جست‌وجوی استاد و پژوهشگر

Daniel Patelمشاهده پروفایل

پژوهشگر

• Geological Visualization
• Computer Graphics
• Scientific Visualization
• +۳ مورد دیگر

Daniel Patel is an Adjunct Researcher at the University of Bergen's Department of Informatics. His work focuses on advancing visualization techniques for geological and geophysical data, with a particular emphasis on interactive modeling, real-time rendering, and domain-specific tools for scientific communication. He has contributed to projects such as 'Geological Storytelling' and 'Rapid Sketch-Based Modeling,' which aim to bridge the gap between geologists' conceptual ideas and computational modeling. Patel’s research integrates computer graphics innovations with domain-specific challenges in geology and marine science. His research interests include volume rendering, sketch-based modeling, seismic data interpretation, and educational approaches to PhD training. He collaborates with interdisciplinary teams, including the VisGroup at UiB, to develop tools for visualizing complex geological structures and sonar data. Patel holds a PhD in expressive visualization from TU Wien (2009) and has published extensively on topics ranging from chromatic shadows in medical visualization to statistical transfer functions for noisy data. Key contributions include the development of occlusion shading models, instant convolution shadows, and knowledge-assisted visualization frameworks for seismic exploration. His work emphasizes rapid prototyping and user-centric design, enabling domain experts to rapidly communicate and explore geological hypotheses.

Hamid Eskandariمشاهده پروفایل

مدرس

• Business Analytics
• Evolutionary Optimization
• Healthcare Operations
• +۲ مورد دیگر

Hamid Eskandari is Lecturer in Strategic Operations and Analytics at Swansea University's School of Management. He holds a PhD in Industrial Engineering from University of Central Florida and previously served as Associate Professor at Tarbiat Modares University. Research Focus: Applies computational intelligence techniques to business analytics challenges, including multi-objective optimization in supply chains, healthcare operations, and energy systems. Recent work emphasizes interpretable machine learning for energy forecasting and sustainability transitions. Publications demonstrate strong methodological focus on evolutionary algorithms, fuzzy decision-making, and simulation-based optimization applied to industrial and environmental problems.

Robert Balogمشاهده پروفایل

استاد

• Power Electronics
• Renewable Energy Systems
• Solar Photovoltaic Technology
• +۵ مورد دیگر

Robert Balog is a Professor in the Department of Electrical & Computer Engineering at Texas A&M University. He serves as Director of the Renewable Energy and Advanced Power Electronics Laboratory (REAPER), Co-Director of the NSF I/UCRC on Next Generation Photovoltaics (NGPV), and Assistant Director for Grid-Edge Modernization at the Smart Grid Center. He holds affiliations with the National Academy of Inventors as a Fellow (2024). Education: Ph.D. (2006), M.S.E.E. (2002) from University of Illinois at Urbana-Champaign; B.S.E.E. (1996) from Rutgers University. He is a licensed Professional Engineer in Texas and Illinois, and an IEEE Senior Member since 2007. Research focuses on advanced power electronics systems, solar photovoltaics, energy storage, microgrids, and power quality. Key innovations include photovoltaic balance-of-systems optimization, non-planar PV integration, and arc fault detection technologies. Over 30 patents highlight his work in power conversion, smart grid security, and energy harvesting. Recent articles emphasize capacitor-less D-STATCOM systems, PV arc fault mitigation, and smart farming energy solutions. His work bridges academic research with industry applications through NSF collaborations and technology commercialization efforts. Awards include NAI Fellowship (2024), IEEE Distinguished Lecturer designation (2020-2021), and TAMEST Patent Innovation Award (2017). Active in standards development (UL 1741/1699B) and conference organization, including ECCE 2016 Technical Program Chair. Labs/Teams: REAPER Lab pioneers PV system optimization and grid-edge technologies. NGPV I/UCRC collaborates with industry partners on next-gen photovoltaic materials and configurations.

