جست‌وجوی استاد و پژوهشگر

• Laser Plasma Acceleration
• Particle Acceleration Physics
• Quantum Beam Applications
• +۴ مورد دیگر

Tomonao Hosokai is a Professor at the Department of Quantum Beam Physics within the Institute of Scientific and Industrial Research (SANKEN) at Osaka University. He also serves as Team Leader at RIKEN Spring-8 Center Laser Acceleration Development Team. His research focuses on developing compact particle acceleration technologies using high-intensity lasers, with applications spanning from fundamental plasma physics to medical and industrial applications. Professor Hosokai's research interests center around laser-plasma acceleration, where his team develops table-top GeV-class laser accelerators using plasma waves excited by intense laser pulses. His work extends to quantum beam applications including beam-driven drug discovery, electron linear accelerator applications, and high-power laser processing for material modification and strengthening. His laboratory employs advanced numerical simulations to understand relativistic plasma behavior and develops plasma control techniques for stable electron acceleration. His recent publications reveal a strong focus on multi-stage laser wakefield acceleration, electron injection control, and practical applications of quantum beams. Key trends include developing modular supersonic nozzles for stable acceleration, controlling electron self-injection with asymmetric gas-jet nozzles, and exploring applications in drug discovery and material science. Hosokai leads the Hosokai Laboratory which operates several major research facilities including the LAPLACIAN laser acceleration platform, L-band electron accelerator/THz FEL system, laser peening test equipment, gas jet laboratory, and a high-performance PC cluster with 1024 CPU cores. His team actively participates in international collaborations including the EuPRAXIA project, which aims to develop compact, cost-efficient particle and radiation sources.

• Numerical Linear Algebra
• Optimization Methods
• Statistical Methods
• +۳ مورد دیگر

Dr. Mei Yang is an Assistant Professor of Instruction in the Division of Data Science at The University of Texas at Arlington, where she has been serving since September 2024. Previously, she held positions as a Visiting Assistant Professor of Instruction in the Department of Mathematics (2023-2024) and as a Postdoc Researcher at both UT Arlington and Shanghai Jiao Tong University. Her academic journey includes a PhD from UT Arlington focused on Krylov subspace optimization. Her research spans three primary domains: Numerical Linear Algebra : Developing advanced iterative methods for matrix computations Optimization Methods : Creating efficient algorithms for mathematical programming Statistical Methods : Applying mathematical techniques to data analysis Her recent publications demonstrate a consistent focus on computational mathematics, particularly in developing novel Krylov subspace methods for linear systems, saddle point problems, and inverse problems. This work bridges theoretical mathematics with practical applications in engineering and measurement sciences. Dr. Yang has received significant recognition for her contributions: Innovative Teaching Award (College of Science, 2024) UTA CARES GRANT (2025) She teaches extensively in data science and mathematics, covering courses such as Machine Learning, Optimization, Statistical Inference, and Linear Algebra across undergraduate and graduate programs. No information is currently available regarding student advising or laboratory leadership roles.

• Medical Physics
• Radiation Therapy
• Radiation Dosimetry
• +۳ مورد دیگر

Christopher Schneider is an Academic Medical Physicist at Mary Bird Perkins Cancer Center (2021–present) and an Adjunct Assistant Professor in the Department of Physics and Astronomy at Louisiana State University (2022–present). He holds a Ph.D. in Medical Physics (Louisiana State University, 2019) and a B.S. in Physics (Southeastern University, 2010). He is board-certified in Therapeutic Medical Physics by the American Board of Radiology (DABR, 2022). His research focuses on radiation dosimetry in radiotherapy, including stray radiation exposure characterization via Monte Carlo simulations, analytical models, and measurements. Current interests include reducing radiation-induced side effects and advancing adaptive radiation therapy. He has published extensively on topics like absorbed dose calculations and neutron dosimetry in proton therapy. Key scientific contributions include developing models to predict therapeutic and stray radiation doses, validated through experimental measurements. He has also contributed to integrating analytical models into proton therapy planning systems. Awards: Diplomat of the American Board of Radiology (DABR) in Therapeutic Medical Physics (2022). Grants/Advising: Details not explicitly provided in text. Labs/Teams: Collaborates with LSU’s Physics and Astronomy Department and clinical teams at Mary Bird Perkins Cancer Center.

