جست‌وجوی استاد و پژوهشگر

Arjan Markusمشاهده پروفایل

استادیار

• Collaboration Networks
• Innovation Ecosystems
• Econometrics
• +۳ مورد دیگر

Dr. Arjan Markus is a tenured Assistant Professor at the Eindhoven University of Technology (TU/e) in the Department of Industrial Engineering and Innovation Sciences , specifically the Innovation, Technology Entrepreneurship & Marketing (ITEM) group. His research focuses on how collaboration networks and ecosystems influence innovation, using methods like econometrics , network analysis , and design science to examine dynamics from intraorganizational to multi-stakeholder levels. His work appears in journals such as Strategic Management Journal , Research Policy , and Social Networks . Ph.D. in Economics and Management, Copenhagen Business School Postdoctoral visitor, Wharton School, University of Pennsylvania Former Assistant Professor, Tilburg University He investigates innovation ecosystems across agrifood and R&I systems, including network symmetry in knowledge structures and patent approval dynamics . Current projects like REWIRE and COOPERATE address collaboration in circular agriculture and interregional knowledge ecosystems . Focus on Sustainable Development Goals (SDGs) related to zero emissions and smart cities Co-supervisor of seven Ph.D. candidates Scientific Awards : Best Reviewer Award, Academy of Management (2021) Code Re-farm Grant (2020) Diversity Fund TU/e (2019) ZonMW Sportinnovator Fase 1 (2020) He co-secures large-scale grants from Horizon Europe and NWO for projects like CUES and REWIRE , emphasizing collaboration improvement and ecosystem development . As an educator, he leads courses such as Strategic Management of Technology and Entrepreneurship in Action , having supervised over 40 master's theses and bachelor's projects.

• Stochastic Control
• Computational Finance
• Machine Learning
• +۷ مورد دیگر

Christoph Reisinger is a Full Professor of Applied Mathematics at the Mathematical Institute, University of Oxford, a position he has held since 2017 (previously Associate Professor from 2006-2017). He is a Fellow at St Catherine's College (since 2006) and an Associate Member of the Oxford-Man Institute of Quantitative Finance (since 2008). He serves as Director of Graduate Studies (Teaching) at the Mathematical Institute (2023-2026) and was Co-Director of the Centre for Doctoral Training on The Mathematics of Random Systems (2022/23). He is also a member of the Mathematical and Computational Finance Group (since 2006) and the Data Science Group (since 2018). Professor Reisinger's research lies at the intersection of stochastic modeling, computational mathematics, and machine learning. His work spans stochastic control, numerical analysis of nonlinear PDEs, mathematical foundations of reinforcement learning and deep learning, and computational finance. Current research focuses on approximation and reinforcement learning of stochastic control problems, mathematical analysis of neural networks, mean-field limits of particle systems, numerical approximation of high-dimensional SDEs and PDEs, and mathematical modeling of financial markets. His publication record shows a strong trend toward integrating machine learning with traditional computational finance methods. Recent work explores policy gradient methods for stochastic control, mean-field game theory applications to financial markets, and novel numerical schemes for McKean-Vlasov equations. His research bridges theoretical mathematics with practical applications in quantitative finance, particularly in option pricing, risk management, and market microstructure modeling. Professor Reisinger has supervised numerous doctoral students, many of whom have gone on to prestigious positions in academia and industry. His current PhD students include Boris Baros, William Gibson, Matthieu Meunier, Zihan Guo, Maria Olympia Tsianni, Filippo De Angelis, and Michael Giegrich. His past students include several who have received awards such as the Graduate Paper Competition First Prize and G-Research DPhil Prizes. He serves as Editor-in-Chief of the Journal of Computational Finance (since 2018) and holds associate editor positions at Applied Mathematics and Optimization (2022-2024), Applied Mathematical Finance (since 2018), and International Journal of Computer Mathematics (since 2012).

