جستجوی استاد راهنمای دانشگاهی

Naresh N. Thadhaniمشاهده پروفایل

استاد

• High-strain-rate mechanical properties
• Shock-compression
• Shock-induced chemical reactions
• +۷ مورد دیگر

Naresh N. Thadhani is a Professor and Chair of Materials Science and Engineering at Georgia Tech, with an adjunct role in the Woodruff School of Mechanical Engineering. His research focuses on shock-induced material changes, high-strain-rate mechanics, and dynamic compaction of powders. He leads a lab equipped with advanced facilities like gas guns and laser-accelerated systems for studying impacts up to 1200 m/s. Education: Ph.D., New Mexico Institute of Mining and Technology (1984); M.S., South Dakota School of Mines and Technology (1981); B.E., University of Rajasthan, India (1980). Research interests include shock compression of metals/ceramics, phase transformations in metallic glasses, and structural energetic materials. His work combines experimental diagnostics (e.g., VISAR, photonic Doppler velocimetry) with computational modeling (CTH/ALE3D codes). Key awards: APS Fellow (2007), ASM International Fellow (2000). Editorial roles include Associate Editor of Shock Waves and Key Reader for Metallurgical and Materials Transactions . Lab & Group: A team of 1 postdoctoral fellow, 11 PhD students, and 3 undergraduates. Over 30 graduates to date. Active in advisory roles for national/international conferences and industrial consultancies. Future work emphasizes nanocomposite magnets and meso-scale modeling of heterogeneous materials under shock.

Christopher M. Wolvertonمشاهده پروفایل

استاد

• Computational Materials Science
• Energy Storage Materials
• First-Principles Quantum Mechanics
• +۷ مورد دیگر

Dr. Christopher M. Wolverton is a Professor of Materials Science and Engineering at Northwestern University , where he leads the Wolverton Research Group . His work focuses on computational materials science with applications in energy sustainability , particularly in batteries , hydrogen storage , and thermoelectrics . PhD in Physics from University of California, Berkeley BS in Physics (summa cum laude) from University of Texas, Austin His research leverages first-principles quantum mechanical simulations and machine learning to enable virtual materials synthesis before laboratory testing. The group specializes in hybrid computational methods integrating Density Functional Theory (DFT) , Monte Carlo simulations , and phase-field microstructural models . The article portfolio shows leadership in energy storage materials , with recent work on data-driven nanoparticle facet control , mixed-anion semiconductors , and machine learning-accelerated discovery . Publications span top journals including Nature Energy , Nature Materials , and Science . 2006 Ford Motor Company Technical Achievement Award 2005 Ford Patent & Publication Awards 2003 Ford Environmental/Physical Sciences Recognition As advisor to PhD candidates Zhenpeng Yao , Shiqiang Hao , and Shane Patel , he fosters interdisciplinary research connecting materials informatics with experimental validation . The group maintains active collaborations with Argonne National Lab and MIT/Harvard teams.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +۸ مورد دیگر

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.

Jack Beuthمشاهده پروفایل

استاد

• Additive Manufacturing
• Manufacturing Processes
• Solid Mechanics
• +۶ مورد دیگر

Jack Beuth is a Professor of Mechanical Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. He has been on the faculty since 1992 and leads the NextManufacturing Center, focusing on additive manufacturing (AM) research. His work emphasizes process mapping for AM, material science, and machine learning integration in manufacturing processes. Key affiliations include the Engineering Research Accelerator and the Manufacturing Futures Institute. Education: Ph.D. in Engineering Sciences, Harvard University (1992) M.S. in Engineering Sciences, Harvard University (1989) M.S. in Engineering Science and Mechanics, Virginia Tech (1987) B.S. in Engineering Science and Mechanics, Virginia Tech (1984) Research Interests: Additive Manufacturing (process modeling, material characterization, and defect analysis) Melt pool dynamics and thermal modeling Machine learning for process optimization and quality control Advanced materials for AM (e.g., Ti-6Al-4V, Inconel 718) His research has led to innovations like 'process map' approaches for AM, enabling better control over variables such as melt pool geometry and microstructure. Awards and Recognition: Ralph R. Teetor Educational Award (1998) George Tallman and Florence Barrett Ladd Development Professorship (2000) ASME Curriculum Innovation Award (2005) Benjamin Richard Teare Teaching Award (2009) Grants and Collaborations: $3.5M cooperative agreement with the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory (ARL) for AI-driven AM process optimization. Collaborations with Westinghouse Electric Company on 3D-printed nuclear components, such as spacer grids for pressurized water reactors. Labs and Teams: NextManufacturing Center: A research hub for AM innovation, emphasizing industrial partnerships and applied research. Beuth’s Additive Lab: Specializes in melt pool analysis, process mapping, and material behavior under AM conditions.

