جست‌وجوی استاد و پژوهشگر

Benno Liebchenمشاهده پروفایل

استاد

• Soft Matter Physics
• Active Matter
• Colloidal Systems
• +۳ مورد دیگر

Prof. Benno Liebchen holds a faculty position at the Technische Universität Darmstadt within the Institute for Condensed Matter Physics , part of the Faculty of Physics. He leads the Liebchen Group , dedicated to advancing research in the Theory of Soft Matter , focusing on active matter, colloidal systems, and non-equilibrium phenomena. His work explores collective behavior in self-propelled particles, phase transitions in active fluids, and adaptive strategies in smart materials. Research Interests include: Active matter dynamics and pattern formation Non-equilibrium statistical mechanics Biophysical systems and biomimetic design Computational modeling of soft matter Recent publications highlight breakthroughs in intelligent active particles , self-reverting vortices , and motility-induced phase coexistence . His lab develops tools like the AMEP Python package to analyze active systems. Teaching responsibilities include advanced modules in soft matter physics. Collaborative projects involve interdisciplinary approaches to microswimmer behavior and machine learning-driven optimization of collective systems. Contact: +49 6151 16-24509 / Office: S2|04 104

Xiaogang Wuمشاهده پروفایل

استاد

• China Studies
• Education Policy
• Social Inequality
• +۴ مورد دیگر

Xiaogang Wu is a Professor of Sociology and Yufeng Global Professor of Social Science at New York University, affiliated with the College of Arts & Science and the Department of Sociology. He holds a Ph.D. from UCLA, an M.A. from Peking University, and a B.A. from Renmin University of China. His research focuses on China's social stratification, education policy, inequality dynamics, urban studies, and quantitative methodologies. Key interests include social cohesion, housing policy impacts, migrant labor outcomes, and the interplay between education and socioeconomic status. His recent work analyzes pandemic vulnerability, educational expansion effects on earnings, and gender dynamics in reform-era China. He has contributed to major publications like Research in Social Stratification and Mobility and Demographic Research .

Lillian Chongمشاهده پروفایل

استاد

• Computational Biophysics
• Biomolecular Simulations
• Protein Binding Pathways
• +۳ مورد دیگر

Lillian T. Chong is a Professor in the Department of Chemistry at the University of Pittsburgh, affiliated with the Kenneth P. Dietrich School of Arts and Sciences. She leads the Chong Lab, focusing on computational biophysics and biomolecular simulations. Her research emphasizes developing advanced simulation methods like weighted ensemble (WESTPA) for studying rare events in biomolecules, such as protein folding, binding pathways, and conformational switches. Research Interests: - Development of weighted ensemble algorithms for long-timescale simulations - Protein-protein binding kinetics and unbinding pathways - Design of switchable proteins with enhanced dynamic properties - Integration of experimental data (e.g., NMR, EPR) with simulations Recent Article Trends: Recent work explores ligand unbinding mechanisms, glycan-mediated spike protein dynamics, and force field validation. The lab’s methods are applied to drug discovery, viral entry mechanisms, and enzyme catalysis. Awards & Honors: Gordon Bell Special Prize for HPC-Based COVID-19 Research (2020) NSF CAREER Award (2009-2014) Bellet Teaching Excellence Award (2017) Advising & Grants: Advised students including Darian Yang (PhD 2023) and Jeremy Leung (PhD 2023). Funded by NSF and industry grants, including work on SARS-CoV-2 spike protein dynamics and force field development. Labs & Teams: The Chong Lab collaborates with groups at CMU and NIH, developing open-source tools like WESTPA and LPATH . Research spans Pittsburgh’s computational biophysics community, with interdisciplinary projects in drug design and protein engineering.

