جست‌وجوی استاد و پژوهشگر

Ayguade Parra, Eduardمشاهده پروفایل

استاد

• High-Performance Computing
• Parallel and Distributed Architectures
• Programming Models
• +۶ مورد دیگر

Eduard Ayguade Parra is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Faculty of Computer Science of Barcelona (FIB). He is a leading researcher in the UPC PM - Programming Models group and maintains a significant affiliation with the Barcelona Supercomputing Center (BSC-CNS), a premier national supercomputing facility. His primary research interests lie in High-Performance Computing (HPC) , with a deep focus on parallel and distributed architectures , programming models (especially task-based models like OmpSs), multicore and multiprocessor systems , and compilers for high-performance architectures . His work bridges hardware and software to optimize performance for complex computational problems. The trends in his recent publications highlight a sustained and evolving research program. He is actively advancing task-based programming for distributed memory and hybrid systems, exploring FPGA acceleration for key HPC kernels like SpMV, and innovating in memory system design, including active compute memory and hybrid memory object placement. His research also extends into applying AI techniques to hardware reliability and creating high-quality datasets for computer vision evaluation. HiPEAC Paper Award 2024 Professor Ayguade has been instrumental in securing and leading numerous competitive and non-competitive R&D+i projects, often funded by national and European programs. He has advised a significant number of doctoral students, whose theses cover topics such as task-based programming, FPGA acceleration, HPC compilers, and machine learning for systems. His collaborations are extensive, with frequent co-authorship with prominent figures at UPC and BSC-CNS, such as Jesús Labarta and Mateo Valero. His research has led to advancements in runtime systems, compiler technology, and FPGA-based acceleration. He is a core member of the UPC PM - Programming Models research group and his work is deeply integrated with the resources and mission of the Barcelona Supercomputing Center (BSC-CNS), one of Europe's leading institutions in supercomputing.

Luis Soares Barbosaمشاهده پروفایل

استاد

• Program Semantics
• Modal Logic
• Coalgebra
• +۵ مورد دیگر

Luis Soares Barbosa is a full professor at the Computer Science Department of Universidade do Minho and a senior researcher at HASLab INESC TEC. He is also affiliated with the International Iberian Nanotechnology Laboratory (INL) as part of the Quantum and Linear Optical Computation Group. He holds a second academic role as Deputy Head of the UNU-EGOV unit at the United Nations University. Barbosa chairs IFIP Technical Committee TC1 on Foundations of Computer Science and serves on IFIP WG1.3 and WG1.11/2.17. His research focuses on program semantics, coalgebra, modal logic, and quantum computation, aiming to establish rigorous mathematical foundations for software engineering. He teaches courses on quantum logic, concurrency, and quantum computation at the master’s level. Research Interests: Barbosa’s work bridges theoretical computer science with practical applications, emphasizing formal methods, coalgebraic systems, and quantum algorithms. His quantum research explores computational advantages and privacy-preserving protocols, while his classical work addresses software verification and concurrency. Recent efforts include paraconsistent transition systems for modeling inconsistent states and quantum policy gradients in machine learning. Lab/Tech Teams: Leads the Logic and Formal Methods group and collaborates with the High Assurance Software lab (HASLab). Active in interdisciplinary teams at INL and UNU-EGOV. Grants/Advising: Supervises students/postdocs in quantum computing, formal methods, and cyber-physical systems. Coordinates projects funded by national/international bodies, including EU initiatives on quantum algorithms and digital governance.

• Internet of Things (IoT)
• Sensor Networks
• Vehicular Networks (VANETs)
• +۷ مورد دیگر

José María Barcelò Ordinas is a Professor in the Department of Computer Architecture at the Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is a leading researcher in the UPC CNDS (Computer Networks and Distributed Systems) group, focusing on IoT, sensor networks, vehicular networks, and wireless systems. His research interests include Internet of Things (IoT), sensor networks, vehicular networks (VANETs), wireless networks, and more specifically, data quality, anomaly detection, and calibration in IoT-based environmental monitoring systems. He applies advanced techniques such as graph signal processing, machine learning, and distributed algorithms to improve the reliability and accuracy of low-cost sensor networks. The recent articles published by Dr. Barcelò and his team demonstrate a strong focus on air quality monitoring using IoT platforms. Key themes include black carbon estimation, sensor calibration, anomaly detection using deep learning, and data reconstruction via graph-based methods. These works frequently appear in high-impact journals such as IEEE IoT Journal, IEEE Transactions on Network Science and Engineering, and Sensors. His scientific recognition includes: Reconeixement als mèrits docents d’especial qualitat (Recognition for Teaching Merits of Special Quality) Dr. Barcelò has led and participated in multiple competitive R&D+i projects, including those funded by the H2020 program and Spanish national research plans. He has advised PhD students such as Pau Ferrer-Cid and collaborates extensively with researchers like Jorge Garcia-Vidal and Manel Guerrero. He also serves on the scientific committees of international conferences and contributes as a reviewer for journals including IEEE Sensors Journal and Transportation Research Part D. He is actively involved in research teams and leads projects related to IoT air pollution monitoring, urban environmental sensing, and digital twins. His work bridges theoretical advances with real-world applications in smart cities and environmental protection.

