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Jeremy SchofieldView profile

Professor

• Theoretical Chemical Physics
• Molecular Dynamics Simulation
• Quantum-Classical Systems
• +4 more

Jeremy Schofield is a Professor of Theoretical Chemical Physics at the Department of Chemistry, University of Toronto. He holds a position at the Lash Miller Chemical Laboratories and leads the Chemical Physics Theory Group. His research focuses on the structure, dynamics, and phases of complex liquid and biological systems, employing analytic theory, computer simulations, and computational methods to study molecular mechanisms in systems like proteins, active colloids, and proton transfer reactions. Education: A.B. in Mathematics and Chemistry (Amherst College, 1988), Ph.D. in Theoretical Chemical Physics (MIT, 1993), and postdoctoral fellowships at the University of Chicago and Northwestern University (1993–1997). Research Interests: The Schofield group investigates non-equilibrium active systems (e.g., Janus motors), protein folding dynamics, and protonated biopolymers. Key projects include understanding charge-dependent macromolecular structure, non-exponential relaxation in glassy systems, and the role of energy landscapes in folding pathways. Recent work emphasizes developing microscopically accurate models for active matter and quantum-classical transitions in proton transfer reactions. Teaching: Teaches graduate courses on dynamics in liquids, molecular simulation, and statistical mechanics, as well as undergraduate courses in statistical mechanics and environmental physics. Lab & Collaborations: Supervises a research group including PhD students and visiting scholars. Collaborates with Ray Kapral’s group on active matter and quantum-classical systems. Active in developing simulation tools for discontinuous molecular dynamics and free energy calculations.

Sarah PiehlView profile

Researcher

• Environmental Monitoring Methods
• Marine Litter
• Eutrophication
• +3 more

Dr. Sarah Piehl is a researcher at the Leibniz-Institute for Baltic Sea Research (IOW) in Rostock, Germany, specializing in environmental monitoring, marine litter, and eutrophication. She holds a PhD in Animal Ecology from the University of Bayreuth (2020), a Master's in Marine Biology from the University of Rostock (2014), and a Bachelor's in Biology from the University of Marburg (2011). Her research focuses on environmental monitoring methodologies, marine litter dynamics, and coastal ecosystem health, particularly in the Baltic Sea. Key projects include analyzing microplastic emissions in estuaries, modeling oxygen deficiency in coastal waters, and assessing the socio-economic impacts of marine hazards through initiatives like the Baltic Sea Action Plan. She has contributed to transnational projects funded by BMBF, EU Horizon Europe, and the German Environment Agency (UBA). Dr. Piehl's work bridges field observations, modeling, and policy, addressing challenges like microplastic transport, hypoxia in marine ecosystems, and pollution mitigation strategies. Her publications span environmental science, marine chemistry, and ecological modeling, with a focus on Baltic Sea ecosystems and sustainable management practices.

Scott Calabrese BartonView profile

Professor

• Electrochemical Engineering
• Catalysis
• Electrochemical Energy Systems
• +7 more

Dr. Scott Calabrese Barton is a Professor in the Department of Chemical Engineering and Materials Science at Michigan State University's College of Engineering. His research focuses on electrochemical engineering, catalysis, and materials for energy systems, particularly fuel cells and bioelectrocatalytic processes. He holds a Ph.D. in Chemical Engineering from Columbia University (1999), an M.S. in Aerospace Engineering from MIT (1991), and a B.A. in Aerospace Engineering from the University of Notre Dame (1989). Education: Ph.D., Chemical Engineering, Columbia University (1999) M.S., Aerospace Engineering, Massachusetts Institute of Technology (1991) B.A., Aerospace Engineering, University of Notre Dame (1989) His research group develops advanced materials and computational models for electrochemical systems, emphasizing sustainable energy solutions. Key areas include: Electrochemical hydrogenation of biomass Molecular dynamics simulations of substrate channeling in enzymatic cascades Nanomaterial design for efficient catalytic reactions Process intensification via nanoscale confinement Recent work includes studies on zinc-catalyzed biomass conversion, electrostatic channeling mechanisms in enzyme systems, and the design of bioelectrochemical interfaces. His team collaborates widely, with projects funded by NSF, DOE, and industry partners. He advises a dynamic group of graduate and undergraduate researchers, including recent students Yan Xie (Ph.D., 2023), Kanchan Chavan (Ph.D., 2020), and Christina Wark (current Ph.D. candidate). His lab is located in the Engineering Building, with active participation in conferences like the Electrochemical Society meetings.