Sinan Kockaraمشاهده پروفایل

مدرس

• Medical Image Processing
• Virtual Reality (VR) Surgical Simulation
• Arthroscopic Surgery Training
• +۳ مورد دیگر

Sinan Kockara is a Lecturer in the Department of Computer Science at Rice University. He holds adjunct roles at the University of Central Arkansas (Computer Science and Engineering) and the University of Arkansas at Little Rock. With a Ph.D. in Applied Computing and a BS in Computer Engineering, his expertise spans medical image processing, virtual reality (VR) surgical simulation, and high-performance computing. His research focuses on dermatology imaging and arthroscopic surgery training systems, with $2.4+ million in funding from NIH, NSF, and industry partners. He has authored 40+ publications and holds over 640 citations. Education: BS, Computer Engineering, Dokuz Eylul University (Izmir, Turkey) PhD, Applied Computing, University of Arkansas at Little Rock Research Interests: Dr. Kockara develops VR-based surgical simulators for orthopedic training, including fluid dynamics simulations and bone drilling procedures. His work integrates machine learning for skill assessment and medical image analysis for dermatology applications. His lab (Biomedical Image Processing & HPC Lab) emphasizes real-time visualization and HPC optimization for medical systems. Grants & Validation: He secured major grants to validate surgical training platforms like ViRCAST and VATDEP. His studies compare novice/expert surgeon performance metrics and employ systematic validation frameworks for medical simulations. Labs & Future Work: Leads the Biomedical Image Processing & High-Performance Computing Lab, advancing real-time medical visualization and GPU-accelerated algorithms. Ongoing projects include cloud-based surgical simulators and AI-driven medical education tools.

Sarah Shanderaمشاهده پروفایل

استاد

• Gravity and Cosmology
• Particle Physics
• Quantum Systems
• +۳ مورد دیگر

Sarah Shandera is a Professor of Physics at Pennsylvania State University and Director of the Institute for Gravitation and the Cosmos. She holds a Ph.D. in Physics from Cornell University (2006) and a B.S. from the University of Arizona (2001). Her research focuses on gravitational physics, cosmology, and quantum systems, particularly exploring how quantum phenomena like open quantum systems and dissipative dark matter relate to early-universe dynamics. She has pioneered work on gravitational wave signatures of subsolar-mass black holes and the interplay between quantum information and cosmological evolution. Shandera has received the Gordon and Betty Moore Foundation Award (2020) and the C.I. Noll Teaching Award (2019). She has been a Visiting Fellow at the Perimeter Institute since 2015 and holds a Robinson-Appel Humanitarian Award (2005). Her research group investigates dark matter interactions, quantum thermodynamics, and non-equilibrium systems. She collaborates with institutions like LIGO/Virgo on gravitational wave analyses and leads the CMB-S4 consortium’s efforts to constrain primordial gravitational waves. Her lab and team include assistant Courtney L. Shaffer, and she actively contributes to initiatives addressing systemic inequities in academia through models like RASE (2021).

Xinghua Luمشاهده پروفایل

استاد

• Biomedical Informatics
• Translational Bioinformatics
• Systems Biology
• +۵ مورد دیگر

Dr. Xinghua Lu is a Professor in the Department of Biomedical Informatics at the University of Pittsburgh. He specializes in computational methods for identifying signaling pathways in biological processes and diseases, and statistical methods for knowledge extraction from biomedical literature. His work focuses on translational bioinformatics, systems biology, and natural language processing for text mining. Education: PhD from University of Connecticut Health Center Research Interests: Dr. Lu develops computational models to simulate biological signaling systems and applies latent variable models to analyze genomic and transcriptomic data. His recent projects include establishing a Center/Institute in Translational Bioinformatics and advancing NLP techniques for biomedical text analysis. Publications: His research emphasizes genomic alteration analysis, causal discovery from big data, and integrative modeling of gene expression. Key themes include cancer mechanism identification, systems biology applications, and machine learning-driven biomedical informatics. Affiliations: Department of Biomedical Informatics School of Computing and Information McGowan Institute for Regenerative Medicine (implied via research context)