Frank Alsmeyerمشاهده پروفایل

استاد

• Sustainable Energy Systems
• Energy Efficiency
• Sector Coupling
• +۱۱ مورد دیگر

Prof. Dr.-Ing. Frank Alsmeyer is a faculty member in the Department of Mechanical and Process Engineering at Niederrhein University of Applied Sciences, where he leads research in Energy and Process Systems Engineering. He is affiliated with the Institute for Energy Technology and Energy Management (SWK E²) and supervises a wide range of student projects and theses focused on sustainable energy systems, optimization, and process engineering. His research interests include: Sustainable energy system technology Climate-friendly restructuring of energy supply Sector coupling across buildings, industry, transport, and commerce Non-fossil energy sources such as hydrogen and biogenic substances Computer-aided simulation and optimization using MILP and phenomenological models Improving process sustainability, quality, and cost-effectiveness His recent publications and research projects (e.g., Bilinear Optimization of Energy Systems, Green Heat for Krefeld) reflect a strong focus on modeling, simulation, and optimization of integrated energy systems, particularly in urban and industrial contexts. The work spans thermodynamics, energy cadastres, waste-to-energy integration, and digital education tools for engineering. There is a clear trend toward applied research with real-world implementation, especially in Krefeld and surrounding regions. Scientific awards include: Best FB04 Bachelor Project 2016/17 for the 'Energiebaukasten' interactive energy visualization tool Prof. Alsmeyer actively supervises numerous student theses and projects, covering topics such as energy efficiency, heat integration, hydrogen systems, and process optimization in industrial settings. He is involved in multiple funded research initiatives, including Bilinear Optimization of Energy Systems (2023–2026), Herzogenrath Energy Park, and OK!Thermo, an open educational resource for thermodynamics. His work bridges engineering fundamentals with practical applications in sustainability and digitalization. He leads the development of digital tools for energy analysis and education, such as interactive touch tables and online learning platforms. He is associated with the following labs and research groups: Institute for Energy Technology and Energy Management (SWK E²) Research group on Computer-Aided Analysis, Simulation and Optimization of Energy and Process Systems Team developing OK!Thermo and other OER materials for engineering education

Pedro Simões Coelhoمشاهده پروفایل

استاد

• Statistics
• Sampling Techniques
• Structural Equation Modeling
• +۱۲ مورد دیگر

Pedro Simões Coelho is a Full Professor and President of the Scientific Board at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, where he also serves as an Integrated Researcher in the Information Management Research Center (MagIC). He is a senior expert for the European Commission in statistical methods and sampling techniques, and holds leadership roles in several national and European bodies, including the European Master of Official Statistics (EMOS) board and the Portuguese Health Technologies Commission. His research interests lie at the intersection of statistics, data analysis, and public policy. He specializes in sampling techniques, structural equation modeling, survey methodology, and data quality, with applications in health economics, business intelligence, and official statistics. His work bridges theoretical statistical innovation with real-world policy impact, particularly in health and administrative systems. His recent publications demonstrate a strong trend toward integrating artificial intelligence and advanced modeling techniques into public administration and health policy. Topics include AI-based detection of legislative burdens, economic burden assessments in dermatology, consumer segmentation via social media, and cardiovascular health optimization. These works reflect a multidisciplinary approach combining statistics, machine learning, and domain-specific knowledge to solve complex societal challenges. Senior Expert, European Commission (Statistical Methods & Sampling) Member, EMOS Board Member, Ischools Accreditation Committee Head of Information and Statistics, NOVA Clinical Research Unit (NOVA CRU) Former President, Portuguese Association for Classification and Data Analysis (CLAD) Former Member, Portuguese High Council for Statistics (CSE) Former President, Fiscal Board, CESD-Lisboa He has supervised numerous graduate and undergraduate courses and has been a consultant and trainer for major institutions including Eurostat, the Portuguese Statistical Office, and the Portuguese Central Bank. His work includes over 100 peer-reviewed publications and around 200 research projects resulting in more than 500 reports. He has delivered nearly 100 invited talks and conference presentations worldwide. He is actively involved in research labs and teams such as MagIC and NOVA CRU, where he leads initiatives in data-driven public health and statistical innovation. His ongoing projects focus on AI for policy assessment, sustainable health systems, and advanced statistical modeling for small area estimation and data integration.