• MCMC Theory
• MCMC Methodology
• Bayesian Inference
• +۷ مورد دیگر

Professor Chris Sherlock is a Professor of Statistics at Lancaster University , affiliated with the School of Mathematical Sciences and the Department of Mathematics and Statistics . His research spans MCMC theory , methodology , and applications in epidemiology, ecology, and environmental science, with a focus on stochastic processes (SDEs, hidden Markov models) and spatial statistics . Key research areas : Non-reversible MCMC algorithms (e.g., Bouncy Particle Sampler), pseudo-marginal methods, and inference for reaction networks. Recent publications address flood risk estimation, scalable Bayesian learning, and clinical trial recruitment prediction using novel MCMC frameworks. He leads the Computational Statistics group (2019-present) and the MARS UG program (2024-present). Supervised PhD students have explored topics like extreme value theory, particle filters, and disease modeling. Collaborations include researchers such as Paul Fearnhead , Chris Nemeth , and Andrew Golightly , with EPSRC funding (EP/P033075/1) supporting non-reversible MCMC innovations.

Christian Rauhمشاهده پروفایل

استاد

• Public politicisation of global governance
• European Union politics
• Quantitative text analysis
• +۵ مورد دیگر

Christian Rauh is a Professor for the Politics of Multilevel Governance at the University of Potsdam since March 2023 and a Research Fellow at the WZB Berlin Social Science Center since April 2012. He is affiliated with the Research Department Global Governance at WZB, where he previously served as Interim Director (2020). His academic journey includes a guest professorship at Freie Universität Berlin (2020) and foundational research roles at WZB since 2008. His educational background features a Dr. rer. pol. summa cum laude from Freie Universität Berlin (2012) with an award-winning thesis on EU consumer policies, and an M.A. in Politics and Management from Universität Konstanz (2008) where his diploma thesis received national recognition. Early career experience includes editorial work for European Union Politics and teaching assistantships at Konstanz. Rauh's research centers on the politicization of global governance through quantitative text analysis of political discourse. He investigates EU institutional dynamics, crisis communication, and supranational actor behavior using computational methods and large-scale text corpora. His work bridges theoretical frameworks from international relations with empirical innovations in political methodology, emphasizing data visualization and programming for text analysis. Analysis of his publication trajectory (2016-2025) reveals consistent methodological evolution: from foundational work on EU bureaucracy toward advanced computational approaches examining crisis responses, social media, and longitudinal discourse patterns. His research increasingly integrates cross-national parliamentary speech datasets (ParlSpeech) with global governance analysis, demonstrating growing sophistication in handling multimodal political communication data. His scientific recognition includes: ECPR Jean Blondel Prize (2013) for best doctoral thesis in politics VEUK e.V. Award (2009) for best diploma thesis in Politics and Management Rauh leads significant research initiatives including the WZB-funded 'Politicization of European Integration' project (ongoing since 2011) and the European Commission-funded OPTED project (2020-2023) analyzing political texts across democracies. His grant portfolio demonstrates sustained institutional support for computational social science infrastructure development, particularly through ParlSpeech data repositories. While student supervision details aren't explicit in source materials, his professorship at Potsdam implies graduate mentorship responsibilities. He operates within the WZB's Global Governance research ecosystem, collaborating with leading scholars like Michael Zürn and Pieter de Wilde. His GitHub presence (ChRauh) showcases methodological contributions including Twitter scraping tools and survey applications, reflecting his commitment to open science and reproducible research infrastructure.

Jianxuan Liuمشاهده پروفایل

استادیار

• Semiparametrics
• Causal Inference
• Measurement Error Models
• +۵ مورد دیگر

Jianxuan Liu is an Assistant Professor in the Department of Mathematics at Syracuse University, affiliated with the Applied Statistics program within the College of Arts and Sciences. His research focuses on semiparametric methods, causal inference, measurement error models, and high-dimensional data analysis. He holds a Ph.D. in Statistics from the University of South Carolina (2017), with prior degrees in Statistical Science (Indiana University, 2012), Economics (East Carolina University, 2010), and a B.S. in Economics from Capital University of Economics and Business (2007). Dr. Liu’s scholarly work emphasizes statistical methodologies for complex data structures, including recent contributions on beta regression for high-dimensional covariates, causal effects of violence exposure on juvenile crime, and longitudinal substance abuse analysis using machine learning. His publications appear in journals like Stat , Journal of Data Science , and Annals of Statistics . He has received grants for projects such as fusion of semiparametric methods with machine learning in causal inference and statistical analysis of error-prone data. Notable awards include an NSF Travel Grant (2022). Dr. Liu is actively involved in academic service, including supervising the MAT 222 course, chairing statistics hiring committees, and advising the Syracuse University ASA Chapter in Statistics and Data Science.

