Professor Dmitry Turaev is a Professor in Dynamical Systems at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His primary role includes teaching courses such as Dynamical Systems and Bifurcation Theory. He is affiliated with the Applied Mathematics and Mathematical Physics groups and the Mathematics research and teaching staff. His research focuses on dynamical systems, chaos theory, bifurcation theory, and their applications in physics and engineering. Education: Ph.D. in Mathematics (details inferred from academic position). Research interests span applied and pure mathematics, with a strong emphasis on dynamical systems, including Hamiltonian systems, homoclinic tangencies, and chaotic behavior in reversible systems. Turaev's work explores complex phenomena such as the emergence of Lorenz-like attractors, Fermi acceleration, and the breakdown of symmetry in dynamical systems. His recent publications highlight studies on pseudohyperbolic attractors, chaotic dynamics in symmetric networks, and nonuniformly expanding random systems. Turaev advises numerous PhD students, reflecting his active role in nurturing the next generation of researchers in dynamical systems. He maintains a lab/working group within the Dynamical Systems group at Imperial College, collaborating with colleagues like Jeroen Lamb and Martin Rasmussen. His research often intersects with interdisciplinary topics like quantum physics and nonlinear optics.
Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.
Anna Perona is a Research Fellow at the University of Warwick's Department of Physics, affiliated with the Physics Centre for Fusion, Space and Astrophysics. Her research focuses on plasma physics phenomena, including magnetic reconnection and energetic particle behavior in fusion and space plasmas. She has conducted postdoctoral work at leading institutions like JET (Culham) and CEA (Cadarache), investigating fast ion dynamics and plasma stability. Her current work emphasizes electron acceleration during magnetic reconnection events, supported by a custom numerical tool analyzing 3D magnetic field effects on electron trajectories. Collaborations include projects with Julia Branke on visualizing plasma dynamics. No awards are explicitly stated, but her work contributes to advancing fusion energy and space plasma understanding. Education & Experience: Postdoctoral Research Fellow at University of Warwick Research stages at JET (Tokamak disruptions) and CEA (fusion physics) Research Interests: Numerical modeling of energetic electrons and ions in magnetized plasmas Magnetic reconnection dynamics and its impact on particle acceleration Development of 3D plasma simulation tools Advising & Grants: No formal advisees listed Collaborations with institutions like JET and CEA Labs & Teams: Part of the Physics Centre for Fusion, Space and Astrophysics at Warwick Contributions to the HAGIS code for energetic ion studies
Benedikt Günther is a research scientist at the Technical University of Munich (TUM) working within the Chair of Biomedical Physics led by Prof. Dr. Franz Pfeiffer. His research focuses on the Munich Compact Light Source (MuCLS), a laboratory-scale inverse Compton X-ray source that provides synchrotron-like radiation for biomedical applications. Günther plays a key role in developing, optimizing, and characterizing this innovative technology, contributing to both its fundamental physics and practical medical applications. His primary research interests center around X-ray physics and imaging techniques, particularly laser enhancement cavities for inverse Compton X-ray sources, X-ray microscopy, dynamic phase-contrast imaging, and X-ray spectroscopy. Günther's work bridges fundamental physics with practical medical applications, developing instrumentation that brings synchrotron-quality imaging to conventional laboratory settings. His research has significant implications for improving medical diagnostics while making advanced imaging techniques more accessible. Analysis of Günther's publication record reveals a consistent focus on advancing compact X-ray source technology and its applications. His work demonstrates expertise in both theoretical modeling and experimental implementation, with publications spanning instrument development, imaging techniques, and specific medical applications. The research shows progression from fundamental source characterization to increasingly sophisticated biomedical applications, particularly in breast imaging, dental diagnostics, and materials science. 2019 Best Poster Award at the combined meeting of the 68th Denver X-ray Conference (DXC) & 25th International Congress on X-ray Optics and Microanalysis (ICXOM) for 'Full-Field Structured Illumination Super-Resolution X-ray Transmission Microscopy' Günther regularly presents his work at major international conferences including the International Particle Accelerator Conference, High-Brightness Sources and Light-driven Interactions Congress, and specialized X-ray imaging meetings. His research is conducted within the Munich Compact Light Source facility, a collaborative project involving physicists, engineers, and medical researchers working to develop laboratory-scale synchrotron technology for widespread biomedical use.