Edgar Solomonikمشاهده پروفایل

دانشیار

• Tensor computations
• Numerical optimization
• Parallel and high performance computing
• +۵ مورد دیگر

Edgar Solomonik is an Associate Professor at the University of Illinois at Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. His research focuses on high performance computing, parallel algorithms, and tensor computations. He earned a PhD from UC Berkeley (2014) and a BS from UIUC (2010). Notable contributions include the Cyclops Tensor Framework and work on communication-avoiding algorithms. Awards include the NSF CAREER Award (2020) and the Jack Dongarra Early Career Award (2024). He teaches courses on parallel numerical algorithms, tensor computations, and numerical analysis. Current research explores quantum computing, graph algorithms, and efficient tensor operations for scientific computing. Education: PhD, UC Berkeley (2014); BS, University of Illinois (2010) Research interests span numerical linear algebra, parallel programming systems, and quantum algorithms. Key projects include optimizing tensor contractions for quantum chemistry simulations and developing scalable algorithms for distributed-memory systems. His work has been published in top venues like SISC, SIAM Review, and SC conferences.

Aydin Buluçمشاهده پروفایل

دانشیار مدعو

• Parallel Computing
• High Performance Graph Analysis
• Machine Learning
• +۴ مورد دیگر

Aydin Buluç is a Senior Scientist at Lawrence Berkeley National Laboratory (LBNL) and an Adjunct Associate Professor of EECS at UC Berkeley. His work focuses on parallel computing, high-performance graph analysis, machine learning, sparse computations, and computational biology. He leads the Sparsitute DOE center and directs the PASSION Lab, emphasizing scalable algorithms and their applications in genomics and data science. Education: PhD in Computer Science from UC Santa Barbara (2010); BS in Computer Science and Engineering from Sabanci University, Turkey (2005). Research: Develops parallel algorithms for graph analysis, sparse linear algebra, and communication-avoiding techniques. Key projects include HipMCL for metagenome analysis and GraphBLAS for combinatorial computing. Awards: DOE Early Career Award (2013), IEEE TCSC Early Career Award (2015), ACM Gordon Bell Prize Finalist (2022). Teaching: Co-teaches CS267 (Applications of Parallel Computers) at UC Berkeley, emphasizing practical high-performance computing. Professional Roles: Editor of ACM Transactions on Parallel Computing, PC member for major conferences (SPAA, IPDPS), and leader in ExaGraph and DOE initiatives.

Rolf Follathمشاهده پروفایل

پژوهشگر

• X-ray Optics
• Synchrotron Radiation
• Free Electron Lasers (FEL)
• +۳ مورد دیگر

Dr. Rolf Follath is a senior Researcher at the Paul Scherrer Institute (PSI) within the Center for Photon Science , Laboratory for Non-linear Optics . He has led major projects including the ATHOS soft X-ray beamlines at SwissFEL, focusing on beamline design, alignment strategies, and commissioning procedures for hard/soft X-ray systems. Education: PhD in X-ray Interferometry from University of Ulm Key Contributions: Development of collimated plane grating monochromators, X-ray induced damage analysis, and high-resolution spectrometer systems His research spans X-ray optics , beamline engineering , and free-electron laser (FEL) technology , with emphasis on soft/hard X-ray interaction , photon diagnostics , and multi-layer grating applications . Recent publications highlight advancements in tender X-ray spectromicroscopy , stochastic X-ray spectroscopy , and beamline transmission efficiency . Scientific Recognition: Innovation Award on Synchrotron Radiation (with Friedmar Senf) Dr. Follath's work supports SwissFEL and SESAME projects, advancing ultrafast X-ray science and third-generation synchrotron applications . His team's developments enable element-specific 3D imaging and high-brightness FEL operation .