• Medical Imaging
• Radiation Detectors
• Biomedical Engineering
• +۳ مورد دیگر

Mats Danielsson is a Professor at KTH Royal Institute of Technology, leading the Medical Imaging research group within the Department of Particle Astrophysics and Medical Imaging. He has coordinated major projects like the ERC Advanced Grant for the Si3 project (starting 2024) and the EIC Pathfinder's 1MICRON project (starting 2025). His work focuses on advancing photon-counting detectors, X-ray technologies, and medical imaging systems. Notable recognitions include the 2024 KTH Innovation Award and the 2022 Hans Wigzell Science Prize. Danielsson has co-founded companies such as Sectra Mamea AB and C-RAD AB, and holds 135 patents with over 150 scientific publications. Education: MSc (1990) and PhD (1996) from KTH, followed by postdoctoral research at Lawrence Berkeley National Lab (1996–1998). He joined KTH in 1999, where he has held his current professorship since then. His research spans medical imaging, detector innovation, and radiation physics applications in healthcare. Research Interests: Development of high-resolution CT detectors, photon-counting technologies, compact X-ray sources, and AI-driven image processing. His recent work emphasizes minimizing radiation exposure while enhancing diagnostic precision through novel detector designs and machine learning algorithms. Key Projects: ERC Si3 project (3D detector for nuclear medicine), EIC 1MICRON (micrometer-scale imaging), and MedTechLabs collaboration with Karolinska Institutet. He has pioneered innovations such as MicroDose mammography and advanced photon-counting spectral CT systems. Awards: KTH Innovation Award (2024), Hans Wigzell Prize (2022), IVA membership (2017), Polhem finalist (2014), and INGVAR Award (2004). Advising & Grants: Over 150 scientific publications, 135 patents, and leadership in multi-institutional projects. Teaches courses on medical imaging and modern physics at KTH. Labs/Teams: Director of the Medical Imaging Group at KTH, co-founder of MedTechLabs, and collaborator across academia and industry in medical imaging innovation.

Shuo Zhangمشاهده پروفایل

استادیار

• High-Energy Astrophysics
• Particle Astrophysics
• Supermassive Black Holes
• +۵ مورد دیگر

Dr. Shuo Zhang is an Assistant Professor in the Department of Physics & Astronomy at Michigan State University's College of Natural Science. Her research focuses on observational high-energy astrophysics and particle astrophysics, with particular emphasis on supermassive black holes, Galactic cosmic-ray origins, and large dataset analysis. As a member of the Event Horizon Telescope collaboration, she leads X-ray observation campaigns of the Galactic center supermassive black hole and its vicinity. Dr. Zhang received her educational training at prestigious institutions: Ph.D. in Physics, Columbia University, 2016 B.S. in Engineering Physics, Tsinghua University, 2010 Her research interests span observational high-energy astrophysics and particle astrophysics, focusing on supermassive black holes including Sgr A* flaring activities, outburst history, and radiation in quiescence. She investigates Galactic cosmic-ray origins and exotic physics, particularly TeV electrons and PeV protons pointing to Galactic PeVatrons. Her work constrains MeV-GeV proton/electron populations in the central 1 kpc of the Galaxy and examines supernova remnant and molecular cloud interaction sites. Dr. Zhang's recent publications reveal a strong emphasis on multi-messenger astronomy, combining neutrino, X-ray, and radio observations to understand cosmic particle acceleration. Her work spans from Galactic center studies of Sgr A* to extragalactic investigations of active galactic nuclei like M87. The research demonstrates increasing sophistication in analyzing complex datasets from multiple observatories including IceCube, ALMA, NuSTAR, and Chandra. Her notable scientific achievements include: NASA Hubble/Einstein Fellowship at Boston University (2019-2020) Heising-Simons Fellowship at MIT (2016-2019) NASA Earth and Space Science Fellowship for research on Galactic center supermassive black hole Dr. Zhang's career path demonstrates a steady progression from her doctoral work at Columbia University through prestigious postdoctoral fellowships to her current faculty position. She has developed significant expertise in X-ray observations using the NuSTAR space telescope and has been instrumental in Galactic plane survey campaigns. Her research group combines high-energy photon and neutrino signals from PeVatron candidates to address fundamental questions about cosmic-ray origins and particle acceleration mechanisms. As a member of the Event Horizon Telescope collaboration, Dr. Zhang contributes to cutting-edge research on black hole physics, utilizing multi-wavelength observations to understand accretion, feedback, and particle acceleration mechanisms around supermassive black holes. Her work bridges observational astronomy with theoretical astrophysics to address some of the most fundamental questions in modern astrophysics.