Professor Michael Manhart is affiliated with the Technical University of Munich (TUM) as an Extraordinary Professor in the Department of Hydromechanics . His research focuses on fluid mechanics, turbulent flow dynamics, and computational fluid dynamics (CFD) simulations, particularly in porous media and environmental fluid mechanics. Education: Not explicitly stated in the provided text. His recent publications investigate turbulent flow over random sphere packs, scalar transport at porous-turbulent interfaces, acoustic resonances in HVAC systems, and nonlinear oscillatory flow modeling. He employs advanced numerical techniques like direct numerical simulations (DNS) and large-eddy simulations (LES) to study flow structures, energy budgets, and particle transport mechanisms. Professor Manhart collaborates with researchers such as Yoshiyuki Sakai, Simon Wenczowski, and Daniel Quosdorf. His work addresses applications in environmental engineering, hydraulic modeling, and industrial fluid dynamics, with a strong emphasis on validating computational models against experimental data (e.g., PIV measurements). He leads the Professorship for Hydromechanics at TUM, conducting high-fidelity simulations and experimental studies on topics like wall shear stress estimation, sediment erosion around cylinders, and turbulence decomposition in complex flows.
Matias Zaldarriaga is the Richard Black Professor in the School of Natural Sciences at the Institute for Advanced Study (IAS), Princeton. His research focuses on theoretical cosmology, gravitational waves, and the Cosmic Microwave Background (CMB). He has held previous faculty positions at Harvard University (2003-2009) and New York University (2001-2002). Education: Ph.D. in Physics, Massachusetts Institute of Technology, 1998 Licenciado en Ciencias Físicas, Universidad de Buenos Aires, 1994 Zaldarriaga's work centers on decoding the early universe through CMB analysis and gravitational-wave astrophysics. He investigates inflation, large-scale structure formation, and black hole dynamics, leveraging advanced statistical methods to probe fundamental physics from cosmological data. His recent publications (2023-2025) demonstrate a strong focus on gravitational-wave data analysis, including novel algorithms for detecting binary black hole mergers, constraints on inflationary physics from large-scale surveys, and modeling supermassive black hole evolution. Key themes include higher-order waveform harmonics, pulsar timing arrays, and computational innovations for gravitational-wave astronomy. Awards and Honors: Gruber Cosmology Prize (2021) MacArthur Fellowship (2006) European Physical Society Gribov Medal (2005) Sloan Fellowship (2004) Helen B. Warner Prize, American Astronomical Society (2003) Packard Fellowship (2001) He collaborates extensively with international teams (e.g., LIGO-Virgo-KAGRA, DESI) and mentors researchers in cosmology and astrophysics. His group develops open-source tools for gravitational-wave inference and cosmological parameter estimation.
Dag Hanstorp is a Professor at the Department of Physics, University of Gothenburg. His office is located at Fysikgränd 3, Göteborg (Room F8032), and he can be contacted via email or telephone. His research focuses on experimental atomic/molecular physics and laser applications, including: Quantum phenomena in levitated droplets Ultraprecise spectroscopy of radioactive molecules (e.g., radium monofluoride) Laser-induced dynamics in fuels and aerosols Electron affinity measurements of alkali metals Vacuum laser particle acceleration techniques Spin Hall nano-oscillator characterization Recent publications (2023-2025) demonstrate interdisciplinary work combining atomic physics, fluid dynamics, quantum optics, and nanotechnology. Common themes include advanced laser spectroscopy, quantum system control, and novel imaging techniques applied to fundamental physical processes.
Dr. Andrew Blake is a Lecturer in Experimental Neutrino Physics at the Department of Physics, Lancaster University. His research focuses on experimental neutrino physics, particularly neutrino event reconstruction and oscillation analysis using Liquid Argon Time Projection Chamber (LAr-TPC) technology. He collaborates on major international experiments including MINOS, MicroBooNE, SBND, and the future DUNE project at Fermilab. His primary research interests include: Precision measurements of neutrino oscillations Development of LAr-TPC detection technology Search for new physics beyond the Standard Model Neutrino interaction cross-section measurements CP symmetry violation in the neutrino sector Blake's recent publications (2025) demonstrate strong focus on: Advanced neutrino interaction measurements using MicroBooNE data Development of reconstruction algorithms for LAr-TPCs DUNE experiment capabilities for supernova detection and CP violation studies Validation methods for neutrino-nucleus interaction models He currently advises three PhD students: Krittika Adhikari (Experimental Particle Physics) Rachel Coackley (Experimental Particle Physics) Bethany McCusker (Experimental Particle Physics)
Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.
Martha Constantinou is an Associate Professor of Physics at Temple University, specializing in Theoretical/Computational Nuclear Physics with a focus on Lattice Quantum Chromodynamics (QCD). Her research addresses fundamental questions in hadron structure, including nucleon spin content and proton radius puzzles, leveraging supercomputing resources. She leads a group conducting advanced numerical simulations at major computational facilities. Constantinou holds a Ph.D. in Theoretical Computational Physics (University of Cyprus, 2008) and a BS in Physics (University of Cyprus, 2003). Her work aligns with the upcoming Electron-Ion Collider (EIC) at Brookhaven National Lab, aiming to explore nucleon structure and dark matter connections. Key research areas include generalized parton distributions (GPDs), axial form factors, and high-performance computing applications. Notable awards include the US Department of Energy Early Career Award (2019) and the Selma Lee Bloch Brown Professorship (2020). Her publications (15 most recent listed) emphasize Lattice QCD advancements, with contributions to GPDs, quark-gluon momentum partitioning, and EIC theory. She actively promotes STEM outreach and public engagement through collaborative initiatives.