Diana Weinholdمشاهده پروفایل

دانشیار

• Development Economics
• Environmental Economics
• Applied Econometrics
• +۳ مورد دیگر

Diana Weinhold is an Associate Professor of Development Economics at the London School of Economics and Political Science (LSE), Department of International Development. She also directs the doctoral research programme in International Development. Her work focuses on land use, environmental economics, applied econometrics, and the socioeconomic impacts of deforestation in tropical regions like the Amazon and Bolivia. Education: PhD in Economics from the University of California and BSc in Economics with Math from the University of Wisconsin. She has held academic positions at Vanderbilt University, Cambridge University (as external PhD examiner), and various research institutes globally, including the Inter-American Development Bank and IPEA in Brazil. Research emphasizes hybrid models analyzing land-use policy trade-offs, noise pollution’s health effects, and deforestation’s socioeconomic consequences. She collaborates with international organizations like NEMESIS and the Institute for Applied Economics Research (IPEA). Diana also develops interactive conservation simulation tools, such as the Conservation Strategies Game, to study contested environmental decision-making. Her articles span topics like urban noise health impacts, deforestation carbon emissions, and biodiversity spending. She advises Ph.D. students on topics ranging from land reform in Southern Africa to historical economic development.

• Energy-Efficient Computing
• High-Performance Computing
• Compiler Techniques
• +۴ مورد دیگر

Kanishkan Vadivel is a Researcher in the Electronic Systems group at Eindhoven University of Technology (TU/e), specializing in energy-efficient hardware architectures and compiler-based code-generation techniques. He holds a Master’s degree in Embedded Systems from TU/e (2017) and a Bachelor’s from Coimbatore Institute of Technology (India). Prior to academia, he worked in embedded systems at Tata Engineering and Arm Ltd. Research Interests : His work focuses on computation-in-memory architectures using resistive devices, optimal code generation for CGRA (Coarse-Grained Reconfigurable Architecture), and high-performance computing. Key projects include the MNEMOSENE initiative and development of the CIM-SIM simulator for computation-in-memory systems. Awards : HiPEAC collaboration grant (2019) Advising & Grants : His research is supported by grants focused on neuromorphic processors and edge-AI hardware. He collaborates on projects like NEUROKIT2E for embedded deep learning systems. Labs/Teams : Active member of TU/e’s Electronic Systems Center and Efficient Stream Processing Lab, contributing to neuromorphic and energy-efficient computing initiatives.

Ignasi Josep Soler Poquetمشاهده پروفایل

پژوهشگر ارشد

• Solar Physics
• Machine Learning/Deep Learning
• Observational Astronomy
• +۲ مورد دیگر

Ignasi Josep Soler Poquet is a Doctoral Research Fellow at the Rosseland Centre for Solar Physics, University of Oslo. His research focuses on solar physics and computational science, with a strong emphasis on applying machine learning techniques to analyze solar observations. **Education**: Bachelor's in Physics from University of Valencia (specializing in astrophysics/theoretical physics) Master's in Advanced Physics (University of Valencia, focusing on supernovae and simulations) **Research Interests**: Development of deep learning models for detecting solar phenomena like Ellerman bombs Integration of Python-based tools for observational astronomy Computational analysis of solar dynamics using instruments like SST and SDO **Publications Trends**: His recent work demonstrates a clear trajectory in leveraging neural networks and deep learning to interpret solar observational data, with a focus on automated detection systems for transient solar events. **Advising & Grants**: No specific grants or advisees listed, though his PhD work is institutionally supported through the Rosseland Centre.

