جست‌وجوی استاد و پژوهشگر

• Multi-Phase Flow
• Turbulence
• Lagrangian Dynamics
• +۳ مورد دیگر

Rohith Jayaram is a Postdoctoral Fellow in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU), specializing in multi-phase flows and turbulence dynamics. His research focuses on particle-laden flows, bubble-turbulence interactions, and computational fluid dynamics. Education: PhD in Particle suspensions from NTNU MSc in Aerospace Mechanics from ISAE-SUPAERO, France BEng in Mechanical Engineering from BMS College of Engineering, India His work investigates Lagrangian dynamics of solid particles and spheroids in evolving Taylor-Green vortex flows, with recent emphasis on bubble-turbulence interactions. He develops numerical algorithms for flow simulations and contributes to turbulence modeling in both 2D and 3D systems. Research trends include computational modeling of multi-phase flows, DNS validation of particle behavior in unsteady vortices, and extension to spheroid alignment dynamics. Publications span leading journals like Physics of Fluids and Physical Review E . Scientific Awards: Selected as Featured Article in Physics of Fluids (2020) Outreach activities include conference lectures at the European Fluid Dynamics Conference (2024) and European Turbulence Conference (2019). Contact: 238 Kjemiblokk 5 Gløshaugen, Trondheim; rohith.jayaram@ntnu.no

• Artificial Intelligence in Accounting
• Machine Learning Applications
• Fraud Detection
• +۳ مورد دیگر

Benjamin Lee is a Senior Lecturer of Accounting and Director of Student Matters at the School of Accountancy, Singapore Management University. He serves as an SMU-X Academic Champion, driving experiential learning initiatives and promoting AI integration in accounting education and practice across multiple institutional roles. His academic background includes: Doctor of Philosophy in Management (Strategy and Technology Management), University of Glasgow, 2025 Master of Science in Business Analytics with Distinction, University of Surrey, 2017 Bachelor of Accountancy, Singapore Management University He is a Chartered Accountant of Singapore with prior industry experience as a consulting data analyst at Singapore's Accountant-General’s Department. Dr. Lee's research centers on AI-driven analytics (AIDA) applications for accounting problem-solving, including fraud detection, financial forecasting, and credit risk analysis. His work explores organizational dynamic capabilities in digital transformation and AI governance frameworks within professional service firms. His publication portfolio (2017-2025) reveals consistent focus on digital transformation across accounting and SME contexts, with recent emphasis on machine learning applications in fraud detection and public transport optimization. The research bridges academic theory with practical industry implementation through numerous case studies. His teaching excellence has been recognized through multiple awards including: Dean’s List for Teaching Excellence (2023-2025) SMU-X Excellent Teacher Award (2023) Howard Teall Award for Innovation in Accounting Education (2023) Wharton-QS Reimagine Education Digital Readiness Award (2022) While specific doctoral advising isn't documented, Dr. Lee actively mentors students through SMU-X experiential projects and industry collaborations. His industry partnerships with ISCA and media contributions to The Business Times demonstrate practical knowledge transfer beyond academia. He holds leadership positions including Director of Student Matters, SMU-X Academic Champion, and member of ISCA's AI Taskforce Sub-Committee on Policy and Regulation, where he shapes professional standards for AI implementation in Singapore's accounting sector.

Blerim Rexhaمشاهده پروفایل

استاد

• Cybersecurity
• Blockchain Technology
• Machine Learning
• +۵ مورد دیگر

Prof. Blerim Rexha is a full professor at the University of Prishtina's Faculty of Electrical and Computer Engineering, Kosovo. With a Ph.D. in Computer Engineering from Vienna University of Technology (2004), he has led research in cybersecurity, blockchain, machine learning, and electronic voting systems. His teaching portfolio includes data, computer, and internet security courses. Education : Ph.D. in Computer Engineering (Vienna), Electrical Engineer MSc (Prishtina), specialized certifications in software engineering, biometrics, and .NET programming. His research spans cybersecurity (DDoS mitigation, face authentication attacks), blockchain applications (electronic voting bridges, transaction privacy), and machine learning integration (boosted trees for intrusion detection, LSTM for vulnerability scanning). He has contributed to cloud security through novel encryption methods and AI-driven attack detection. Recent publications focus on energy efficiency in cloud vs on-premises systems, XGBoost/CatBoost/LightGBM comparisons for network security, and blockchain bridges for e-voting. His work has addressed privacy preservation in video data, SMS encryption, and eID card pseudo-profiles. Awards include the 2024 Marin Barleti Prize for academic contributions and Best Paper Awards in election security (2015) and Kosovo website vulnerabilities (2013). Honors : Marin Barleti Prize (2024) Cyber Security Ambassador (2018) ICT Academician of the Year (2016) Best Paper Awards (2015, 2013) As academic advisor to the KosovaCyberTeam , he mentors students like Korab Keqekolla and Abian Morina. His leadership extends to Kosovo's Cyber Security State Training Center curriculum development and jury roles in Albanian ICT Awards .

