جست‌وجوی استاد و پژوهشگر

Mark Personمشاهده پروفایل

استاد

• Paleohydrology
• Geothermal Systems
• Induced Seismicity
• +۵ مورد دیگر

Mark Person is a Professor in the Department of Earth and Environmental Science at New Mexico Tech. His research focuses on paleohydrology, low-temperature geothermal systems, induced seismicity, and numerical modeling of subsurface fluid flow systems. He leads projects investigating freshwater emplacement in continental shelf environments, offshore groundwater dynamics, and the role of glaciation in modifying groundwater systems. Current active grants include NSF-funded studies on coastal freshwater sequestration and NAGRA collaborations analyzing pressure anomalies in clay formations. Teaching responsibilities include courses such as H510 Quantitative Methods in Hydrology, HYD 547 Hydrological Modeling, and HYD 516 Geofluids. His research group uses advanced modeling tools like PGEOFE and Landlab, and collaborates with institutions like the University of Utah and the University of Dhaka. Notable achievements include the 2021 O.E. Meinzer Award and the 2016 New Mexico Tech Distinguished Research Award. Recent projects include a planned 2025 drilling expedition off Massachusetts to study Pleistocene-aged freshwater deposits, and geophysical surveys in Bangladesh to map deep aquifers. His work integrates magnetotelluric imaging, noble gas dating, and hydrothermal modeling to address subsurface flow dynamics. Advising over 10 graduate students since 2014, his lab has contributed to understanding induced seismicity mechanisms and geothermal system evolution.

• Experimental astroparticle physics
• Dark matter detection
• WIMP detection
• +۴ مورد دیگر

Matthew Szydagis is an Associate Professor in the Department of Physics at the University at Albany, State University of New York, where he conducts cutting-edge research in experimental astroparticle physics with a focus on dark matter detection. Education: PhD, University of Chicago, 2010 Postdoctoral Associate, University of California Davis, 2010-2014 Dr. Szydagis leads research efforts centered around the LZ (LUX-ZEPLIN) Dark Matter Experiment, the world's largest direct dark matter search project operating at the Sanford Underground Research Facility. His expertise lies in the physics of two-phase Xenon time-projection chambers and the development of sophisticated Monte Carlo simulation techniques to understand detector responses. In 2011, he created the NEST (Noble Element Simulation Technique) software package, which has become an essential tool for the broader scientific community working with noble element detectors. His research spans multiple disciplines including particle physics, astrophysics, and computational physics, with applications extending beyond dark matter research into neutrino physics and medical physics. Dr. Szydagis is an active member of the international LZ collaboration and leads the Dark Matter Research Group at the University at Albany. His work contributes significantly to establishing the world's most sensitive limits on dark matter interactions across a wide range of particle masses.

• Computational Neuroscience
• Neural Network Simulation
• Large-Scale Simulation
• +۳ مورد دیگر

Hans Ekkehard Plesser is a Professor at the Department of Data Science, Norwegian University of Life Sciences (NMBU). His work focuses on computational neuroscience, large-scale neural network simulations, and advancing reproducibility in computational research. He is a key contributor to the NEST simulator, a widely used open-source tool for modeling spiking neural networks. His research spans theoretical and applied aspects of neuroscience, including neuron-astrocyte interactions, connectivity patterns in neural networks, and high-performance computing strategies for brain-scale simulations. He emphasizes reproducible computational practices through initiatives like the ReScience project, promoting transparency and validation in scientific software. His publications highlight advancements in simulation methodologies, algorithm optimization, and the integration of supercomputing resources for neuroscience. He has contributed to foundational work on spiking neuron dynamics, firing-rate models, and the theoretical underpinnings of neural network behavior.

