جست‌وجوی استاد و پژوهشگر

Heather Keathleyمشاهده پروفایل

دانشیار

• Management Systems Engineering
• Organizational Change
• Performance Measurement Systems
• +۵ مورد دیگر

Heather Keathley is an Associate Professor in the Department of Industrial Engineering and Management Systems at the University of Central Florida (UCF), part of the College of Engineering and Computer Science. She holds a BS in Systems Engineering from the University of Arkansas at Little Rock, an MS in Industrial and Systems Engineering from Virginia Tech, and a dual doctoral degree from Virginia Tech’s Grado Department of Industrial and Systems Engineering and the Royal Military Academy in Belgium. Her research focuses on Management Systems Engineering, particularly organizational change and transformation, with an emphasis on performance measurement systems in healthcare and defense sectors. She actively contributes to professional societies like IISE and SEMS. Her research interests include system dynamics, performance measurement implementation challenges, and the integration of technology like augmented reality in managerial tasks. She has published extensively on strategic performance measurement frameworks for defense organizations, lean six sigma in healthcare, and causal modeling in organizational processes. Her work often bridges interdisciplinary gaps, combining quantitative methodologies with real-world applications. Notable trends in her publications include methodological advancements (e.g., systematic reviews, SEM analysis), critical sector analyses (healthcare, defense), and emerging technologies (e.g., AR). No scientific awards or grants are explicitly mentioned in the provided texts. She has no listed advisees or specific lab affiliations.

Fahad Ahmadمشاهده پروفایل

مدرس

• Machine Learning
• Cybersecurity
• Medical Informatics
• +۶ مورد دیگر

Fahad Ahmad is a Lecturer in the School of Computing within the Faculty of Technology at the University of Portsmouth. He holds affiliations with the Portsmouth AI and Data Science Centre, Centre for Cybercrime and Economic Crime, and Portsmouth Centre for Advanced Materials and Manufacturing. His research focuses on machine learning applications in healthcare, cybersecurity, and quantum computing. He supervises PhD students in topics like quantum machine learning for securing IoT medical devices. Key research areas include: Medical imaging diagnostics using deep learning (e.g., echocardiograms, X-rays) Cybersecurity for financial systems and SDN-NFV networks Quantum key distribution for post-quantum security AI-driven health management systems Recent work emphasizes: Human activity recognition through machine learning Cancer subtype classification using RNA expression data Emotional empathy modeling in intelligent agents His articles span healthcare technology, cybersecurity frameworks, and hybrid AI architectures. He actively contributes to international conferences and journals, with over 60 peer-reviewed publications. Research collaborations include institutions in Pakistan and the UK.

Matthew R. Helmusمشاهده پروفایل

دانشیار

• Ecology
• Evolutionary Biology
• Invasion Biology
• +۸ مورد دیگر

Matthew R. Helmus is an Associate Professor of Ecology at Temple University in Philadelphia, USA. His research focuses on the intersection of ecological and evolutionary processes in generating biodiversity, with emphasis on invasion biology, island biogeography, and quantitative methods. He employs big data statistics, mathematical modeling, and field research in systems across the Eastern U.S. and Caribbean Islands, with additional work in entomology, ichthyology, and herpetology. Ph.D. in Zoology, University of Wisconsin Madison (2008) B.S. in Biology and Sociology, University of Central Arkansas (2001) Postdoctoral Fellow, Amsterdam Global Change Institute, Vrije Universiteit (2012-2016) Postdoctoral Fellow, University of Chicago (2010-2011) Postdoctoral Fellow, Xishuangbanna Tropical Botanical Garden (2009-2010) Dr. Helmus specializes in invasive species forecasting , particularly the spotted lanternfly's global threat to the wine industry. His work merges island biogeographic theory with anthropogenic drivers to update classical models for the Anthropocene. He also develops open-access quantitative frameworks like the R package lydemapr to enhance biodiversity science communication and stakeholder engagement. Recent publications (2022-2024) emphasize mathematical models for pest resistance , anthropogenic extinction patterns , and climate-buffered invasive species dynamics . While no explicit awards are listed in the provided text, his work has been published in high-impact journals including Nature , Communications Biology , and Global Ecology and Biogeography . Dr. Helmus leads the iEcoLab , which actively engages in ecological forecasting and software development for invasive species research. The lab recently advertised a postdoctoral position focused on urban ecology of invasive pests and host-plant phenology through the 2023 job posting .