Gian Antonio Sustoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Predictive Maintenance
• +۶ مورد دیگر

Gian Antonio Susto is an Associate Professor at the Department of Information Engineering , University of Padova . With a Ph.D. in Information Technology and post-doctoral experience at National University of Ireland, Maynooth, he leads research in Machine Learning , Semiconductor Manufacturing , and Industrial IoT . His work bridges Anomaly Detection , Continual Learning , and Algorithmic Fairness with applications in Hydroelectric Power Plants , Particle Accelerators , and Smart Mobility . B.Sc. and M.Sc. in Controls Engineering, University of Padova (cum laude) Ph.D. in Information Technology, University of Padova (2013) Post-Doc at National University of Ireland, Maynooth (2012-2013) Assistant Professor at University of Padova (2013-2021) His research focuses on Explainable AI , Virtual Metrology , and Deep Learning for manufacturing and infrastructure monitoring. Recent projects include the AIMS5.0 (AI for Manufacturing Sustainability) and MICS (Circular Economy in Italy) initiatives. His publications span Engineering Applications of Artificial Intelligence , IEEE Transactions , and Information Processing & Management , with 15+ recent papers on topics like Fault Diagnosis , Continual Learning , and Fair Ranking . Key scientific awards include: IEEE CCTA Best Student Paper Award (2021) IP&M 2020 Ph.D Paper Award Best Industry Paper Award, European Workshop on Advanced Control and Diagnosis (ACD 2019) He has supervised Ph.D. students on projects involving Particle Accelerators , Plant Behavior Modeling , and Explainable AI , with alumni now at institutions like Max Planck Institute , IBM , and Scripps Research . Current teaching includes Reinforcement Learning and Explainable Machine Learning at graduate and Ph.D. levels.

• Particle Physics
• Cosmology
• Brain Dynamics
• +۱۳ مورد دیگر

Prof. Dr. Ekrem Aydıner is a distinguished physicist at Istanbul University, Faculty of Science, Department of Physics, specializing in High Energy and Plasma Physics. He also serves as a visiting researcher at Koç University and has been invited by Princeton University. With 78 WoS publications and an H-index of 726, his work spans particle physics, cosmology, and complex systems.

Hadi Daneshmandمشاهده پروفایل

استادیار

• Foundations of Machine Learning
• Understanding neural networks
• Optimization for machine learning
• +۳ مورد دیگر

Hadi Daneshmand is an Assistant Professor of Computer Science at the University of Virginia, specializing in theoretical machine learning. Prior to joining UVA, he completed postdoctoral research at FODSI (jointly hosted by MIT and Boston University), Princeton University, and INRIA Paris following his 2020 PhD in Computer Science from ETH Zurich. Education Ph.D. in Computer Science, ETH Zurich, 2020 His research bridges computational perspectives and neural network theory, focusing on theoretical guarantees for deep learning systems. Key interests include understanding neural network mechanisms through optimization frameworks, foundations of machine learning, and stochastic processes in learning systems. His work reveals how neural networks implement computational primitives like gradient descent and optimal transport through architectural components. Recent publications demonstrate a cohesive trajectory analyzing transformers' computational capabilities, batch normalization's theoretical properties, and optimization dynamics in deep learning. His studies consistently establish formal connections between neural architectures and classical optimization methods, particularly in in-context learning scenarios. Scientific Awards Stanford CPAL Rising Star Award Spotlight award at ICML In-context Learning workshop (2024) Postdoc fellowship of the Foundation of Data Science Institute (FODSI) Early Postdoc Mobility grant from SNSF Best poster award at Max Planck ETH deep learning workshop (2016) Reviewer awards for ICML (2022, 2019) and NeurIPS (2020) Dr. Daneshmand actively mentors graduate students, with advisees including PhD candidates at ETH Zurich who have secured positions at Harvard, Yale, Meta, and NVIDIA. His research is supported by competitive grants including the SNSF Early Postdoc Mobility award and FODSI fellowship. He serves the community as Area Chair for NeurIPS 2023-2024 and ICML 2025, and regularly reviews for top machine learning conferences and journals. He teaches specialized courses including "Neural Networks: A Theory Lab" at UVA, emphasizing experimental-theoretical connections in neural computation through hands-on coding exercises.