• Biomedical Signal Processing
• Sleep Apnea Detection
• Heart Rate Variability (HRV)
• +۴ مورد دیگر

Daniel Romero Pérez is a researcher at the Institute for Bioengineering of Catalonia (IBEC) , specializing in Biomedical Signal Processing and Interpretation . His work focuses on developing computational models for detecting respiratory events in sleep apnea, analyzing cardiovascular responses to hypoxia, and applying machine learning to improve diagnostics for conditions like Brugada syndrome and COPD. Key Research Areas : Heart Rate Variability (HRV), ECG analysis, obstructive sleep apnea, COPD, Brugada syndrome, computational modeling Technologies : Dynamic Bayesian Networks, time-warping analysis, quaternion-based algorithms His recent publications demonstrate cross-disciplinary applications of artificial intelligence in medicine, particularly for non-invasive diagnostic systems. Collaborations include clinical institutions and biomedical engineering teams across Europe. Current projects involve: Prediction of obstructive apnea episodes using ECG time-series Non-linear HRV quantification in OSA models Respiratory rate estimation from vectorcardiograms

• Mathematics Education
• Computing Education
• STEM Equity
• +۳ مورد دیگر

Sarah D Castle serves as Assistant Professor of Mathematics Education at the University of Idaho with dual appointments in the Department of Mathematical and Statistical Sciences (College of Science) and Curriculum and Instruction Department (College of Education, Health and Human Sciences). Her research bridges computational methods and mathematics education to advance equity and foster creativity in undergraduate STEM contexts. Her educational background includes: B.A. in Mathematics, Whitworth University (2012-2016) B.S. in Engineering Physics, Whitworth University (2012-2016) M.S. in Mathematics, Michigan State University (2019-2022) Ph.D. in Mathematics Education, Michigan State University (2018-2023) Castle's research centers on computational approaches to uncover systemic inequities in STEM education while creating environments that nurture mathematical creativity, particularly for marginalized students. She investigates how computational modeling—such as Jupyter Notebook implementations in linear algebra—can transform mathematical engagement and understanding. Her work critically examines power structures in educational spaces and develops pedagogical frameworks that leverage coding to deepen conceptual learning and promote equitable participation. Analysis of her 13 publications (2020-2024) reveals three dominant trends: (1) computational tools as catalysts for mathematical creativity and conjecture, (2) quantitative critical analyses of systemic advantages in STEM courses, and (3) investigations of student agency development in proof-based mathematics. These works span ACM computer science education conferences, mathematics education research forums, and interdisciplinary learning sciences venues. Castle actively contributes to major research initiatives: SEISMIC Project: Multi-institutional analysis of equity in introductory STEM courses Transition to Proof Project: Studying sense-making in advanced mathematics Computational Education Research Lab: Developing computational modules for mathematics learning Her teaching portfolio includes Secondary Mathematics Methods (EDCI 434), Proof and Viable Argumentation (MTHE 410), and Calculus I (MTH 132), reflecting her dual expertise. She has extensive experience as instructor of record, teaching assistant, and developer of STEM curricula including high-altitude balloon launch modules for K-12 students.

Dariusz Siudakمشاهده پروفایل

دانشیار

• Relational perspective of the organization
• Complex networks in financial markets
• Artificial intelligence in management
• +۲ مورد دیگر

Dr hab. Dariusz Siudak is an Associate Professor at the Institute of Management , part of the Faculty of Organization and Management at Lodz University of Technology . He serves as Head of the Division of Economics and Finance and is a member of the Management and Quality Studies Discipline Council , IFE Study Program Council , and IDS Scholarship Committee . His work spans interdisciplinary research at the intersection of finance, network science, and artificial intelligence. Research Interests : Relational organizational theory, complex financial networks, AI-driven management systems, system dynamics in enterprise governance, and value migration analytics. Scientific Contributions focus on: Financial multiplex networks and cross-correlation dynamics AI applications in value migration modeling Network topology impacts on stock returns Corporate governance and board interlock analysis Markov chain modeling of network transitions Scientific Recognition : First place, NetMiner 20th Event (2022) Lecture of Marek Siudak award (2018) Dean’s award for scientific achievements (2018) Beta 2014 award nomination (2014)