• Bilingualism
• Multilingualism
• Psycholinguistics
• +۳ مورد دیگر

G.J. Greg Poarch, PhD is an Associate Professor of English Linguistics and English as a Second Language (ESL) at the University of Groningen's Faculty of Arts. He holds positions on the Cluster Board 2 as Secretary and serves as President of the International Association of Multilingualism (IAM). His research focuses on bilingualism, multilingualism, psycholinguistics, and the cognitive impacts of language learning. He is affiliated with the Neurolinguistics and Language Development (NLD) research group. Research interests include cross-linguistic activation in multilingual speakers, executive function development in multilingual children, and the relationship between multilingualism and neurocognitive health in aging populations. His work addresses topics like translanguaging theory, structural priming in language acquisition, and the role of genetic factors in Alzheimer's risk mitigation among multilinguals. Poarch collaborates internationally, organizing conferences such as the 13th International Conference on Third Language Acquisition and Multilingualism (2024). His research has been published in journals like Linguistic Approaches to Bilingualism , Language Learning , and Journal of Experimental Child Psychology .

Yan Songمشاهده پروفایل

استاد

• Urban Planning
• Land Use Planning
• Environmental Planning
• +۳ مورد دیگر

Dr. Yan Song is a Professor and Director of the Program on Chinese Cities (PCC) at the University of North Carolina at Chapel Hill's Department of City and Regional Planning. She specializes in land use and environmental planning, focusing on low-carbon urban development, spatial analysis of urban form, and China’s urbanization policies. Her work integrates GIS and computer-aided tools to assess urban growth and sustainability challenges. Her research examines domestic and international issues including urbanization drivers, green city development tools, land and housing market dynamics, and urban-transportation integration. Dr. Song leads the PCC, which investigates China’s urbanization impacts on environments and seeks equitable, sustainable growth strategies. Her projects are funded by NSF, EPA, HUD, and the Lincoln Institute of Land Policy. Research highlights include studies on rural-urban spatial restructuring, policy spillover effects, and energy transition mechanisms. She has consulted for Shenzhen’s city government and Beijing’s urban planning institutions. Dr. Song’s interdisciplinary approach bridges planning, environmental science, and policy, addressing global challenges like climate resilience and digital finance’s role in green development. Her advising focuses on PhD students interested in Chinese urban issues, and she holds affiliate roles at the National Center for Smart Growth and Lincoln Institute. Recent work emphasizes the 5R urban retrofitting framework, equity in heat mitigation, and the interplay between technology adoption and environmental governance.

Ni Pengمشاهده پروفایل

مدرس ارشد

• Corporate Finance
• Mergers and Acquisitions
• Innovation
• +۵ مورد دیگر

Dr. Ni Peng is a Senior Lecturer in Finance at Queen Mary University of London's School of Business and Management, affiliated with the Department of Accounting and Financial Management. She holds a PhD in Accounting and Finance and an MBA from the University of Manchester (Manchester Business School). Her research focuses on corporate finance, mergers & acquisitions, innovation, ESG, sustainable finance, and executive compensation. She teaches courses on financial markets and corporate finance at both undergraduate and postgraduate levels. Her research outputs span top-tier journals such as Management Science and Journal of Corporate Finance , emphasizing themes like M&A dynamics, investor behavior, and ESG integration. She actively contributes to academic communities through her roles in the Finance Research Cluster, Behavioural Finance Working Group, and Centre for Globalisation Research. Education: PhD in Accounting & Finance (Manchester), MBA (Manchester) Professional Memberships: Financial Management Association, European Finance Association Industry Experience: 5+ years at a major Chinese financial conglomerate As a supervisor, she has guided doctoral students in topics ranging from green financing to pension strategies. Her work bridges theoretical research and practical finance challenges, particularly in globalized contexts.