Maria Eriksdotterمشاهده پروفایل

استاد

• Alzheimer's Disease
• Geriatrics
• Dementia
• +۶ مورد دیگر

Professor Maria Eriksdotter is a leading academic in geriatrics and dementia research at the Karolinska Institutet , holding the Department of Neurobiology, Care Sciences and Society . She also serves as a Senior Consultant in Themes Inflammation and Ageing at Karolinska University Hospital Huddinge and previously as Dean of KI South (2019–2023). Her work spans translational research, clinical trials, and national registry development, with a focus on Alzheimer's disease, cholinergic therapies, and aging. Her research group pioneered NGF cell therapy for Alzheimer's patients, demonstrating safety and cognitive stabilization in clinical trials. She chairs the SveDem registry , tracking over 100,000 dementia patients to refine diagnostics and care. Studies from SveDem revealed mortality reduction with cholinesterase inhibitors and highlighted pandemic-era diagnostic delays. Recent publications analyze dementia subtypes , comorbidities , and precision medicine in neurodegeneration. Her work intersects neuroimaging , epidemiology , and public health policy , addressing ageism and improving geriatric care systems. Collaborations span Karolinska University Hospital , NSGene Inc , and international institutions.

Erica C Jansenمشاهده پروفایل

استادیار

• Sleep
• Nutrition
• Dietary Patterns
• +۵ مورد دیگر

Dr. Erica C Jansen serves as Assistant Professor in the Department of Nutritional Sciences at the University of Michigan School of Public Health and holds a secondary appointment as Research Assistant Professor in Neurology. Her work bridges nutritional epidemiology and sleep science, focusing on lifespan health impacts with specialized attention to adolescent and women's health outcomes. Her educational foundation includes: PhD in Epidemiology, University of Michigan School of Public Health (2016) MPH in Epidemiology, University of Michigan School of Public Health (2014) BS in Biology, Hope College (2012) Dr. Jansen's research program centers on bidirectional sleep-nutrition interactions and their cardiometabolic consequences. Key pillars include: Early-life nutritional environments and puberty timing Objective sleep metrics (duration, timing, quality) and cardiometabolic risk Epigenetic mechanisms in sleep-diet-health pathways Toxicant exposures through dietary sources Life-stage specific analyses (adolescence, pregnancy, midlife) She primarily leverages the ELEMENT cohort—a 25+ year Mexican birth cohort—and complementary datasets like NHANES and BioCycle. Analysis of her 2021-2025 publications reveals consistent methodological approaches: Integration of actigraphy for objective sleep measurement Dietary pattern analysis over single-nutrient approaches Epigenome-wide assessments of circadian genes Focus on health disparities through socioeconomic and environmental lenses Strong emphasis on Mexican and US minority populations Her work increasingly incorporates machine learning for sleep data analysis and examines policy-relevant questions like food expenditure patterns. Current funding includes: NHLBI K01 grant (K01HL151673) examining sleep-cardiometabolic links in adolescents Gilmore Grant and MNORC Pilot funding for circadian gene methylation studies in pregnancy and midlife Dr. Jansen actively mentors through: NUTR703: Guiding MS students in thesis development and scientific communication PUBHLTH417: Teaching undergraduates to investigate sleep-nutrition interplay through hands-on research Her collaborative network spans Michigan Medicine, environmental health scientists, and international cohorts studying developmental origins of health. Her research team operates within the ELEMENT study infrastructure and partners with clinical departments to translate findings into public health interventions targeting sleep hygiene and dietary patterns for cardiometabolic risk reduction.

