جست‌وجوی استاد و پژوهشگر

Nicole Eikmeierمشاهده پروفایل

دانشیار

• Network Science
• Higher-Order Interactions
• Dynamic Networks
• +۳ مورد دیگر

Nicole Eikmeier is an Associate Professor at Grinnell College , holding the Luebke-Sproehnle Endowed Chair in Computer Science . Her research focuses on network science , particularly higher-order interactions , spectral clustering , and dynamic network modeling . She teaches courses in algorithms, theory, and the societal implications of technology. Education: Ph.D. in Mathematics, Purdue University (August 2019) B.A. in Computer Science and Mathematics, Concordia College (May 2012) Nicole’s research examines the structure and dynamics of complex networks. She develops models for nonuniform hypergraphs , analyzes centrality measures in dynamic systems , and investigates the topological properties of higher-order networks . Her work bridges theoretical mathematics and applied computer science, with implications for social, epidemiological, and technological systems. Her publications span topics like COVID-19 transmission modeling in educational institutions , tensor decomposition for dynamic topic analysis , and stochastic processes on tree networks . She has presented at premier conferences including ACM SIGKDD and IEEE ICDM . Scientific Awards: NSF Grant Research Corporation for Science Advancement Grant Luebke-Sproehnle Endowed Chair Nicole actively explores educational frameworks integrating ethics into computer science curricula and investigates alliances and leadership in competitive networks . She maintains affiliations with institutions like the Alan Turing Institute .

• Quantum Computing
• Optimization
• Fluid Dynamics
• +۳ مورد دیگر

Zeguan Wu is a NASA Postdoctoral Fellow in the Department of Computer Science at the School of Computing and Information, University of Pittsburgh, collaborating with Professors Juan Jose Mendoza Arenas, Peyman Givi, and Junyu Liu on quantum computing applications for fluid dynamics. Previously, he completed his Ph.D. in Industrial Engineering at Lehigh University under Tamás Terlaky and Xiu Yang. His educational background includes: Ph.D. in Industrial Engineering, Lehigh University (2020-2025) M.S. in Operations Research, Columbia University (2018-2020) B.S. in Material Physics, Nanjing University (2014-2018) Dr. Wu's research centers on quantum algorithm development for optimization and linear algebra problems, with specific focus on adapting interior point methods and tensor-based computations to quantum architectures. His work bridges theoretical quantum computing with practical applications in fluid dynamics simulation and regression analysis, targeting exponential speedups over classical methods through novel preconditioning and inexact solution techniques. His publication trajectory since 2022 reveals concentrated advancement in quantum interior point methodologies across linear and quadratic optimization domains, increasingly incorporating tensor representations and fluid dynamics applications. These contributions appear in leading quantum computing venues including ACM Transactions on Quantum Computing and IEEE workshops, demonstrating rigorous mathematical foundations with practical quantum hardware considerations. His primary scientific recognition is: NASA Postdoctoral Fellowship Funded by the NASA Postdoctoral Program, Dr. Wu contributes to quantum fluid dynamics research while maintaining active professional service. He has not yet served as primary advisor for graduate students but gained teaching experience as a graduate TA at Lehigh University for core engineering courses including Mathematical Optimization and Simulation. At the University of Pittsburgh, he operates within collaborative quantum computing research groups focused on computational fluid dynamics, leveraging institutional expertise in high-performance computing to develop quantum-classical hybrid simulation frameworks for aerospace applications.

Stephen Ashwalمشاهده پروفایل

استاد

• Pediatric Neurology
• Neuroimaging
• Traumatic Brain Injury
• +۵ مورد دیگر

Stephen Ashwal, MD, is a Distinguished Professor in the Department of Neurology at the School of Medicine. With a career spanning over five decades, he has made significant contributions to pediatric neurology, neuroimaging, and traumatic brain injury research. Doctor of Medicine, New York University, 1970 Bachelor of Arts, New York University, 1966 His research focuses on: Pediatric neurology and neurocritical care Advanced neuroimaging techniques (MRI, MRS, DTI) Brain death determination in infants and children Stem cell therapy for hypoxic-ischemic injury Metabolomics in traumatic brain injury Neuroinflammatory mechanisms in juvenile brain trauma Analysis of his 15 most recent articles reveals trends in Neuroimaging biomarkers for pediatric brain injury Development of automated diagnostic tools (CT, MRI) Stem cell interventions in neonatal hypoxia-ischemia Consensus guidelines for disorders of consciousness Metabolic predictors of cognitive outcomes Neuroinflammatory pathways in pediatric neurotrauma Collaborations highlight partnerships with researchers at Loma Linda University (Holshouser, Tong, Obenaus) Elsevier publishers (Swaiman's Pediatric Neurology) Neurology and Pediatrics departments

