جست‌وجوی استاد و پژوهشگر

• Distributed Computing
• Grid Computing
• Cloud Computing
• +۳ مورد دیگر

Chronopoulos Antonis is a Professor at the University of Patras, specializing in distributed computing, grid and cloud computing, numerical algorithms, high performance computing, and scientific computing. His research spans both theoretical numerical methods and practical applications in distributed systems. His primary research interests focus on Distributed Computing , Grid and Cloud Computing , Numerical Algorithms , High Performance Computing , and Scientific Computing . His work bridges theoretical numerical methods with practical distributed systems implementations, particularly in the areas of load balancing, parallel iterative methods, and computational efficiency. Analysis of his publication record reveals a career trajectory that began with foundational work in numerical linear algebra and iterative methods in the late 1980s and 1990s, then evolved toward distributed and cloud computing applications in the 2000s and 2010s. His recent work shows increasing focus on healthcare applications of AI and machine learning, particularly in medical diagnostics and decision support systems. His research demonstrates consistent contributions to both theoretical numerical methods and practical distributed computing systems, with recent work expanding into healthcare applications of artificial intelligence.

Jonas Berxمشاهده پروفایل

پژوهشگر ارشد

• Non-equilibrium Statistical Mechanics
• Stochastic Thermodynamics
• Theoretical Biophysics
• +۶ مورد دیگر

Jonas Berx is a Research Fellow at the Niels Bohr Institute , University of Copenhagen, specializing in non-equilibrium statistical mechanics and theoretical biophysics. His research integrates concepts from knot theory, complex networks, and stochastic thermodynamics to model biochemical replication and chiral active matter dynamics.

Zhong Chenمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Mining
• +۷ مورد دیگر

Zhong Chen is an Assistant Professor in Data Science and Machine Learning at the School of Computing, Southern Illinois University (SIU), where he serves as Director of the Learning, Optimization, and Analysis from Data Lab (LOAD Lab). He holds a Ph.D. in Computer Science from Wuhan University of Technology and has previously worked as a Research Assistant Professor at the University of Kansas Medical Center and as a Computational Scientist at Xavier University of Louisiana. His research focuses on data-centric AI, Large Language Models, machine learning, deep learning, big data mining, online optimization, and anomaly detection, with applications in healthcare and medical physics. His work addresses fundamental challenges in handling streaming data with varying feature spaces, imbalanced classification problems, and developing interpretable AI systems for medical applications. Chen's recent publications demonstrate expertise in online learning frameworks, sparse representation techniques, and applications in healthcare domains including cancer treatment, patient outcome prediction, and medical imaging. His research combines theoretical innovation with practical applications in medical physics and bioinformatics. Excellence Reviewer Award of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23) Outstanding Reviewer Award (top 10% of reviewers) of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25) Chen serves as Associate Editor for Medical Physics and Editorial Member of Computational Biology and Bioinformatics. He is actively involved in the academic community as Program Committee member for major AI conferences including AAAI, IJCAI, KDD, and ECML-PKDD. He has advised numerous research projects and served on thesis committees at SIU, with a focus on developing the next generation of data scientists and AI researchers. His LOAD Lab at SIU focuses on foundational innovation in artificial intelligence and machine learning with emphasis on online optimization, machine learning techniques, and applications in big streaming data, bioinformatics, and medical physics.