• Terahertz Physics
• High-Field Physics
• Laser Physics
• +۳ مورد دیگر

Alexey Ponomaryov is a Researcher at the Helmholtz Center Dresden-Rossendorf (HZDR), leading the High-Field THz-Driven Phenomena Group within the Institute of Radiation Physics. Based at HZDR's Dresden campus, he conducts experimental research using advanced radiation sources like the ELBE electron linear accelerator to investigate matter under extreme terahertz electromagnetic fields. His primary research focuses on Terahertz Physics, High-Field Physics, and Laser-Matter Interactions, with specialization in ultrafast dynamics in condensed matter systems. Key areas include high-intensity terahertz pulse generation, nonlinear material responses, and applications in semiconductor spectroscopy. This work bridges fundamental radiation physics and applied optoelectronics, leveraging HZDR's unique high-field infrastructure for breakthroughs in light-matter interaction studies. Dr. Ponomaryov operates within HZDR's Institute of Radiation Physics, contributing to Germany's strategic research in radiation-based sciences. His group collaborates internationally using facilities like the ELBE Center for High-Power Radiation Sources, advancing methodologies for probing transient states in quantum materials under extreme electromagnetic conditions.

Ivor Simpsonمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Medical Image Analysis
• +۱۲ مورد دیگر

Dr. Ivor Simpson is an Associate Professor in Informatics at the University of Sussex, affiliated with the School of Engineering and Informatics and part of the AI research group. He serves as the Academic Lead for Sussex AI and convenes the MRes in Advanced Artificial Intelligence. His academic career includes prior roles as a Senior Lecturer (2022–present) and Lecturer (2020–2022) in Artificial Intelligence at the same institution. His educational background includes a DPhil in Medical Image Analysis from the University of Oxford (2008–2012) and an MEng in Computer Science from the University of Southampton (2004–2008). Prior to his academic appointments, he worked as a Senior Research Scientist and Head of Machine Learning at Anthropics Technology (2014–2019) and as a Research Associate at University College London (2012–2014). Ivor’s research lies at the intersection of machine learning and applied statistics, with a focus on probabilistic modeling for imaging and temporal data. His work emphasizes uncertainty quantification, interpretable AI, and deep generative models, applied to medical image analysis, computer vision, and ecological monitoring. He is particularly interested in inverse problems, morphometry, image registration, and multi-modal MRI analysis. His recent publications (2022–2025) reflect a strong trend toward uncertainty-aware models in medical imaging, ecoacoustics, and computer vision. Topics include compressed sensing MRI, variational autoencoders, deep ensembles, and interactive ecoacoustic dashboards. These works demonstrate interdisciplinary collaboration and methodological innovation, particularly in quantifying spatial uncertainty and building interpretable representations. MICCAI Young Scientist Award (2013) for work on probabilistic non-linear registration Dr. Simpson actively supervises students, currently serving as primary supervisor for 6 PhD candidates and secondary supervisor for several others. He has led or participated in multiple research grants from EPSRC, NERC, and Innovate UK, supporting projects in ecoacoustics, MRI modeling, and machine learning applications. His teaching includes advanced machine learning, computer vision, and the MRes in Advanced AI. He leads a research team focused on interdisciplinary AI applications and collaborates widely across academia and industry.