Christian Engwerمشاهده پروفایل

استاد

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.

• Dark Matter
• Particle Astrophysics
• Cosmology
• +۲ مورد دیگر

Pearl Sandick is a Professor in the Department of Physics and Astronomy and Interim Dean in the College of Science at the University of Utah. She has previously served as Associate Chair of the Department of Physics and Astronomy and Associate Dean for Faculty and Research in the College of Science. Her academic journey at the University of Utah began in 2011 as an Assistant Professor, progressing to Associate Professor in 2017, and achieving the rank of Professor in 2022. Her educational background includes: BA in Mathematics from New York University (2003) PhD in Physics from the University of Minnesota (2008) Sandick is a theoretical particle physicist whose research focuses on physics beyond the Standard Model, with particular emphasis on dark matter. Her work spans theoretical modeling, connections to astrophysical observations, and implications for experimental detection. She investigates various dark matter candidates and their potential signatures in current and future experiments, including collider searches, direct detection experiments, and indirect detection through astrophysical observations. Her research also extends to connections between particle physics and cosmology, including early universe phenomena and implications for cosmic structure formation. She has developed computational tools like MADHAT for dark matter analysis and has made significant contributions to understanding how stellar evolution can constrain axion physics. Her scholarly contributions have been recognized with several prestigious awards: University of Utah Early Career Teaching Award (2016) University of Utah Distinguished Mentor Award Linda K. Amos Award for Distinguished Service to Women University of Utah Presidential Scholar Sandick has been actively involved in mentoring graduate students, as evidenced by her teaching of PhD thesis research and Master's research courses. She has secured significant research funding from the National Science Foundation and other agencies to support her work on dark matter, dark energy, and new physics. Her grant portfolio includes projects on theoretical particle physics, connections to astrophysical observations, and studies on graduate education reform following a departmental tragedy. She is an active member of the American Physical Society, having served as Chair of the regional Four Corners Section in 2021-2022, demonstrating her commitment to the broader physics community and leadership in her field.

William Newmanمشاهده پروفایل

استاد

• Geophysics
• Planetary Science
• Astrophysics
• +۵ مورد دیگر

William Newman is a Professor in the Department of Earth, Planetary, and Space Sciences at the University of California, Los Angeles (UCLA), currently on sabbatical at the Institute for Advanced Study in Princeton. His primary academic home resides within UCLA's geoscience and planetary science division. His educational credentials include: B.Sc. (Hon.) in Physics from the University of Alberta, Canada (1971) M.Sc. in Physics from the University of Alberta, Canada (1972) M.S. in Astronomy and Space Science from Cornell University (1975) Ph.D. in Astronomy and Space Science from Cornell University (1979) Professor Newman applies theoretical physics and applied mathematics to solve critical real-world problems across multiple disciplines. His research spans statistical techniques for climate change assessment, earthquake hazard modeling, solar system evolution (including collision risks from trans-Jovian bodies), astrophysical jet dynamics, and pattern emergence in complex systems. This interdisciplinary work bridges geophysics, planetary science, and astrophysics through rigorous mathematical frameworks. His publication record (2024-2016) reveals three dominant research thrusts: (1) Semiconductor electron emission physics (GaAs nanotips, photoemission sources), (2) Solar system dynamics and celestial mechanics (N-body simulations, impact hazards), and (3) Complex systems analysis (earthquake patterns, statistical record-breaking events). These intersect physics, earth sciences, and computational mathematics through shared methodologies in statistical modeling and nonlinear dynamics. At UCLA, Newman developed innovative courses including a natural disasters undergraduate GE course (satisfying diversity requirements) and graduate-level planetary atmospheres and continuum mechanics curricula. His academic contributions include over 100 refereed papers and graduate textbooks published by Princeton and Cambridge University Presses, focusing on mathematical methods for geophysics and space physics.