Nicolas Rodrigueمشاهده پروفایل

دانشیار

• Molecular Evolution
• Bayesian Statistics
• Computational Biology
• +۳ مورد دیگر

Nicolas Rodrigue is an Associate Professor in the Department of Biology at Carleton University, with a cross appointment in Biology. He holds degrees from Bishop's University (BA), McGill University (BSc), École de technologie supérieure (MSc), and Université de Montréal (PhD). His research focuses on Bayesian modeling of molecular evolution, particularly protein-coding genes and evolutionary forces like mutation, selection, and drift. He also explores Markov chain Monte Carlo methods, protein structure modeling, and next-generation sequencing for viral/microbial evolution studies. Education: BA, Bishop's University BSc, McGill University MSc, École de technologie supérieure PhD, Université de Montréal Research Interests: Bayesian phylogenetic models Mutation-selection dynamics Protein sequence and structural evolution High-performance statistical computing Microbial genomics and clinical evolution Recent publications highlight advancements in phylogenetic modeling, adaptive evolution detection, and computational methods for analyzing molecular data. His work integrates statistical frameworks with evolutionary biology to address complex questions about genomic adaptation and sequence evolution. Awards: None explicitly listed. Advising/Grants: No student advising or grant details provided in the text. Lab/Teams: No specific lab or team affiliations mentioned.

Berthold Lausenمشاهده پروفایل

استاد

• Machine Learning
• Medical Statistics
• Data Science
• +۳ مورد دیگر

Berthold Lausen is a Professor in Data Science & Statistics at the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex, and concurrently holds a professorship in Biometry and Epidemiology at the University of Erlangen-Nuremberg. He has led the SMSAS as Head of Department (2016–2021) and significantly expanded its academic and student body. His research focuses on machine learning, medical statistics, and data science applications in healthcare and industry. He is a Fellow of the Royal Statistical Society (since 2009) and has been nominated for the KTP Academic of the Year award (2022). Education: Diplom-Statistiker (1987), Dr. rer. nat. (PhD, 1990), and Privatdozent (habilitation in biometry and epidemiology, 2003), all from German institutions. Research Interests: Development of nonparametric methods, classification algorithms, survival analysis, and machine learning applications in medicine. He has pioneered techniques like Maximally Selected Rank Statistics and p-Value Adjusted Classification Trees. His work bridges theoretical statistics with practical data science challenges in healthcare and industry. Grants & Partnerships: Principal Investigator for Innovate UK-funded KTPs with Prophasis, Mondaq, and Rolls-Royce Motor Cars/BMW. These projects focus on data-driven solutions for industry and legal sectors. His leadership in academic-industry collaborations has been recognized nationally. Professional Roles: President of the British Data Science Society (since 2022), Vice-President of the European Association for Data Science (since 2023), and former President of the International Federation of Classification Societies (IFCS, 2018–2022). He serves on editorial boards of journals like Advances in Data Analysis and Classification and Archives of Data Science .