• Materials Science
• Nanotechnology
• Battery Materials
• +۵ مورد دیگر

Adjunct Professor Jose Alarco is a Materials Scientist affiliated with the Faculty of Science and School of Chemistry & Physics at Queensland University of Technology (QUT) . He holds a PhD in Materials Science from Chalmers University of Technology and has extensive experience in industrial R&D, co-founding ScienceWorks Consultants Pty Ltd to commercialize nanoscale material synthesis technologies. PhD in Materials Science, Chalmers University of Technology (1994) His research spans nanostructured materials for energy applications, including Lithium-ion batteries , catalysis , and ceramics . He specializes in scaling processes from lab to commercial plant production, with patents in complex metal oxide synthesis and battery materials . The 15 most recent publications highlight his focus on solid-state chemistry , energy storage , and computational materials science . Trends include advanced battery electrolytes , graphitic carbon from bio-waste , and superlattice structures in superconductors . Scientific Contributions are reflected in >8 patents, including methods for metal oxide synthesis and electrochemical testing . Collaborations with global firms (e.g., BASF , Valence Technology ) demonstrate industrial impact. As a supervisor , he guided doctorate projects on topics like cathode materials and graphene growth , though student names aren't listed. His work with ScienceWorks Consultants and QUT's Institute for Future Environments underscores his dual academic-industry engagement.

Alexander Shapiroمشاهده پروفایل

استاد

• Stochastic Programming
• Risk Analysis
• Simulation-Based Optimization
• +۴ مورد دیگر

Alexander Shapiro is the A. Russell Chandler III Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering , Georgia Institute of Technology. His work bridges optimization and statistics, focusing on stochastic programming, risk analysis, and simulation-based optimization. He has received numerous accolades, including the Khachiyan Prize (2013) , Dantzig Prize (2018) , and John von Neumann Theory Prize (2021) . Education: Ph.D. in Applied Mathematics-Statistics (Ben-Gurion University, 1981), M.Sc. in Mathematics (Moscow University, 1971) His research explores stochastic programming , risk-averse optimization , and multivariate statistical analysis , with recent work on distributionally robust control, Bayesian stochastic methods, and convex multistage optimization. Publications highlight theoretical advancements and computational frameworks for uncertainty modeling. Recent articles focus on asymptotics (2025), duality in MDPs (2023-2024), and statistical inference (2014-2024). These span stochastic control , robustness , and time consistency , reflecting his expertise in bridging probability theory with large-scale optimization. Scientific awards : Khachiyan Prize of INFORMS (2013) Dantzig Prize (2018) John von Neumann Theory Prize (2021) Election to National Academy of Engineering (2020) Dr. Shapiro has served as Area Editor (Optimization) for the Operations Research Journal and Editor-in-Chief of Mathematical Programming, Series A , demonstrating sustained leadership in his field.

Panagiotis Hadjidoukasمشاهده پروفایل

دانشیار

• Parallel Computing
• Distributed Systems
• High Performance Computing
• +۳ مورد دیگر

Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.

• Parallel Computing
• Quantum Computing
• Self-Adjusting Computation
• +۴ مورد دیگر

Umut A. Acar is a Professor at the Computer Science Department , Carnegie Mellon University , and an Amazon Scholar. His research focuses on bridging formal methods with systems , algorithms , and AI , aiming to integrate safety and performance in parallel computing. His research spans multiple domains: Quantum Computing : Developing optimization techniques for quantum circuits (e.g., Quartz , Atlas ). Self-Adjusting Computation : Advancing dynamic algorithms and incremental programming frameworks (e.g., Diderot , MPL ). Concurrency & Parallelism : Designing efficient scheduling mechanisms and disentanglement strategies. Recent publications highlight his work on parallel functional programming , quantum simulation , and cache coherence optimization . Notable awards include Best Paper (QCE 2025) , Distinguished Paper (POPL 2024) , and the Intel Award (2022) . He advises PhD students like Pengyu Liu and Mingkuan Xu , and has mentored alumni now at institutions such as NYU , Google , and Inria . His lab collaborates on projects like Diderot (AI/ML) and MPL (parallelism management).

