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Kwang-Je KimView profile

Professor

• Accelerator Physics
• Synchrotron Radiation
• Free-Electron Lasers (FEL)
• +4 more

Kwang-Je Kim is a Professor of Physics at the University of Chicago and an Argonne Distinguished Fellow at Argonne National Laboratory (ANL). He holds dual affiliations with the Accelerator Systems Division at ANL’s Advanced Photon Source (APS) and the Enrico Fermi Institute (EFI). His research focuses on accelerator physics, synchrotron radiation, and free-electron lasers (FELs), with over 300 publications and a seminal textbook, Synchrotron Radiation and Free Electron Lasers (2017). Kim’s career includes pioneering work at Lawrence Berkeley National Laboratory (LBNL) as Deputy Director of the Center for Beam Physics and founding the Argonne Accelerator Institute. He has developed theoretical frameworks for FELs, synchrotron radiation, and beam dynamics, including groundbreaking contributions to coherent amplification and self-amplified spontaneous emission (SASE). His awards include the APS Fellowship (1995), International FEL Award (1997), USPAS Award (2013), and Robert Wilson Prize (2014). He teaches advanced courses like Introduction to Accelerator Physics and Accelerator Physics and Technologies for Linear Colliders , emphasizing cutting-edge topics such as high-energy colliders and future light sources. Kim collaborates with institutions like Fermilab, ANL, and CERN, advancing accelerator technologies for applications in quantum beam cooling, ionization cooling channels, and next-generation X-ray sources. His research groups explore topics ranging from Smith-Purcell FELs to transverse-emittance exchange mechanisms.

Robert M. FreundView profile

Professor

• Nonlinear optimization theory
• Computational complexity
• Interior-point methods
• +5 more

Robert M. Freund is the Theresa Seley Professor in Management Science at the MIT Sloan School of Management, specializing in Operations Research. His research focuses on nonlinear optimization, computational complexity, and first-order methods with applications in management and engineering. He holds a PhD in Operations Research from Stanford University and has authored influential textbooks like Data, Models, and Decisions: The Fundamentals of Management Science . Education: B.A. in Mathematics (Princeton, 1975), M.S. and Ph.D. in Operations Research (Stanford, 1979–1980). Roles: Faculty Director of MIT Sloan MBA Program, Deputy Dean for Faculty (2008–2011), Co-Director of MIT Program in Computation for Design and Optimization (2004–2008). His research interests span optimization theory, computational science, and applications in machine learning. Notable contributions include advancements in Frank-Wolfe algorithms, interior-point methods, and photonic crystal design. He has received the Longuet-Higgins Prize (2007) and multiple teaching awards. Recent work includes the development of restarted PDHG algorithms, Taylor-approximated gradients for empirical risk minimization, and analysis of first-order methods' computational guarantees. His collaborative efforts with students and researchers like Zikai Xiong and Haihao Lu have produced impactful publications in Mathematical Programming and SIAM Journal on Optimization . Freund has also contributed to educational initiatives, including MIT's Professional Certificate in Data Science and Analytics. His work bridges theoretical optimization advancements with practical applications in engineering and data-driven decision-making.

Riccardo PencoView profile

Associate Professor

• Cosmology
• Astrophysics
• Condensed Matter Physics
• +7 more

Riccardo Penco is an Associate Professor at the Department of Physics, Carnegie Mellon University, within the Mellon College of Science. His research spans cosmology, astrophysics, and condensed matter physics, utilizing quantum field theory and effective field theory (EFT) to connect diverse phenomena such as inflation, dark matter, black hole dynamics, and collective excitations in solids. Education: Ph.D., Syracuse University (2012) Laurea Specialistica, University of Trieste, Theoretical Physics (2006) Laurea, University of Trieste, Physics (2003) His work focuses on symmetry realization in finite-density systems, including EFT descriptions of black hole tidal dynamics, superradiance, and dark matter models. He has contributed to the coset construction for superfluids and Goldstone's theorem at finite density, alongside EFT approaches to black hole quasinormal modes and the classical double copy formalism. Recent publications highlight applications of EFT in cosmology (dark matter-baryon interactions, inflationary perturbations), gravitational physics (Kerr black hole symmetries, Love numbers), and condensed matter (magneto-elasticity, symmetry breaking in fluids). His work often bridges broad disciplines through symmetry analysis and EFT frameworks.

Lilia KrivodonovaView profile

Professor

• Numerical Analysis
• Computational Physics
• Discontinuous Galerkin Methods
• +2 more

Lilia Krivodonova is a Professor in the Department of Applied Mathematics at the University of Waterloo. Her research focuses on developing advanced numerical methods including discontinuous Galerkin techniques, error estimation, adaptive methods, and efficient algorithms for solving partial differential equations on complex geometries. She specializes in computational physics applications such as gas dynamics, acoustics, and wave propagation. Her recent work includes simulations of sound propagation in musical instruments and high-performance computing implementations. Krivodonova maintains an active publication record with recent articles focusing on conservation laws, numerical stability, and GPU-accelerated computations.

