جست‌وجوی استاد و پژوهشگر

Barry Dunietzمشاهده پروفایل

دانشیار

• Quantum Chemistry
• Electronic Structure Modeling
• Charge Transport
• +۴ مورد دیگر

Barry D. Dunietz is an Associate Professor of Chemistry at Kent State University, specializing in electronic structure modeling and quantum chemistry. His research focuses on excited state dynamics, charge transport, and optoelectronic devices such as solar cells and organic light-emitting diodes (OLEDs). He also explores energy transfer mechanisms in natural photosynthetic systems and nanotechnology applications like molecular conductance. Education: Ph.D. in Chemistry from Columbia University (2000). Dunietz's work is funded by agencies such as the Department of Energy (DOE) and the National Science Foundation (NSF). His group collaborates closely with experimentalists to bridge theoretical insights with practical applications in materials science. Research interests include developing advanced computational methods (e.g., time-dependent density functional theory) to study molecular systems under non-equilibrium conditions. Key areas of investigation are charge transfer dynamics at organic interfaces, molecular conductance predictions using hybrid functionals, and designing materials for improved optoelectronic performance. Publications span over 60 peer-reviewed articles, with recent work addressing triplet excitations in biological systems, computational modeling of photovoltaic interfaces, and novel approaches to enhance charge mobilities in semiconductors. His group's tools, such as the CTRAMER software package, enable correlating molecular structures with charge transfer rates in organic materials. Advising includes current graduate students Roshan Khatri, Aswathy Jayachandran, and Abhishek Bagale, along with former students now in academic and industrial roles. Dunietz’s lab is equipped to tackle interdisciplinary challenges at the molecular-scale, combining theory with experimental validation to advance energy conversion technologies and molecular-level understanding of complex systems.

• Space Physics
• Magnetospheric Dynamics
• Plasma Turbulence
• +۱ مورد دیگر

Dr. Matthew Argall is a Research Professor in Physics & Astronomy at the University of New Hampshire's College of Engineering and Physical Sciences. His research focuses on space plasma physics, magnetospheric dynamics, and space weather phenomena using NASA mission data. Research explores magnetic reconnection processes, plasma turbulence, and energy conversion mechanisms in space environments. Recent work develops machine learning tools for space weather prediction and VR systems for science education. Publications extensively utilize data from MMS, Wind, and Ulysses missions. Educational Contributions: Teaches graduate-level Optics (PHYS 708) Supervises senior design projects (PHYS 797) Mentors doctoral researchers (PHYS 999)

Gregor Betzمشاهده پروفایل

استاد

• Philosophy of Science
• Computational Philosophy
• Argumentation Theory
• +۶ مورد دیگر

Gregor Betz is a Professor of Philosophy of Science at the Karlsruhe Institute of Technology (KIT), where he leads the Institute for Philosophy. His work spans the philosophy of science, computational philosophy, and argumentation theory with applications to climate science, economics, and AI ethics. He has developed formal theories of argumentative debate and computational models that enhance critical thinking teaching. Education : M.A. in Philosophy, Political Sciences, and Mathematics (2002), Dr. phil (Ph.D.) in Philosophy (2004), Dr. habil. in Philosophy (2008) – all from Freie Universität Berlin His research focuses on the limits of scientific prediction (particularly in economics and climate science), the role of values in science, democratic scientific policy advice, and the ethics of climate engineering. Recently, he has pioneered computational philosophy projects using large language models since 2019. Gregor Betz’s publications and projects (like Argunet , Argdown , and the 2020 textbook Argumentationsanalyse ) have significantly impacted argumentation visualization and analysis. In 2023, he founded Logikon AI , a startup applying critical thinking methods to generative AI. His work on NLP and AI includes the first empirical studies of LLM argumentation skills, multi-agent debate simulations, and fallacy detection frameworks. Notably, his 2021 paper on Chain of Thought reasoning preceded similar approaches in GPT-3 by Wei et al.