Tiffany A. Radcliffمشاهده پروفایل

استاد

• Health Economics
• Health Policy
• Health Services Research
• +۵ مورد دیگر

Tiffany A. Radcliff, PhD , is Professor of Health Policy & Management in the Texas A&M University School of Public Health and concurrently serves as the Senior Associate Dean for Research . In this dual role she guides the school’s strategic research agenda while maintaining an active, federally funded research portfolio. Education PhD, Health Services Research, Policy & Administration – University of Minnesota BS (Magna Cum Laude), Economics; Minor, Political Science – Texas A&M University Research Interests Radcliff’s work lies at the intersection of health economics , health policy , and health-services research . Her overarching goal is to improve the quality, accessibility, and affordability of care—particularly for rural and underserved populations . Current emphases include: Economic evaluation of behavioral interventions for obesity and diabetes in rural settings Post-acute care transitions and long-term care quality Industrial organization of healthcare markets and provider responses to policy Disaster preparedness and health-system resilience Publication Trends Across 200+ peer-reviewed articles spanning 1997–2025, Radcliff’s scholarship shows a clear evolution from foundational work on rural hospital markets to large-scale comparative-effectiveness and implementation-science studies. Recent clusters (2020–2025) focus on COVID-19 policy impacts, telehealth cost-effectiveness, and VA transitional-care programs, reflecting a sustained commitment to policy-relevant, methodologically rigorous research. Scientific Awards & Honors (No specific awards listed in the provided text; extensive federal funding and invited lectureships implied by CV and publication record.) Grants & Advising Principal or Co-Investigator on multiple NIH, AHRQ, and VA Health Services Research & Development grants Faculty mentor for PhD, MPH, and post-doctoral trainees in health policy, economics, and implementation science Serves on dissertation and thesis committees for students across Texas A&M and collaborating institutions Labs & Teams Radcliff leads an active research group of graduate research assistants, post-docs, and biostatisticians housed within the Center for Health Organization Transformation (CHOT) and collaborates closely with the VA Health Services Research Center of Innovation and the Southwest Rural Health Research Center at Texas A&M.

Christine Heimمشاهده پروفایل

استاد

• Psychobiology of Stress
• Developmental Psychobiology
• Epigenetics
• +۵ مورد دیگر

Prof. Dr. Christine Heim is a University Professor (W3) and Director of the Institute of Medical Psychology at Charité – Universitätsmedizin Berlin, where she leads interdisciplinary research on stress psychobiology. She holds concurrent appointments as Research Professor at Pennsylvania State University's College of Health and Human Development and serves as Principal Investigator in the NeuroCure Cluster of Excellence (DFG EXC 2049). Her faculty affiliations include the Berlin School of Mind & Brain, Max Planck School of Cognition, and Einstein Center for Neurosciences. Her research program investigates mechanisms through which early-life stress fundamentally alters biological adaptability across the lifespan, increasing disease risk. Key focuses include: Molecular and epigenetic embedding of stress effects Gene-environment interactions in stress vulnerability Neuroendocrine and immunological consequences of trauma Development of biomarkers and early interventions Her publications demonstrate consistent focus on stress biology across multiple levels of analysis, with recent work emphasizing epigenetic clocks, neural plasticity, and childhood maltreatment outcomes. Research employs multimodal methods integrating psychological, neuroendocrine, immunological, neural, and molecular approaches. She coordinates the BMBF-funded research network 'Programming Effects of Childhood Stress on Lifelong Disease Risk (Kids2Health)' and serves on the Steering Committee for 'Promoting Talent' in the Berlin University Alliance.

Seojeong Leeمشاهده پروفایل

دانشیار

• Econometrics
• Statistical Inference
• Generalized Method of Moments (GMM)
• +۸ مورد دیگر