Souhayl Sadikمشاهده پروفایل

استادیار

• Nonlinear Elasticity
• Kirigami Mechanics
• Continuum Mechanics
• +۵ مورد دیگر

Souhayl Sadik is a Tenure Track Assistant Professor at the Department of Mechanical and Production Engineering Mechanics and Materials, Aarhus University's College of Engineering. His research focuses on nonlinear elasticity, geometric mechanics, and kirigami-based material design. Recent publications address topics like hyper-anelasticity, kirigami mechanics, and geometric theories of morphoelastic shells. His work combines differential geometry with continuum mechanics to analyze complex material deformations and thermoelastic accretion processes. Key research trends include Development of geometric frameworks for nonlinear elasticity Kirigami-inspired design of stretchable and patterned materials Tensor field analysis on deformable surfaces Thermoelastic accretion modeling He leads the ECK: Elasto Capillary-Kirigami (ECK) Test Setup (2020-2021), exploring mechanical behavior of materials with creases and eigenstrains. Contact: sosa@mpe.au.dk | Phone: +45 93 52 26 53

• Muon Physics
• Quantum Chromodynamics
• Theoretical Physics
• +۳ مورد دیگر

Pere Masjuan Queralt is a Professor at the Universitat Autònoma de Barcelona (UAB) in the Department of Physics, affiliated with the Grup de Recerca de Física Teòrica and IFAE. His research focuses on theoretical particle physics, particularly in muon physics, quantum chromodynamics, and advanced mathematical methods like Padé approximants. He holds a Llicenciat from UAB (2004) and has supervised 14 research works. Key research interests include calculating the muon g-2 anomaly through hadronic vacuum polarization and light-by-light scattering, applying Padé approximants to QCD correlators, and modeling complex systems like pandemic dynamics. His work contributes to UN SDG 4 (Quality Education) through advanced physics education and training. Recent publications (2024-2025) explore novel data-driven approaches to precision physics measurements and interdisciplinary modeling of public health crises. Collaborations span international institutions, with active participation in projects like MUonE and pandemic response simulations.

Farida Sohrabjiمشاهده پروفایل

استاد

• Neuroendocrinology
• Neuroinflammation
• Stroke Research
• +۳ مورد دیگر

Farida Sohrabji is a Regents Professor and Department Head of Neuroscience and Experimental Therapeutics at Texas A&M University's School of Medicine. She directs the Women's Health in Neuroscience Program, advocating for sex/gender considerations in medicine. Her research focuses on neuroendocrinology, neuroinflammation, and aging-related stroke mechanisms with emphasis on female vulnerability. Key projects include gut-brain axis interactions, microRNA therapies, and translational stroke treatments. Funded by NIH institutes (NIA, NINDS, NIAAA), her work bridges basic research and clinical applications. Affiliations : Texas A&M College of Medicine, Women's Health in Neuroscience Program Education : Doctorate in Neuroscience Research interests integrate stroke pathophysiology with sex differences, investigating how aging, hormonal status, and comorbidities influence outcomes. Recent studies highlight gut microbiome changes post-stroke and novel therapies targeting peripheral systems like IGF-1 pathways. Behavioral models assess long-term deficits like depression and cognitive impairment. Publications emphasize sex-based disparities in stroke recovery, miRNA neuroprotection, and prenatal alcohol effects on adult health. Her grants include Cox Endowed Chair support and Woodnext Foundation funding. She trains graduate/postdoctoral scholars in translational neuroscience and gender medicine. Current lab projects explore stem cell transplants and epigenetic modifiers for stroke recovery. Labs/Teams: Neurovascular Research Lab, Women's Health in Neuroscience Collaborative Network.

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.

Oliver Pechenikمشاهده پروفایل

استادیار

• Algebraic Combinatorics
• Schubert Calculus
• K-Theory
• +۴ مورد دیگر

Oliver Pechenik is an Assistant Professor in the Department of Combinatorics & Optimization at the University of Waterloo, with a cross-appointment in Pure Mathematics. His research focuses on algebraic combinatorics, particularly Schubert calculus and K-theory, with contributions to crystal graphs, tableau combinatorics, and cyclic sieving phenomena. He completed his PhD at the University of Illinois at Urbana-Champaign, followed by postdoctoral positions at Rutgers University and the University of Michigan. Education: PhD in Mathematics, University of Illinois at Urbana-Champaign (2016) Bachelor’s degree from Oberlin College (2010) Research Interests: Algebraic Combinatorics, Schubert Calculus, K-Theory, Crystal Graphs, Tableau Algorithms, Cyclic Sieving. Key Contributions: Developed new Littlewood-Richardson rules and crystal structures for Grothendieck polynomials. Proved the Cameron-Fon-Der-Flaass conjecture on plane partition periodicity. Advanced combinatorial K-theory via genomic tableaux and glide polynomials. Contributed to the study of plabic graphs and web bases in representation theory. Awards & Fellowships: Philippe Tondeur Dissertation Prize (2016) NSF Research Fellowship (2013–2016) Illinois Distinguished Fellowship (2013–2016) Advising & Grants: Advised over 20 graduate and undergraduate students, with grants from NSERC and the NSF. Active in mentoring and promoting algebraic combinatorics research. Labs & Collaborations: Collaborates internationally on projects in Schubert calculus, K-theory, and combinatorial dynamics. Hosts the annual Algebraic Combinatorics Virtual Expedition (AlCoVE).