Ugo Piomelliمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Turbulence Modeling
• Large Eddy Simulation (LES)
• +۳ مورد دیگر

Ugo Piomelli is a Professor in the Department of Mechanical and Materials Engineering at Queen's University (since 2008). He holds the Tier I Canada Research Chair in Turbulence Simulation and Modelling (2008-2022) and previously held the HPCVL-Sun Microsystems Chair in Computational Science and Engineering (2008-2015). His academic career includes positions at the University of Maryland (Professor 2000-2008, Associate Professor 1993, Assistant Professor 1987-1993). He is a Fellow of multiple prestigious societies: Royal Society of Canada (2015), American Physical Society (2002), American Institute of Aeronautics and Astronautics (2004), American Society of Mechanical Engineers (2009), and Canadian Academy of Engineering (2021). PhD in Mechanical Engineering (Stanford University, 1988) MASc in Aerospace Engineering (University of Notre Dame, 1984) Dr. Ing. in Aerospace Engineering (Università degli Studi di Napoli, 1979) His research focuses on turbulence modeling, large eddy simulation (LES), geophysical flows, and computational fluid dynamics (CFD) with applications to rough-wall boundary layers, flow separation, and environmental/hydroelectric systems. Recent work explores resolvent-based wall models, scalar transport over rough surfaces, and unsteady boundary layer dynamics under variable freestream conditions. Scientific awards include: Fellow of the American Physical Society (2002), Associate Fellow of AIAA (2004), Fellow of ASME (2009), Fellow of the Royal Society of Canada (2015), and Fellow of the Canadian Academy of Engineering (2021). Key contributions include: 2023-2025 studies on LES wall models using resolvent analysis, roughness effects in boundary layers, and scalar transport dynamics. Earlier work (2013-2022) examines turbulence structures in dunes, rotating channel flows, and erosion-rate modeling for sediment transport. He operates the Turbulence Modelling and Simulation Laboratory, advancing numerical frameworks for complex flows and mentoring graduate students. His research has been supported by NATO-AVT349 for naval applications and collaborations in hydroelectric/hydraulic engineering.

• Animal Genetics
• Genomic Selection
• Livestock Breeding
• +۷ مورد دیگر

Samuel Clark is an Associate Professor in Animal Genetics at the University of New England , affiliated with the School of Environmental and Rural Science and the Faculty of Science, Agriculture, Business and Law . His work focuses on leveraging genomic technologies to enhance livestock breeding programs. Research Interests : Dr. Clark's research spans genetic resilience in sheep , genomic selection in beef cattle , and environmental sensitivity in livestock . He investigates how genetic variation interacts with environmental factors to optimize traits like meat quality, feed efficiency, and methane emissions. Publication Trends : Recent articles emphasize genomic prediction accuracy , genotype-environment interactions , and microbial profiling for health traits . His work includes large-scale studies on Angus and Hanwoo cattle and sheep resilience metrics , often integrating bioinformatics and statistical models. Collaborative Efforts : Dr. Clark contributes to initiatives like the Southern Multi-Breed Resource Population , which addresses complex phenotypes for genomic selection, and explores the impact of reproductive technologies on breeding programs .

Libal Andrasمشاهده پروفایل

دانشیار

• Computer Simulation
• Soft Condensed Matter Physics
• Active Matter
• +۵ مورد دیگر

Dr. Libal András is an Associate Professor at the Faculty of Mathematics and Computer Science of Babes-Bolyai University , Romania. His research spans soft condensed matter physics , active matter systems , and artificial spin ice , with a focus on computational modeling and GPU/CPU optimization. He collaborates with institutions like Los Alamos National Laboratory and has contributed seminal work on colloidal spin ice. Alma Mater: Bathory Istvan High School (Cluj), Babes-Bolyai University (BSc), University of Notre Dame (PhD) Past affiliations: Argonne National Lab, Los Alamos National Lab, Johns Hopkins University, Universiteit Antwerpen Research interests include: Computer simulation of soft condensed matter and active matter systems Topological defects in spin ice and granular materials Development of Arduino-based microcontroller applications and IoT systems GPU-accelerated simulation methods Publications focus on vortex dynamics , jamming transitions , colloidal crystals , and active matter across physics, materials science, and computational epidemiology. No scientific awards are explicitly mentioned in available sources.

