جست‌وجوی استاد و پژوهشگر

• Biomedical Signal Processing
• Computational Neuroscience
• Brain-Computer Interfaces (BCI)
• +۷ مورد دیگر

David B. Grayden is a Professor at The University of Melbourne, affiliated with the Melbourne School of Engineering and the Department of Electrical and Electronic Engineering . His work spans Biomedical Signal Processing , Computational Neuroscience , and Brain-Computer Interfaces (BCI) , focusing on applications in Epilepsy Research and Cochlear Implants . Melbourne Neural Engineering Laboratory member Collaborator in multidisciplinary biomedical research Key research interests include: Developing Seizure Prediction Algorithms using long-term EEG/iEEG data Neural mass modeling for Epilepsy and Inhibitory Network Behavior Optimizing Cochlear Implants via computational models Advancing Endovascular BCI Systems and Neural Stimulation Recent publications highlight trends in Machine Learning , Path Signatures , and Multi-Frequency Stimulation for SSVEP-based BCIs . His work integrates Computational Modeling with Biomedical Engineering to address clinical challenges in neuroprosthetics and sensory processing. Grayden leads projects on Neural Network Dynamics , Biomedical Signal Analysis , and Neurostimulation , often collaborating with institutions like Monash University and Royal Melbourne Hospital .

• High-Performance Computing
• Distributed Systems
• Graph Algorithms
• +۳ مورد دیگر

Tahsin Reza is an Assistant Professor at the University of Waterloo, affiliated with the Faculty as a full-time member. His research focuses on high-performance computing, distributed systems, and large-scale graph processing. His work emphasizes algorithmic optimization for irregular parallelism, distributed approximation algorithms, and efficient handling of massive graphs with billions of edges. Key research interests include developing frameworks like YGM for HPC, HyGN for NUMA architectures, and tools such as PruneJuice for graph pruning. His contributions span graph algorithms for Steiner trees, temporal graphs, and metadata-driven pattern matching. He has extensively explored GPU and hybrid CPU-GPU systems to accelerate graph processing tasks in domains like InSAR data analysis and VANET tracking. No scientific awards or grants are explicitly mentioned in the provided materials. His work has been published in top venues, consistently addressing challenges in scalability, efficiency, and real-world applicability of graph-based solutions.

Cranos Williamsمشاهده پروفایل

استاد

• Agricultural Analytics
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Cranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at North Carolina State University, holding primary and secondary appointments in the Department of Electrical and Computer Engineering and the Department of Plant and Microbial Biology, respectively. He serves as Platform Director of the NC Plant Sciences Initiative and leads the EnBiSys Research Laboratory. His research integrates electrical engineering methodologies with plant biology to address challenges in biofuel production, environmental adaptability, and stress resilience in plants. Dr. Williams holds a Ph.D. in Electrical Engineering from NC State (2008), an M.S. from the same institution (2002), and a B.S. from NC A&T State University (2001). His work focuses on applying machine learning, signal processing, and systems analysis to quantify genetic and environmental impacts on plant responses. His research highlights include advancing sweetpotato quality assessment via AI, studying gene expression dynamics in Drosophila, and developing high-throughput phenotyping tools. His articles span topics like plant microbiome analysis, freezing tolerance mechanisms, and diversity in STEM education. Dr. Williams has received notable awards, including the University Faculty Scholar (2023), Alumni Association Outstanding Research Award (2021), and Alcoa Foundation Engineering Research Achievement Award (2019). He advocates for broadening participation in STEM and leads initiatives like the GRAD-AID for Ag program to train AI-driven plant science researchers. As head of EnBiSys Lab, Dr. Williams fosters interdisciplinary innovation at the intersection of engineering, data science, and agriculture. His contributions bridge computational tools and biological systems to address global agricultural challenges.

