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Rui QiaoView profile

Professor

• Fluid Dynamics
• Heat Transfer
• Nanotechnology
• +6 more

Rui Qiao is a Professor and John R. Jones III Faculty Fellow in the Department of Mechanical Engineering at Virginia Tech. He leads the Laboratory of Transport Phenomena for Advanced Technologies, focusing on fluid dynamics, heat transfer, and interfacial phenomena with applications in energy systems, advanced manufacturing, and nanotechnology. His research integrates molecular, mesoscopic, and continuum modeling to address challenges in fields like thermal management and electrochemical energy storage. Education: Ph.D. (2004) in Mechanical Engineering from the University of Illinois, Urbana-Champaign; M.S. (1999) in Thermal Engineering from Tsinghua University; B.S. (1996) in Power Engineering from Huazhong University of Science and Technology. Research Interests include nanoscale fluid transport, colloidal physics, and computational methods such as molecular dynamics and dissipative particle dynamics. His work addresses applications in hydrocarbon extraction, thermal management, and aeroengine reliability. Awards include the 2023 ASME Fellow, John R. Jones III Faculty Fellowship (2017), and multiple teaching and research awards from Clemson University. He has secured $10.5M in grants from agencies like NSF, DOE, and industrial partners. Professional memberships include the American Society of Mechanical Engineers (ASME), American Physical Society (APS), and American Chemical Society (ACS).

Nenad S. SimonovicView profile

Research Professor

• Atomic Physics
• Quantum Mechanics
• Mesoscopic Physics
• +5 more

Nenad S. Simonovic is a Research Professor at the Institute of Physics Belgrade (IPB) and Full Professor (part-time) at the Faculty of Sciences, University of Banja Luka (UniBL). He leads the Laboratory for Atomic Collision Processes (LACP) at IPB. His research focuses on atomic and mesoscopic systems under strong fields, quantum dots, and semiclassical dynamics. He has held visiting positions at institutions like MPIPKS Dresden and TU Wien. Education: BSc (1985), MSc (1990), PhD (1993) in Physics from the University of Belgrade. Research Interests: Strong-field ionization, quantum entanglement, laser-atom interactions, computational methods in atomic physics. His work bridges classical and quantum dynamics, with applications in semiconductor physics and surface interactions. Collaborations: Long-term partnerships with international groups in Germany, Austria, Spain, and Colombia. Labs/Teams: Head of LACP, active in theoretical and computational atomic physics.

Mark PritzkerView profile

Professor

• Electrochemical Engineering
• Fuel Cells
• Batteries
• +5 more

Mark Pritzker is a Professor in the Department of Chemical Engineering at the University of Waterloo, specializing in electrochemical engineering, fuel cells, batteries, and nanotechnology. His research focuses on advanced materials for energy storage systems (e.g., redox flow batteries, PEM fuel cells) and nanomaterials for biomedical applications. Collaborations include industry partners like BlackBerry and the Automotive Fuel Cell Cooperation Corporation. Education: PhD (Virginia Tech, 1985), MS (UC Berkeley, 1978), BEng (McGill University, 1976). Research emphasizes electrodeposition processes, catalyst layer defect analysis, and hybrid materials development. Notable projects include zinc-cerium redox flow batteries, platinum-based catalysts for PEMFCs, and peptide-carbon nanotube hydrogels for tissue engineering. His work integrates experimental and computational approaches, such as kinetic Monte Carlo simulations and mesoscopic modeling. Teaching includes courses on electrochemical engineering, wastewater treatment, and applied mathematics (e.g., CHE 331, CHE 574). Recent publications (2020–2025) address redox flow battery optimization, oxidative stress modeling in cell cultures, and advanced electrode materials. His research has led to innovations in sustainable energy systems and biomedical scaffolds.

Wenrong CaoView profile

Associate Professor

• Continental Crust Evolution
• Tectonic Processes
• Structural Geology
• +3 more