Mahmut Parlarمشاهده پروفایل

استاد

• Stochastic Modeling
• Dynamic Optimization
• Game Theory
• +۲ مورد دیگر

Dr. Mahmut Parlar is a Distinguished University Professor and Professor of Management Science at McMaster University's DeGroote School of Business, where he holds a Business Research Chair (2014-2025). He also serves as an Adjunct Professor at Lingnan University, Hong Kong (2024-2027). His research focuses on stochastic modeling, dynamic optimization, and game theory applications in supply chain management, supported by continuous NSERC funding since 1981. His primary research areas include: Stochastic inventory and queueing systems Dynamic pricing and contract design Game-theoretic supply chain coordination Optimization in healthcare and retail operations Analysis of his 15 most recent publications (2020-2025) reveals strong emphasis on: supply chain incentive design, dynamic pricing strategies, operational risk management, and retail/e-commerce optimization. Key methodologies include differential games, stochastic modeling, and empirical validation. Major scientific recognitions: CORS Award of Merit (2018) Stanford's Top 2% Scientists (2023) President's Award for Graduate Supervision Excellence (2013) DeGroote Research Excellence Award (2006) Dr. S. J. Basu Teaching Award (2014) He has supervised numerous graduate students and authored the book Interactive Operations Research with Maple (2000). NSERC grants have continuously funded his research program for over four decades.

Nicholas C. Jacobsonمشاهده پروفایل

دانشیار

• Digital Phenotyping
• Digital Mental Health
• Anxiety and Depression
• +۶ مورد دیگر

Nicholas C. Jacobson is an Associate Professor of Biomedical Data Science and Psychiatry at the Geisel School of Medicine, Dartmouth College. He serves as the Director of the Treatment Development & Evaluation Core within the Center for Technology and Behavioral Health (CTBH) and leads the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory. His work bridges computational methods with clinical applications to transform mental healthcare through technology. Dr. Jacobson earned his PhD in Psychology from Pennsylvania State University in 2019, following an MSc in Psychology from the same institution in 2015. He completed his Postdoctoral and Clinical Fellowships in Psychology at Massachusetts General Hospital/Harvard Medical School in 2019. Dr. Jacobson's research focuses on harnessing artificial intelligence and passive sensor data from smartphones and wearable devices to develop scalable, personalized interventions for anxiety and depression. His work has three main pillars: (1) enhancing precision assessment of anxiety and depression using intensive longitudinal data, (2) conducting multimethod assessment utilizing passive sensor data from smartphones and wearable devices, and (3) providing scalable, personalized technology-based treatments utilizing smartphones. As a computational psychologist, he created the Differential Time-Varying Effect Model (DTVEM), an innovative statistical package in R that allows researchers to discover and model optimal lag times in intensive longitudinal data. His methodological expertise encompasses machine learning, structural equation modeling, multilevel modeling, time-series techniques, and dynamical systems modeling. His recent publications demonstrate a strong focus on digital phenotyping, machine learning applications in mental health, and personalized interventions. The research spans multiple domains including depression symptom networks, anxiety disorder assessment, eating disorder prevention, and the use of passive sensing to understand mental health conditions. A notable trend is the application of advanced computational methods to create more precise and personalized mental health assessments and interventions, with increasing emphasis on real-world implementation and accessibility. Principal Investigator of an R01 Award from the National Institute of Mental Health studying personalized deep learning models to predict rapid changes in major depressive disorder symptoms Secured over $6 million in funding as Principal Investigator and over $20 million as a co-Investigator Featured on NBC Nightly News and CBS Morning News for pioneering work in AI-powered mental health applications Dr. Jacobson has developed several impactful digital tools including Therabot, a generative AI therapy chatbot that demonstrated substantial reductions in symptoms of major depressive disorder, generalized anxiety disorder, and feeding and eating disorders in its first randomized controlled trial. He also developed Mood Triggers, a smartphone sensing platform that integrates ecological momentary assessment and intervention to help users identify and manage anxiety and depression triggers. His suite of smartphone applications has reached over 50,000 users in more than 100 countries. Dr. Jacobson is actively recruiting team members and encourages interested individuals to contact him through his personal website. He directs the AIM HIGH Laboratory, which focuses on advancing AI applications in mental healthcare. The lab develops innovative computational approaches to enhance mental health assessment and treatment through technology. Current projects include using passive sensor data to predict symptom changes, developing personalized just-in-time adaptive interventions, and creating quantitative tools that enable precision mental healthcare.