Claudia Solis-Lemusمشاهده پروفایل

استادیار

• Statistical Phylogenomics
• Microbiome Analysis
• Network Modeling
• +۵ مورد دیگر

Claudia Solis-Lemus is an Assistant Professor at the University of Wisconsin-Madison with dual appointments in the Department of Plant Pathology within the College of Agricultural and Life Sciences and the Wisconsin Institute for Discovery. Her work bridges statistical methodology development with biological applications, focusing on creating computationally efficient models that maintain biological interpretability and theoretical guarantees. Dr. Solis-Lemus earned her PhD in Statistics and MA in Mathematics, both from the University of Wisconsin-Madison. Her academic journey has positioned her at the intersection of statistics, computational biology, and evolutionary genomics. Her research program centers on three interconnected areas: statistical phylogenomics, microbiome analysis, and the application of deep learning in biological contexts. In statistical phylogenomics, she develops novel methods to reconstruct phylogenetic networks that can account for reticulate evolutionary processes like hybridization and horizontal gene transfer. Her work in microbiome analysis focuses on creating statistical frameworks to model microbial communities as networks and use these networks to predict host phenotypes. In deep learning applications, she addresses challenges in applying neural networks to biomedical data by developing methods with statistical guarantees and improved interpretability. Analysis of Dr. Solis-Lemus's recent publications reveals a strong emphasis on developing computationally efficient methods for analyzing complex biological networks. Her work spans from theoretical advances in phylogenetic network inference to practical software implementations for microbiome analysis. A notable trend is her focus on creating methods that balance statistical rigor with computational tractability, particularly for large-scale biological datasets. Her research increasingly integrates machine learning approaches with traditional statistical methods to address challenges in evolutionary biology and microbiome research. Dr. Solis-Lemus has received significant recognition through competitive grants including: NSF CAREER Award (DEB 2144367) for "Towards Scalable and Robust Inference of Phylogenetic Networks" USDA Individual Hatch grant for "Enhanced interaction and network statistical models for microbiome data" DOE Computational Tool Development award for "Harnessing the power of big omics data" USDA-NIFA hatch project 1023699 for "Novel interaction and network statistical models for microbiome data" NSF IntBIO Collaborative Research DEB Award 2316269 USDA NIFA award for "Unraveling The Microbial Mechanisms That Mediate Disease Resurgence In Plants" Dr. Solis-Lemus actively mentors a diverse research group including postdoctoral researchers, PhD students, MS students, and undergraduates. Her lab is supported by multiple grants totaling millions of dollars, enabling research that spans from theoretical statistics to practical applications in agriculture and medicine. She has successfully secured funding from NSF, DOE, USDA, and industry partners, demonstrating the broad relevance of her work across multiple domains. The Solis-Lemus Lab operates as an interdisciplinary team bringing together statisticians, biologists, computer scientists, and domain experts. The lab maintains strong collaborations with researchers across UW-Madison and nationally, particularly in plant pathology, microbiology, and evolutionary biology. They develop open-source software tools that are widely used in the scientific community, including PhyloNetworks for phylogenetic network analysis and CMiNet for microbiome network construction.

• Hydrology
• Flood-runoff processes
• Catchment regionalization
• +۵ مورد دیگر

Dr. Larisa Tarasova is a tenured Research Group Leader of "Watershed Dynamics & Hydrological Extremes" at the Helmholtz Centre for Environmental Research – UFZ, Department of Catchment Hydrology, Germany. She leads projects on large-scale flood analysis, catchment regionalization and hydrological modelling uncertainty, and has published extensively on these topics since 2016. Education: PhD in Hydrology, Department of Catchment Hydrology, UFZ (2016-2020) M.Sc. Water Resources & Environmental Management, Leibniz University Hannover (2013-2015) B.Sc. Ecology & Environmental Engineering, Peoples' Friendship University of Russia (2007-2012) International exchange semesters at Nanjing Normal University and Xiamen University, China Research interests revolve around understanding and predicting hydrological extremes across large domains. She develops data-driven and process-based approaches to classify flood events, quantify their space-time dynamics, and unravel how catchment properties and climate patterns control runoff generation. Her work integrates statistical learning, large-sample datasets (e.g., CAMELS-DE) and uncertainty analysis to improve predictive models for floods and water quality under change. Her publications (≥60 peer-reviewed since 2016) exhibit a clear trajectory from event-based process studies to continental-scale modelling and machine-learning applications, culminating in recent high-impact papers on compound flood drivers, catchment descriptors and neural-network flood forecasting. Scientific awards include the Gordon Research Conference Rising Star Award, AGU and Wiley citations for excellence, and national Best Dissertation and UFZ Best PhD awards in 2021, underlining her early-career leadership. She convenes sessions on hydrometeorological extremes at EGU and AGU meetings, serves as reviewer for top journals (Science, Nature Climate Change, WRR, etc.) and acts as referee for NSF and Hungarian Academy grant programmes, underlining her active service to the community.

