جست‌وجوی استاد و پژوهشگر

Tony Hanssonمشاهده پروفایل

استاد

• Chemical Physics
• Atomic and Molecular Processes
• Laser Spectroscopy
• +۳ مورد دیگر

Tony Hansson is a Professor in the Department of Physics at Stockholm University, focusing on chemical physics and surface reaction dynamics. His research employs advanced spectroscopic techniques like femtosecond photoelectron spectroscopy and sum frequency generation to study molecular interactions with laser pulses and catalytic surfaces. Research Areas: Ultrafast laser-matter interactions, hydrocarbon decomposition, catalyst passivation, and excited state molecular relaxation. Methodologies: Combines experimental approaches (TPD, SFG, XPS, STM) with computational methods (DFT, molecular dynamics). Recent publications highlight his work on naphthalene dehydrogenation on nickel surfaces, sulfur's role in carbon formation, and oxide-derived gold electrode characterization. His studies bridge fundamental atomic-level processes with industrial catalysis applications. Key collaborations include Oliver Schalk and Ting Geng, with affiliations to Stockholm University's Fysikum facility. Contact: thansson@fysik.su.se

• Wave Energy Conversion
• Marine Current Energy
• Power Electronics
• +۱۲ مورد دیگر

Mats Leijon is Professor of Electrical Engineering at Uppsala University, Sweden. His work centers on renewable energy systems, particularly wave and marine current energy conversion, with a focus on direct-driven linear generators, power electronics, and grid integration. He has led projects at the Lysekil Research Site, Sweden, and contributed to experimental hydrokinetic power stations like the Söderfors Project. Key Affiliations: Department of Electrical Engineering, Uppsala University; Ångström Laboratory; Lysekil Research Site. Research Interests span wave energy converter design, electromagnetic systems, control strategies for renewable energy, and marine substation technology. He explores: Hydrodynamic and electromagnetic modeling of point-absorbing wave energy devices Power optimization via resonance circuits and predictive control Robotized manufacturing for electric machines Publication Trends highlight collaborations on Wave Energy Converters , Marine Current Turbines , and Three-Level Inverter Systems , with applications in the Baltic Sea and Norwegian fjords. His work addresses extreme wave survivability, power fluctuation reduction, and environmental impact assessments. Grants and Projects include offshore wave energy deployments, thermal rating of submerged substations, and experimental validation of marine power systems. He has advised on robotics for cable winding and stator slot geometry optimization. Labs and Teams operate at the Ångström Laboratory and Lysekil Research Site, focusing on full-scale offshore experiments, CFD simulations, and grid-connected marine substations.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Luca Modeneseمشاهده پروفایل

مدرس ارشد

• Computational biomechanics
• Musculoskeletal modeling
• Neuromuscular modeling
• +۲ مورد دیگر

Dr. Luca Modenese is a Senior Lecturer in Biomechanics at the Graduate School of Biomedical Engineering, University of New South Wales (UNSW). A recipient of the prestigious Scientia Fellowship , his research focuses on computational biomechanics with specialization in musculoskeletal and neuromuscular modeling. He has extensive experience across institutions including Imperial College London, Griffith University, and Sheffield University. Education : Mechanical Engineering (summa cum laude), University of Padua (2008) PhD in Structural Biomechanics, Imperial College London (2013) Research Expertise spans musculoskeletal modeling, neuromuscular simulation, orthopaedic biomechanics, and predictive computational methods. His work integrates patient-specific modeling with finite element analysis and predictive simulations. Recent publications highlight advancements in GAN-based motion data generation , electromyography-informed models , and AI-enhanced biomechanical analysis . Scientific Recognition : Scientia Fellowship (UNSW) Athanasiou ABME Award (2021) Publication of the Year (Australia and New Zealand Society of Biomechanics, 2017) Griffith University Awards (2015) OpenSim Fellows Program (Stanford, 2014) Supervision & Collaboration : Currently supervising PhD students Arnault Caillet and Metin Bicer at Imperial College London as external supervisor. Actively involved in research partnerships with institutions including Imperial College London, Stanford University, and the Menzies Health Institute Queensland.