• Materials Science
• Atomistic Modeling
• Magnetic Systems
• +3 more

Prof. Dr. Peter Michael Derlet is an Adjunct Professor at ETH Zurich's Department of Materials and Senior Scientist at Paul Scherrer Institute's Laboratory for Theoretical and Computational Physics. His research specializes in atomistic modeling of materials with complex defect structures, magnetic systems, and radiation effects. Education includes a PhD in Physics from the University of Hamburg. Research integrates computational methods with experimental validation: Dislocation dynamics and irradiation damage in metals Magnetic nanoparticle behavior and spin systems Metallic glass relaxation and aging phenomena Development of magnetic interatomic potentials Recent publications focus on radiation damage in nuclear materials, magnetic reversal mechanisms, and glass transition physics. Collaborative works frequently bridge nanoscale simulations with experimental characterization techniques like neutron scattering and electron microscopy. Dr. Derlet teaches Thermodynamics and Computational Materials Modeling at ETH Zurich. He coordinates multiple SNF-funded PhD projects on correlated disorder magnetism and has supervised 6 doctoral students to completion. Current projects include anomalous thermal transport in spin ices and nanostructure-specific X-ray tomography.

• Sustainable Aviation
• Aerospace Computation
• Aircraft Design Optimization
• +7 more

Dr. Rhea Liem is an Associate Professor in Sustainable Aviation at Imperial College London's Department of Aeronautics, affiliated with the Brahmal Vasudevan Institute for Sustainable Aviation. She holds a PhD from the University of Toronto and prior degrees from MIT and Nanyang Technological University. Her work emphasizes computational science integration into sustainable aviation, focusing on operation-aware aircraft design and air traffic optimization. Her research bridges computational methods with real-world applications, addressing systemic inefficiencies through data-driven frameworks. Education: PhD in Aerospace Engineering (University of Toronto, 2015) S.M. in Aeronautics and Astronautics (MIT, 2010) S.M. in Computation for Design and Optimization (MIT, 2007) B.Eng. in Mechatronics (Nanyang Technological University, 2003) Research Interests: Optimization of aircraft design across flight missions Data-infused physics-based models for fuel efficiency Integration of operational constraints into design frameworks Noise and fuel tradeoffs in trajectory optimization Her work on AirTrafficSim and AeroFuse platforms highlights contributions to education and industry. Awards include the Amelia Earhart Fellowship and Royal Aeronautical Society Fellowship. She has held roles at Hong Kong University of Science and Technology and industry positions in Singapore.

• Geophysics
• Machine Learning
• Differential Equations
• +5 more

Facu Sapienza is a Postdoctoral Scholar in Geophysics at Stanford University, focusing on interdisciplinary research that combines paleomagnetism , differential equations , machine learning , and quantum thermodynamics . His work bridges computational methods with Earth and planetary sciences, particularly through physics-informed machine learning models for glaciology and magnetospheric studies. His recent publications highlight expertise in Universal differential equations for geophysical modeling Statistical learning for remagnetization analysis Quantum thermodynamic resource theory Planetary magnetic field interactions (Mars) Distance learning algorithms with Fermat principles trends in applying Gaussian processes, causal inference, and differentiable programming to geoscience challenges. He maintains an active research profile with collaborations in computational geophysics and quantum information theory, though no formal awards or student advising details are publicly listed in the provided materials.

• Scientific Machine Learning
• Stochastic Modeling
• Numerical Methods for (Stochastic) PDEs
• +4 more

Xiaoliang Wan is a Professor in the Department of Mathematics at Louisiana State University (LSU), with an affiliation at the Center for Computation and Technology (CCT). His research focuses on Scientific Machine Learning, Stochastic Modeling, Numerical Methods for Partial Differential Equations (PDEs), and the Minimum Action Method for Large Deviation Principles. He has developed software tools such as the Multi-Element Probabilistic Collocation Method (ME-PCM) and the hp-adaptive Minimum Action Method for computational physics and uncertainty quantification. Wan has taught numerous advanced courses, including Numerical Analysis, Numerical Linear Algebra, and Stochastic PDEs. His work bridges computational mathematics with applications in fluid dynamics, oceanography, and climate modeling. Key contributions include integrating machine learning with traditional numerical methods to solve high-dimensional PDEs and stochastic systems. His research emphasizes adaptive algorithms, rare event simulation, and parameterization techniques for complex systems. Recent work includes applications of deep learning to ocean turbulence modeling and the development of hybrid FEM-PINN methods for time-dependent PDEs.