Ellen Zweibel is the W. L. Kraushaar Professor of Astronomy and Physics at the University of Wisconsin–Madison , where she has been a faculty member since 2003. She holds a joint appointment in the Department of Astronomy and Physics . Zweibel earned her undergraduate degree in Mathematics from the University of Chicago and her Ph.D. in Astrophysical Sciences from Princeton University. Her research focuses on plasma astrophysics , particularly the evolution of astrophysical magnetic fields , cosmic ray feedback in galactic and intergalactic environments, and stellar differential rotation dynamics. Recent work examines cosmic ray interactions with the interstellar medium, magnetic instabilities in galaxy clusters, and turbulence-driven dynamo processes. Zweibel's publications highlight collaborations on missions like HelioSwarm and SOFIA/HAWC+ , including the discovery of a magnetized dust ring in the Galactic Center . She leads NSF-funded research on microscale plasma processes in high-beta environments and contributes to understanding magnetic reconnection across astrophysical contexts.
K.S. Babu, Ph.D. , is a Regents Professor in the Department of Physics at Oklahoma State University . His research focuses on theoretical physics beyond the Standard Model, particularly in neutrino mass models, grand unification, and baryon/lepton number violation. Email: kaladi.babu@okstate.edu Contact: 405-744-5810 | 232 Physical Sciences, OSU Dr. Babu's work spans several key areas: Grand Unified Theories (GUTs): Studies of SO(10), SU(5), and E6 unification frameworks. Neutrino Physics: Development of models like the Zee-Babu mechanism for neutrino masses and research on oscillations. Dark Matter & Cosmology: Proposals for dark matter candidates and connections to inflation and baryogenesis. CP Violation & Leptogenesis: Mechanisms for generating matter-antimatter asymmetry. His recent publications highlight advancements in: Spontaneous CP violation in SO(10) (2025) Left-right symmetric models for leptogenesis (2025) Ultraviolet-completed two-loop neutrino mass models (2025) Probing baryon number violation at IceCube and LHC (2024) Accidental Peccei-Quinn symmetry for axion models (2024) Dr. Babu actively collaborates on international initiatives such as the Center for Theoretical Underground Physics (CETUP) and contributed to the Snowmass 2013 Community Planning Study. His lab has mentored numerous graduate and postdoctoral researchers, including Kirtiman Ghosh, Sudip Jana, and Shaikh Saad.
Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).
Kiwon Um is a tenured Assistant Professor in the Computer Graphics group at Télécom Paris, France, since October 2019. He focuses on physics-based simulations and data-driven approaches using deep learning, with an emphasis on human visual perception in computer graphics and engineering applications. Education: Ph.D. in Computer Science and Engineering from Korea University His research explores effective simulation of natural phenomena through refined data utilization and develops reliable data acquisition methods for machine learning. He also investigates perceptual evaluation of simulations to advance numerical method understanding. Recent work trends include: (1) turbulence modeling with machine learning integration, (2) elastic material simulation stability, (3) fluid dynamics optimization, and (4) differentiable physics frameworks. His publications range from 2008 to 2025, covering topics like SPH solvers, porous shell simulations, and numerical method validation.
Foteini Oikonomou is an Associate Professor at the Department of Physics, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU). She specializes in theoretical astroparticle physics, focusing on extreme astrophysical environments that accelerate particles to energies exceeding 10 20 eV. Current research includes multimessenger emission modeling of active galactic nuclei Expertise in cosmic ray acceleration and high-energy neutrino origin Active in teaching advanced astrophysics and particle physics Her work bridges astrophysics, particle physics, and cosmology, with particular attention to blazars, tidal disruption events, and ultra-high-energy cosmic rays. She contributes to major collaborations like GRAND and GCOS, developing future instrumentation for astroparticle detection. Recent publications explore cosmic ray propagation in diverse source populations, neutrino emission from transient astrophysical phenomena, and magnetic field line effects on particle acceleration. Her research has been featured in journals such as Nature Reviews Physics , Physical Review D , and The Astrophysical Journal . She teaches AST-3451 Astrophysics II and has previously taught particle physics (FY3403/FY8913) and general astrophysics (FY2450). Her outreach includes public explanations of ultra-high-energy cosmic ray research through popular science articles.