Ioan Kosztinمشاهده پروفایل

استاد

• Biological Physics
• Theoretical Physics
• Computational Biophysics
• +۵ مورد دیگر

Ioan Kosztin is a Professor in the Department of Physics & Astronomy at the University of Missouri, where he conducts cutting-edge research at the intersection of physics, biology, and computational science. His work focuses on understanding the organization and function of living matter across multiple scales, from atomic to mesoscopic levels. Dr. Kosztin earned his Ph.D. from the University of Illinois at Urbana-Champaign in 1997 under Nobel Laureate Prof. A.J. Leggett, with his doctoral research centered on superconductivity. His academic journey also includes an M.S. from the University of Illinois (1993) and a Dipl. from the University of Cluj, Romania (1985). Professor Kosztin's research spans theoretical and computational biophysics, employing molecular dynamics simulations and non-equilibrium statistical mechanics to investigate biomolecular systems. His work explores mechanical force generation in proteins, energy transfer mechanisms, and transport phenomena in biological systems. Recent publications reveal a strong emphasis on peptide-lipid membrane interactions, utilizing combined theoretical, computational, and experimental approaches to achieve single-residue resolution in understanding these fundamental biological processes. His research has earned significant recognition including the MU Chancellor's Award for Outstanding Research and Creative Activity (2012), the P-G. de Gennes Prize (2010), and the Physics Alumni Faculty Fellow designation (2010). Over the past decade, his work has increasingly incorporated advanced computational techniques, machine learning, and atomic force microscopy to push the boundaries of biomolecular understanding. While specific details about his students and grants aren't provided in the available information, his extensive publication record spanning more than two decades demonstrates a highly productive research program with substantial contributions to biophysics and computational biology. His laboratory appears to maintain active collaborations with experimental groups, bridging theoretical physics with practical biological applications.

Thomas E. Bakerمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Entanglement Renormalization
• +۴ مورد دیگر

Dr. Thomas E. Baker is an Assistant Professor and Canada Research Chair in Quantum Computing for Modelling of Molecules and Materials at the University of Victoria. He holds joint appointments in the Departments of Physics & Astronomy and Chemistry. His research focuses on quantum computing, quantum algorithms, and entanglement renormalization, with applications to quantum chemistry and density functional theory. He leads the sensing and quantum materials cluster under the Office of the VPRI and is a member of Quantum BC and the Centre for Advanced Materials and Related Technology. Education: M.Sc. (California State University Long Beach), PhD (University of California, Irvine), postdoctoral fellow at the Institute for Quantum Computing (IQ) at Université de Sherbrooke, and Fulbright U.S. Scholar at the University of York, UK. Research interests include quantum information processing, quantum error correction, and tensor networks. His team develops the DMRjulia library, a Julia-based tool for entanglement renormalization computations. Recent work emphasizes quantum algorithm design for near-term quantum computers and theoretical foundations of quantum materials. Scientific Awards: Fulbright U.S. Scholar (2018). Teaching includes graduate courses on quantum physics, computational methods, and tensor networks. He actively mentors students across physics, chemistry, and computer science backgrounds. Labs/Teams: Principal Investigator of the sensing and quantum materials cluster, co-developer of DMRjulia, collaborator in Quantum BC.

• Complex Systems
• Statistical Physics
• Network Theory
• +۶ مورد دیگر

Hernan Makse is a Distinguished Professor of Physics at the City College of New York (CCNY) , affiliated with the Levich Institute and Department of Physics. He directs the Complex Networks and Data Science Lab , pioneering research in statistical physics approaches to complex systems. His work bridges brain networks, biological synchronization, social media dynamics, and granular materials. Research Focus: Makse's lab develops emergent laws for complex systems using statistical mechanics, network theory, and machine learning. Key areas include: Brain network optimization via Collective Influence algorithms for neural integration and control Symmetry fibrations in biological systems (e.g., C. elegans connectome) COVID-19 contact tracing through superspreader k-core analysis Election/fake news forecasting via Twitter data mining Granular matter phase transitions and jamming physics Publication Trends: His recent articles focus on network-based prediction systems (elections, pandemics), neural synchronization mechanisms, and AI-driven solutions for biological and social systems. Earlier foundational work established fractality and renormalization in complex networks. Awards & Honors: Elected Member, Academia Brasileira de Ciencias (2023) Academic Leadership: He mentors PhD students in physics/data science and collaborates with Memorial Sloan Kettering (brain tumors), University of Vienna (neural symmetry), and Brazilian health agencies (COVID apps). Grants include NSF-CRCNS and NIH Brain Initiative funding. Lab Initiatives: The lab develops open-source tools for COVID-19 contact tracing and neural symmetry analysis. Current projects include MRI-based breast cancer prediction and neurotransmitter dynamics modeling.