Weijing Tangمشاهده پروفایل

استادیار

• Network Analysis
• Machine Learning
• Survival Analysis
• +۵ مورد دیگر

Weijing Tang is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on developing statistical methodologies for network analysis, machine learning, and survival analysis, addressing challenges in large-scale interdisciplinary datasets across health and social sciences. He holds a Ph.D. in Statistics from the University of Michigan (2022) and a B.S. in Mathematics from Tsinghua University (2016). Prior to CMU, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2023). Education: Ph.D. in Statistics, University of Michigan, 2022 B.S. in Mathematics, Tsinghua University, 2016 Research Interests: Network analysis and signed network dynamics Survival analysis and Cox models Machine learning applications in biostatistics and public policy Optimization and scalable statistical methods Nonparametric methods and subgroup heterogeneity analysis Key Awards: ProQuest Distinguished Dissertation Award (2023) IMS Hannan Graduate Student Travel Award (2022) Multiple awards from ASA and ENAR (2021-2020) Grand Prize in American Heart Association's COVID-19 Data Challenge (2020) Funding: NSF Grant DMS-2412853 (sole PI) CMU Dietrich College Seed Grant (co-PI)

• Diffusion MRI
• Medical Physics
• Neuroimaging
• +۳ مورد دیگر

Dr. Chantal Tax is a Research Fellow at Cardiff University's School of Physics and Astronomy, specializing in diffusion MRI and its applications in neuroimaging. Her work focuses on advancing quantitative MRI techniques, including ultra-strong gradient diffusion MRI and tractography, to study brain microstructure, white matter abnormalities, and developmental differences in preterm infants. Tax has contributed significantly to methodological advancements in diffusion MRI preprocessing, harmonization, and clinical applications in oncology and neurology. Her research interests span medical physics, neurodevelopmental imaging, and the integration of machine learning for optimizing MRI protocols. Notable studies include exploring myelin sheath quantification via diffusion models, assessing cancer microstructure, and investigating white matter changes in movement disorders. Tax collaborates extensively with global institutions, contributing to open challenges in diffusion MRI metadata standards (e.g., aDWI-BIDS) and reproducibility in tractography. Publications highlight Tax's expertise in diffusion MRI's clinical and research applications, including pediatric neuroimaging, brain connectivity mapping, and the development of novel imaging biomarkers. Her work bridges physics, computer science, and clinical medicine, addressing challenges in neuroimaging accuracy and accessibility.

• Developmental Biology
• Molecular Genetics
• Embryology
• +۶ مورد دیگر

Caroline Hoppe is an Associate Research Scientist in the Genetics Department at Yale School of Medicine, Yale University. She holds a PhD in Developmental and Molecular Biology from the University of Manchester (2019) and a BSc in Biochemistry and Cell Biology from Jacobs University (2015). Her research focuses on developmental genetics, transcriptional regulation, CRISPR technologies, and molecular imaging. She collaborates with prominent researchers such as Antonio Giraldez and Mina Kojima on topics like maternal-to-zygotic transition and embryonic development. Key research areas: Embryology, Drosophila genetics, genomic regulatory elements, and bioinformatics. Notable work includes live imaging techniques for mRNA and transcription dynamics, CRISPR-Cas9 applications, and analysis of BMP signaling gradients. Her publications span developmental biology to cancer genomics, with a focus on molecular mechanisms driving embryonic development and genetic regulation in model organisms. She has contributed to tools like the MS2/MCP system for transcription visualization and GEARs for protein function studies.