Bing Yaoمشاهده پروفایل

استادیار

• Big data analytics
• Physics-informed machine learning
• Computer simulation and optimization
• +۱۲ مورد دیگر

Bing Yao is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee Knoxville, where she holds the Dan Doulet Early Career Assistant Professor title and serves as Director of the RME Program. She previously served as an Assistant Professor at Oklahoma State University's School of Industrial Engineering and Management from Fall 2019 to Summer 2022. Education: Dual-Title PhD in Industrial Engineering and Operations Research, The Pennsylvania State University, 2019 MS in Physics, The Pennsylvania State University, 2015 BS in Physics, University of Science and Technology of China, 2012 Dr. Yao's research centers on developing physics-informed machine learning models for decision optimization in complex systems, with applications in healthcare and advanced manufacturing. Her work integrates statistical learning with physical constraints to model spatiotemporal dynamics, particularly in cardiac electrophysiology and EHR-based disease prediction. She employs deep learning, simulation optimization, and signal processing to address challenges in data quality, missing values, and system complexity. Her recent publications demonstrate a strong trend toward AI-driven healthcare solutions, including personalized cardiac surgery planning, diabetic retinopathy screening, and sepsis prediction using longitudinal EHR data. The research consistently leverages multi-branching neural networks, physics constraints, and tensor-based imputation to improve model robustness and accuracy. Scientific Awards: Best Poster Award, NERCCS, 2018 Susan Schall Fellowship, Penn State, 2018 First Place, IISE Healthcare Systems Student Paper, 2017 IMS/ASA Spring Research Conference Scholarship, 2017 First Place, Penn State IEGA/IME Poster Competition, 2017 Best Poster Finalist, INFORMS MIF, 2016 Samsung Scholarship, USTC, 2011 Dr. Yao actively mentors PhD students, including Jianxin Xie, Zekai Wang, and others, many of whom have received prestigious awards such as the Gilbreth Fellowship and Best Paper Finalist recognitions. She is the Lead PI on a $1.1M NSF/NIH grant for cardiac surgical planning and a Co-I on a $1.2M grant for diabetic retinopathy screening. Her research is supported by the AI TENNessee Initiative as well. She leads a research group focused on spatiotemporal system dynamics, EHR analytics, and deep learning for medical diagnostics, currently recruiting PhD students with strong backgrounds in data science and engineering.

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.

• Artificial Intelligence
• Neuro-Symbolic Integration
• Knowledge Graphs
• +۴ مورد دیگر

Luciano Serafini is a researcher at Fondazione Bruno Kessler in Trento, Italy, specializing in Artificial Intelligence with a focus on Neuro-Symbolic Integration and Knowledge Graphs . His work bridges Machine Learning and Symbolic Reasoning , emphasizing Planning , Relational Learning , and Visual-Textual Grounding . Key Research Areas : Neuro-symbolic systems, logic-based knowledge representation, planning under uncertainty, and computer vision. Recent Publications highlight trends in Embodied AI for open-world tasks, Weighted Model Counting , and Graph Generative Models . His contributions include Logic Tensor Networks for integrating deep learning with formal logic and methods to mitigate Data Sparsity through knowledge transfer. Collaborations span institutions like the University of Trento and research teams in Computer Vision and Reasoning , with applications in Social Navigation and Event Recognition .

• Neuroscience
• Medical Imaging
• Regenerative Medicine
• +۳ مورد دیگر

Øystein Olsen is an Associate Professor at the Radiography BSc program, UiT The Arctic University of Norway, with research expertise in Magnetic Resonance Imaging (MRI) and neuroregenerative applications using manganese-based contrast agents . His work spans Central nervous system injury visualization Optic nerve regeneration tracking Alginate hydrogel contrast delivery systems Academic program evaluation Research trends include neuroimaging innovation , axon regeneration monitoring , and multimodal MRI techniques for neural repair assessment. Recent publications focus on 2019 education policy analysis 2015 radiography curriculum evaluation 2013 glial cell therapy for nerve repair

• Parallel and Distributed Systems
• High Performance Computing
• GPU Programming
• +۹ مورد دیگر