• Multiscale Modeling
• Multiphysics Modeling
• Biophysics
• +۷ مورد دیگر

Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.

Mark Troddenمشاهده پروفایل

استاد

• Cosmology
• High Energy Theory
• Dark Energy
• +۷ مورد دیگر

Mark Trodden is the Dean of the School of Arts & Sciences and Thomas S. Gates Jr. Professor of Physics and Astronomy at the University of Pennsylvania. He previously served as the Fay R. and Eugene L. Langberg Professor of Physics, Department Chair, and Co-Director of the Center for Particle Cosmology. His career includes faculty roles at Syracuse University (2000–2009) and visiting positions at Case Western Reserve University and Cornell University. Ph.D. and M.Sc. in Physics, Brown University (1992–1995) Advanced Study in Mathematics, University of Cambridge (1990–1991) M.A. in Mathematics, University of Cambridge (1987–1990) Trodden’s research focuses on the intersection of cosmology and particle physics, addressing fundamental questions such as the nature of dark energy, dark matter, the baryon asymmetry of the universe, inflation, and modified gravity theories. His work explores how cosmological data can constrain physics beyond the Standard Model and general relativity. His publications span topics like dark energy models , inflationary spacetimes , topological defects , and BPS states in supersymmetric theories , reflecting his expertise in connecting high-energy physics to cosmological observations. At Penn, Trodden has held editorial roles for journals like Physics Letters B and Journal of Cosmology and Astroparticle Physics , and has contributed to collaborative workshops advancing cosmology and particle physics.

Julia Thom-Levyمشاهده پروفایل

استاد

• Experimental Particle Physics
• Heavy Quark Physics
• Hadron Collider Physics
• +۲ مورد دیگر

Julia Thom-Levy is a Professor of Physics in the College of Arts and Sciences at Cornell University, serving as Deputy Director of the Cornell Laboratory of Accelerator-Based Sciences and Education and Vice Provost for Academic Innovation. Her career at Cornell progressed from Assistant Professor (2005-2012) to Associate Professor (2012-2018) and Professor (2018-present), following prior research roles at SLAC and Fermilab. She earned her Physics Dipl. (1997) and Ph.D. (2001) from Hamburg University. Her research centers on experimental particle physics, with expertise in heavy quark physics, hadron collider physics, and silicon detector development for both particle physics and X-ray science applications. She leads critical work on CMS experiment upgrades at CERN's LHC, focusing on radiation-hard pixel detectors and Higgs boson/top quark physics analysis. Her 2013-2018 publications reveal a consistent focus on precision measurements in top quark physics and Higgs boson studies through CMS collaboration, alongside innovative detector technology development. The research demonstrates strong interdisciplinary connections between high-energy physics and advanced materials science. Awards and Honors: Fellow, German National Scholarship Foundation (1993-1997) She advises graduate student Lena Franklin and has mentored postdocs Joseph Reichert and Jose Monroy, along with undergraduates Hannah Hu, Benjamin Myers, Jeffrey Backus, and Alexander Albert. Her group secures significant funding including a $3.8M NSF grant for early universe research and $2.7M for the Active Learning Initiative. She directs detector R&D for CHESS light source applications and leads Cornell's academic innovation efforts through the Active Learning Initiative, which has transformed classroom instruction across STEM disciplines.