• Neuroscience
• Neuroimaging
• Brain Connectivity
• +۴ مورد دیگر

Kangjoo Lee is an Associate Research Scientist in Psychiatry at Yale School of Medicine, specializing in neuroimaging and computational neuroscience. Their research focuses on understanding brain dynamics, particularly in mental health conditions like schizophrenia and mood disorders, using advanced neuroimaging techniques and AI-driven analysis. Education: PhD in Neuroscience from McGill University (2019), Postdoctoral Research Associate at Yale University (2023). Research interests include brain connectivity, sleep deprivation effects, and developing AI tools for neuroscience. Lee has contributed to studies on ketamine's neural mechanisms, sleep recovery networks, and large language models' predictive capabilities in neuroscience. Notable achievements include the Merit Abstract Award (2021) and leadership roles in the Organization for Human Brain Mapping's Diversity Committee. Their work bridges clinical neuroscience with computational methods, emphasizing reproducibility and inclusivity in scientific practices. Current roles include Guest Editor for Biological Psychiatry and committee memberships in neuroimaging and diversity initiatives. Lee collaborates across disciplines, advancing translational research in mental health biomarkers and neurotechnology.

Amy Muellerمشاهده پروفایل

دانشیار

• Biogeochemistry
• Environmental Engineering
• Sensor Technology
• +۳ مورد دیگر

Amy Mueller is an Associate Professor in Civil and Environmental Engineering and Marine and Environmental Sciences at Northeastern University, with affiliations in Electrical and Computer Engineering and membership in The Plastics Center. She holds dual academic appointments and leads research in biogeochemistry, sensor technology, and environmental sustainability. Education: PhD in Environmental Chemistry (MIT, 2012), MEng in Electrical Engineering and Computer Science (MIT, 2003). Awards include the NSF Ocean Sciences Postdoctoral Fellowship (2015–2016), NSF Graduate Research Fellowship (2004–2007), and Northeastern’s CEE Excellence in Teaching Award (2021). Research focuses on in-situ sensors for environmental monitoring, remediation strategies in coastal environments, and machine learning applications. Projects include the Common SENSES initiative for climate resilience, DOE-funded methane sensing, and sensor-driven resource optimization in engineered systems. Collaborations span disciplines such as robotics (e.g., robotic aquaculture systems) and community engagement in Boston’s air quality initiatives. Key grants include NSF, DOE, Schmidt Marine Technology Partners, and the Woodard & Curren consortium. She directs the Environmental Sensors Lab and contributes to institutes like iSUPER Impact Engine and the Global Resilience Institute. Recent work emphasizes equitable, community-led environmental solutions and hyperlocal pollution monitoring in urban areas.

Bart De Moorمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Optimization
• Algebraic Geometry
• +۱۱ مورد دیگر

Bart De Moor is a Full Professor at the Department of Electrical Engineering, KU Leuven, Belgium, and a guest professor at the University of Siena. He leads the STADIUS research group and has supervised 85 PhD students. His roles include chairman of Health House (2016–present), member of the Board of VIB (Biotech Institute), and former Vice-Rector for International Policy (2009–2013). Education: Master Degree in Electrical Engineering (1983), KU Leuven PhD in Engineering (1988), KU Leuven Research Interests: His work spans numerical linear algebra, optimization, algebraic geometry, systems and control theory, data-driven AI, machine learning, and applications in process industry and biomedical big data. He has contributed to subspace identification, tensor decomposition, bioinformatics, and quantum computing. Publications Trends: His publications highlight subspace identification methods, tensor decomposition, bioinformatics, and biomedical data analysis. These reflect interdisciplinary advancements in control theory, quantum physics, and mathematical engineering, with applications in industrial and healthcare domains. Scientific Awards and Honors: Leslie Fox Prize (1989) Laureate of the Belgian Royal Academy of Sciences (1992) Bi-annual Siemens Award (1994) Fellow of IEEE (since 2004) Member of the Royal Academy of Belgium for Science and Arts (since 2000) Fellow of IFAC (since 2022) Commander in the Order of King Leopold I (2020) Fellow of SIAM (since 2017) FWO Excellence Award (2010) Advising and Grants: He has led a research group of 20 PhD students and postdocs, co-founded 8 spinoff companies, and secured the ERC Advanced Grant ‘Back to the roots’ (2020–2025). He also co-holds the KU Leuven Chair on healthcare systems (2018–present). Labs and Organizations: Active in the STADIUS research group (KU Leuven), he has served on boards of the Flemish Interuniversity Institute for Biotechnology (VIB), the Alamire Foundation, and the Health Tech Experience Center Health House. His spinoffs include Trendminer, Cartagenia, and Ugentec.