Dr. Wenrong Cao is an Associate Professor in the Department of Geological Sciences and Engineering at the University of Nevada Reno. His research focuses on the compositional, structural, thermal, and rheological evolution of continental crust at convergent plate boundaries, integrating field studies, laboratory analyses (LA-ICPMS, EPMA, EBSD), and numerical simulations. Key study areas include the Sierra Nevada Batholith (California), Basin and Range Province (Nevada), and Gangdese Batholith (southern Tibet). He teaches courses such as Structural Geology, Field Methods, and Lithospheric Dynamics. Education includes a Ph.D. from the University of Southern California (2015) and a B.S. from China University of Geosciences, Beijing (2009). Research emphasizes tectonic-climate interactions, magma emplacement mechanisms, and crustal-scale deformation. Dr. Cao is actively seeking motivated Ph.D./M.S. students to join his research group. Research Themes: Continental crust evolution, metamorphic core complexes, crustal-scale deformation, lithospheric dynamics Methodologies: Field mapping, geochemical analysis, numerical modeling, FDM simulations Key Regions: Sierra Nevada, Basin and Range, Gangdese Batholith His work bridges micro-scale structural analysis with large-scale tectonic processes, contributing to understanding how crustal thickening, magmatism, and erosion interact across geologic time. Recent studies highlight the role of buoyancy-driven upwelling in crustal dynamics and the impact of climate-tectonic feedbacks on continental evolution.

• Atomic, Molecular, and Optical Physics
• Quantum Optics
• Light-Matter Interaction
• +4 more

Ana Asenjo Garcia serves as Associate Professor of Physics in the Department of Physics at Columbia University, where she leads the Asenjo-Garcia Lab. Her research bridges theoretical quantum optics, quantum information science, and many-body physics, focusing on emergent phenomena in open quantum systems. She joined Columbia in January 2019 after completing her PhD at Universidad Complutense de Madrid and prestigious postdoctoral fellowships at ICFO Barcelona and Caltech. PhD: Universidad Complutense de Madrid (2014) Marie Curie Postdoctoral Fellow: Institute of Photonic Sciences (ICFO) IQIM Fellow: California Institute of Technology Her research centers on theoretical quantum optics with emphasis on light-matter interactions in far-from-equilibrium quantum systems. Key interests include quantum many-body dynamics, superradiance, quantum simulation, and protocols for quantum information processing. Her work explores how collective quantum effects enable novel phenomena like self-organization and emergent order in atomic arrays, with applications in quantum metrology and quantum computing. The lab develops theoretical frameworks connecting fundamental quantum optics to experimental platforms in atomic physics and photonics. Analysis of recent publications reveals strong focus on quantum entanglement engineering, dissipative state preparation, and topological aspects of light-matter interactions. Her group pioneers methods for controlling quantum dynamics in structured environments, with particular emphasis on correlated decay phenomena and quantum-enhanced sensing protocols. The research demonstrates consistent innovation in connecting abstract quantum theory to experimental implementations. Packard Fellowship NSF CAREER Award AFOSR Young Investigator Prize Sloan Fellowship Professor Asenjo Garcia mentors four PhD students in the Asenjo-Garcia Lab, including Silvia Fernanda Cardenas Lopez, Edgar Guardiola Navarrete, Joseph T Lee, and Eric Sierra-Garzo. Her research is supported by multiple federal grants including NSF and AFOSR funding. The lab maintains active collaborations with experimental groups at UC Berkeley (Stamper-Kurn), Caltech, and international institutions including MPQ and the University of Copenhagen. The Asenjo-Garcia Lab operates as a theoretical research group specializing in quantum optics and many-body physics. The team combines analytical methods with numerical simulations to investigate emergent phenomena in quantum systems, with particular focus on atom-photon interactions in structured environments. Current projects explore quantum information storage, non-linear quantum optics, and metrology applications using engineered atomic arrays and photonic structures.

Yong HanView profile

Researcher

• Computational Materials Science
• Condensed Matter Physics
• Surface and Interface Science
• +3 more

Yong Han is a Research Scientist at the Ames National Laboratory (U.S. Department of Energy) and the Department of Physics and Astronomy at Iowa State University, where he has been employed since 2011. He holds a Ph.D. in Materials Science and Engineering (2007) and an M.S. in Physics (2004) from the University of Utah. His professional trajectory includes positions as a Postdoctoral Research Associate at Ames Laboratory (2007–2011) and Graduate Research/Teaching Assistant roles at the University of Utah. Dr. Han's research integrates computational simulation and physical modeling across atomic to mesoscopic scales. Key focus areas include: Thermodynamic/kinetic properties of bulk materials, surfaces, and interfaces Growth mechanisms of nanofilms and self-assembly of nanostructures Quantum effects in graphene, topological insulators, and silica nanopores Electronic structure analysis using density functional theory, kinetic Monte Carlo, and molecular dynamics His work frequently collaborates with experimental groups utilizing scanning tunneling microscopy. Analysis of recent publications (2023–2025) reveals dominant themes in computational materials science, including intercalation chemistry in 2D materials, metal nanocluster dynamics, surface diffusion phenomena, and method development for nanostructure simulation. Research consistently bridges theoretical frameworks with experimental validations. Dr. Han contributes to major projects including Development of Exascale Software for Heterogeneous & Interfacial Catalysis and Theoretical & Computational Tools for Modeling Energy-Relevant Catalysis . He maintains active collaborations with national laboratories and experimental research teams.