Florian Reitzمشاهده پروفایل

پژوهشگر

• Computer Science
• Database Systems
• Information Retrieval
• +۵ مورد دیگر

Florian Reitz is a Scientific Staff member at Schloss Dagstuhl - Leibniz Center for Informatics, specializing in the dblp computer science bibliography. His current role involves enhancing bibliographic data infrastructure and managing projects like SmartER. He holds a PhD from the University of Trier (2018), focusing on historical metadata analysis. Previously, he was a research assistant in the Databases and Information Systems group under Prof. Bernd Walter. His work centers on metadata quality, author disambiguation, and leveraging historical corrections for test collections. Research interests include information retrieval, database systems, and digital libraries. He contributes to projects like Scalable Author Disambiguation and has co-authored numerous publications on topics such as LLM applications, knowledge graphs, and FAIR data principles. Contact: florian.reitz@dagstuhl.de.

Han van der Aaمشاهده پروفایل

استاد

• Process Mining
• Business Process Management
• Natural Language Processing
• +۳ مورد دیگر

Han van der Aa is a Professor at the Faculty of Computer Science, associated with the Research Group Workflow Systems and Technology. His research focuses on advancing Process Mining, Business Process Management, and Natural Language Processing. He leads projects like the 2025 initiative on Semantic Process Discovery from User Interaction Logs, funded until 2027. With over 82 publications and 11 awards, including Best Paper Awards at CAISE 2023 and ICPM 2024, his work bridges theoretical contributions with practical applications. Notable projects include tools like AgentSimulator for data-driven process simulation and the PET dataset for process extraction from text. His research emphasizes semantic-aware process mining, privacy-preserving techniques, and integration with AI/ML. Key publications address concept drift detection, multi-agent systems, and large language model applications in process analysis. Awards highlight his impact on both academic and applied process mining fields.

Shijie Zhouمشاهده پروفایل

استادیار

• Cardiac Electrophysiology
• AI in Biomedical Engineering
• Clinical Translational Research
• +۳ مورد دیگر

Shijie Zhou is an Assistant Professor of Biomedical Engineering at Worcester Polytechnic Institute (WPI), previously serving as an Assistant Professor at Miami University, Ohio. He holds a PhD from Dalhousie University and completed a postdoctoral fellowship at Johns Hopkins University. His research focuses on cardiac electrophysiology and biomedical engineering, particularly in developing AI-driven 3D computational heart models for personalized diagnostics and therapies. Dr. Zhou’s work bridges clinical practice and research, emphasizing ventricular tachycardia localization and translational studies to improve patient outcomes. He leads the Computational Clinical Cardiac Electrophysiology Laboratory (CCCEL), which has secured grants including the 2025 American Heart Association Innovative Project Award and National Heart, Lung, and Blood Institute R15 Grant. His lab relocated to WPI in 2023, focusing on clinical applications of virtual-heart technologies. Education PhD in Biomedical Engineering, Dalhousie University, Canada (2018) Postdoctoral Research, Biomedical Engineering, Johns Hopkins University (2018–2020) Research Interests Dr. Zhou’s research integrates computational modeling with clinical data to address arrhythmia mechanisms. Key areas include: AI-based ventricular tachycardia localization using ECG and imaging fusion Personalized heart digital twins for guiding ablation therapies Non-invasive ECG imaging techniques (ECGI) Fat infiltration’s role in post-infarct arrhythmogenesis Professional Recognition Fellow of the Heart Rhythm Society Fellow of the European Society of Cardiology Awarded multiple grants including AHA Innovative Project Award (2025), AHA Career Development Award (2025), and NHLBI R15 Grant (2025) Lab Activities CCCEL develops real-time systems for arrhythmia origin localization, including the RAPID-VT platform. Current projects aim to enhance clinical decision-making through automated algorithms and patient-specific models.