Chunyi Gaiمشاهده پروفایل

استادیار

• Reaction-Diffusion Systems
• Pattern Formation
• Dynamical Systems
• +۵ مورد دیگر

Dr. Chunyi Gai is an Assistant Professor in the Department of Mathematics and Statistics at the University of Northern British Columbia (UNBC), part of the Faculty of Science and Engineering. She holds a PhD in Mathematics from Dalhousie University (2021) and completed a postdoctoral fellowship at the University of British Columbia (2021-2024). Her research focuses on reaction-diffusion systems, pattern formation, and dynamical systems analysis with applications in ecology, morphogenesis, and crime modeling. She employs both analytical methods (e.g., asymptotic analysis) and numerical simulations to study localized patterns, bifurcations, and stochastic influences on system behavior. Education: PhD in Mathematics (Dalhousie University, 2021), Postdoctoral Research (University of British Columbia, 2021-2024). Research emphasizes theoretical development of reaction-diffusion models and their real-world applications, including: Pattern formation in ecological systems and urban crime Stability analysis of spike-like patterns Influence of domain growth and stochasticity on spatiotemporal dynamics Teaching experience includes courses at UNBC (e.g., MATH 335 Numerical Methods), UBC (e.g., MATH 307 Applied Linear Algebra), and Dalhousie University (e.g., MATH 2120 ODE Methods). Her work bridges pure mathematics with applied problems, maintaining a focus on interdisciplinary collaboration.

Jongguen Leeمشاهده پروفایل

استاد

• Combustion Engineering
• Aerospace Propulsion
• Fluid Dynamics
• +۳ مورد دیگر

Professor Jongguen Lee is a faculty member at the University of Cincinnati, specializing in combustion and propulsion systems. His research focuses on combustion dynamics in gas turbines, ramjets, and rocket propulsion, with expertise in laser-based diagnostics, supersonic combustion, and combustion control. He collaborates with major industries like GE Aviation, Pratt & Whitney, and NASA. His work addresses challenges in combustion instability, fuel-air mixing, and multi-phase combustion processes. Recent projects include studies on O2-CO2 combustion for carbon capture and satellite data analysis for riverine phosphorus quantification using machine learning. He has secured federal and industry grants totaling over $140,000, including roles as PI in programs like the Aerospace Propulsion Outreach Program (APOP). His publications span journals such as Flow, Turbulence and Combustion and Atomization and Sprays , with a focus on jet dynamics, spray characterization, and combustion diagnostics. Notable collaborations include AFRL-Wright Patterson, NASA-GRC, and DOE NETL. His research bridges theoretical models and practical applications in aerospace propulsion and environmental sustainability.

Ali Farhadzadehمشاهده پروفایل

دانشیار

• Nearshore Hydrodynamics
• Sediment Transport
• Coastal Flooding
• +۳ مورد دیگر

Ali Farhadzadeh is an Associate Professor at the School of Marine and Atmospheric Sciences, Stony Brook University. His research focuses on nearshore hydrodynamics, sediment transport, and coastal protection systems. He holds a Ph.D. from the University of Delaware's Center for Applied Coastal Research (2011). His work addresses resilient coastal infrastructure, wave-structure interactions, and disaster resilience for vulnerable communities. Education: Ph.D., 2011 - Center for Applied Coastal Research, University of Delaware. Research interests include coastal flooding mitigation, sediment dynamics under extreme events, and marine renewable energy resource assessment. His recent studies explore scour processes, oyster reef functionality, and human-centric flood resilience strategies. Publications highlight trends in numerical modeling of wave impacts, physical experiments on bluff recession, and collaborative projects on debris dynamics. His research integrates computational tools with experimental data to improve coastal hazard prediction and infrastructure design. Grants include SCC-CIVIC-PG Track A (2022) for socioeconomically disadvantaged coastal communities and collaborative NSF projects on scour mechanisms. He leads efforts in experimental facilities and field observations for coastal processes analysis. Labs/Teams: Directs research on coastal hazards and resilience, with contributions to the School's marine and atmospheric science initiatives.