Robert Johanssonمشاهده پروفایل

دانشیار

• Discrete Mathematics
• Graph Theory
• Combinatorics
• +۳ مورد دیگر

Robert Johansson is an Associate Professor at the Department of Mathematics and Mathematical Statistics, Umeå University, and holds an affiliated position with the Faculty Office of Science and Technology. His research focuses on discrete mathematics and graph theory, with a strong emphasis on combinatorial structures and theoretical foundations. Research Focus His work explores critical areas in graph theory, including edge-decompositions, chromatic numbers, and path-factors. He has contributed to understanding extremal properties of graphs and combinatorial optimization problems in planar and r-partite graph systems. Publications The majority of his publications fall under discrete mathematics and theoretical computer science, with recurring themes in graph theory, combinatorial analysis, and network structures. His 2012 educational text on advanced mathematics further highlights his contributions to pedagogical frameworks. Labs & Teams Member of the Discrete Mathematics research group at Umeå University

Bernardino Casas Fernándezمشاهده پروفایل

استاد مدعو

• Quantitative Linguistics
• Natural Language Processing
• Polysemy
• +۳ مورد دیگر

Bernardino Casas Fernández is an Adjunct Professor at the Departament de Llenguatges i Sistemes Informàtics (LSI) of Universitat Politècnica de Catalunya (UPC). He holds offices at both the Vilanova i la Geltrú campus (EPSEVG-VG1, Room 120) and the Barcelona campus (Campus Nord-Edifici Omega, Room 224). His research focuses on quantitative linguistics, natural language processing, polysemy analysis, and computational linguistics. He has contributed to open-source tools like FreeLing and projects such as CARPANTA for email summarization. Recent work explores semanticity in Catalan using large language models and ethical implications of generative AI in education. He maintains active involvement in computational linguistics and child language development studies. Education details are not explicitly listed, but his academic career spans from 2003 (earliest article) to 2024. He collaborates with initiatives like LAPPS (Language Acquisition and Processing Systems) and has participated in projects involving churn prediction systems and entropy estimation techniques.

LIU Boxiangمشاهده پروفایل

استادیار

• Genomics
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Asst Prof LIU Boxiang holds the position of Assistant Professor and NUS Presidential Young Professorship at the Department of Pharmacy and Pharmaceutical Sciences, National University of Singapore (NUS), within the Faculty of Science. His research focuses on integrating multi-omics approaches with computational methods to study complex diseases such as coronary artery disease and age-related macular degeneration. He specializes in developing statistical and machine learning tools for genomic analysis, including eQTL mapping and deep learning architectures for gene expression regulation. Education: BA in Biophysics (Illinois Wesleyan University), MS and PhD in Bioinformatics (Stanford University). He contributed to the GTEx consortium and is part of the Asian Immune Diversity Atlas (AIDA) initiative. His lab develops methods like ANTseq for ancestry determination and scPrediXcan for cell-type-specific transcriptome studies. Research Interests: Functional genomics, eQTL analysis, deep learning in biomedicine, and computational tools for omics data integration. His work bridges disciplines such as natural language processing and computer vision with biological questions. Scientific Awards: NUS Presidential Young Professorship (2021). His lab's innovations include ParaMed, a biomedical translation dataset, and LinearDesign for optimized mRNA stability. Advising and Grants: Leads the Liu Lab (boxiangliulab.com), focusing on single-cell genomics, mitochondrial dynamics, and computational biomedicine. Collaborates on projects like the RESET cohort study for cardiovascular disease prevention.