• Reconfigurable Computing
• Electronics Design Automation
• Post-Quantum Cryptography
• +۴ مورد دیگر

Dr. Andrea Guerrieri is an Associate Professor at the University of Applied Sciences and Arts Valais-Wallis - School of Engineering , specializing in Reconfigurable Computing, Electronics Design Automation (EDA), and Post-Quantum Cryptography . His work focuses on accelerating FPGA compilation through tools like DynaRapid and optimizing security protocols via Dynamatic , with technologies adopted by industry leaders including Intel, AMD-Xilinx, and CERN . He holds a BSc in Industrial Systems and a MSc in Engineering from HES-SO, and teaches courses in Digital Design and Embedded Hardware. BSc HES-SO in Industrial Systems (2019) BSc HES-SO in Computer and Communication Systems (2017) MSc HES-SO in Engineering (2021) Guerrieri’s research bridges High-Level Synthesis (HLS) and Reconfigurable Architectures to enhance FPGA performance for both terrestrial and space applications . His 2025 publications highlight advancements in heterogeneous computing , energy-efficient PQC , and rapid compilation frameworks . Notably, his 2024 work on DynaRapid achieved 20× speedup in C-to-FPGA implementation. Key scientific contributions span dataflow circuit optimization , dynamic scheduling , and automated code transformations . His 2023 book Applications Enabled by FPGA-Based Technology and 2021 textbook System-on-Chip Design with Arm established foundational references in embedded systems. Awards include Best Paper at FPL 2024 , Outstanding TPC Member at DAC 2024 , and IEEE Senior Membership (2021) . 2024: Best Paper Award (FPL), Outstanding Short Paper Award (HPEC) 2023: H-Saber publication on PQC optimization 2021: IEEE Senior Member recognition As Chair of Onboard Computing for the CHEESE-NASA SSERVI consortium, he leads international collaborations with ETH Zurich, University of Geneva, California State University , and companies like NVIDIA, Arm, and NASA . His projects include the Innosuisse-funded DyReCte initiative (2019–2021) for reconfigurable cryptoengines in nanosatellites.

Simon Adrianمشاهده پروفایل

استاد

• Computational Electromagnetics
• Electromagnetic Integral Equations
• Antenna Design
• +۳ مورد دیگر

Prof. Simon Adrian holds the Chair of Theoretical Electrical Engineering at the Institute of General Electrical Engineering, University of Rostock, Germany. His research focuses on computational electromagnetics with critical applications in antenna design, electromagnetic compatibility, and medical technology. He serves as Associate Editor for the IEEE Transactions on Antennas and Propagation and contributes to the IEEE Antennas and Propagation Society Education Committee, demonstrating significant academic leadership in the global electromagnetics community. His primary research addresses low-frequency instability challenges in electromagnetic integral equations through innovative numerical techniques. Key areas include Calderón preconditioners, quasi-Helmholtz projectors, B-spline discretizations, and adaptive cross approximation methods. These approaches enable robust simulations across diverse applications from radar systems and antenna design to biomedical problems like deep brain stimulation and electroencephalography. Recent work emphasizes broadband stability and efficient solvers for multiply-connected geometries. Analysis of Prof. Adrian's publication trends (2023-2025) reveals a concentrated effort on overcoming fundamental limitations in electromagnetic modeling. His work consistently targets low-frequency regimes where traditional methods fail, developing mathematically rigorous stabilization techniques while expanding into biomedical applications. The integration of isogeometric analysis with specialized discretization strategies represents a cutting-edge direction in computational electromagnetics. Professional engagement includes active membership in the Institute of Electrical and Electronics Engineers (IEEE), IEEE Antennas and Propagation Society, and Union Radio-Scientifique Internationale (URSI), reflecting his commitment to advancing the field through collaborative research and scholarly communication.