Bosen Lianمشاهده پروفایل

استادیار

• Cooperative Control
• Graphical Games
• Inverse Reinforcement Learning
• +۹ مورد دیگر

Bosen Lian is an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University's College of Engineering. He received his Ph.D. in Electrical Engineering from the University of Texas at Arlington, where he also served as a postdoctoral researcher and adjunct professor before joining Auburn in August 2023. His research focuses on distributed control, reinforcement learning, game theory, and their integration in cyber-physical and robotic systems. Research Interests: His work centers on designing safe, optimal, and resilient multi-agent systems through data-driven control and inverse reinforcement learning. Key areas include cooperative control, graphical games, robotics, distributed estimation, and anomaly detection in autonomous systems. He applies these methods to robotic platforms such as robot arms, unmanned vehicles, and sensor networks. His recent publications show a strong trend in leveraging inverse reinforcement learning for multi-agent and adversarial systems, with applications in tracking, estimation, and optimal control. He frequently publishes in top IEEE journals and Automatica, demonstrating consistent contributions to control theory and machine learning integration. Scientific Awards and Recognition: Appointed Associate Editor at IEEE Transactions on Neural Networks and Learning Systems Appointed Associate Editor at IEEE Transactions on Cybernetics Appointed Associate Editor at Transactions of the Institute of Measurement and Control Published first textbook: 'Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games' Advising and Grants: He actively mentors students and researchers such as Wenqian Xue, Vrushabh S. Donge, and others, many of whom are co-authors on his publications. While specific grant details are not listed, his continuous publication output and editorial appointments suggest strong research support and funding. His lab conducts rigorous theoretical analysis alongside practical robotic applications. Labs and Teams: His research group focuses on cyber-physical feedback systems, working on both theoretical algorithms and real-world implementations. The team explores dynamic clustering, consensus, and learning-based control in networked environments, maintaining a balance between provable guarantees and data efficiency.

Aziza Mahomedمشاهده پروفایل

استاد

• Engineering Education
• Generative Artificial Intelligence
• Feedback and Assessment in Higher Education
• +۷ مورد دیگر

Aziza Mahomed is a Professor in Engineering Education and Deputy Head of the School of Engineering at the University of Birmingham, where she is affiliated with the Department of Mechanical Engineering. She holds a PhD in Mechanical Engineering (Biomaterials) from the University of Birmingham and an MEng from Durham University. She has held academic positions progressing from Teaching Fellow (2015) to Lecturer (2017), Senior Lecturer/Associate Professor (2019), and Professor (2024). Education: PhD in Mechanical Engineering (Biomaterials), University of Birmingham, 2009 MEng in Mechanical Engineering, Durham University, 2005 Postgraduate Certificate in Academic Practice, University of Birmingham, 2017 Her research spans two major domains: biomaterials and biomechanics , focusing on elastomers for joint replacements, accelerated aging, and wear/friction in orthopaedic implants; and engineering education , where her current work centers on generative artificial intelligence, digital feedback tools, and student self-regulated learning. She has published over 20 peer-reviewed articles in journals such as Journal of Biomechanics , Medical Engineering & Physics , and Advances in Engineering Education . Her recent publications show a clear shift toward educational technologies and pedagogical innovation, particularly in feedback systems and inclusive teaching methods. Scientific Awards and Honors: Senior Fellow of the Higher Education Academy (Advance HE), 2020 Fellow of the Institution of Mechanical Engineers (FIMechE), 2019 Chartered Engineer (CEng), 2011 Fellow of the Higher Education Academy (FHEA), 2017 HEFi Education Innovation Award, 2017 Aziza has secured funding from the Royal Academy of Engineering and internal university sources (HEFi). She serves on the academic standards committee of IMechE and is an Ethics Ambassador for the Engineering Professors’ Council. She also acts as an external examiner for UK institutions. She has contributed to public engagement through drama-based STEM outreach and inclusive design projects, such as an aircraft wheelchair seat. Laboratories and Research Teams: Her early research was conducted in biomechanics and biomaterials laboratories at the University of Birmingham, focusing on polymer testing and implant simulation. Currently, her work is associated with engineering education research groups, likely within the School of Engineering, exploring digital learning tools and AI applications in pedagogy.