• Healthcare AI
• Deep Learning
• Machine Learning
• +۵ مورد دیگر

Dr. Peng Xu is a Research Fellow in the Department of Engineering Science at the University of Oxford, affiliated with the Institute of Biomedical Engineering. His roles include advancing Healthcare AI, Deep Learning, and Machine Learning applications in biomedical research. Previously, he held positions at Nanyang Technological University (postdoctoral research fellow) and earned his Ph.D. from Beijing University of Posts and Telecommunications. Dr. Xu’s research focuses on integrating computational methods with clinical data to address respiratory diseases, particularly asthma and COPD. His work emphasizes novel lung function assessment techniques like Computed Cardiopulmonography (CCP), which evaluates ventilation-inhomogeneity indices and small-airway abnormalities. He also investigates therapeutic interventions, including biologics and corticosteroids, and their effects on Type-2 inflammation biomarkers. His recent studies explore post-COVID-19 pulmonary physiology and the role of gut microbiota in cardiovascular diseases linked to diabetes. His publications highlight contributions to lung function technology (e.g., the TREETOP Study) and the mechanisms of asthma treatment. Collaborations span clinical, engineering, and computational domains, aiming to bridge AI-driven diagnostics and precision medicine. Dr. Xu has contributed to interdisciplinary initiatives such as the Bellhouse BioEngenuity Keynote Lecture at IBME and projects promoting underrepresented students in engineering. His work underscores a commitment to advancing medical technology for improved patient outcomes.

• Dementia
• Parkinson's Disease
• Cognitive Neuroscience
• +۵ مورد دیگر

Professor Simon Lewis is a Consultant Neurologist and Professor of Cognitive Neuroscience at the University of Sydney's Brain and Mind Centre within the Faculty of Medicine and Health. He serves as Clinical Director of the Ageing Brain Clinic and Director of the Parkinson's Disease Research Clinic, while also heading the NSW Movement Disorders Brain Donor program. With over 200 peer-reviewed publications, 2 books, and 8 book chapters to his name, Professor Lewis has secured more than $10 million in research funding from prestigious sources including the NHMRC, ARC, and Michael J Fox Foundation. Professor Lewis's research primarily focuses on dementia and Parkinson's disease, with particular expertise in cognitive neuroscience, movement disorders, and REM sleep behavior disorder. His work spans from basic neuroimaging studies to clinical trials investigating novel interventions for neurodegenerative conditions. He has pioneered research on freezing of gait in Parkinson's disease, visual hallucinations in Lewy body disorders, and the relationship between circadian rhythms and neurodegeneration. His laboratory employs a multidisciplinary approach combining clinical assessment, neuroimaging (structural and functional MRI), neurophysiological measurements, and innovative computational methods to understand disease mechanisms. The trajectory of Professor Lewis's recent publications reveals a growing emphasis on biomarker discovery for early diagnosis and disease progression, with increasing integration of artificial intelligence and machine learning approaches to analyze complex movement and sleep data. His work increasingly bridges basic neuroscience with clinical applications, particularly in developing targeted interventions for non-motor symptoms of Parkinson's disease. The collaborative nature of his research is evident in the international authorship patterns across his publications. Among his notable accolades is the Leonard Cox Award (2014) from ANZAN recognizing his significant contributions to neuroscience as an early career neurologist. Professor Lewis serves on multiple editorial boards including Movement Disorders, Translational Neurodegeneration, and Journal of Neurology, Parkinson's and Related Disorders. He actively contributes to international committees such as the Movement Disorders Society Asian Oceanian Section Executive Committee and the International REM Sleep Behaviour Study Group. Professor Lewis leads the world-leading Parkinson's and Related Diseases Research Group at the Brain and Mind Centre, which attracts PhD students interested in understanding disease mechanisms, developing biomarkers, and creating novel treatment modalities for Lewy body-related diseases. His international collaborations span institutions in the Netherlands (Radboud UMC), United Kingdom (King's College London, Cambridge, Newcastle), and other global centers of excellence in Parkinson's research. His work exemplifies a translational approach from bench to bedside, with particular focus on improving quality of life for patients with neurodegenerative conditions.