Helmut Breitmeierمشاهده پروفایل

استاد

• International Relations
• Global Governance
• International Environmental Politics
• +۶ مورد دیگر

Helmut Breitmeier has been Professor of International Relations at the Institute of Political Science, Justus Liebig University Giessen, since 2013. In parallel he serves on the project management board of the DAAD-funded SDGnexus Network (since 2020), on the board of directors of the LOEWE Cluster “Minority Studies: Language and Identity” (since 2019) and on the board of the Center for international Development and Environmental Research (ZEU) at JLU (since 2019). From 2008-2013 he was Professor of Political Science at the Open University Hagen, and previously held positions at TU Darmstadt, University of Stuttgart, University of Mannheim and the International Institute for Applied Systems Analysis (IIASA). Education 1994 – Dr. phil., University of Tübingen (dissertation on formation of global environmental regimes) 2004 – Habilitation & Venia Legendi, TU Darmstadt (topic: legitimacy of international regimes) M.A. Political Science & Contemporary History, University of Tübingen / Uppsala University Research Interests Prof. Breitmeier’s work centres on global governance and the effectiveness and legitimacy of international institutions. He analyses how state and non-state actors jointly address global problems, with particular attention to: Environmental and climate governance (international regimes, regime effectiveness, compliance) Food system governance (sustainability norms, SDG 2 implementation, regime complexity) Transnational security threats (climate-conflict nexus, prevention of violence) Role of religion and non-state actors in global politics Methodological focus on quantitative and qualitative comparative regime analysis Across these themes he investigates questions of democratic legitimacy, distributive justice and policy learning. Publication Profile His recent peer-reviewed articles (2021-2009) map the evolution of global sustainability governance. Collectively they trace how inter-institutional arrangements align (or misalign) norm understandings on food security and climate change, how non-state actors enhance or challenge regime legitimacy, and how quantitative regime analysis can assess problem-solving effectiveness and equity. The work combines large-n database analysis (International Regimes Database) with case-based inquiry, producing policy-relevant insights for scholars and practitioners. Grants & Scientific Service Project Management Board, DAAD “exceed” SDGnexus Network (2020-) Board of Directors, LOEWE Cluster “Minority Studies” (2019-) Board Member, Center for international Development and Environmental Research (ZEU) (2019-) Chair, Doctoral Committee Faculty 02 (2015-2020) Deputy Chair, Development and Peace Foundation (SEF) (2015-2020) Member, Scholarship Selection Committee, German Federal Foundation for Ecology (2008-2013) Teaching & Supervision At JLU he teaches undergraduate and graduate courses on global governance, climate politics, the United Nations, minority issues and development theory. He supervises bachelor and master theses and emphasises early, structured consultation with students.

• Power Electronics
• Electrical Engineering
• Energy Conversion
• +۴ مورد دیگر

Morten Nymand is a Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark (SDU), with dual affiliation to the Centre for Industrial Electronics. His research focuses on advanced power electronics, including wide bandgap semiconductor devices (GaN/SiC), high-frequency converters, and energy-efficient systems for renewable energy applications. Key affiliations: SDU, Centre for Industrial Electronics Academic focus: Power electronics, converter design, GaN/SiC devices Projects: HPC (Ultra-High Efficiency DC-DC Converters), PtXKonvertering (Power-to-X conversion) Nymand’s work emphasizes minimizing power losses, optimizing converter topologies (e.g., ANPC, NPC), and developing novel magnetic components (planar transformers/inductors). He has contributed to EMI noise reduction, predictive control algorithms, and photovoltaic system analysis. His publications highlight collaborations across Europe and applications in grid-connected systems, battery charging, and industrial electronics. Recent research trends include hybrid converter topologies, ultra-high efficiency (>99%), and high temporal resolution weather data platforms for renewable energy storage. He has supervised students like Amin Kouchaki in EMI studies and converter design projects. Scientific activities include organizing the 13th IEEE CPE-POWERENG conference (2019) and participation in EUDP-funded projects (2022–2023). His lab work centers on FPGA-based control platforms, digital modulation techniques, and compact converter prototypes.