Davit PotoyanView profile

Associate Professor

• Computational Biophysics
• Molecular Dynamics
• Biomolecular Condensates
• +7 more

Davit Potoyan is an Associate Professor in the Department of Chemistry at Iowa State University's College of Liberal Arts and Sciences, with courtesy faculty appointments in both the Biochemistry, Biophysics and Molecular Biology (BBMB) and Bioinformatics and Computational Biology (BCB) programs. His research group, CATBio (Computation, AI & Theory-Driven Biophysics), develops computational approaches to study biomolecular systems. Professor Potoyan's research interests focus on computational biophysics, particularly using multi-scale computational tools rooted in statistical physics, machine learning, and data analytics to study biologically motivated problems. His group investigates the condensation of disordered proteins and nucleic acids, enzyme dynamics, chromatin organization, and epigenetic networks of stem cells. Current research is organized into four main directions: developing AI- and physics-based methods to predict allosteric communication in biomolecular assemblies; uncovering sequence-encoded dynamics in biomolecular condensates using multi-scale computational models; constructing mesoscopic and systems-level models to elucidate transcriptional and epigenetic regulation of chromatin; and advancing generative and explainable AI approaches for modeling and interpreting biomolecular dynamics. Analysis of his recent publications (2024-2025) reveals a strong focus on biomolecular condensates, chromatin organization, and protein-RNA interactions. His work combines computational approaches with theoretical biophysics to address fundamental questions in phase separation, allosteric regulation, and nuclear organization. The research spans from atomistic simulations to mesoscale modeling, demonstrating an integrated approach to understanding biological phenomena across multiple spatial and temporal scales. Professor Potoyan actively recruits postdoctoral researchers, PhD students, and undergraduate students with backgrounds in Physics, Chemistry, Engineering, Computer Science, or Applied Mathematics who have experience with scientific coding (C/C++/Python).

Paola CarboneView profile

Professor

• Computational and Theoretical Chemistry
• Soft Matter Simulations
• Multiscale Modelling
• +6 more

Paola Carbone is a Professor of Computational and Theoretical Chemistry at the University of Manchester's Department of Chemistry, leading research in multiscale simulation techniques. She obtained her PhD in Material Science from Università Bicocca (Milan, 2004) and held postdoctoral positions at the University of Bologna and the Humboldt Foundation (Darmstadt, Germany). Her work focuses on soft matter simulations, including electrolyte/graphene interfaces, polymer composites, and surfactant solutions. She chairs CCP5 and has contributed to strategic roles such as EPSRC's Strategic Advisory Team for E-Infrastructure. Research interests include developing hybrid quantum-mechanical/molecular dynamics models to study complex fluids. Her group explores applications in energy storage, nanomaterials, and sustainable technologies aligned with UN SDGs. Over 98 publications and a project on graphene-based membranes highlight her impact. Awards include the IOP/APS Polymer Lecture Exchange (2015). Labs/Teams: Her research group specializes in multiscale modeling, with recent projects on lithium-ion battery separators and graphene oxide membranes. Supervised 15 PhD students, emphasizing interdisciplinary training.

Anne DraelosView profile

Assistant Professor

• Real-time neural data analysis
• Machine learning for neuroscience
• Adaptive experimental designs
• +7 more