Thomas A. Runklerمشاهده پروفایل

استاد مدعو

• Machine Learning
• Data Analytics
• Pattern Recognition
• +۳ مورد دیگر

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

David John Bakerمشاهده پروفایل

استاد

• Philosophy of Physics
• Philosophy of Science
• Metaphysics
• +۵ مورد دیگر

David John Baker is a Professor of Philosophy at the University of Michigan, where he also serves as Undergraduate Chair and PPE (Philosophy, Politics, and Economics) Chair. His primary research focuses on the philosophy of physics, metaphysics, and the philosophy of science, with special emphasis on quantum field theory, general relativity, and symmetry principles in fundamental physics. He holds a Ph.D. in Philosophy from Princeton University (2008) and a B.S. in Philosophy and Physics from the University of Michigan (2003). Education: 2008: Ph.D., Philosophy, Princeton University 2003: B.S., Philosophy (highest honors) and Physics, University of Michigan Research Interests: Baker’s work bridges the philosophy of physics, metaphysics, and ethics. His core research examines the ontological implications of dynamical symmetries in quantum field theory, spacetime structure, and the metaphysics of quantities. He also explores moral philosophy, particularly questions related to free will, responsibility, nuclear arms control, and mortality. Recent Work Trends: His publications address foundational issues in quantum mechanics, interpretations of supersymmetry, spacetime functionalism, and the philosophical implications of string theory. He critiques field interpretations of quantum field theory and investigates how scientific theories shape our understanding of fundamental physical quantities and symmetries. Advising & Grants: While no specific student advising records are noted here, his academic role suggests active mentorship in philosophy and interdisciplinary programs like PPE. Grants and funding details are not explicitly mentioned in the provided texts. Labs/Teams: No specific research groups or laboratories are listed, though his affiliation with the University of Michigan’s philosophy department implies collaborative work within the institution’s academic community.

Ralf Schlüterمشاهده پروفایل

مدرس

• Automatic Speech Recognition
• Machine Learning
• Pattern Recognition
• +۴ مورد دیگر

Ralf Schlüter is a senior researcher and Lecturer (Privatdozent) at RWTH Aachen University, where he leads the Automatic Speech Recognition Group within the Department of Computer Science. His academic roles include serving as Academic Director and leading research initiatives at Lehrstuhl Informatik 6: Human Language Technology and Pattern Recognition. He holds a Dipl. in Physics (1995), a Dr. rer. nat. in Computer Science (2000), and a Habilitation in Computer Science (2019), all from RWTH Aachen University. His research focuses on advanced speech recognition technologies, including end-to-end architectures, acoustic modeling, discriminative training, and machine learning applications. He has led numerous national and international projects, such as EU-funded initiatives like BABEL, BOLT, and GALE. Schlüter has published extensively in top-tier conferences like Interspeech and ICASSP, contributing to advancements in neural network frameworks, feature extraction, and error analysis. He teaches courses on speech recognition, pattern recognition, and signal processing, and serves as a Subject Editor for Speech Communication . His work also involves developing the RWTH ASR toolkit, emphasizing scalable and efficient speech recognition systems. Schlüter’s contributions to speech technology have earned recognition, including the ISCA Best Student Paper Award (2019).

Alexander Sahnمشاهده پروفایل

استادیار

• Political Economy
• Urban Politics
• American Political Development
• +۵ مورد دیگر

Alexander Sahn is an Assistant Professor of Political Science and the Thomas J. Pearsall Fellow at the University of North Carolina at Chapel Hill. He specializes in political economy and representation, focusing on subnational U.S. governments, housing policies, and urban governance. His research examines racial backlash in exclusionary zoning, public comment influence on housing approvals, and municipal civil service reforms. Education: PhD in Political Science from the University of California, Berkeley (2020); Postdoctoral Fellow at Princeton University’s Center for the Study of Democratic Politics (2020–2023). Research Interests: Political economy of housing and urban development Institutional design of local governments Racial dynamics in policy implementation Public participation mechanisms Awards: Recognized with the Susan Clarke Young Scholar Award (APSA Urban Politics) and Emerging Scholar Award (APSA American Political Economy Section). Advising/Grants: Currently mentoring students in urban politics and political methodology. No active grants explicitly mentioned, but his research often addresses applied policy questions. Labs/Teams: Active in UNC’s political science department, collaborating on projects about neighborhood representation and climate communication.