Laurent Heldenمشاهده پروفایل

پژوهشگر

• Colloidal Physics
• Critical Casimir Forces
• Statistical Mechanics
• +۳ مورد دیگر

Laurent Helden is affiliated with the Department of Physics at the University of Stuttgart's Faculty of Mathematics and Physics. His research focuses on colloidal systems, critical Casimir forces, and light scattering techniques, with contributions to self-assembly phenomena and experimental validation of theoretical models. He has collaborated extensively with Prof. C. Bechinger and other researchers, publishing in journals like Physical Review Letters and Nature . Key research areas include quasicrystalline surface tiling, boundary-condition effects in critical Casimir interactions, and advanced microscopy techniques like Total Internal Reflection Microscopy (TIRM). His work bridges experimental physics with computational modeling, addressing fundamental questions in condensed matter and soft matter physics. Recent publications highlight advancements in colloidal assembly patterns, direct force measurements at critical points, and evanescent wave scattering simulations. Collaborative projects involve interdisciplinary teams exploring symmetry-breaking mechanisms and phase transition dynamics in complex fluids.

Andrew Harrisonمشاهده پروفایل

مدرس ارشد

• Bioinformatics
• Functional Genomics
• Biostatistics
• +۳ مورد دیگر

Dr. Andrew Harrison is a Senior Lecturer in the School of Mathematics, Statistics and Actuarial Science at the University of Essex. Originally an astrophysicist specializing in star formation, he transitioned to bioinformatics to explore life detection on exoplanets and apply mathematical-statistical methods to functional genomics. His work involves collaborating with biologists and medics to analyze data-rich experiments like microarrays. Qualifications include a PhD in Astrophysics and BSc in Physics with Astrophysics from the University of Manchester (1991). He has held his current position since 2004. Research interests span bioinformatics applications, computational genomics, and interdisciplinary collaborations in life sciences. Publications reflect expertise in gene selection methods, evolutionary cooperation models, and bioinformatics tools for genomic data analysis. He has contributed to rice stress-resistance databases and protein interaction studies. His work bridges astrophysics-derived analytical techniques with modern biological problems, emphasizing data-driven solutions. Grants and funding details are listed in institutional records, though specific projects are not detailed here. Teaching and supervision activities are integral to his role, though student names are not provided in available texts.

Samy Wu Fungمشاهده پروفایل

استادیار

• Inverse Problems
• Optimization
• Deep Learning
• +۴ مورد دیگر

Samy Wu Fung is an Assistant Professor in the Department of Applied Mathematics and Statistics and affiliated with the Department of Computer Science at Colorado School of Mines. He is part of the Mines Optimization and Deep Learning (MODL) group. Prior to this, he was an Assistant Adjunct Professor at UCLA's Department of Mathematics. He holds a PhD in Applied Mathematics from Emory University (2019) and a BSc in Applied Mathematics from Brown University (2014). His research focuses on the intersection of applied mathematics and data science, particularly in inverse problems, optimization, deep learning, optimal control, and mean field games. His work spans theoretical developments and practical applications, including numerical solutions for Hamilton-Jacobi PDEs and algorithms for swarm control. Recent publications highlight advancements in mean-field games, optimization methods for SAT solving, and generalization bounds for implicit networks. He has received notable awards, including the 2024 Laney Early Career Alumni Award and the 2022 MGB-SIAM Early Career Fellowship. Advising includes PhD students like Soraya Terrab and Alex Vidal. He has secured grants such as the NSF-DMS award (2023) supporting learning-to-optimize research. Teaching roles include courses like Numerical Optimization and Scientific Computing at Colorado School of Mines. His lab, MODL, emphasizes interdisciplinary projects in optimization and deep learning, with collaborations spanning academia and industry. Ongoing work explores explainable AI, hybrid SAT solving, and high-dimensional mean-field control.