Prof. Dr. Sergei Gorlatch is a full professor at the University of Münster, Germany, in the Department of Mathematics and Computer Science, where he holds the Chair of Practical Computer Science (Parallel and Distributed Systems) within the Institute of Computer Science. He has been a leading figure in high-performance and parallel computing since joining the university in 2003. University: University of Münster School: Department of Mathematics and Computer Science Department: Institute of Computer Science Academic Rank: Professor His research focuses on algorithm and software development for modern computer systems, particularly in parallel and distributed computing, high-performance computing (HPC), GPU-based systems, cloud and grid computing, and performance optimization. His work bridges theoretical formal methods and practical applications, especially in real-time online interactive systems such as online games and simulations. He has pioneered frameworks like SkelCL, dOpenCL, and the Real-Time Framework (RTF) to simplify parallel programming and improve performance portability. The recent publications (2020–2024) reflect a strong trend in GPU programming, performance optimization, formal verification, and distributed systems. Key themes include the development of safe and high-level GPU languages (e.g., Descend), autotuning and model checking for performance, multi-cloud orchestration, and performance modeling of legacy and real-time systems. His work often combines compiler techniques, functional programming, and systematic transformations to achieve efficient and portable code. Best Poster Award – PUMPS+AI, 2019 Best Paper Award – CGO, 2018 Alexander von Humboldt Research Fellowship, 1991 Prof. Gorlatch has supervised numerous students and researchers, many of whom are frequent co-authors on his publications. He has led multiple funded projects from DFG, EU (e.g., CoreGrid, MONICA), and industry (e.g., NVIDIA Graduate Fellowship). His work includes both theoretical contributions (e.g., algorithmic skeletons, formal verification) and applied systems development, demonstrating a strong record of advising, grant acquisition, and interdisciplinary collaboration. He is actively involved in several research labs and teams at the University of Münster, particularly those focused on parallel computing, GPU programming, and real-time systems. His group develops high-level programming models and tools to make parallel computing more accessible and efficient across diverse architectures.

• Reading and Writing
• Cognitive Neuropsychology
• Literacy Skills
• +۶ مورد دیگر

Jacqueline Cummine is a Professor in the Communication Sciences & Disorders department within the Faculty of Rehabilitation Medicine at the University of Alberta. Her research focuses on understanding reading and writing processes, particularly examining how skilled readers adapt their approaches, how reading breaks down, and how sensory systems contribute to reading proficiency. She employs behavioral measurements and brain-based methodologies including fMRI and DTI to investigate these phenomena. Her educational background includes a B.A. Honours in Psychology from the University of Saskatchewan (2005) and a Ph.D. in Cognitive Neuropsychology from the same institution (2009). Cummine is affiliated with several research groups including the Alberta Cognitive Neuroscience Group, the Neuroscience and Mental Health Institute at the University of Alberta, and serves as a Research Affiliate at the Glenrose Rehabilitation Hospital Research. Dr. Cummine's research interests center on literacy skills as critical communication tools across all aspects of life. She investigates the skills that facilitate skilled reading, what happens when reading breaks down, and resources that may benefit individuals with reading impairments. Her current projects include 'Behavioural and Neurobiological Correlates of Basic Reading Processes: The Print-to-Speech Network' (NSERC funded, 2018-2023) and 'Auditory-Visual Integration for Individuals with Impairments in Reading, Hearing and/or Speech.' Her publication record shows a consistent focus on the neural underpinnings of reading processes, with recent work examining cortical thickness relationships, white matter pathways, and structural brain asymmetry in relation to reading abilities. Her research demonstrates an increasing sophistication in neuroimaging techniques and a growing recognition of the complex interactions between motor systems, sensory processing, and reading proficiency. Dr. Cummine teaches several courses including Research Methods and Design (CSD 501), Advanced Univariate Statistics (REHAB 699), and Readings on Selected Topics in Neuroscience (NEURO 450). Her teaching spans both the Rehabilitation Science and Neuroscience programs, reflecting the interdisciplinary nature of her research.

• Numerical Analysis
• Machine Learning
• Computational Mathematics
• +۴ مورد دیگر

Jonas Kusch is an Associate Professor at the Department of Data Science, Norwegian University of Life Sciences, specializing in numerical analysis and its applications in scientific computing and machine learning. His work focuses on dynamical low-rank approximation, particularly in developing low-rank neural networks with geometry-aware training algorithms that respect the differential geometry of matrix manifolds. Research spans computational quantum mechanics , radiation transport , and machine learning . Key contributions include energy-stable integrators for kinetic equations and multi-fidelity optimization algorithms for fission criticality. Publications emphasize low-rank methods for solving inverse problems, time-dependent systems, and uncertainty quantification in hyperbolic equations.