جست‌وجوی استاد و پژوهشگر

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +۵ مورد دیگر

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

• Innovation Sociology
• Sociology of Digital Work
• Organisational Sociology
• +۳ مورد دیگر

Ulrich Meyer serves as Full Professor at Johannes Kepler University Linz since 2019, based in the Department of Sociology with a specialized focus on Innovation and Digitalization (SID). His academic work bridges classical organizational sociology with contemporary digital transformation studies, examining how technological changes reshape work practices, institutional fields, and social relations. Professor Meyer's research centers on three interconnected domains: Innovation Sociology examining path-dependent and path-creating dynamics; Sociology of Digital Work analyzing labor transformations under Industry 4.0; and Organisational Sociology investigating institutional pluralism and field reconfiguration. His scholarship consistently reveals the tension between technological domination and emancipatory potential in workplace digitalization, documenting both worker resistance strategies and organizational adaptation mechanisms. Analysis of his 15 most recent publications shows a clear evolution from foundational Industry 4.0 studies (2019-2020) toward sophisticated frameworks for understanding digitalization narratives, counter-movements, and everyday practices (2022-2025). The work demonstrates increasing methodological diversity while maintaining theoretical coherence through neo-institutional and field-theoretical lenses. His collaborative research spans multiple institutional contexts including: Co-authorship with Austrian labor policy institutions (Arbeiterkammer Wien) International research networks on digital sociology Cross-disciplinary projects with computer science researchers Policy-oriented studies for German and Austrian government bodies Professor Meyer maintains active supervision of doctoral candidates while contributing to academic discourse through editorial roles and conference leadership. His work provides essential theoretical and empirical resources for understanding digital transformation's societal implications, particularly regarding labor relations, organizational adaptation, and democratic governance of technological change.

• Quantitative Linguistics
• Speech Production
• Articulatory Speech Synthesis
• +۴ مورد دیگر

Konstantin Sering is a researcher at the University of Tübingen within the Faculty of Philosophy, specifically working at the Seminar for Linguistics as part of the Quantitative Linguistics group. His academic journey bridges physics and psychology with computational linguistics, culminating in his 2023 PhD thesis on the PAULE (Predictive Articulatory speech synthesis Utilizing Lexical Embeddings) model. He maintains active collaborations with the Cognitive Modeling Group led by Martin V. Butz and the Quantitative Linguistics Group led by Harald Baayen. Sering's research interests center on the computational modeling of human speech production, with particular focus on the articulatory aspects of language. He approaches spoken language from the perspective of computer-aided modeling and simulation, extending the VocalTractLab model to produce spontaneous human speech. His work emphasizes the information-bearing nature of language over its meaning-bearing aspects, investigating how language signals contextual states and conditions. He combines artificial neural networks with predictive forward approaches to model vocal tract control parameters across multiple target spaces, including meaning and acoustic representations. Analysis of Sering's publication record reveals a consistent trajectory in articulatory speech synthesis and quantitative linguistics. His work demonstrates increasing sophistication in modeling the human speech apparatus, with recent publications focusing on improving the PAULE model through speech/non-speech classification, somatosensory feedback integration, and benchmarking methodologies. His research spans both theoretical computational linguistics and practical applications, with strong connections to cognitive science and psycholinguistics. The publications show interdisciplinary collaboration across linguistics, computer science, and cognitive psychology. Sering actively contributes to the academic community through teaching and software development. He has taught courses including Introduction to Psycholinguistics, Physics of Human Speech Production, and seminars on Predictive Articulatory Speech Synthesis. As software author and maintainer, he has developed several important tools for the field: Articubench (an articulatory speech synthesis benchmark), PAULE (his namesake speech synthesis model), create_vtl_corpus, and contributions to pyndl and other packages. His technical expertise spans Python, R, and specialized instrumentation for speech analysis. Based in room 188 at Keplerstr. 2 in Tübingen, Sering maintains an active research program with regular publications and software releases since 2013. His work environment benefits from the resources of the University of Tübingen's linguistics infrastructure and collaborative opportunities across disciplines.