David Lorenzمشاهده پروفایل

مدرس ارشد

• English Linguistics
• Language Variation and Change
• Grammaticalization
• +۵ مورد دیگر

David Lorenz is a Senior Lecturer and Associate Professor at Lund University's Centre for Languages and Literature, specializing in English Linguistics. His work spans language variation, grammaticalization, and phonetic reduction in spoken language. Teaching: Courses in English language, academic writing, and linguistic theory/methods. Research: Focus on usage-based language dynamics, corpus analysis, and experimental linguistics. Recent publications explore epistemic modality, multi-word sequences, and statistical modeling. Projects include CRMWSE (2022-2025) on language cognition and upcoming work on TikTok hate speech rhetoric (2025). Contact: david.lorenz@englund.lu.se | +46 76 720 10 15 | SOL:H317b, Lund University.

Miek Schlangenمشاهده پروفایل

پژوهشگر

• Food Texture Analysis
• Food Mechanical Properties
• Machine Learning Applications in Food Science
• +۳ مورد دیگر

Miek Schlangen is a postdoctoral researcher at SDU FOOD MATTERS (University of Southern Denmark), specializing in food texture engineering, mechanical properties of meat analogues, and machine learning applications in food science. She obtained her PhD in Food Process Engineering from Wageningen University (2024), focusing on dry fractionation of plant proteins, transglutaminase crosslinking, and structuring into meat analogues. Active in AI-driven hierarchical food structure design (FIBROUSNESS project, 2024-2027) Developed image analysis methods for fibrousness quantification Expert in mechanical anisotropy assessment of meat analogues Research Interests: Her work bridges food biophysics, advanced processing technologies, and computational modeling to create plant-based meat alternatives with optimized structural and textural properties. Key focus areas include: Multiscale fibrousness characterization Enzymatic protein modification Machine learning for rheological prediction Polysaccharide-protein interactions Non-linear food material behavior Endogenous starch utilization Scientific Contributions: Miek's research output includes 11 publications (2025-2021) covering meat analogue development, plant protein structuring, and sustainable ingredient applications. Her work has received significant attention with 3 citations in 2025 and 15 citations in 2024. 2023 - Honorable Mention, Women in Science Rising Talent Prize 2022 - Structural characterization of plant-based meat replacers 2021 - Texture evaluation methodology review Collaborative Network: Maintains active collaborations with researchers at Wageningen University and SDU Biotechnology, with external partnerships across Europe and Asia. Current work focuses on quantitative relationships between mechanical anisotropy, macrostructure, and microstructure in meat analogues.

• Epilepsy
• Neurology
• Signal Processing
• +۷ مورد دیگر

Dr. Wim Van Drongelen is a Professor at the University of Chicago in the Department of Pediatrics-Neurology. His research focuses on understanding the mechanisms underlying epilepsy through interdisciplinary approaches including advanced statistical and digital signal processing, non-linear dynamics, and modeling. His lab employs mathematical modeling, computational simulations, animal models, patient tissue analysis, and clinical recordings to improve epilepsy treatment.

Haim Avronمشاهده پروفایل

استاد

• Numerical Computing
• High Performance Computing
• Machine Learning
• +۳ مورد دیگر

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

• Speech Processing
• Biomedical Signal Analysis
• Neural Networks
• +۳ مورد دیگر

Adam Robert Kobus is a Lecturer at the Department of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics, and Computer Science, Maria Curie-Skłodowska University (UMCS) in Lublin, Poland. His academic profile includes roles in research and education, with a focus on speech processing and biomedical engineering. University: Maria Curie-Skłodowska University School: Faculty of Mathematics, Physics, and Computer Science Department: Department of Neuroinformatics and Biomedical Engineering Academic Rank: Lecturer Dr. Kobus specializes in speech recognition algorithms for detecting non-fluencies and disorders, combining techniques like linear prediction coefficients , neural networks , and Continuous Wavelet Transform (CWT) . His work integrates machine learning and biomedical signal processing to develop automated tools for analyzing speech patterns. Research trends include speech disorders diagnostics , neural network applications , and wavelet-based signal analysis . His publications emphasize computational linguistics and biomedical informatics . Dr. Kobus is based at the Institute of Computer Science, room 511, Lublin. He can be contacted via email at adam.kobus@mail.umcs.pl .