoForschungsZentrum (GFZ) in Potsdam, Germany, working within the Data and Information Management and Geoinformation departments. Her professional activities focus on developing and implementing research data infrastructure solutions for the geoscience community in Germany and internationally. Her research interests center around geoscience data management, research data infrastructure development, Open Science practices, and FAIR data principles implementation. She is particularly engaged in creating interoperable systems that connect diverse research outputs and facilitate international collaboration in Earth System Sciences. Analysis of her recent publications reveals a strong focus on building national research data infrastructure (NFDI4Earth) and specialized information services (FID GEO) for the geosciences. Her work consistently addresses challenges in research data interoperability, repository development, and cultural change toward Open Science practices within the geoscience community. The publications demonstrate her leadership in connecting data and text repositories while promoting standards that enhance research data discovery and reuse. Melanie Lorenz is actively involved in the FID GEO initiative, which serves as a hub for geoscience research outputs in Germany. Through this work, she contributes to the development of sustainable research data ecosystems that support both current research needs and future scientific discovery in Earth System Sciences.