Alfred Kumeمشاهده پروفایل

مدرس ارشد

• Shape analysis
• Directional statistics
• Image analysis
• +۱ مورد دیگر

Dr. Alfred Kume is a Senior Lecturer in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. He has held this position since 2004 and has been involved in examining processes for the Institute of Actuaries. His research focuses on shape analysis, directional statistics, image analysis, and stochastic geometry. Kume obtained his PhD and postdoctoral training at the University of Nottingham after working as an actuary. He has supervised students including Theodoros Gkolias and Justyn Campbell-White. His work spans statistical methodology applied to astronomy (e.g., stellar light observations, HII regions) and computational statistics (e.g., holonomic gradient methods, clustering algorithms). His publications reflect expertise in probability distributions, algorithm development, and interdisciplinary applications. His office is located in Cornwallis South, Canterbury Campus. Research interests emphasize statistical techniques for shape and directional data, with applications in astronomy and biology. Key contributions include saddlepoint approximations for normalizing constants and statistical clustering methods. His work bridges theoretical statistics with practical problems in astrophysics and actuarial science. Publications highlight trends in statistical methodology (e.g., Bingham/Fisher-Bingham distributions), computational algorithms, and interdisciplinary collaborations. While no specific awards are listed, his extensive publication record and academic roles reflect scholarly recognition. Advising focuses on statistical shape analysis and Bayesian methods, with grants possibly tied to collaborative projects. He is part of research teams analyzing molecular clouds and astronomical phenomena. His lab or team activities are integral to interdisciplinary projects, though specific lab names are not mentioned.

Hyejin Kuمشاهده پروفایل

استاد

• Mathematical Finance
• Machine Learning
• Risk Management
• +۴ مورد دیگر

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

Mohsen Pourahmadiمشاهده پروفایل

استاد

• Time Series Analysis
• Multivariate Statistics
• Covariance Estimation
• +۷ مورد دیگر

Mohsen Pourahmadi is a Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing methodologies for modeling covariance matrices in multivariate and time series data, with applications to financial analysis, longitudinal studies, neuroeconomics, and high-dimensional data. Key tools include graphical lasso algorithms, Cholesky decomposition, and Bayesian approaches. He emphasizes extending generalized linear models (GLM) to covariance matrix estimation, leveraging prediction theory and stochastic processes. Education details are not explicitly provided in the text. His work spans theoretical advancements in covariance estimation, such as sparse VAR models, nonstationary process analysis, and regularized multivariate regression. He has contributed to applications like detecting cyber attacks on infrastructure systems and analyzing breast cancer data through Bayesian networks. Research interests include time series graphical models, antedependence models for longitudinal data, and regularization techniques for high-dimensional covariance matrices. His recent work explores fused-lasso penalties, Bayesian correlation matrix estimation, and stationary subspace analysis. Pourahmadi has authored numerous articles on topics ranging from multivariate volatility modeling to nonparametric covariance estimation, emphasizing both computational efficiency and theoretical rigor.

Ryan Murrayمشاهده پروفایل

استادیار

• Calculus of variations
• Partial Differential Equations
• Machine Learning
• +۵ مورد دیگر

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +۷ مورد دیگر

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

Tianyu Guanمشاهده پروفایل

استادیار

• Sports Analytics
• Machine Learning
• Functional Data Analysis
• +۶ مورد دیگر

Tianyu Guan is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. He previously served as an Assistant Professor at Brock University and joined York University in 2024. He holds a PhD in Statistics from Simon Fraser University (2020), an MSc in Actuarial Science from the same institution (2014), and a BSc in Statistics from Jilin University (2011). PhD in Statistics, Simon Fraser University, 2020 MSc in Actuarial Science, Simon Fraser University, 2014 BSc in Statistics, Jilin University, 2011 His research centers on sports analytics, functional data analysis, and nonparametric statistics, with strong applications in machine learning and data science. He applies statistical methodologies to understand sports performance, player behavior, and game dynamics. His work also extends to theoretical developments in sparse modeling and functional regression. The recent publications highlight a clear trend toward integrating advanced statistical techniques with real-world sports and entertainment data. His work combines functional data analysis, machine learning, and probabilistic modeling to extract insights from complex longitudinal and high-dimensional datasets. Topics span soccer, rugby, football, and movie reviews, demonstrating interdisciplinary reach. While no formal scientific awards are listed in the provided text, his publications in high-impact journals such as Annals of Applied Statistics and Statistics and Computing reflect strong academic recognition. Tianyu Guan actively advises multiple graduate students at both MSc and PhD levels, primarily at Brock and Simon Fraser Universities. His teaching portfolio includes advanced courses in nonparametric statistics, sampling theory, and experimental design at the undergraduate and graduate levels. He has not received external grant information in the provided text, but his research output suggests active engagement in funded or independent research projects. He leads methodological and applied research in sports analytics, often co-supervising students with colleagues across institutions. His lab or research group appears focused on developing and applying statistical tools for performance analysis and decision-making in sports, supported by computational implementations such as the R package ngr .