• Parallel Computing
• Systolic Arrays
• Matrix Operations
• +۴ مورد دیگر

Емина И. Миловановић је редовни професор на Електронском факултету у Нишу, Универзитета у Нишу, где је изабрана 2002. године. Припада Катедри за рачунарство и ради у области рачунарства и информатике. Њена образовна позадина укључује: Докторат из рачунарства и информатике (1993, Електронски факултет у Нишу) Магистратура из рачунарства и информатике (1989, Електронски факултет у Нишу) Диплома из аутоматике (1981, Електронски факултет у Нишу) Професорка Миловановић се бави истраживањима у области паралелног рачунарства, са посебним нагласком на систоличке низове, матричне операције и хардверске имплементације. Њена радови показују систематски приступ оптимизацији алгоритама за специјализоване рачунарске архитектуре. Последњих година, њена истраживања су се проширила на Грид рачунарство и FPGA имплементације. Анализом њених научних радова може се закључити да њена истраживања еволуирају од основних алгоритама за матричне операције (1990-их) ка комплекснијим системима за обраду графова и реорганизације података (2000-их). Посебан допринос представљају њена истраживања у области систоличких низова за ефикасну имплементацију линеарне алгебре. Професорка Миловановић је објавила укупно 25 радова у часописима са IMPACT фактором. Најзначајнији радови обухватају: Радове о систоличким низовима за матричне операције Истраживања у области паралелне обраде графова Грид рачунарство и суперрачунарске платформе Фаулт-толерантне системе за матричне операције Тренутно учествује на националном пројекту. Њена научна активност укључује менторство младих научника и сарадњу на развоју нових паралелних алгоритама за хардверску имплементацију. Иако текст не наводи експлицитно име лабораторије, њена специјализација указује на рад у лабораторији за паралелно рачунарство и хардверску оптимизацију на Електронском факултету.

• Urology
• Interventional Pulmonology
• Anesthesiology
• +۴ مورد دیگر

Peter Rose Zepernick is a Clinical Professor at Aalborg University's Faculty of Medicine and serves as Chief Physician in the Department of Urology at Aalborg University Hospital since June 1, 2024. Previously, he held the position of specialist consultant in the Department of Anaesthesiology and Intensive Care at the same institution. His professional email is prz@rn.dk. His research spans Urology, Interventional Pulmonology, Anesthesiology, and Radiation Oncology, with core expertise in lung biopsy techniques, apneic oxygenation protocols, fiducial marker development for radiation therapy, and respiratory motion management during interventions. His work frequently employs porcine models to simulate human physiological conditions and advance preclinical validation of medical devices. Publication analysis reveals consistent focus on image-guided procedural innovation, particularly CT-guided biopsies under apneic conditions, NiTi stent applications as radiation fiducials, and real-time tumor tracking systems. These contributions bridge anesthesiology support with precision oncology interventions, emphasizing translational solutions for respiratory and urological pathologies.

Joel S. Emerمشاهده پروفایل

استاد

• Accelerator Architectures for Sparse Computation
• Accelerator Architectures for Deep Learning
• Spatial Processing Architectures
• +۵ مورد دیگر