Xiantao LiView profile

Professor

• Quantum Computing
• Computational Physics
• Molecular Dynamics
• +5 more

Xiantao Li is a Professor in the Department of Mathematics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds a B.S. from Peking University (1998) and a Ph.D. from the University of Wisconsin, Madison (2002). His research focuses on quantum algorithms, computational physics, and multiscale modeling, with particular emphasis on quantum dynamics, molecular simulations, and numerical methods for complex systems. He teaches advanced courses such as Math 551 and is actively involved in developing novel computational frameworks for quantum systems and material science. His research interests span quantum computing applications, such as quantum speedup for classical simulations and noise mitigation techniques, alongside classical methods like molecular dynamics and reduced-order modeling. He has contributed to the development of structure-preserving algorithms for Hamiltonian systems and has explored hybrid quantum-classical models for biological and chemical systems. His work bridges applied mathematics, theoretical physics, and computational engineering. Dr. Li’s recent publications emphasize quantum algorithms for nonlinear dynamics, error analysis in quantum simulations, and efficient control of open quantum systems. Though no specific awards are listed, his contributions reflect significant innovation in computational methodologies. His academic activities include advising on advanced research topics and collaborating on interdisciplinary projects, though specific student names or grant details are not provided in the text.

Gerassimos PetratosView profile

Professor

• Experimental Nuclear Physics
• Nuclear Structure
• Particle Physics
• +2 more

Dr. Gerassimos (Makis) Petratos is a Professor in the Department of Physics at Kent State University. He earned his Ph.D. in Experimental Nuclear Physics from The American University in 1988 and has held research positions at the University of Rochester and Stanford Linear Accelerator Center before joining Kent State in 1994. His research focuses on the internal structure of nucleons and light nuclei using electron scattering techniques. Education includes: Physics Diploma, University of Athens (1980) M.S. in Physics, The American University (1983) Ph.D. in Physics, The American University (1988) Research interests center on experimental nuclear physics, particularly electromagnetic studies of proton/neutron structure, few-body nuclear systems, spin physics, and deep inelastic scattering experiments conducted at major facilities like Jefferson Lab. His work advances understanding of quark distributions and nuclear dynamics. Publication analysis reveals consistent focus on experimental nuclear physics, with recent work emphasizing precision measurements of nucleon structure functions, electromagnetic form factors, and few-body nuclear dynamics using advanced scattering techniques at Jefferson Lab facilities. Scientific awards include: Excellence in Education Award (2013) Excellence in Advising Award (2012) Distinguished Professor Award (2011) Fellow, American Physical Society Nuclear Physics Division (2008) Research infrastructure includes access to Jefferson Lab facilities for scattering experiments with polarized electron beams and specialized detectors. Current projects examine nucleon structure through MARATHON experiment data analysis.

Xiaojian XuView profile

Professor

• Statistics
• Mathematical Modeling
• Data Analysis
• +3 more

Xiaojian Xu is a Professor of Mathematics at Brock University, holding a PhD from the University of Alberta. His research focuses on optimal regression designs, robust statistical methods, and reliability engineering, particularly in accelerated life testing. He has published extensively in statistics and interdisciplinary fields such as biomedical engineering and sociology. Education: PhD, University of Alberta Research Interests: Dr. Xu specializes in developing robust statistical methodologies for non-standard data scenarios, including censored data, model misspecification, and heteroscedasticity. His work bridges theoretical statistics and applied problems in engineering, health sciences, and social sciences. Key areas include optimal experimental design, sequential sampling strategies, and mixed-effects modeling. Teaching: Recent courses include Mathematical Statistics I (MATH 2P82), Multivariate Statistics (MATH 5P86), and Linear Models (MATH 5P83). Labs/Teams: Affiliated with Brock University's Department of Mathematics and Statistics, contributing to interdisciplinary research initiatives involving reliability analysis and data science applications.

Wenjie NiView profile

Research Fellow

• Nonlinear Partial Differential Equations
• Mathematical Biology
• Reaction-Diffusion Systems
• +1 more

Wenjie Ni is a Research Fellow in the School of Science and Technology at the University of New England. He earned his Ph.D. in Mathematics from Harbin Institute of Technology in 2018, specializing in reaction-diffusion equations and free boundary problems. His research focuses on nonlinear partial differential equations with applications in mathematical biology, particularly examining propagation dynamics in ecological and epidemiological systems. He teaches Linear Algebra and Differential Equations and maintains memberships in professional organizations including the Australian Mathematical Society. His recent publications analyze spatial spread mechanisms in biological systems through advanced mathematical frameworks, with consistent focus on nonlocal diffusion phenomena and free boundary formulations across ecological and epidemic contexts.