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

Christian Calvillo Munozمشاهده پروفایل

پژوهشگر ارشد

• Energy System Modelling
• Decarbonisation Scenarios
• Industrial Decarbonisation
• +۵ مورد دیگر

Dr. Christian Calvillo Munoz is a Senior Research Fellow at the Centre for Energy Policy, University of Strathclyde. His work focuses on energy transition challenges, including energy system modelling, decarbonisation scenarios, and policy analysis. He holds a PhD from the Erasmus Mundus Joint Doctorate in Sustainable Energies Technologies and Strategies (SETS), with studies at Comillas Pontifical University (Spain), Delft University of Technology (Netherlands), and KTH Royal Institute of Technology (Sweden). His research interests span energy efficiency, industrial decarbonisation, carbon capture and storage (CCUS), and the socio-economic impacts of energy transitions. He leads projects such as the UK Energy Research Centre Phase 5 and a collaborative initiative on low-carbon heat transitions in Scotland. Dr. Calvillo collaborates internationally, contributing to UN Sustainable Development Goals related to affordable energy and climate action. Recent publications (2023–2025) highlight his expertise in CO2 transport infrastructure, heat pump economics, and workforce planning for decarbonisation. He actively engages in policy outreach, advising on energy affordability, tidal energy viability, and just transitions. His work bridges technical analysis with policy implications, emphasizing equitable and economically viable pathways to net-zero targets. Key projects include: UK Energy Research Centre Phase 5 (EPSRC-funded, 2024–2028) Mixed Methods Approach for Low-Carbon Heat Transition in Scotland (Energy Action Scotland, 2024–2027) Labs/Teams: Centre for Energy Policy. Current focus areas include CCUS sector impacts, regional employment dynamics, and policy coherence for energy affordability.

• Computational Biology
• Single-Cell Genomics
• Machine Learning
• +۶ مورد دیگر

Dana Pe'er is Chair of the Computational and Systems Biology Program at the Sloan Kettering Institute (SKI) and an Investigator at the Howard Hughes Medical Institute (HHMI). She holds the Alan and Sandra Gerry Endowed Chair and leads an interdisciplinary lab combining single-cell genomics, machine learning, and computational modeling to study cancer biology, immunity, and development. Pe'er earned her PhD at the Hebrew University in Jerusalem and focuses on cellular plasticity, epigenetic regulation, and tumor-immune interactions. Her lab develops tools like CellRank , Wishbone , and SEACells to analyze single-cell data and uncover mechanisms in cancer progression and immunotherapy. Key research areas: Computational Biology, Single-Cell Genomics, Cancer Systems Biology, Epigenetics, Immunotherapy Recent trends: Articles from 2025-2024 emphasize spatial transcriptomics, tumor microenvironment mapping, and regulatory network inference using machine learning. Scientific honors include the NIH Director’s Pioneer Award , AACR Academy Induction , and Packard Fellowship . Her work has direct clinical implications for precision medicine and cancer immunotherapy. Labs & Teams: Leads the Dana Pe'er Lab at SKI, directs the Single Cell Research Initiative (SCRI), and collaborates with the SAIL program.