Anne Draelos, Ph.D. is an Assistant Professor with dual appointments in the Department of Biomedical Engineering and the Department of Computational Medicine & Bioinformatics at the University of Michigan. She is also affiliated with the Michigan Neuroscience Institute, the Michigan Institute for Data Science, and the Michigan Institute for Computational Discovery and Engineering. Her research focuses on developing statistically efficient methods for real-time and adaptive neuroscience experiments. Dr. Draelos received her Ph.D. in Physics and M.S. in Electrical & Computer Engineering from Duke University, followed by undergraduate degrees in Physics and Computer Science from North Carolina State University. Her academic journey spans quantum physics, electrical engineering, and neuroscience, reflecting her interdisciplinary approach to complex scientific problems. Her research interests center on real-time analysis of neural and behavioral data using machine learning and statistical techniques. The Draelos Lab develops methods for adaptive stimulation of neural dynamics, multimodal latent space modeling, real-time brain-computer interfaces with neural stimulations, and Bayesian optimization for visual stimuli. Her work bridges the gap between simplistic and complex stimulus spaces to provide new insights into how sensory stimuli are represented in the brains of behaving animals. Analysis of her recent publications reveals a strong focus on developing computational methods that enable real-time analysis and intervention in neural systems. Her work spans from low-dimensional neural manifold modeling to large-scale neural connectivity estimation, with applications ranging from larval zebrafish to non-human primates. She has pioneered approaches that combine streaming data analysis with adaptive experimental designs to accelerate neuroscience discovery. Sloan Fellowship in Neuroscience (2024) Career Award at the Scientific Interface from Burroughs Wellcome Fund (2021) Swartz Foundation Fellow for Theory in Neuroscience (2020) Best Poster Award, IEEE Brain Workshop on Advanced NeuroTechnologies (2020) Ruth K. Broad Postdoctoral Fellowship (2019-2020) Dr. Draelos actively mentors students across multiple levels, including undergraduate, master's, and Ph.D. candidates. Her lab has received funding from multiple prestigious sources including the University of Michigan Research Scout Award, Sloan Fellowship, Burroughs Wellcome Fund, and the National Institute on Aging. Her research philosophy emphasizes tight integration between computational models and experimental neuroscience, creating a feedback loop where models inform experiments and experimental results refine models. The Draelos Lab is located at the North Campus Research Complex (NCRC) at the University of Michigan, where they develop and implement cutting-edge methods for real-time neural data analysis and adaptive experimentation. The lab maintains strong collaborations with several other research groups including the Kaczorowski Lab, Chestek Lab, Savier Lab, Burgess Lab, and Naumann Lab.

Madeleine MsallView profile

Professor

• Condensed Matter Physics
• Acoustic Wave Propagation
• Phonon Dynamics
• +2 more

Madeleine Msall is the Josiah Little Professor of Natural Science in the Department of Physics and Astronomy at Bowdoin College. Her research focuses on condensed matter physics, acoustic wave propagation, phonon dynamics, and semiconductor nanostructures. She holds a PhD in Physics from the University of Illinois at Urbana-Champaign (1994), an MA in Physics from the same institution (1989), and a BA in Physics from Oberlin College (1987). Her work has received support from multiple research fellowships including the Max Planck Institut für Festkörperforschung Research Fellowship, DAAD Faculty Research Visit Fellowship, and Japan Society for the Promotion of Science Invitation Fellowship. She has secured significant grant funding, including a Fletcher Family Research Award and an Air Force Multidisciplinary Research Program award. Currently, she teaches PHYS 3010: Methods of Experimental Physics and serves on program committees for international conferences like PHONONS 2021. She is Bowdoin's representative to the Maine Space Grant Program and actively contributes as a referee for multiple scientific journals.

Adrian FeiguinView profile

Professor

• Computational Condensed Matter Physics
• Quantum Transport
• Strongly Correlated Systems
• +4 more

Adrian Feiguin is a Professor in the Department of Physics at Northeastern University, Boston. His research focuses on computational condensed matter physics, particularly quantum mechanical systems with strong correlations. He explores topics such as quantum transport, exotic phases in cold atom systems, and topological quantum matter. His group also investigates quantum magnetism, spin-charge separation phenomena, and high-temperature superconductivity. Feiguin's work emphasizes developing advanced numerical methods like the Density Matrix Renormalization Group (DMRG) and GPU-accelerated simulations to tackle complex many-body problems. His research interests include: Time-dependent and non-equilibrium quantum dynamics Electronic and spin transport in mesoscopic systems Exotic phases in ultracold atomic gases Decoherence in quantum systems (e.g., N-V centers in diamond) Quantum magnetism in low dimensions Topological order and high-temperature superconductivity Recent work highlights advancements in ultrafast magnetism, quantum spintronics, and neural network approaches to quantum many-body systems. His publications explore novel phenomena like quasi-Fermi liquids, unconventional pairing states, and skyrmion-based spintronic devices. Feiguin has contributed to algorithmic innovations in GPU computing for atomistic spin dynamics simulations and machine learning applications in quantum physics. His research is supported through collaborations with experimental groups and computational infrastructure development. The Feiguin group is actively involved in theoretical modeling of novel materials and quantum systems, with an emphasis on bridging computational methods and experimental observations in extreme conditions (e.g., low temperatures, high magnetic fields).