Edo Bergerمشاهده پروفایل

استاد

• Time-domain Astrophysics
• Supernovae
• Gamma-ray Bursts
• +۳ مورد دیگر

Edo Berger is a Professor of Astronomy at Harvard University's Department of Astronomy and the Center for Astrophysics. He leads the Cosmic Transients Lab, focusing on time-domain astrophysics including supernovae, gamma-ray bursts, tidal disruption events, gravitational wave events, and magnetic activity in substellar objects. His team employs multi-wavelength observations (radio to γ-rays) and machine learning techniques for transient classification and gravitational wave analysis. His research explores explosive astrophysical phenomena through advanced observational methods. Recent work emphasizes: Multi-messenger studies of gamma-ray burst afterglows Long-term evolution of tidal disruption events Machine learning approaches for real-time transient classification Radio and submillimeter follow-up of gravitational wave counterparts Berger received his PhD from Caltech in 2004 and was a Hubble/Carnegie-Princeton Postdoctoral Fellow (2004-2008). Honors include the 2016 APS Division of Astrophysics Thesis Award and 2016 Star Family Challenge Prize. He maintains extensive observational programs using ground/space-based facilities including ALMA, VLA, HST, and JWST.

• Biodiversity and Ecosystem Patterns
• Sustainable Society Transitions
• Biogeography
• +۷ مورد دیگر

Prof. Peter van Bodegom is a Professor of Environmental Biology and Circular Horticultural Systems at Leiden University's Institute of Environmental Sciences (CML). He leads research on biodiversity-ecosystem linkages and sustainable transitions, focusing on global vegetation modeling, plant-soil interactions, and trait-based approaches. His work integrates ecological theory with applied challenges like mosquito-borne disease dynamics and oil spill remediation. Education details are not explicitly provided in the text, but his academic roles suggest advanced training in environmental sciences and engineering, given titles like 'dr.ir.' (indicating PhD and MSc in technical sciences). Research interests span ecosystem functioning, climate impacts on vector species (e.g., Culex pipiens), and biotechnological solutions for environmental crises. Notable projects include C-SCAPE coastal adaptation strategies and the Sand Motor Building with Nature initiative. Recent studies highlight interactions between climate, land use, and soil type on mosquito abundance, as well as ecological consequences of Bti insecticide use. Current PhD advisees include Jennifer Anderson, Anugrah Aditya Budiarsa, and others focusing on topics like tropical vegetation dynamics and urban biodiversity. Former students include Joeri Morpurgo and Sam Boerlijst. Labs and affiliations include the CML's Environmental Biology Department and collaborations with interdisciplinary teams on projects like MULTIPLY (Earth Observation integration) and COMBINED (biodiversity resilience strategies).

• Computational Chemistry
• Quantum Chemistry
• Theoretical Chemistry
• +۳ مورد دیگر

Robin Grotjahn is an Assistant Professor in the Department of Chemistry & Biochemistry at Santa Clara University (SCU), affiliated with the Sobrato Campus for Discovery and Innovation. Their research focuses on computational and theoretical chemistry, particularly in the application of advanced quantum mechanical methods to solve complex problems in catalysis, electronic structure, and material science. Academic Rank: Assistant Professor Department: Chemistry & Biochemistry Institution: Santa Clara University Location: Sobrato Campus for Discovery and Innovation Dr. Grotjahn's work centers on developing and validating high-accuracy quantum mechanical methods, including local hybrid and range-separated functionals, for ground and excited-state properties. Key research areas include: Density Functional Theory (DFT) and Time-Dependent DFT (TDDFT) for excited-state calculations Metalloenzyme reaction mechanisms and catalytic design Electronic coupling and spin-forbidden excitations in transition-metal systems Quantum interference effects in mixed-valence complexes Coordination chemistry of lanthanides and rare-earth elements Computational modeling of phosphorescence, fluorescence, and vibronic spectra Recent publications demonstrate a consistent emphasis on method development, mechanism elucidation, and applications to organometallic systems. Dr. Grotjahn has contributed to software frameworks like TURBOMOLE and explored phenomena such as reactive wetting in high-temperature chemical transport reactors and smectic liquid crystal behavior under flow conditions.