Roberto Tronمشاهده پروفایل

استادیار

• Control Theory
• Robotics
• Computer Vision
• +۴ مورد دیگر

Roberto Tron is an Assistant Professor in the Mechanical Engineering and Systems Engineering departments at the Boston University College of Engineering , with his office located at 110 Cummington Mall. His research integrates control theory, robotics, and computer vision to solve complex multi-agent coordination problems. His primary research interests focus on Riemannian geometry applications , distributed multi-agent systems , and safety-critical control . Key methodologies include Control Barrier Functions (CBFs), Riemannian optimization, and distributed consensus algorithms, with applications spanning autonomous aerial vehicles, robotic manipulation, and multi-robot security systems. Analysis of his recent publications reveals a strong emphasis on safety verification and real-time optimization for autonomous systems. His work consistently bridges theoretical foundations in nonlinear control with practical implementations in robotics, particularly addressing challenges in limited sensor fields of view, distributed task allocation, and noise-robust navigation. The research shows increasing integration of formal methods like Signal Temporal Logic with learning-based approaches. Tron received his Ph.D. from The John Hopkins University and previously conducted post-doctoral research at the GRASP Lab, University of Pennsylvania. His work demonstrates significant contributions to provably safe autonomous systems through frameworks like the Control Barrier Function Toolbox.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۵ مورد دیگر

Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.

• Hydrogen Production
• CO2 Capture
• Solar Thermal Energy
• +۳ مورد دیگر

Dr. Mehdi Jafarian is a Senior Lecturer in the School of Chemical Engineering at the University of Adelaide . His work focuses on hydrogen production , CO2 capture , solar thermal energy , and chemical looping combustion . Key research areas include: Solar thermal integration in industrial processes Hydrogen generation via methane pyrolysis CO2 sequestration technologies Advanced water treatment systems Thermochemical energy storage Research Trends : Recent publications emphasize hydrogen production optimization , PFAS removal , and molten metal reactor systems . Sub-fields span flash reactor modeling , hydrodynamic cavitation , and membrane-free electrolysis . Contact : mehdi.jafarian@adelaide.edu.au

• Spectroscopy
• Photophysics
• Organic Optoelectronics
• +۴ مورد دیگر

Professor Fernando Dias is a distinguished academic in the Department of Physics at Durham University, specializing in advanced photophysics and optoelectronic materials. His research focuses on developing novel organic molecules for applications in light-emitting devices, particularly exploring the fundamental mechanisms behind near-infrared emission, room-temperature phosphorescence, and thermally activated delayed fluorescence. Professor Dias' research interests center on spectroscopy and photophysics of organic molecules for optoelectronic and photonic applications. He investigates how molecular structure influences emission properties, with particular emphasis on NIR emitters and the interplay between different excited states. His work bridges fundamental photophysical understanding with practical applications in organic light-emitting diodes (OLEDs), where he explores how molecular design can optimize device efficiency and performance. The research group examines energy transfer mechanisms, excimer formation, and the relationship between molecular conformation and luminescent properties. Analysis of Professor Dias' recent publications reveals a strong trend toward developing advanced materials for next-generation optoelectronic devices. His work consistently focuses on platinum and iridium complexes, boron-containing compounds, and molecular designs that exploit thermally activated delayed fluorescence mechanisms. The research spans fundamental photophysical characterization to device implementation, with a clear trajectory toward improving efficiency and color purity in organic light-emitting technologies. Recent work shows increasing sophistication in molecular engineering approaches, including dendronized structures, multimolecular excited states, and precise control of molecular conformation to optimize emission properties. Professor Dias actively supervises PhD students including Rongjuan Huang, Piotr Pander, Carolina Francener, and Lucy Weatherill. His departmental responsibilities include serving as Chair of the Health & Safety Committee, International Coordinator for Physics, and Exchange Coordinator for Physics. He teaches fourth-year undergraduate courses in Optical Devices and Postgraduate courses in Optical Spectroscopy, in addition to directing Level 1 Discovery Labs, tutorials, Team Projects, and Level 4 project supervision.

Shangyou Zhangمشاهده پروفایل

دانشیار

• Finite Element Methods
• Numerical Analysis
• Partial Differential Equations
• +۳ مورد دیگر