• Structural Biology
• Chemical Engineering
• Protein Biochemistry
• +7 more

Brent Nannenga is an Associate Professor of Chemical Engineering at Arizona State University (ASU), affiliated with the School for Engineering of Matter, Transport and Energy and the Biodesign Center for Applied Structural Discovery. He co-directs the NIH-funded MicroED Imaging Center at UCLA, focusing on advancing microcrystal electron diffraction (MicroED) methodology and applications. His research bridges chemical engineering, structural biology, and materials science, emphasizing high-resolution structure determination and biomolecule/material engineering. Education: Ph.D. in Chemical Engineering (University of Washington, 2011); M.S. and B.S.E. in Chemical Engineering (ASU, 2007 and 2005). Research interests include: MicroED development, protein-nanoparticle interactions, cyanobacterial bicarbonate transporters, and bio-inspired materials. His work has led to breakthroughs in structural biology, such as DNA crystal analysis and ferritin biomineralization studies. Articles trends reflect a focus on MicroED applications, crystallography innovations, and interdisciplinary collaborations. Recent work explores 2D covalent organic frameworks, gold complex reactivity, and semiconductor structures. Awards: NSF CAREER Award, AFOSR Young Investigator Award, 2020 Burton Medal, 2022 Margaret C. Etter Early Career Award Advising/Grants: Leads NIH-funded MicroED center; supervises research projects across chemical engineering and bioengineering. Courses taught include advanced lab techniques, transport phenomena, and thesis supervision. Labs/Teams: Biodesign Center for Applied Structural Discovery, MicroED Imaging Center (UCLA), and collaborations in materials science and biochemistry.

Marc DelarueView profile

Research Professor

• Computational biology
• Statistical physics
• Ligand-gated ion channels
• +6 more

Marc Delarue is a Research Professor at Institut Pasteur, Paris, where he leads UMR 3528 CNRS and the "Dynamique Structurale des Macromolecules" unit since 2012. He has been active in structural biology since the early 1990s, combining experimental and computational approaches to study macromolecular systems. Habilitation in Biological Sciences, University Louis Pasteur (1993) Crystallography of Biological Macromolecules (advisor D. Moras), IBMC CNRS (1989) PhD in Molecular Biophysics (advisor Cl. Hélène), University of Paris VI (1984) His research spans multiple disciplines: Structural Biology : Crystallography of biological macromolecules, cryo-EM of complexes Computational Biology : Normal mode analysis, transition path exploration Synthetic Biology : Non-canonical nucleotide incorporation Biochemistry : DNA polymerases and ligand-gated ion channels Recent publications show sustained impact in neuroscience (ion channels), molecular evolution (polymerase architecture), and structural bioinformatics (normal mode analysis). His work bridges experimental structural biology with computational modeling and systems-level approaches. 2012 Prix Thérèse Lebrasseur (Académie des Sciences) 2007 ARTS Laureate (Apple) 2007 Prix La Caze-Lacassagne (Académie des Sciences) As mentor, Delarue has supervised 7 PhD students, 10 Masters students, and 15 postdoctoral fellows. He contributes to scientific governance as reviewer for major journals and grant agencies (ERC, NIH, NSF), and organized three international workshops on structural modeling.

Jianfeng LuView profile

Professor

• Mathematical Analysis
• Algorithm Development
• Computational Physics
• +5 more

Jianfeng Lu is the James B. Duke Distinguished Professor of Mathematics at Duke University, with affiliations in the Department of Mathematics, Chemistry, and Physics within the Trinity College of Arts & Sciences. He holds a Ph.D. from Princeton University (2009). His research focuses on mathematical analysis, algorithm development for computational physics, theoretical chemistry, materials science, and machine learning, particularly in high-dimensional PDEs, quantum dynamics, and multiscale modeling. Notable achievements include the 2024 James B. Duke Distinguished Professorship. His work spans interdisciplinary collaborations, with grants from the National Science Foundation (NSF) and Department of Energy (DOE), addressing challenges in quantum computing, electronic structure theory, and AI-driven material design. Research interests include quantum Markov processes, reinforcement learning theory, and numerical methods for PDEs. He advises numerous graduate students and postdocs, mentoring in applied mathematics and computational physics. His labs and teams contribute to projects like the Duke Materials Initiative and the ELSI collaboration for electronic structure theory.

• Computational Mathematics
• Applied Mathematics
• Stochastic Processes
• +5 more

Di Liu is a Professor in the Department of Mathematics at Michigan State University. His research focuses on computational mathematics, stochastic processes, and quantum mechanics, with applications in chemical kinetics and nano-optics. He teaches courses such as Applied Linear Algebra and Readings in Mathematics. Education: Ph.D. in Applied and Computational Mathematics, Princeton University (1999–2003) B.S. in Computational Mathematics and Its Applied Softwares (1994–1999) His work spans numerical methods for solving Kohn-Sham equations, multiscale modeling of nano-optical systems, and stochastic simulation algorithms with applications in chemical reaction networks. Recent publications highlight network embedding techniques for energy landscape analysis and error estimation in delayed reaction systems. Professor Liu's research trajectory shows consistent innovation in computational methods for quantum dynamics and stochastic systems, with a particular emphasis on adaptive algorithms and multiscale approaches. His publications bridge theoretical mathematics and practical applications in physics and chemistry.