Martin Jaggiمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Martin Jaggi is an Associate Professor at EPFL, leading the Machine Learning and Optimization Laboratory (MLO). He holds academic positions within the School of Computer and Communication Sciences (IC), including roles in the SIN, SSC, and EDIC departments. His research focuses on machine learning optimization, federated learning, and large language models. He earned his PhD in Machine Learning and Optimization from ETH Zurich (2011) and a MSc in Mathematics from the same institution. Education: PhD in Machine Learning and Optimization, ETH Zurich (2011) MSc in Mathematics, ETH Zurich Research Interests: Dr. Jaggi's work integrates optimization theory with machine learning applications, emphasizing scalable algorithms and ethical AI. He explores topics like distributed learning systems, privacy-preserving techniques (e.g., federated learning), and model interpretability. His lab develops foundational methods for large language models (LLMs) and their ethical deployment. Teaching & Courses: Optimization for Machine Learning Topics in Machine Learning Systems Advising & Grants: He currently advises 9 PhD students and has supervised 11 past students. His work is supported by grants focusing on federated learning, optimization algorithms, and medical AI applications like Meditron-70b. Collaborations include projects on respiratory disease detection and mental health modeling. Labs & Teams: Academic Director of RCP-GE and oversees MLO lab, which develops open-source tools like FLamby for federated learning benchmarks and Meditron for clinical AI. The lab also advances techniques in distributed optimization and low-precision training.

Reinhard Heckelمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Medical Image Processing
• +۳ مورد دیگر

Reinhard Heckel is a Professor of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM). His career includes positions as a Tenure-Track Assistant Professor at Rice University (2017–2019) and postdoctoral fellow at UC Berkeley's Berkeley Artificial Intelligence Research Lab. He holds a PhD from ETH Zurich (2014) and conducted doctoral research at Stanford University’s Statistics Department. Recognitions include being named one of Germany's 'Top 40 under 40' (2022) and the Werner von Siemens Ring Foundation Award (2022). Education & Professional Background: PhD in Computer Science, ETH Zurich (2014) Visiting Doctoral Fellow, Stanford University (Statistics Department) Postdoctoral Fellowship, UC Berkeley (EECS Department) Research Focus: His work bridges theoretical foundations and practical applications in machine learning, including: Algorithm development for deep learning and medical image processing Mathematical foundations of machine learning DNA data storage technology (error correction, synthesis methods) Computational imaging and inverse problem solutions Awards & Highlights: 2022: Capital 40 under 40, Werner von Siemens Ring Foundation Award 2015: ETH Zurich Medal for Doctoral Thesis, IBM Invention Achievement Award Grants & Collaboration: His research has been supported by grants focusing on DNA storage scalability and MRI reconstruction. He collaborates with institutions like IBM Research and the Berkeley AI Lab. Key projects include developing DNA synthesis methods and AI-driven medical imaging tools. Labs & Teams: Leads TUM's machine learning initiatives in computational imaging and biological data storage systems. Active in interdisciplinary teams bridging computer science, bioengineering, and statistics.

• Computational Statistics
• Machine Learning
• Bayesian Inference
• +۳ مورد دیگر

Saifuddin Syed is a Florence Nightingale Bicentenary Research Fellow at the University of Oxford's Department of Statistics, supervised by Arnaud Doucet and funded by the CoSInES project. His research focuses on computational statistics and machine learning, particularly scalable Bayesian inference, Monte Carlo methods, and non-reversible parallel tempering. He completed his PhD in Statistics at the University of British Columbia under Alexandre Bouchard-Côté. Currently, he contributes to the Next Generation Event Horizon Telescope (ngEHT) collaboration, improving algorithms for modeling and imaging supermassive black holes. Research interests include parallel tempering, sequential Monte Carlo, information geometry, and statistical physics. His work spans methodological development in MCMC schemes and applications in complex systems. He is affiliated with the Computational Statistics and Machine Learning research group and Statistical Theory and Methodology at Oxford. Key contributions include advancing non-reversible parallel tempering techniques to enhance computational efficiency in high-dimensional sampling, with applications to astrophysics and statistical mechanics. His recent work emphasizes scalable algorithms and rigorous theoretical analysis of MCMC performance.