Fang Liuمشاهده پروفایل

استاد

• Differential Privacy
• Statistical Machine Learning
• Bayesian Statistics
• +۷ مورد دیگر

Fang Liu serves as Notre Dame Collegiate Professor and Associate Chair in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame's College of Science. She holds a Ph.D. from the University of Michigan (2003), M.S. from Iowa State University (1999), and B.S. from Peking University (1997). Ph.D., University of Michigan, 2003 M.S., Iowa State University, 1999 B.S., Peking University, 1997 Her research spans five interconnected domains: 1) Development of modern data privacy protection approaches including differential privacy; 2) Statistical and machine learning methodologies with complex model regularization; 3) Bayesian frameworks for medical, biological and social science data analysis; 4) Advanced missing data analysis techniques; and 5) Biostatistical and epidemiological applications. She pioneered the integration of differential privacy concepts from computer science into statistical methodology, identifying numerous research opportunities at this interdisciplinary intersection. Dr. Liu leads the Trustworthy and Ethical Statistical Learning Lab (TESLLa), which focuses on differential privacy, trustworthy AI, synthetic data generation, and probabilistic machine learning. Her lab has produced significant output in privacy-preserving statistical inference and applications to pandemic response, vector-borne disease control, and social media emotional analysis. Key honors include: Elected Fellow of the American Statistical Association Elected Member of the International Statistical Institute (2024) As an advisor, she has mentored over 40 graduate students and researchers since joining Notre Dame in 2011, with alumni securing positions at institutions including Sandia National Laboratories, Los Alamos National Lab, JP Morgan Chase, TikTok, and academic faculty roles. Her research has received substantial funding from NSF, NIH, private foundations, and Notre Dame internal grants, including critical work on campus COVID-19 surveillance testing strategy and privacy-preserving pandemic data collection.

Ralf Salomonمشاهده پروفایل

استاد

• Embedded Systems
• Localization and Positioning
• Time Interval Measurement
• +۱۲ مورد دیگر

Ralf Salomon is a Professor at the Faculty of Computer Science and Electrical Engineering , University of Rostock , Germany. He holds the title of Prof. Dr.-Ing. habil. and is actively involved in research and teaching in embedded systems, localization, and signal processing. University: University of Rostock School: Faculty of Computer Science and Electrical Engineering Position: Full Professor Contact: Room 201, Tel: +49 381 498 7260 His research focuses on high-precision time measurement , ambient assisted living , industrial embedded systems , and localization technologies . He applies bio-inspired principles, particularly from the barn owl auditory system, to develop low-cost, high-accuracy systems. His work spans health monitoring (e.g., fall detection, cochlear implants), sports technology, and animal experimentation systems. The recent publications (2020–2025) show a strong trend in sensor systems , embedded vision , and real-time measurement , with applications in sports, healthcare, and automation. His work combines theoretical innovation with practical implementation, often using FPGAs and microcontrollers. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He advises students such as Theo Gabloffsky , who co-authors multiple publications and assists in teaching. He has led multiple funded projects including Welisa (DFG Graduate College) , iHeal , CurlDat , and BOUNCE . Many of his projects focus on health, localization, and embedded intelligence. Labs and Teams: He collaborates closely with researchers like Ralf Joost , Matthias Hinkfoth , Gerald Bieber , and Marian Lüder . His team develops FPGA-based systems, embedded sensors, and intelligent algorithms for real-world applications in health, industry, and sports.