Reinhold Ludwigمشاهده پروفایل

استاد

• Electromagnetic and Acoustic Nondestructive Evaluation (NDE)
• MRI RF Coil Design
• Electromagnetic Sensors
• +۵ مورد دیگر

Reinhold Ludwig is a Professor and Associate Head in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI). He holds a Dipl.-Ing. (MS) from the University of Wuppertal (1983) and a PhD from Colorado State University (1986). His research focuses on electromagnetic and acoustic nondestructive evaluation (NDE), MRI RF coil design, and biomedical imaging systems. As director of the Center for Imaging and Sensing (CIS), he leads projects in energy/media interaction analysis and advanced MRI technology development. Key contributions include anatomically shaped RF coils for MRI systems up to 7T, including full-body, head, and extremity imaging applications. His team has developed NDE sensing technologies and electronic circuits for clinical and pre-clinical MR scanners. Recent work explores ultra-low-power neuromorphic circuits for autonomous vehicles and AI-driven optical defect detection systems. Professional memberships include IEEE, Eta Kappa Nu, and Sigma Xi. Notable grants include a $3M NIH award (2016) for robot-assisted brain cancer treatment research. His lab also works on microwave imaging innovations, ceramic material testing, and embedded computing systems. Current projects emphasize compact neural networks for real-time NDE, non-contact electromagnetic stimulation devices for pain management, and high-resolution MRI coil optimization. Patents include multi-modal RF coils and moisture analysis systems for industrial materials.

• Laser Physics
• Optics and Laser Technology
• Scintillator Materials
• +۴ مورد دیگر

Emőke Lőrincz is an Honorary Professor in the Department of Atomic Physics at Budapest University of Technology and Economics (BME). Her research focuses on advanced optical and nuclear technologies, including scintillator materials for medical imaging (PET), laser physics, and holographic data storage. She has contributed to the development of high-resolution PET detectors, digital photon counter systems (SPADnet), and novel optical memory technologies using azobenzene polymers. Her work bridges fundamental material science with applied engineering solutions for biomedical and data storage applications. Key research areas include optimizing light-sharing modules for PET detectors, measuring optical properties of scintillator crystals, and advancing polarization-based holographic storage systems. Her interdisciplinary approach combines experimental validation with computational modeling to enhance detector performance and data density in optical systems. Publications span from 1985 to 2017, reflecting her long-term contributions to optics, nuclear instrumentation, and materials science. Notable collaborations include studies on LYSO:Ce scintillators, SiPM arrays, and phase-modulation techniques for holography. While no specific awards are listed, her extensive publication record underscores her influence in these fields. Her work often addresses practical challenges such as improving detector spatial resolution, optimizing light collection efficiency, and ensuring material durability in extreme conditions. This research has direct applications in medical imaging systems and next-generation optical storage technologies.

• Climate Science
• Glaciology
• Paleoclimatology
• +۳ مورد دیگر

Etienne Legrain serves as an unpaid employee in the Department of Water and Climate at Vrije Universiteit Brussel (VUB), Brussels, Belgium. He leads the FWOTM1276 CAPSULE project (2025-2028), a fundamental research initiative modeling Antarctic Blue Ice areas to extract the oldest climatic reconstructions from ice cores. His research centers on climate science with specialization in glaciology and paleoclimatology. Legrain investigates Antarctic blue ice formations to reconstruct historical climate patterns through ice core analysis, while extending his work to model glacial inception thresholds using positive degree-day methodologies. This integrates field observations with numerical simulations to understand long-term climate dynamics. His 2025 publications reveal a dual focus: analyzing elevation-dependent warming absence in Antarctica through paleoclimate records, and establishing global thresholds for glacial inception. Both studies demonstrate interdisciplinary approaches combining geophysics, climate modeling, and ice core chronology to address critical questions in paleoclimatology and contemporary climate change. Scientific awards: None documented in source material. Legrain currently directs the FWOTM1276 CAPSULE project funded for fundamental research on Antarctic ice cores. No student advisees are mentioned, though his publications indicate extensive international collaboration across six institutional profiles. The research involves significant computational modeling and field data synthesis. His work operates within VUB's Department of Water and Climate research ecosystem, leveraging collaborative networks across European and global Antarctic research teams as evidenced by multi-institutional co-authorship on recent publications.