John StufkenView profile

Professor

• Design and Analysis of Experiments
• Data Science
• Optimal Design of Experiments
• +5 more

John Stufken is a Professor in the Department of Statistics at George Mason University. Previously, he held roles such as Director of MS Informatics & Analytics and Bank of America Excellence Professor at UNC Greensboro (2019-2022), Charles Wexler Endowed Professor at Arizona State University (2014-2019), and Head of the Department of Statistics at the University of Georgia (2003-2014). He has also served as Program Director for Statistics at the National Science Foundation (2000-2003). Educations: PhD in Mathematics, University of Illinois at Chicago Doctoraal (MSc equivalent) in Mathematics, Universiteit Nijmegen Kandidaats (BSc equivalent) in Mathematics, Universiteit Nijmegen His research focuses on experimental design, data science, and extracting information from data. Key areas include orthogonal arrays, optimal designs, and sub-data selection. He co-authored the influential book Orthogonal Arrays: Theory and Applications and edited the Handbook of Design and Analysis of Experiments . Scientific Awards and Recognitions: Elected Fellow, American Statistical Association Elected Fellow, Institute of Mathematical Statistics Elected Member, International Statistical Institute Rothschild Distinguished Visiting Fellow (2011) His work has been funded by the National Science Foundation, and he has advised numerous students through his academic roles. He has contributed to interdisciplinary collaborations in informatics, biostatistics, and neuroimaging.

• Magnetic Particle Imaging
• Tomographic Imaging
• Image Reconstruction
• +3 more

Prof. Tobias Knopp holds dual professorships in experimental Biomedical Imaging at the University Medical Center Hamburg-Eppendorf (UKE) and Hamburg University of Technology (TUHH). He leads the Institute for Biomedical Imaging and serves as Editor-in-Chief of the International Journal on Magnetic Particle Imaging (IJMPI). His expertise spans tomographic imaging, signal processing, and magnetic particle imaging (MPI), with a focus on advancing imaging technologies for clinical applications. Education: Tobias Knopp earned a Diplom in Computer Science (2007) and a PhD (2010) from the University of Lübeck, both with highest distinction. His doctoral work on MPI earned the Klee Award (2011). Career highlights include leading the MAPIT project, publishing the first MPI textbook, and developing the first commercial MPI system with Bruker Biospin (2011–2014). Research Interests: His work revolves around MPI innovation, including image reconstruction algorithms, system matrix optimization, and biomedical applications. Recent studies focus on reducing power consumption in MPI systems, improving motion correction in imaging, and applying deep learning for enhanced reconstruction. Publications reflect advancements in MPI fundamentals, such as field calibration using ellipsoidal harmonics, GPU-accelerated algorithms, and neural network-driven solutions. Awards include the Klee Award acknowledging his foundational contributions to MPI technology. As a leader in interdisciplinary research, Knopp collaborates across institutions and industries to bridge gaps between biomedical imaging theory and clinical practices. His lab develops cutting-edge tools like TrainingPhantoms.jl and explores MPI applications in perfusion imaging and catheter guidance.

Gregory OngieView profile

Assistant Professor

• Machine Learning
• Medical Imaging
• Computational Imaging
• +5 more

Dr. Gregory Ongie is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Marquette University. He holds a Ph.D. in Applied Mathematical and Computational Sciences from the University of Iowa (2016), an M.S. in Mathematics (2011), and a B.S. in Mathematics and Physics from Coe College (2008). His research focuses on machine learning, optimization, and compressed sensing applied to medical imaging, particularly in MRI and CT reconstruction. He has developed algorithms for inverse problems in imaging, including deep equilibrium models and Neumann networks. Research Interests: Mathematics of deep learning, applied algebraic geometry in data science, machine learning for medical image reconstruction, and optimization in computational imaging. His work bridges theoretical foundations of neural networks with practical applications in medical imaging, including model observer analysis and compressed sensing techniques. Grants and Awards: NSF CISE Research Initiation Initiative Grant (2022), Way Klinger Early Career Award (2023), D. C. Spriestersbach Dissertation Prize (2018), and Best Student Paper Award at ISBI 2015 for MRI super-resolution work. Teaching: Courses include Applied Linear Algebra, Differential Equations, and Mathematics of Medical Imaging. He has mentored multiple graduate and undergraduate students in imaging-related projects. Active in service roles, including Marquette's Faculty Hiring Committee and seminar coordination. Labs & Collaborations: Engaged in collaborative research with institutions like the University of Chicago and University of Michigan, focusing on computational imaging and machine learning. GitHub repositories include tools for matrix completion and MRI reconstruction.