• Watershed Modeling
• Water Quality
• Agricultural Ecosystems
• +۷ مورد دیگر

Rebecca Muenich is an Associate Professor in the Department of Biological & Agricultural Engineering at the University of Arkansas. Her research bridges watershed modeling, agricultural ecosystems, and the food-energy-water nexus, with a focus on surface hydrology, water quality, and climate impacts. She holds a Ph.D. (2015) and M.S. (2011) in Agricultural & Biological Engineering from Purdue University, and a B.S. in Biological Engineering (2009) from the University of Arkansas. Her research explores watershed and environmental modeling, agricultural management, urban agriculture, and climate impacts on water resources. Key interests include mitigating nutrient pollution, enhancing ecosystem services, and developing sustainable land-use strategies. Recent publications emphasize machine learning applications in environmental monitoring, phosphorus circularity, and climate-resilient water management. Muenich leads significant grants including a USDA NRCS project on PFAS in agriculture (2023-2027), NSF-STC’s Science and Technology for Phosphorus Sustainability (2021-2031), and DISES research on cyanobacterial blooms (2022-2025). She mentors students in her research group (Muenich Lab) and collaborates on interdisciplinary projects. Lori Libbert New Faculty Commendation (2024) Early Career Alumni Award, UA Engineering (2022) New Face of ASABE (2020) National Science Foundation Graduate Research Fellowship (2009)

Markus Schartauمشاهده پروفایل

پژوهشگر

• Marine Biogeochemical Modeling
• Plankton Dynamics
• Ocean Ecosystem Modeling
• +۷ مورد دیگر

Markus Schartau is a Researcher in the Biogeochemical Modelling Research Unit at GEOMAR Helmholtz Centre for Ocean Research Kiel, where he has worked since 2013. His research focuses on marine biogeochemical modeling, plankton dynamics, and parameter optimization in ecosystem models. With over 69 publications spanning two decades, Schartau has established himself as a leading expert in marine biogeochemical modeling, particularly in the areas of model calibration, mesocosm experiment simulation, and plankton community dynamics. His research interests encompass uncertainty and trustworthiness of biogeochemical model results, species composition and size frequency structures of plankton, methods of parameter optimization and model selection, simulations of mesocosm experiments, physiological and ecological variations of plankton, and temporal and spatial variations of organic substances in coastal ocean areas and estuaries. Schartau's work often bridges theoretical modeling with experimental observations to improve our understanding of marine biogeochemical processes. The analysis of his recent publications (2020-2024) reveals a strong focus on marine biogeochemical modeling, with particular emphasis on plankton dynamics, carbon cycling, and the impacts of environmental change. His work spans multiple spatial scales from coastal systems to the global ocean and integrates approaches from data science, biogeochemistry, and ecosystem modeling. Key themes include model calibration techniques, mesocosm experiment analysis, marine particle dynamics, and the effects of ocean acidification on biogeochemical processes. Schartau actively participates in major research projects including OceanNETs (EU project), BASS (funded by DFG), ICEBERG (EU project), and μARC (funded by BMBF), demonstrating his integration into the international marine science community. His collaborative approach is evident in his extensive co-authorship network across multiple institutions and countries. His career trajectory shows progression from postdoctoral positions at Stony Brook University and the Alfred Wegener Institute to permanent research scientist roles at Helmholtz-Zentrum Geesthacht (now HEREON) and finally at GEOMAR. This path reflects his growing expertise and recognition in the field of marine biogeochemical modeling.

Anjali Sandipمشاهده پروفایل

عضو هیئت علمی

• Computational Mechanics
• High-Performance Computing
• Machine Learning
• +۵ مورد دیگر