Shangyou Zhang is an Associate Professor in the Department of Mathematical Sciences at the University of Delaware, affiliated with the College of Arts & Sciences. He holds a B.S. from the University of Science and Technology of China and a Ph.D. from The Pennsylvania State University. His research focuses on finite element methods, numerical analysis, and computational mathematics, with an emphasis on constructing and analyzing finite elements for partial differential equations, including superconvergence and error analysis. He has contributed extensively to the development of conforming and nonconforming elements for various equations such as the Stokes, biharmonic, and Maxwell equations, with a particular interest in divergence-free and H(div)-conforming elements. His work bridges theoretical analysis and practical numerical methods, addressing challenges in mesh adaptivity, stabilization, and high-order accuracy. Education: B.S. (USTC), Ph.D. (Penn State) Affiliations: University of Delaware, Department of Mathematical Sciences Research interests include finite element methods for PDEs, superconvergence, and numerical solutions of fluid dynamics and elasticity. Recent work emphasizes weak Galerkin methods, HDG schemes, and stabilized finite elements for complex geometries. His publications span topics like C1-Pk elements, divergence-free discretizations, and error estimates for mixed formulations. Publications highlight contributions to high-order elements, locking-free plate models, and robust discretizations for singular perturbations. Teaching includes computational mathematics courses such as MATH 426 (Spring 2025).

Scott E. Parkerمشاهده پروفایل

استاد

• Plasma Physics
• Magnetic Fusion Energy
• Computational Physics
• +۴ مورد دیگر

Scott E. Parker is a Professor of Physics at the University of Colorado Boulder and a Fellow at both the Renewable and Sustainable Energy Institute (RASEI) and the Center for Integrated Plasma Studies (CIPS). His expertise lies in theoretical and computational plasma physics for magnetic fusion energy. Education: Ph.D. in Engineering Science, University of California, Berkeley B.S. in Nuclear Engineering and Mathematics, University of Wisconsin, Madison His research centers on plasma turbulence simulation using advanced computational methods. Key areas include gyrokinetic theory, tokamak edge physics, and quantum information science applications to plasma modeling. The group develops particle-based simulations in five-dimensional phase space to study transport in magnetically confined plasmas, utilizing massively parallel computing and scientific visualization. Analysis of his publications reveals consistent focus on numerical techniques for plasma turbulence, particularly trapped electron modes and zonal flows. His work bridges fundamental kinetic theory with practical fusion reactor challenges, demonstrating qualitative agreement with experimental transport observations in tokamaks. Awards: RASEI Fellow CIPS Fellow Professor Parker leads the Parker Research Group, mentoring graduate students in computational plasma physics. The group collaborates on extreme-scale computing projects for fusion energy research, though specific grant details are not provided in source materials. Their work is integral to advancing predictive capabilities for next-generation fusion devices. The research group maintains active projects in magnetic fusion energy, utilizing GEM simulations and multiscale kinetic methods to address edge turbulence and transport phenomena critical for reactor viability.

Katie Morrisonمشاهده پروفایل

استاد

• Mathematical Neuroscience
• Neural Networks
• Neural Coding
• +۳ مورد دیگر

Katie Morrison is an Associate Chair and Professor in the Department of Mathematical Sciences at the University of Northern Colorado (UNC), part of the College of Natural and Health Sciences. Her research focuses on mathematical neuroscience, neural networks, and algebraic coding theory, with a particular emphasis on applying matrix codes and graph-based algorithms to understand neural dynamics. She has been funded by NSF DMS-1951599 and NIH R01 EB022862 grants. Education: Ph.D. in Mathematics (University of Nebraska-Lincoln, 2012), M.S. in Mathematics (2008), B.A. in Mathematics and Psychology (Swarthmore College, 2005), and studies at Budapest Semesters in Mathematics. Her work bridges abstract mathematics with applied neuroscience, including projects like the CTLN initiative. Research trends in her publications emphasize graph-theoretic approaches to neural network dynamics, pattern completion in threshold-linear networks, and algebraic analysis of neural codes. Collaborations with computational topologists and neuroscientists highlight interdisciplinary strengths. Grants and advising: Active in securing federal research funding and mentoring students in interdisciplinary projects. Her lab explores connections between network connectivity and emergent behaviors, with applications to both theoretical and applied neuroscience.

Maxim Raginskyمشاهده پروفایل

استاد

• Probability and Stochastic Processes
• Control Theory
• Machine Learning
• +۵ مورد دیگر