• Quantum Physics
• Atomic, Molecular and Optical Physics
• Electronics and Sensors
• +3 more

Prof Margaret Reid is a Professor at the School of Science, Computing and Emerging Technologies at Swinburne University of Technology. Her research focuses on quantum foundations, mesoscopic and macroscopic quantum correlations, and quantum optics. She has expertise in phase-space methods for simulating quantum networks and validating quantum computational advantage. Education: BSc (Physics, First Class Honours) from the University of Auckland, New Zealand. Professional roles include ARC Panel of Experts, editorial roles in journals like Physical Review A, and leadership in quantum physics committees. Research interests span quantum correlations in photonic and atomic systems, macroscopic realism tests, and quantum simulation techniques. Her work bridges foundational quantum theory with experimental applications in quantum computing and measurement. Key achievements include the Moyal Medal (2019), Fellowships from the American Physical Society (2017), Australian Academy of Science (2014), and Fellowships in the Optical Society of America (2007). She has supervised multiple PhD students exploring topics like quantum phase-space representations and mesoscopic entanglement. Recent grants include a John Templeton Foundation project on quantum foundations (2023–2026) and collaborations with NTT Research on joint quantum projects (2019–2024). Her publications emphasize quantum validation methods, macroscopic paradoxes, and phase-space simulations in photonic networks.

• Granular Dynamics
• Computational Mathematics
• Stochastic Processes
• +3 more

David Horntrop is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on computational mathematics, granular dynamics, and stochastic processes. He has led and collaborated on federally funded projects including 'Spectral Schemes for Stochastic Partial Differential Equations for Surface Processes' (NSF 2004-2008) and 'SYMCAR. Symbolic Computation and Automated Reasoning for Program Analysis' (NSF 2007-2021). His work integrates mesoscopic simulation techniques with material science challenges, particularly in granular systems and coarsening phenomena. He has been recognized with the 2024 NJIT Nexus of Excellence Award. Projects: 8 federal grants spanning computational mathematics, program analysis, and stochastic modeling Key collaborators: Rosato, Kondic, Michalopoulou, and Siegel Research areas: Density relaxation, granular column dynamics, and mesoscopic modeling Recent contributions include neural network models for granular systems and simulations of coarsening processes. His work bridges theoretical mathematics with applied computational physics.

Jiaoyan LiView profile

Assistant Professor

• Multiscale modeling
• Computational mechanics
• High performance computing
• +3 more

Jiaoyan Li is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo , part of the School of Engineering and Applied Sciences . Her research focuses on multiscale modeling, computational mechanics, and high-performance computing with applications in nano-, bio-, and energy systems. She holds a PhD in Solid Mechanics and Materials Science from George Washington University (2015), an MS from Xi’an Jiaotong University (2009), and a BS from Dalian University of Technology (2006). Education: BS: Engineering Mechanics, Dalian University of Technology (2006) MS: Solid Mechanics, Xi’an Jiaotong University (2009) PhD: Solid Mechanics and Materials Science, George Washington University (2015) Her research interests span nanoconfinement effects on fluids, mesoscopic simulation techniques, and energy systems optimization . Recent work includes studies on hydrocarbon flow in nanochannels, tumor growth modeling, and graphene topological design. She has received prestigious awards such as the ICTAM Early Career Fellowship (2016) and multiple research excellence prizes at George Washington University. Dr. Li’s publications highlight advancements in multiscale methods, including finite element analysis for fracture mechanics and micromorphic theories. She has secured grant support from the NSF Summer Institute on Nano Mechanics and Materials (2012). Her research bridges computational innovation with practical applications in energy, healthcare, and nanotechnology.

Łukasz MachuraView profile

Professor

• Statistical Physics
• Stochastic Processes
• Nonlinear Dynamics
• +7 more

Łukasz Machura is a Professor at the University of Silesia, specializing in statistical physics and nonlinear dynamics. His research focuses on stochastic processes, quantum systems, and mesoscopic phenomena, particularly in Brownian motors and molecular motors. He investigates transport phenomena in mesoscopic systems, quantum open systems, and signal processing in complex systems. His work often employs advanced computational methods like GPU-accelerated Monte Carlo simulations. Key research areas include: Stochastic transport in mesoscopic devices Nonlinear dynamics in Josephson junctions Quantum fluctuations in nanoscale systems Signal analysis for biomedical applications His publications demonstrate expertise in quantum Smoluchowski regimes, absolute negative mobility, and energy conversion in superconducting systems. Recent studies address biharmonic-driven systems and multifractal analysis of biological signals. He contributes to both theoretical and computational physics, bridging fundamental research with applications in nanoelectronics and medical diagnostics.