Brian Van KotenView profile

Assistant Professor

• Applied Probability
• Numerical Analysis
• Computer Simulation of Molecular Systems
• +4 more

Brian Van Koten is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on applied probability and numerical analysis, with applications to molecular systems simulation, variance reduction in statistical physics, and numerical methods for saddle point identification. He teaches courses such as Math 652 (Numerical Analysis II), emphasizing computational methods for partial differential equations and optimization in machine learning. Key research interests include computer simulations of biological and materials systems, rare event sampling techniques, and atomistic/continuum coupling methods. He has presented work at institutions like the Institute for Computational and Experimental Research in Mathematics (ICERM) and the Institute for Pure and Applied Mathematics (IPAM), focusing on topics like stratification for Markov chain Monte Carlo and the string method for minimum energy paths. Van Koten's work bridges theoretical numerical analysis with practical computational challenges, addressing issues in multiscale modeling and error analysis. His teaching philosophy integrates advanced numerical methods with real-world applications, supported by detailed course materials on topics like finite difference methods and optimization algorithms for large-scale data science problems.

• Physical Chemistry
• Theoretical Chemistry
• Condensed Matter Physics
• +3 more

John Weeks is a Distinguished University Professor at the University of Maryland, affiliated with the Institute for Physical Science & Technology. His research focuses on theoretical and computational studies of solvation phenomena, interfacial chemistry, and non-equilibrium systems. Key areas include molecular field theory, ion solvation dynamics, hydrophobic effects, and surface electromigration. He investigates long-range forces in aqueous systems, the structural and thermodynamic properties of liquids at interfaces, and the development of advanced simulation methods for complex fluids. His work bridges statistical mechanics, condensed matter physics, and chemical theory to explain molecular-scale phenomena in both equilibrium and non-equilibrium settings. Recent contributions address crystal nucleation mechanisms, the role of distant boundaries in charged particle solvation, and the application of local molecular field theory to ionic systems. His research has implications for understanding biological solvation, materials science, and environmental chemistry. Notable trends in his publications include: (1) advancing multiscale models for long-range interactions in confined systems, (2) analyzing hydrophobic effects through molecular-scale structural changes in water, and (3) developing efficient computational frameworks for simulating Coulombic systems. While no specific awards or grants are listed here, his extensive publication record reflects sustained leadership in theoretical chemistry and materials science. He has advised numerous researchers through collaborative projects at the Institute for Physical Science & Technology.

Ingo ScholtesView profile

Professor

• Machine Learning
• Network Science
• Graph Mining
• +5 more

Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.

Rudolf HanelView profile

Associate Professor

• Complex Systems
• Statistical Physics
• Non-Equilibrium Thermodynamics
• +12 more

Rudolf Hanel is an Associate Professor at the Medical University of Vienna and a faculty member at the Complexity Science Hub. His interdisciplinary research bridges theoretical physics, complex systems, medical imaging, and socio-economic modeling. He is deeply involved in advancing the foundations of statistical mechanics and non-equilibrium thermodynamics. His research interests include complex systems, non-equilibrium thermodynamics, information theory, medical robotics, and social physics. He investigates how systems evolve far from equilibrium, focusing on phase transitions, tipping points, and the emergence of structure. His work applies these principles to diverse domains such as firm dynamics, social cohesion, and pandemic response. The recent trend in his publications reveals a strong focus on generalized entropy, sample space reduction, network-based social modeling, and practical applications in public health. His articles span foundational physics, computational medicine, and socio-economic systems, reflecting a unifying framework of complexity science across disciplines. Thermodynamics of driven systems Generalized entropy and information theory Social fragmentation and homophily Pooled testing for pandemics Firm performance via information consumption Structure-forming systems Rudolf Hanel has received no explicitly mentioned scientific awards in the provided text. He has not been stated to advise any formal students, though he collaborates widely with researchers such as Stefan Thurner, Jan Korbel, and Peter Klimek. He has contributed to major interdisciplinary grants and projects, particularly through the Complexity Science Hub, including work on pandemic testing strategies and economic modeling. He is a key member of the Complexity Science Hub, where he collaborates on foundational and applied research in complex systems. The Hub serves as a central platform for his work in integrating physics-inspired models into social, biological, and economic systems.