Anjali Sandip is a Teaching Assistant Professor in the Mechanical Engineering Department at the University of North Dakota's College of Engineering and Mines. She holds a Ph.D. in Mechanical Engineering from the University of Kansas and maintains an active research program in computational mechanics, high-performance computing, and machine learning. Her educational background includes a Doctor of Philosophy and Master of Science in Mechanical Engineering from the University of Kansas, and a Bachelor of Engineering in Mechanical Engineering from Osmania University in Hyderabad, India. She previously served as a post-doctoral researcher at the University of Nebraska, where she developed patient-specific computational models for peripheral artery disease treatment. Dr. Sandip's research spans computational mechanics, high-performance computing, uncertainty quantification, physics-informed machine learning, and multi-physics modeling. Her work has significant applications in ice sheet dynamics, medical device modeling, and multi-phase flow simulations. She has developed open-source software frameworks that integrate finite element and finite volume methods with uncertainty quantification tools. Her recent publications demonstrate a strong focus on developing computational frameworks for multi-physics problems, with particular emphasis on GPU acceleration, uncertainty quantification, and machine learning integration. The research shows consistent application of these methods to challenging problems in earth sciences, biomedical engineering, and traditional mechanical engineering domains. Scientific Awards: NSF EPSCoR Research Fellow (2024-25) Dr. Sandip actively mentors both undergraduate and graduate researchers, and she is currently seeking Master's and Ph.D. students interested in computational mechanics, applied mathematics, scientific machine learning, and earth sciences. She serves as an active member of the Association of Computational Mechanics (USACM & IACM) and has delivered numerous presentations at professional conferences. Her research has received support from prestigious organizations including the National Science Foundation (NSF), Department of Energy (DOE), and NVIDIA. She teaches courses including Introduction to Mechanical Engineering, Thermodynamics, Machine Component Design Laboratory, Advanced Finite Element Methods, Modeling Glaciers and Ice Sheets, Statics, and Engineering Ethics, demonstrating her broad expertise across mechanical engineering disciplines.

• Semantic Web and Ontology Engineering
• Agent-Based Modeling
• Knowledge Graphs
• +۴ مورد دیگر

Giovanna Di Marzo Serugendo is a researcher affiliated with the University of Geneva (Faculty of Social Sciences, Centre for Informatics) and the Institute of Information Service Science (ISS) . Her work spans semantic technologies, agent-based modeling, and sustainable systems. Research interests focus on Semantic knowledge graphs for regulatory compliance Ontology-driven resource management Self-organizing systems inspired by biological models AI applications in smart grids and urban mobility Digital agriculture platforms for smallholder farmers Recent publications highlight trends in ontology automation using LLMs, agent-based simulations for urban planning, and KG-enhanced compliance frameworks . She leads projects integrating digital twins with smart energy systems and develops bio-inspired coordination paradigms. Supervised works include 17 research projects in these domains. Current technical reports and conference papers explore cybersecurity-safety interdependencies in autonomous vehicles and decentralized event source detection in sensor networks.

Mathias Kneubühlerمشاهده پروفایل

مدرس ارشد

• Remote Sensing
• Imaging Spectroscopy
• Earth Observation
• +۷ مورد دیگر

Mathias Kneubühler is a Senior Lecturer and Scientific Section Head of Remote Sensing Spectroscopy at the Department of Geography, Faculty of Science, University of Zurich. He leads the Spectroscopy Lab and has been a key figure in advancing imaging spectroscopy and Earth observation research. He is actively involved in international remote sensing initiatives, including serving as the Swiss ESA-DOSTAG Delegate and Co-Chair of the EARSeL Special Interest Group on Imaging Spectroscopy. PhD in Remote Sensing, University of Zurich (1997–2002) MSc in Geography, University of Zurich (1992–1996) His research focuses on imaging spectroscopy , spectro-directional measurements , and ecosystem monitoring , with applications in plant phenology , biodiversity assessment , and land surface dynamics . He investigates how multi-angular and hyperspectral data can improve the understanding of vegetation structure, function, and environmental responses. His work integrates field spectroscopy, airborne and satellite remote sensing, and advanced data processing techniques. The recent publications highlight a strong trend in imaging spectroscopy for ecological applications, including plant trait mapping , camouflage detection , tree species identification , and phenology modeling . These studies often involve UAV and satellite data fusion, BRDF correction, and machine learning, demonstrating interdisciplinary innovation in remote sensing science. Co-Chair, EARSeL Special Interest Group on Imaging Spectroscopy (2016–present) President, Swiss Commission for Remote Sensing (2015–2020) Swiss ESA-DOSTAG Delegate (2004–2024) He has supervised several PhD and Master’s students, including Marius Vögtli, Christian Rossi, and Mojdeh Miraki, contributing to the next generation of remote sensing scientists. His leadership in organizing scientific workshops and campaigns underscores his role in fostering collaborative research. He is associated with the Spectroscopy Lab at the University of Zurich, which conducts cutting-edge research in spectro-directional measurements and environmental monitoring.