Mohammad Ali MiriView profile

Associate Professor

• Optics
• Photonics
• Quantum Computing
• +7 more

Mohammad Ali Miri is an Associate Professor of Optics and Photonics at the Department of Physics within the School of Mathematics and Natural Sciences at Queens College of the City University of New York. His research focuses on advanced optical systems, quantum computing, and photonic integrated circuits, with particular emphasis on applications in signal processing, sensor technology, and optimization. He holds a BSc in Electrical Engineering from Shiraz University (2004-2008), followed by MSc degrees in Electrical Engineering (Sharif University of Technology, 2008-2010) and Optics (CREOL, University of Central Florida, 2010-2013), as well as an MSc in Physics (University of Central Florida, 2011-2013). He earned his PhD in Optics from CREOL, University of Central Florida (2010-2014). Miri's research interests span topics such as programmable photonic networks, quantum-assisted optimization, laser array synchronization, and topological photonics. Recent work includes innovations in photonic matrix multipliers, entropy-based quantum computing paradigms, and microwave beam steering systems. His lab actively seeks graduate students interested in experimental and theoretical photonics research. He teaches courses on electromagnetism and electromagnetic theory, including PHYS 311 (Electromagnetism II) and PHYS 615.2 (Electromagnetic Theory II). His work has been published extensively in high-impact journals, with a focus on interdisciplinary applications of optics and photonics.

• Nuclear Engineering
• Nuclear Data Measurement
• Thermal Neutron Scattering
• +3 more

Dominik Fritz is an Assistant Professor in the Department of Mechanical, Aerospace and Nuclear Engineering at Rensselaer Polytechnic Institute (RPI). He holds a B.S. (2018) and Ph.D. (2022) in Nuclear Engineering from RPI, with doctoral research focused on neutron moderator system design and thermal neutron cross-section measurements. After completing his Ph.D., he worked as a Senior Nuclear Engineer at NorthStar Medical Radioisotopes before returning to RPI's faculty in Fall 2024. His research centers on the Gaerttner LINAC facility, a 60 MeV electron linear accelerator, with key focus areas including nuclear data validation, thermal neutron scattering analysis, high-energy photon interactions, medical radioisotope production, and high-heat-flux target design. Student opportunities involve equipment design for radiation environments, experimental execution, multiphysics radiation modeling, and nuclear data validation. Current positions are available for students interested in his research group focused on advanced radiation systems and nuclear applications.

John MitchellView profile

Professor

• Optimization
• Integer Programming
• Conic Optimization
• +6 more

John Mitchell is a Professor and Graduate Program Director in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. His research focuses on optimization theory and applications, including integer programming, conic optimization, and complementarity constraints. He holds an additional affiliation with the Department of Industrial and Systems Engineering. Key research interests include sparse/low-rank optimization with applications in compressed sensing, genome-wide association studies, and financial optimization. His work extends to practical domains like disaster management (resource allocation, humanitarian logistics) and resilient supply chain design. Mitchell's methods address both theoretical challenges (e.g., exact formulations) and real-world problems such as network interdiction and infrastructure restoration. Publications from 2015-2025 highlight interdisciplinary applications: from ridesharing algorithms and trafficking network disruption to community detection in graphs and neuromorphic computing optimization. His work bridges mathematical rigor with societal impact in areas like emergency evacuation modeling and fair division approaches for humanitarian logistics. His research emphasizes algorithm development (e.g., branch-and-cut methods) and convex reformulations for nonconvex problems. Current projects explore quantitative resilience metrics for multi-echelon supply chains and ground-truth community detection via modularity optimization.

Adeel AkhtarView profile

Assistant Professor

• Aerial Robotics
• Control Systems
• Mechatronics
• +3 more

Adeel Akhtar is an Assistant Professor in the Department of Mechanical & Industrial Engineering at the New Jersey Institute of Technology (NJIT), USA. He holds a Ph.D. (2018) and MASc (2012) in Mechanical and Mechatronics Engineering/Electrical and Computer Engineering from the University of Waterloo, Canada, and a B.E. (2006) in Mechatronics Engineering from NUST, Pakistan. His research focuses on aerial robotics, nonlinear control systems, geometric control theory, and multi-agent coordination. His academic career includes work on path-following algorithms for robotic systems, hybrid control frameworks, and stabilization techniques for underactuated systems. Key contributions involve applying global parameterization and control barrier functions to ensure robust performance in challenging environments. Research interests span aerial robotics, control systems design, and mechatronics integration. Recent work emphasizes safety-critical control strategies for autonomous systems and coordinated multi-agent operations in dynamic environments. Award-winning educator with a focus on system dynamics and robotics pedagogy. Maintains an active research lab exploring advanced control methodologies for robotic vehicles and aerospace applications.