Deborah O'Connellمشاهده پروفایل

استاد

• Low-Temperature Plasma Technology
• Plasma Medicine
• Environmental Plasma Applications
• +۴ مورد دیگر

Prof. Deborah O'Connell is a Full Professor and Director of the National Centre for Plasma Science and Technology (NCPST) at Dublin City University (DCU). She holds a PhD from DCU and has held prestigious positions including Chair at the University of York (UK) and EU Marie Curie Fellowship at Queen’s University Belfast. Her research focuses on plasma-based technologies addressing global challenges in biomedical and environmental sectors, including plasma medicine, antimicrobial applications, and green chemistry solutions. Her work emphasizes atmospheric pressure plasmas, plasma activated liquids, and diagnostic techniques. She has pioneered advancements in plasma-jet design and reactive species control. Key achievements include the Quantemol database development and co-founding the International Conference on Data-Driven Plasma Science. Prof. O'Connell has received international recognition, including the EPS/IoP Crookes Prize (2018) and the IoP Hershkowitz Award (2013). She actively contributes to global plasma science through leadership roles in the International Plasma Chemistry Society and IUVSTA's Plasma Science & Techniques Division. Her interdisciplinary research integrates plasma physics, chemistry, and biology to advance therapies and sustainable technologies. Current work explores plasma applications in agriculture, cancer treatment, and environmental remediation.

Zhenzhen Yuمشاهده پروفایل

دانشیار

• Materials Joining
• Welding Metallurgy
• Non-Equilibrium Phase Transformations
• +۵ مورد دیگر

Zhenzhen Yu is an Associate Professor in Metallurgical and Materials Engineering at the Colorado School of Mines. She directs the Center for Welding, Joining, & Coatings Research (CWJCR) and co-directs the Manufacturing and Materials Joining Innovation Center (Ma2JIC). Her research focuses on advanced manufacturing processes, particularly materials joining, non-equilibrium phase transformations during welding, and multi-material system design. She holds a Ph.D. in Materials Science and Engineering from the University of Tennessee, Knoxville, and a B.S. in Mechanical Engineering from East China University of Science and Technology. Research interests include: joining of dissimilar materials, weld microstructure-property relationships, in-situ characterization of transient material states, and stress relaxation cracking susceptibility evaluation. Notable awards include the NSF CAREER Award (2019) and the McKay-Helm Award (2019). Her recent work addresses challenges in high-temperature materials for energy storage systems, stress corrosion cracking mechanisms in nuclear applications, and development of multi-principal element alloys for advanced joining applications. She teaches courses such as Advanced Welding Metallurgy and Solidification. Labs: Center for Welding, Joining, & Coatings Research (CWJCR) Key Projects: Residual stress control in welding, AI-enabled robotic NDE for power plant boilers, molten salt tank failure analysis

Andreas Koutselosمشاهده پروفایل

استاد

• Physical Chemistry
• Molecular Dynamics
• Ion Transport in Gases
• +۴ مورد دیگر

Andreas Koutselos is a Professor of Physical Chemistry at the Department of Chemistry, National and Kapodistrian University of Athens. His research focuses on molecular dynamics, ion transport in gases and supercritical fluids, and computational modeling of chemical systems. He has contributed significantly to understanding electrostatic field effects on ions and biomolecules, as well as environmental applications like pollutant adsorption and photocatalysis. His academic career is marked by studies on gas-phase ion behavior, nonequilibrium thermodynamics, and the interplay between theoretical chemistry and experimental observations. Key research areas include peptide dynamics, ion mobility in supercritical fluids, and the development of novel computational methods for chemical systems analysis. Publications span over three decades, reflecting expertise in both foundational physical chemistry and applied environmental chemistry. Collaboration with international teams has advanced methodologies for simulating complex chemical phenomena, including ion dynamics under external fields and reaction mechanisms in catalytic systems.