Alexander Robelمشاهده پروفایل

دانشیار

• Climate, Oceanography, and Weather
• Cryospheric Science
• Paleoclimate
• +۶ مورد دیگر

Dr. Alexander Robel is an Associate Professor in the School of Earth & Atmospheric Sciences at the Georgia Institute of Technology, part of the College of Sciences. His research focuses on climate, oceanography, and cryospheric science, with a particular emphasis on glaciers, ice sheets, and sea level change. He holds a Ph.D. in Earth and Planetary Science from Harvard University (2015) and a B.S. in Physics and Earth Sciences from Duke University (2010). Research Interests: Dr. Robel’s work addresses critical questions in cryospheric science, including ice-ocean interactions, climate modeling, and the dynamics of ice sheets. His studies integrate computational and mathematical modeling to explore topics such as glacier stability, sea level rise, and the impacts of climate variability on polar regions. Key themes include the stochastic parameterization of ice dynamics, the role of subglacial hydrology, and the statistical analysis of climate forcing on glacial systems. Publications Trends: Recent work emphasizes advancing stochastic models for ice sheet projections, quantifying biases in climate-ice sheet interactions, and analyzing oceanic drivers of glacial retreat. His studies often bridge observational data with theoretical frameworks, offering insights into both near-term and long-term climate impacts. Education: Ph.D., Earth and Planetary Science, Harvard University (2015) B.S., Physics and Earth Sciences, Duke University (2010) Advising & Grants: Specific advising or grant details are not explicitly listed in the provided text. Labs/Teams: Likely affiliated with Georgia Tech’s School of Earth & Atmospheric Sciences research groups, though specific teams are not mentioned here.

Tim Biblarzمشاهده پروفایل

دانشیار

• Family Sociology
• Stratification and Social Mobility
• Gender and Sexuality
• +۲ مورد دیگر

Tim Biblarz is an Associate Professor of Sociology and Gender Studies at the University of Southern California (USC), affiliated with the USC Dornsife College of Letters, Arts and Sciences and the Gerontology Research Institute in the Andrus Gerontology Center. His roles include Faculty Master of the South Residential College and former Chair of the Sociology Department (2007–2012), Director of Graduate Studies (2001–2005), and President of the USC Dornsife Faculty Council (2010–2011). Education: Ph.D. in Sociology, University of Washington, 1992 Research Interests: Focus on social inequalities, family dynamics, and intergenerational outcomes in the U.S., particularly regarding gender roles, sexual orientation, and family structure. Current projects examine parental gender impacts on child development, LGBTQ+ family structures, and social mobility patterns. Publications Trends: Recent work emphasizes LGBTQ+ family studies, intergenerational relationships, and the socio-demographic impacts of family structure. Key themes include challenging traditional family norms, analyzing post-divorce family dynamics, and exploring the societal implications of gender and sexual orientation diversity. Awards/Honors: None explicitly listed in the provided text. Grants/Advising: No specific grants or student advisees listed, though his leadership roles suggest extensive mentorship. Research has been conducted through USC’s institutions and collaborative centers. Labs/Teams: Affiliated with the Gerontology Research Institute, focusing on aging populations and intergenerational relationships.

Sze Ming Leeمشاهده پروفایل

پژوهشگر

• Social Statistics
• Large-Scale Data Analysis
• Latent Variable Modelling
• +۳ مورد دیگر

Sze Ming Lee is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on Social Statistics, with expertise in large-scale data analysis, latent variable modelling, survival analysis, and quantile regression. Lee holds a BSc in Mathematics, an MPhil in Risk Management Science, and a postgraduate diploma in Education, all from the Chinese University of Hong Kong. His academic supervisors are Dr. Yunxiao Chen and Professor Fiona Steele. His research interests emphasize methodological advancements in statistical modelling, particularly in handling high-dimensional data and latent variables. Recent publications (2023-2025) highlight contributions to quantile regression, factor analysis stability, and longitudinal data methodologies. Lee’s work bridges theoretical statistics with practical applications in social sciences and biostatistics. No scientific awards or grants are explicitly mentioned in the provided texts. He is affiliated with LSE’s Social Statistics research group and contributes to the department’s focus on data-driven social science research.