Matthew W. Kunzمشاهده پروفایل

دانشیار

• Astrophysical plasma dynamics
• Instability and turbulence in plasmas
• Transport in weakly collisional plasmas
• +۲ مورد دیگر

Matthew W. Kunz is an Associate Professor of Astrophysical Sciences at Princeton University, serving as Associate Chair of the Department of Astrophysical Sciences and Director of Graduate Studies for the Program in Plasma Physics. He holds a B.S. in Astronomy-Physics and B.A. in Music from the University of Virginia (2003), and a Ph.D. in Physics from the University of Illinois at Urbana-Champaign (2009). His research focuses on astrophysical plasma dynamics, including instability, turbulence, and transport in weakly collisional and poorly ionized plasmas, with applications to galaxy clusters, accretion disks, and the solar wind. Dr. Kunz's work employs analytical and numerical methods to study multi-scale plasma dynamics, aiming to understand angular momentum transport in accretion disks, kinetic turbulence cascades, and magnetic field evolution. His research has been recognized with several awards, including an NSF CAREER Award (2020-25), Alfred P. Sloan Research Fellowship (2017-20), and NASA Einstein Postdoctoral Fellowship (2011-14). He teaches courses on plasma astrophysics (AST 521), irreversible processes in plasmas (AST 554), and astrophysical research methods (AST 303). His publications demonstrate a consistent focus on plasma turbulence, magnetic reconnection, and cosmic ray propagation, with recent work emphasizing collisionless plasma dynamics and high-energy astrophysical phenomena.

Hank Childsمشاهده پروفایل

استاد

• Scientific Visualization
• High Performance Computing
• Computer Graphics
• +۱ مورد دیگر

Hank Childs is a Professor in the School of Computer and Data Sciences at the University of Oregon, specializing in scientific visualization and high-performance computing. He leads the Research Group on Computing and Data Understanding at eXtreme Scale (CDUX) and has held leadership roles including Interim Executive Director of the School of Computer and Data Sciences. His educational background includes a Ph.D. (2006) and B.S. (1999) in Computer Science from the University of California at Davis. Prior to academia, he worked for 14 years at Lawrence Livermore and Lawrence Berkeley National Laboratories, where he served as architect of the VisIt open-source visualization tool. Research interests center on visualizing extreme-scale scientific datasets from supercomputers, with a focus on in situ visualization for cosmology, seismology, and fluid dynamics. He has pioneered projects like VTK-m and Ascent, and his work explores power-performance tradeoffs and data-parallel algorithms for GPUs. Recent publications emphasize scalable visualization techniques for exascale computing, with 15 notable works from 2021-2020 covering particle advection, in situ triggering, and power-aware frameworks. His research has been honored with multiple best paper awards at IEEE LDAV, EGPGV, and SC conferences. DOE Early Career Award (2012) University of Oregon Faculty Excellence Award (2018) 4+ million dollars in research funding since 2013 5 Best Paper awards in 2021 alone As an educator, he received four consecutive CIS Best Teacher Awards (2014-2019). He has served as Associate Editor for IEEE Transactions journals and organized numerous visualization workshops including Dagstuhl seminars and Shonan workshops.

Gene Coopermanمشاهده پروفایل

استاد

• High-Performance Computing
• Computer Systems
• Transparent Checkpointing
• +۴ مورد دیگر

Gene Cooperman is a Professor at the Khoury College of Computer Sciences at Northeastern University, with an affiliation in the College of Engineering. His research focuses on high-performance computing (HPC), transparent checkpoint-restart systems, and model checking. He leads the High Performance Computing Laboratory, where he explores checkpointing technologies like DMTCP, MANA for MPI, and CRAC for CUDA, aiming to enhance HPC workflows on supercomputers such as NERSC's Perlmutter. His work bridges distributed computing, parallel algorithms, and system software to address challenges in fault tolerance, scalability, and resource management. Cooperman has advised 10 PhD students and co-authored over 125 refereed publications, contributing to projects like Geant4-MultiThreaded and Roomy for disk-based computation. His teaching includes courses on computer systems and HPC seminars. Education: Background in computational algebra and parallel computing, transitioning to HPC systems and checkpointing. Research Themes: Transparent checkpointing, MPI agnostic solutions, CUDA integration, and HPC resource optimization. Recent articles emphasize MPI checkpointing, reversible debugging (FReD), and CUDA support, reflecting trends in distributed and GPU-accelerated systems. His grants include NSF, NERSC/DOE, and MemVerge funding. Cooperman collaborates with institutions like CERN and NERSC, advancing applications in particle physics simulations and supercomputing. Current students include Aayushi Gautam, Jiajun Cao, Rohan Garg, and Twinkle Jain.