Joel S. Emer is a Professor of the Practice in MIT's Department of Electrical Engineering and Computer Science (EECS) and a Senior Distinguished Research Scientist at NVIDIA. His research focuses on computer architecture, processor micro-architecture, and performance modeling. He has contributed to advancements in simultaneous multithreading, cache optimization, and reliability analysis. Emer holds over 25 patents and has published over 60 papers, earning awards like the IEEE Rau Award and induction into the National Academy of Engineering. Education: Ph.D., Electrical Engineering, University of Illinois Urbana-Champaign, 1979 M.S., Electrical Engineering, Purdue University, 1975 B.S., Electrical Engineering, Purdue University, 1974 (highest honors) Research interests include accelerator architectures for sparse computation and deep learning, spatial processing, memory hierarchy design, and reliability analysis. His work on Eyeriss and other accelerators has shaped energy-efficient neural network hardware. Recent projects explore hierarchical structured sparsity (HSS) and compute-in-memory (CIM) techniques. Key awards include the ISCA Best Paper Session (2024), IEEE Micro Top Picks (2024), and the SIGMICRO Test of Time Award (2022). He co-advises students with Prof. Vivienne Sze, focusing on sparse tensor acceleration and energy-efficient designs. Awards: 2023 IEEE Rau Award 2022 IASED Lifetime Achievement Award 2020 National Academy of Engineering Membership 2009 Eckert-Mauchly Award Grants and collaborations span industry partnerships (e.g., NVIDIA) and academic initiatives. Emer leads the Emze Group, exploring hardware-software co-design for emerging architectures. Current work includes sparse tensor accelerators (e.g., HighLight, Tailors) and modeling tools like Sparseloop and Accelergy.

Malte von Schevenمشاهده پروفایل

پژوهشگر

• Adaptive structures
• Fluid-structure interaction
• Software Engineering
• +۳ مورد دیگر

Malte von Scheven is a Senior Researcher and Deputy Director at the Institute of Structural Analysis and Dynamics at the University of Stuttgart. He holds a Dr.-Ing. degree (2009) and specializes in adaptive structures, fluid-structure interaction, and computational mechanics. Research Focus: Redundancy matrices for structural assessment, high-performance computing, actuator placement optimization Teaching: Finite element methods, computational mechanics, nonlinear structural analysis Leadership: Deputy Director since 2006, conference organizer for ECCOMAS and SMART symposia His work bridges structural mechanics with bio-inspired design, including studies on sea urchin skeletons as models for segmented shells. He has supervised numerous theses on SFRP composites, topology optimization, and adaptive systems. Scientific Engagement: Published 15+ papers on redundancy matrices and FSI Organized mini-symposia at international conferences (ECCOMAS 2024, SMART 2023) Active in university governance through Faculty Council and TIK committee Recent research investigates mechanical modeling of adaptive structures, with applications in civil engineering and architectural geometry. His redundancy matrix framework provides novel performance indicators for robust design and assemblability assessment.

Samira Shirzaei Nicholsمشاهده پروفایل

استادیار

• Operations Research
• Public Health
• Gender Equity
• +۴ مورد دیگر

Samira Shirzaei Nichols serves as an Assistant Professor in the Department of CISA (Computer Information Systems and Analytics) at the College of Business, University of Central Arkansas (UCA), with contact details including email sshirzaei@uca.edu, office COB 305D, and phone (501) 450-5331. Her academic role integrates quantitative analytics with real-world problem-solving across public health, social equity, and business operations. Her research spans Operations Research, Public Health, and Gender Equity, employing machine learning, statistical modeling, and simulation to address epidemiological forecasting (HIV/COVID-19), gender pay disparities in academia/business, and service system optimization. Key methodologies include ARIMA/SARIMA time-series analysis, RNN neural networks, mixed-methods approaches, and Society 5.0 simulation frameworks, demonstrating interdisciplinary rigor in translating theoretical models to societal challenges like refugee health access and grocery store operational efficiency. Analysis of her 15 publications (2014-2025) reveals an accelerating focus on public health crises since 2020, with 60% of recent work dedicated to pandemic response modeling and gender equity analysis. While early research centered on supply chain optimization (2014-2018), post-2020 outputs pivot toward urgent health applications—evidenced by 5 COVID-19 forecasting studies—and structural inequities, using comparative data (2018 vs. 2021) to quantify gender pay gaps across academic disciplines. Her work consistently bridges technical analytics with human-centric outcomes. No information is available regarding student advising, grant funding, laboratory facilities, or research teams from the provided sources, though her prolific publication record indicates active scholarly engagement.