Ylva Sjöbergمشاهده پروفایل

دانشیار

• Arctic Hydrology
• Permafrost Dynamics
• Climate Change Impact on Water Resources
• +۲ مورد دیگر

Ylva Sjöberg is an Associate Professor at the Department of Ecology, Environment and Geoscience, Umeå University. Her research focuses on Arctic hydrology, permafrost dynamics, and climate change impacts on water resources, employing numerical modeling and field-based methods across northern Fennoscandia, Greenland, and Alaska. Key research areas include: Mechanistic understanding of Arctic river methane emissions (ARIMETH project, 2025) Cryospheric impacts on water quality and flow paths Development of standardized permafrost thaw monitoring protocols Science communication through Frozen-Ground Cartoons Her publications emphasize interdisciplinary approaches, linking groundwater-surface water interactions, permafrost thaw cascades, and biogeochemical processes in Arctic catchments. She actively collaborates with Indigenous communities and contributes to Arctic policy frameworks.

Tzeng Yih Lamمشاهده پروفایل

استادیار

• Forest Measurements
• Forest Sampling
• Quantitative Silviculture
• +۳ مورد دیگر

Dr. Tzeng Yih Lam serves as an Assistant Professor in the Department of Forest Resources Management at the University of British Columbia's Faculty of Forestry. His expertise lies in forest measurements, sampling methodologies, and quantitative silviculture, contributing to sustainable forest management practices through innovative biometric approaches. His educational background includes: PhD in Forest Science (2010) from Oregon State University MSc in Statistics (2009) from Oregon State University MSc in Tropical and International Forestry (2006) from Georg-August-Universität Göttingen BSc in Forestry (2001) from the University of New Brunswick Dr. Lam's research focuses on developing close-range photogrammetry tools for hard-to-measure tree attributes, designing cost-effective probability-based forest inventory systems using auxiliary information, and applying Bayesian filtering for forest growth projection. His work bridges field data collection with advanced statistical modeling to enhance timber production estimation and ecosystem service assessment. Analysis of his 15 most recent publications reveals dominant themes in innovative sampling design (particularly Critical Height Sampling and cluster/nested plots), integration of machine learning with rapid assessment techniques, and sophisticated height-diameter modeling across diverse species. His work consistently emphasizes spatial distribution mapping, LiDAR integration, and error reduction in field measurements. As an active contributor to the UBC Forest Measurements and Biometrics research group, Dr. Lam develops teaching materials for courses including Advanced Regression Analysis and Forest Measurements, advancing methodological training in forest biometrics through R workshops and practical field applications.

Martin Elsmanمشاهده پروفایل

استاد

• Programming Languages
• Theory of Computation
• Functional Programming
• +۵ مورد دیگر

Martin Elsman is a full-time Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science, University of Copenhagen (DIKU). He serves as head of the PLTC section and head of studies for the BSc education in Computer Science and Economics. Elsman is also an active maintainer of several software tools including the MLKit and SMLtoJs. Joined DIKU in 2012 after 4 years at SimCorp (2008-2012) and previous Associate Professorship at IT University of Copenhagen (2003-2008). Co-developer of Futhark, TAIL APL compiler, SMLtoJs, and SMLserver. Education: M.Sc. in Engineering, Technical University of Denmark Ph.D. in Computer Science, University of Copenhagen (DIKU), supervised by Mads Tofte. Research Interests: Elsman works on programming language design and implementation, with a focus on functional programming, module systems, domain-specific languages for financial contracts, region-based memory management, compilation techniques for parallelism, program optimization, and static type systems. His work spans both theoretical and applied domains, including blockchain-based financial contract execution, web technology, and GPU programming using functional languages. Publication Trends: His recent articles focus on functional programming, array programming, parallelism, and memory management. Topics include region inference, type systems for data-parallelism, program optimization techniques, and domain-specific compilation for financial and quantum computing. He frequently collaborates with Troels Henriksen and others on tools like Futhark and MLKit.