• gender inequality
• European values
• transformations in the world of work
• +۳ مورد دیگر

Vera Lomazzi is an Associate Professor of Sociology at the Department of Management, University of Bergamo. She holds leadership roles in the Center on Economic, Social and Cooperative Dynamics (CESC) and Center for Teaching Quality, Innovation and Learning (CQIIA). President of the European Survey Research Association Secretary of the Executive Committee of the European Values Study Member of OECD Global Technical Advisory Group on masculinities Her research focuses on: Comparative analysis of European values Evolution of gender role attitudes Workplace transformation and labor values Survey methodology and cross-national data harmonization Current projects include: PoweR - Promoting inclusive companies (Coordinator) MEN4DEM - Masculinity and European democracy (Work Package Leader) Missione Bergamo - Sociological investigation (Co-coordinator) European Values Study Wave 6 (Co-coordinator) Her recent publications address immigration attitudes, gender inequality measurement, work values transformation, and survey methodological challenges across European countries. Key scientific contributions: Measurement invariance testing in cross-national surveys Gender role attitude polarization analysis Pandemic impact on work-life balance Conceptualization of solidarity in European contexts

Robert Strzodkaمشاهده پروفایل

استاد

• Parallel algorithms on parallel hardware (GPUs, many-core CPUs, FPGAs)
• Numerical methods (AMG, Krylov, preconditioners, FMM, multilevel methods, SVD)
• Graph algorithms (partitioning, coloring, decomposition, BFS, MST)
• +۷ مورد دیگر

Robert Strzodka is a full professor at Heidelberg University, holding the chair on Application Specific Computing since 2015. He is renowned for pioneering work in GPU-based scientific computing, focusing on optimizing interactions between mathematics, algorithms, and parallel hardware architectures. Current affiliation: Institute of Computer Engineering (ZITI), Heidelberg University Prior roles: NVIDIA (AmgX group), Max Planck Institute (Integrative Scientific Computing), Stanford University (visiting assistant professor) PhD: Numerical mathematics from University of Duisburg-Essen (2004) His research spans parallel algorithms (GPUs, FPGAs), numerical methods (AMG, Krylov, preconditioners), and graph algorithms (partitioning, MST). Publications emphasize GPU acceleration , cache optimization , and mixed precision computing across domains like astrophysics, fluid dynamics, and finite element methods. The 15 most recent articles (2024-2008) demonstrate consistent innovation in parallel computing , GPU algorithms , and numerical methods . Key subfields include stochastic modeling , tridiagonal solvers , level set visualization , and cache-aware algorithms .

• Power Electronics
• Electrical Traction Systems
• Electromagnetic Compatibility
• +۳ مورد دیگر

Dr. Eng. Bartosz Piotr Woszczyna is a Lecturer at the Department of Electrical Engineering within the Faculty of Electrical and Computer Engineering at Cracow University of Technology. His research focuses on optimizing electrical traction systems and power electronics, with emphasis on real-world applications in urban transportation infrastructure. Research Interests: His work spans power quality, semiconductor technologies, and electromagnetic compatibility in traction systems. Primary areas include: Switching loss reduction in multilevel inverters Harmonics analysis in tramway power grids SiC transistor applications for high-efficiency converters Load variability modeling in traction substations EMC solutions for power electronics Voltage stability in on-board vehicle networks Publication Trends: His 14 recent publications demonstrate consistent focus on empirical solutions for tram traction systems. Dominant themes include experimental validation of SiC-based converters, measurement of harmonic distortions in traction networks, and optimization of substation load distribution. Collaborative works frequently address practical engineering challenges in urban rail transport.