• Existential Psychotherapy
• Phenomenological Psychology
• Clinical Psychology
• +۳ مورد دیگر

Daniel Sousa serves as Professor of Health and Clinical Psychology and Director of Clinica ISPA at ISPA - University Institute in Lisbon, Portugal, holding these positions continuously since February 2012. His academic leadership bridges clinical practice and research within the university's mental health infrastructure. Education: Ph.D. from Wales University, London, GB His research pioneers existential-phenomenological approaches to psychotherapy, with core investigations into therapist persuasiveness, significant therapeutic events, and the mechanics of the working alliance. He systematically examines how self-awareness and verbalization catalyze meaning-making in clients, while developing innovative scales like the Facilitative Interpersonal Skills (FIS) instrument to quantify therapeutic effectiveness. His methodology emphasizes descriptive phenomenological exploration of client experiences within existential frameworks. Analysis of his 2022-2025 publications reveals three converging trends: rigorous testing of deliberate practice models against traditional training methods, adaptation of existential therapy to digital environments including online presence challenges, and cross-cultural validation of therapeutic factors like persuasiveness in anxiety treatment. His work consistently integrates qualitative depth with quantitative validation across Portuguese and international contexts. Sousa actively shapes the field through leadership roles as Founder Member of the Portuguese Society for Existential Psychotherapy (since 2006) and editorial contributions to the European Journal for Qualitative Research in Psychotherapy. As Director of Clinica ISPA, he oversees a university-based clinic that functions as both a service provider and training ground for emerging clinicians, embedding his research directly into clinical supervision and practice.

• Network Science
• Dynamical Systems
• Stochastic Processes
• +۶ مورد دیگر

Professor István Z. Kiss is a Professor of Network Science at the Network Science Institute, Northeastern University London. His research bridges network science, dynamical systems, and stochastic processes with applications in epidemic modeling, computational neuroscience, and complex systems analysis. He leads the Systems Processes and Networks Lab (SPAN Lab) and maintains active collaborations across multiple institutions globally. Current position: Professor of Network Science Institution: Northeastern University London Research unit: Network Science Institute (SPAN Lab) Key collaborators: Péter L. Simon (Eötvös Loránd University), Joel C. Miller (La Trobe University), Gregory A. Rempała (Ohio State University) His research focuses on theoretical and data-driven problems at the intersection of network science and dynamical processes. Key areas include network inference, exactness of mean-field models, temporal and higher-order networks, adaptive/dynamic networks, and resilience of power networks. He has made significant contributions to understanding epidemic dynamics on complex networks, developing mathematical frameworks that connect approximate models with rigorous counterparts. Recent work emphasizes higher-order network structures, network inference from system-level data, and applications to public health and infrastructure resilience. His publication record demonstrates consistent output in top journals including Journal of Mathematical Biology, Bulletin of Mathematical Biology, and Physical Review E. The research shows a clear progression toward increasingly complex network structures, with recent publications focusing on higher-order interactions, temporal dynamics, and practical applications in epidemiology and infrastructure networks. His work bridges theoretical mathematics with real-world applications in disease control and network resilience. Professor Kiss actively supervises postdoctoral researchers and PhD students, with current advisees including Federico Cosimo Malizia (higher-order network contagion), Kevin Teo (shipping networks), and Yan Li (trade dynamics). His supervision spans mathematical theory, computational modeling, and data analysis across multiple domains. Leverhulme Trust grant RPG-2017-370: Bayesian Inference and Approximations in High-dimensional Network Models Network Science Institute Boston funding for higher-order network research EPSRC grant EP/H001085/1 for information diffusion modeling He leads the Systems Processes and Networks Lab (SPAN Lab), which develops mathematical frameworks for complex networked systems. The lab focuses on both theoretical advancements in network science and practical applications to epidemiology, neuroscience, and infrastructure networks. Current projects include developing tools for sequential temporal network analysis, understanding failure prediction in utility networks, and disentangling contact network structure effects on system-level outputs.