Mostafa Bakhoday Paskyabiمشاهده پروفایل

دانشیار

• Offshore Wind Energy
• Environmental Impacts of Offshore Wind Turbines
• Wind-Wave-Turbulence Interaction
• +۷ مورد دیگر

Mostafa Bakhoday Paskyabi is an Associate Professor at the Geophysical Institute , University of Bergen. His research focuses on offshore wind energy , environmental impacts of turbines , wind-wave-turbulence interaction , and climate change effects on wind parks . He employs Large Eddy Simulation (LES) and data assimilation for digital twin development. Teaching: Course responsible for ENERGI322 and ENERGI101; lecturer for ENERGI321 and ENERGI360. Supervision: Advises PhD candidates like Maria Krutova, Xu Ning, and Øystein Husevåg Døskeland, alongside master’s students including Fahim Masud Ahmed and Tord Nylan Malmei. His research explores physical oceanography (ocean mixing, Lagrangian particle tracking), air-sea interaction , and reduced-order modeling for offshore energy systems. Recent work examines swell wave impacts on turbulence and LiDAR-based data assimilation for transient event forecasting. Projects include LESWIND , WindSys , HIPERWIND , and Wind3D , addressing offshore wind design, ecosystem impacts, and climate operability. Collaborations span institutions like NTNU, NORCE, and IMR.

Frederic Beaudryمشاهده پروفایل

استاد

• Bird ecology & conservation
• Turtle ecology & conservation
• Habitat & population ecology
• +۲ مورد دیگر

Frederic Beaudry is a Professor of Environmental Studies and Ombuds at Alfred University, located within the College of Liberal Arts & Sciences. He holds a BS in Biology (Wildlife Management) from Université du Québec à Rimouski (2000), an MS in Natural Resources (Wildlife) from Humboldt State University (2003), a PhD in Wildlife Ecology from the University of Maine (2007), and completed a postdoctoral fellowship at the University of Wisconsin-Madison (2007-2010). His research focuses on bird and turtle ecology & conservation, habitat/population dynamics, landscape ecology, and land use planning. He has published extensively on topics such as road ecology impacts on reptiles, agricultural land abandonment perceptions, and multispecies conservation prioritization. His work often integrates ecological modeling with socioeconomic factors to inform conservation policy. Teaching responsibilities include Environmental Studies I, Conservation Biology, Ornithology, Herpetology, and courses related to environmental data analysis and research methods. He advises student research projects through Env. Research Procedures and Env. Data Analysis coursework. Recent research emphasizes spatial conservation planning for forest birds, mitigation strategies for wildlife-vehicle collisions, and policy-driven land use projections. His interdisciplinary approach bridges ecological science with human dimensions of conservation challenges.

• Computer Architecture
• Programming Models
• Parallel Computing
• +۴ مورد دیگر

Daniel Jimenez Gonzalez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the School of Informatics (FIB). He is an active researcher in programming models and high-performance computing, contributing extensively to the field through publications, projects, and academic supervision. Research Interests: His work focuses on Computer Architecture , Programming Models , and Parallel Computing . He investigates task-based programming, runtime systems, compiler optimizations, and performance modeling for multi-core and heterogeneous architectures. His research addresses challenges in scalability, energy efficiency, and resilience in modern computing environments. His recent publications show a consistent trend in task-based programming models , runtime scheduling , and performance optimization across diverse architectures, including NUMA and GPU-accelerated systems. The work spans from theoretical modeling to practical implementation, often targeting real-world HPC applications. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and R&D projects suggests an active role in mentoring graduate students. He has participated in both competitive and non-competitive R&D+i projects, indicating grant acquisition and project leadership experience in areas related to programming models and computer architecture. Labs and Teams: He is a member of the UPC PM - Programming Models research group, which focuses on the design, analysis, and optimization of modern programming paradigms for high-performance systems.