Associate Professor Seojeong Lee is a faculty member at the University of New South Wales (UNSW) Business School, School of Economics, specializing in advanced econometric theory. She joined UNSW in 2012 after completing her PhD at the University of Wisconsin-Madison and has established herself as a leading researcher in robust inference methods under complex data conditions. Her educational background includes: Ph.D. in Economics, University of Wisconsin-Madison (2008-2012) M.A. in Economics, Seoul National University (2006-2008) B.A. in Economics and Political Science (dual major), Seoul National University, summa cum laude (2000-2006, with military service 2002-2004) Professor Lee's research centers on developing theoretically rigorous methods for econometric inference, with primary focus on generalized method of moments (GMM), instrumental variables (IV), and two-stage least squares (2SLS) under model misspecification. Her work addresses critical challenges including invalid/many/weak instruments, heterogeneous treatment effects, and clustered sampling, contributing foundational advances to statistical inference in economics. Analysis of her recent publications reveals a strong trajectory in refining methods for many-instrument settings and misspecified models, with increasing emphasis on computational implementations (e.g., Stata packages) and applications to causal inference. Her work bridges theoretical econometrics with practical policy-relevant analysis. Her scientific achievements include: Australian Research Council DECRA Fellowship (2017-2019) UNSW Dean's Research Fellowship (2020-2022) Zellner Thesis Award Honorable Mention from American Statistical Association (2014) Multiple competitive UNSW research awards Professor Lee actively supervises PhD candidates Wei Tian and Fangzhou Yu, and has secured over AUD 700,000 in research funding including ARC Discovery Projects. She teaches undergraduate and postgraduate econometrics courses, integrating her research into pedagogy. Her ongoing work continues to push boundaries in robust econometric methodology for modern data challenges.

• Graph Theory
• Graph Databases
• Knowledge Graphs
• +۵ مورد دیگر

Carlos R. Rivero is an Associate Professor in the Department of Computer Science at the Rochester Institute of Technology (RIT), located within the Golisano College of Computing and Information Sciences. His primary research focuses on graph theory applications in knowledge graphs, graph databases, and computer-aided program comprehension. He holds a PhD from the University of Seville (Spain), completed in 2012, with postdoctoral work at the University of Idaho (USA). His teaching responsibilities include courses such as Principles of Data Management, Data Mining, and Big Data exploration. Rivero has advised numerous PhD and Master’s students, contributing to research projects in link prediction, knowledge graph completion, and educational technology. He actively serves on program committees for conferences like The Web Conference and SIGKDD, and has reviewed for journals including the VLDB Journal and Communications of the ACM. His research emphasizes evaluating knowledge graph embeddings, improving link prediction methodologies, and developing tools for educational feedback in programming. He has contributed to projects like AYNEXT, which streamlines link prediction evaluation, and CAFE, a neighborhood-aware knowledge graph completion tool. Rivero’s work bridges theoretical advancements with practical applications in education and industry. Notable contributions include frameworks for automated feedback in programming courses and methodologies for assessing inference patterns in knowledge graphs. His grants and service roles reflect a commitment to advancing computational methods and fostering academic collaboration in data science and education.

Robert J Brunnerمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Statistical Analysis
• +۳ مورد دیگر

Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.

Stefan Klusمشاهده پروفایل

مدرس

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +۸ مورد دیگر

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis

Sharad Mehrotraمشاهده پروفایل

استاد

• Data Management
• Distributed Systems
• Database as a Service
• +۷ مورد دیگر

Sharad Mehrotra is a Distinguished Professor at the University of California, Irvine (UCI), leading the Center for Emergency Response Technologies (CERT) and directing the NSF-funded RESCUE project. He previously served at the University of Illinois, Urbana-Champaign, and holds a Ph.D. from the University of Texas at Austin (1993). His research focuses on data management, IoT systems, privacy-preserving technologies, and smart spaces, with contributions to frameworks like TIPPERS and MARS. Education: Ph.D., Computer Science, University of Texas at Austin, 1993 Research Interests: His work bridges database systems, security, and IoT, emphasizing privacy in smart environments. Notable projects include sentient space technologies for disaster response, cryptographic methods for encrypted data queries, and semantic IoT integration. Recent efforts address privacy in multi-owner data systems and resilient community water infrastructure. Awards & Recognition: ACM Fellow (2024) SIGMOD Best Paper (2001), DASFAA Best Paper (2004) NAVWAR Innovation Award (2021) Outstanding Graduate Mentor (2005) Grants & Leadership: As RESCUE PI, he managed $12.5M NSF funding, developing crisis-response software deployed by emergency agencies. Collaborations include the Cal-IT2 institute (UCSD/UCI) and the US Navy’s TIPPERS platform. He co-leads initiatives like the NSF Civic Innovation Challenge for disaster resilience in aging communities. Labs & Teams: Directs UCI’s Information Systems Group and CERT, fostering interdisciplinary research with 60+ members. His teams produce open-source tools (e.g., SEMIoTIC, PrivacySphere) and engage in global partnerships via Fulbright Visiting Scholar programs.