• Mental Health Statistics
• Suicidal Behavior
• Posttraumatic Stress Disorder (PTSD)
• +۶ مورد دیگر

Jing Wang is a Research Associate Professor in the Department of Psychiatry at the Uniformed Services University of the Health Sciences (USUHS) and a Research Psychologist at the Center for the Study of Traumatic Stress (CSTS), with secondary appointment in the Department of Medical and Clinical Psychology. Education: Postdoctoral Fellowship, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH PhD in Quantitative Psychology, Bowling Green State University MA and MS, Morehead State University BS, Ocean University of China Research Focus: Dr. Wang specializes in applying advanced statistical methodologies to military mental health research. Her core expertise includes structural equation modeling, latent class analysis, and longitudinal data analysis to investigate suicidal behavior , PTSD symptom variability , and ecological momentary assessment in military populations, with emphasis on risk/protective factors for mental health disorders. Publication Trends: Recent work (2020-2023) consistently examines suicide risk trajectories among National Guard/Reserve personnel during military transitions, attachment theory applications in new soldiers, and PTSD-sleep interrelationships using intensive longitudinal data. All studies employ sophisticated statistical approaches to address military-specific mental health challenges. Current Projects: Actively leads research initiatives at CSTS including the Daily Dairy Study, Army STARRS, Family Violence Study, and Mental Health Service Utilization among Reserve/National Guard forces.

Marco Giesselmannمشاهده پروفایل

پژوهشگر ارشد

• Migration Studies
• Refugee Integration
• Labor Market Analysis
• +۴ مورد دیگر

Marco Giesselmann serves as a DIW Fellow at the German Institute for Economic Research (DIW Berlin), focusing on the Socio-Economic Panel (SOEP) study, while maintaining an affiliation with the Sociological Institute at the University of Zurich. His research centers on migration and refugee integration in Germany, labor market dynamics, and socio-economic inequality using longitudinal panel data methodology. Giesselmann's research interests span migration studies, refugee integration, labor market analysis, poverty and inequality research, and longitudinal data methodology. His work particularly examines how refugee influxes impact German society, including labor market consequences, social cohesion, political attitudes, and exclusionary behaviors. He has made significant contributions to SOEP data infrastructure, developing specialized modules for youth, residential history, and socialization processes that enhance the panel's research capabilities. His recent publications demonstrate a strong focus on the social and economic consequences of refugee movements to Germany, with particular attention to labor market integration, political consequences, and social dynamics. Giesselmann employs sophisticated panel data analysis techniques, including fixed effects models and multilevel modeling, to extract causal insights from complex longitudinal datasets. As a key contributor to the Socio-Economic Panel infrastructure, Giesselmann has developed specialized SOEP-Core modules that have expanded the study's capacity to investigate life course transitions, residential mobility, and youth development. His methodological expertise in panel data analysis has advanced the field's understanding of how to properly model cross-level interactions and fixed effects in longitudinal research designs.

Michael Auslenمشاهده پروفایل

استادیار

• State and Local Politics
• Democratic Representation
• Media and Politics
• +۳ مورد دیگر

Michael Auslen serves as an Assistant Professor in the Department of Government at the University of Texas at Austin's College of Liberal Arts, where he teaches Media and American Democracy and Honors Tutorial courses. His research examines democratic representation mechanisms with emphasis on state/local politics, media influences, and public opinion's policy role. His educational background includes: Ph.D. in Political Science from Columbia University Master in Public Policy from Harvard Kennedy School B.A. in Journalism and Political Science from Indiana University Auslen's research spans state/local governance, media-representation dynamics, and political methodology. He investigates how local news organizations and political parties mediate public-official connections while developing advanced techniques for subnational opinion estimation. His work bridges empirical political science and journalistic practice, leveraging prior experience covering Florida state politics. Recent publications reveal three interconnected research strands: methodological innovations for subnational polling (particularly cluster-sampled data), historical analysis of partisan polarization through state party platforms, and media's watchdog role in state legislatures. His 2024-2025 work increasingly integrates multivariate modeling with granular policy analysis. His scientific recognition includes: Best Graduate Student Poster at 2022 State Politics and Policy Conference Christopher Z. Mooney Best Dissertation Prize from APSA State Politics and Policy Section Auslen advises honors theses while maintaining active research collaborations. His prior journalism career at the Tampa Bay Times and Miami Herald directly informs his academic focus on state political institutions. Although specific grant details aren't public, his methodological papers suggest support for computational social science infrastructure. No dedicated labs or research teams are documented, though his work involves large-scale text analysis of newspaper archives and state legislative data.