Maxim Raginsky is a Professor at the University of Illinois at Urbana-Champaign, holding appointments in the Department of Electrical and Computer Engineering, Coordinated Science Laboratory, and a courtesy appointment in Computer Science. His work bridges probability, stochastic processes, control theory, machine learning, optimization, and information theory , focusing on modeling, learning, and simulation of nonlinear dynamical systems with applications to advanced electronics, autonomy, and artificial intelligence. Research Interests Nonlinear dynamical systems in machine learning and control Statistical machine learning theory Information-theoretic methods in learning Stochastic control and filtering Scientific Contributions Co-author of foundational monographs on concentration inequalities and generalization bounds Recipient of the NSF CAREER Award (2013) , IEEE Fellow (2025) , and Roberto Tempo Best CDC Paper Award (2024) Editorial roles in Foundations and Trends in Machine Learning , Journal of Machine Learning Research , and SIAM Journal on Mathematics of Data Science Academic Leadership Advising 15+ graduate students and postdocs including Joshua Hanson, Belinda Tzen, and Tanya Veeravalli Teaching core graduate courses: Control of Stochastic Systems , Statistical Learning Theory , Optimization by Vector Space Methods

Michael E. Oskinمشاهده پروفایل

استاد

• Geology
• Structural Geology
• Geomorphology
• +۴ مورد دیگر

Michael E. Oskin is a Professor and former Department Chair (2017-2021) in the Department of Earth and Planetary Sciences at the University of California, Davis. He also serves as faculty director of the Lassen Field Station, part of the UC Natural Reserve System. His research focuses on structural geology, geomorphology, and active tectonics, particularly the interplay between earthquakes, surface processes, and landscape evolution. He specializes in using high-resolution topography and geochronological techniques to study deformation rates, fault mechanics, and tectonic processes in regions like California and the Tibetan Plateau. Education: B.S. in Geology/Engineering Geology, UC Los Angeles (1995) Ph.D. in Geology, California Institute of Technology (2002) Research Interests: His work addresses deformation rates linked to earthquakes, continental deformation mechanisms, and topographic responses to geologic structures. Techniques include field observations, cosmogenic dating, and lidar analysis to quantify processes like fault slip, erosion, and landscape evolution. Publications: Recent work includes studies on fault rupture dynamics (e.g., San Andreas and Ridgecrest earthquakes), geochronological modeling, and tectonic evolution in regions like the Tibetan Plateau. His research bridges structural geology with quantitative methods to advance understanding of active crustal processes. Awards: Recipient of the UC Davis Graduate Program Advising and Mentoring Award (2020). Advising & Grants: Mentor to over 20 graduate students, emphasizing collaborative research, fieldwork, and open science practices. Prioritizes student summer support and funding for field/analytical costs. Leads a lab focused on earthquake geology and hosts weekly reading groups. Labs/Teams: Oversees the Lassen Field Station, teaching the summer field course GEL 110A and conducting research in the region. Active in the UC Natural Reserve System and collaborates with international research groups.

Louis Ohlمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Unsupervised Learning
• Clustering Algorithms
• +۳ مورد دیگر

Louis Ohl is a Research Fellow and Postdoc at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on unsupervised learning methods, particularly clustering algorithms and PU learning, with applications to medical data (e.g., aortic stenosis phenogroups) and material science (e.g., MAX-phase to MXen structure changes). He holds a PhD from Côte d'Azur University (France) and Laval University (Canada), where he developed discriminative clustering techniques for cardiac disease analysis. Education: PhD in Computer Science from Côte d'Azur University and Laval University (France/Canada). Research interests include: Unsupervised and semi-supervised machine learning Clustering algorithm development Medical informatics applications in cardiology Material science computational modeling PU learning frameworks His recent work emphasizes bridging theoretical advancements in clustering with practical applications in healthcare and materials research. Collaborations include projects on aortic stenosis progression prediction and computational studies of structural phase transitions in materials. No scientific awards listed. Active in advising and research grants related to his current projects at STIMA. Associated with the STIMA division's international master's program in Statistics and Machine Learning.

Ian Howatمشاهده پروفایل

استاد

• Glaciology
• Remote Sensing
• Climate Change
• +۲ مورد دیگر

Ian Howat is ENGIE-Axium Professor and Distinguished University Scholar at Ohio State University, where he directs the Byrd Polar & Climate Research Center. His research focuses on ice sheet dynamics using remote sensing and field observations to predict sea-level rise. Research Leadership: Develops advanced satellite observation techniques to monitor glacier retreat, ice shelf stability, and ice-ocean interactions. Leads international collaborations on cryospheric change including NASA-funded projects. Field Expertise: Conducted extensive fieldwork in Greenland, Antarctica, and Iceland. Current research examines Greenland Ice Sheet response to climate forcing using high-resolution elevation models and velocity mapping. Recent Advances: Pioneered methods for generating continent-scale elevation models and detecting glacial lake outburst risks. Authored foundational studies linking ice sheet mass loss to sustained glacier retreat.