جست‌وجوی استاد و پژوهشگر

Nuno Miguel Britesمشاهده پروفایل

دانشیار

• Stochastic Differential Equations
• Fisheries Management
• Epidemiological Modeling
• +۳ مورد دیگر

Nuno Miguel Brites is an Associate Professor of Statistics and Actuarial Science at the Department of Mathematics, Instituto Superior de Economia e Gestão (ISEG), Universidade de Lisboa. He holds a PhD in Mathematics (Statistics) from the University of Évora. His research focuses on stochastic differential equations applied to fisheries management, mortality rate modeling, and epidemiological systems. He has led over 150 publications in peer-reviewed journals and supervised multiple master's and doctoral students. Brites is also an active member of the Centro de Matemática Aplicada à Previsão e Decisão Económicas (CEMAPRE) and has held visiting professorships at the University Aberta and Polytechnic Institute of Beja. Education includes a PhD in Mathematics (2017), MSc in Mathematics (2010), and BSc in Mathematics (2008) from Universities of Évora and Aveiro. His work integrates stochastic models with real-world applications in ecology, public health, and actuarial science. Recent articles explore optimal harvesting strategies under environmental stochasticity and comparative mortality rate analysis between France and Portugal. He coordinates advanced courses in stochastic processes, actuarial computation tools, and mathematical finance methodologies at ISEG. Brites' professional experience spans academic leadership roles including Management Council membership at CEMAPRE and evaluation roles at A3ES. He has developed pedagogic materials for PhD and master's programs in applied mathematics, economics, and finance. His research addresses pressing issues in sustainable resource management and stochastic modeling of complex systems. Current projects include advancing computational methods for stochastic differential equations in actuarial and ecological contexts.

• Probability
• Combinatorics
• Theoretical Computer Science
• +۵ مورد دیگر

Erik Slivken is an Associate Professor of Mathematics at the University of North Carolina Wilmington . Previously, he held positions as a Krener Assistant Professor at UC Davis, a CNRS Postdoctoral Researcher at Laboratoire de Probabilités in Paris VII, and a Visiting Instructor at Dartmouth College. He earned his Ph.D. in Mathematics from the University of Washington in 2014 under Christopher Hoffman. His research focuses on the intersection of probability and combinatorics , with applications to theoretical computer science and statistical mechanics. Key areas include scaling limits of discrete random objects, discrete stochastic processes, bootstrap percolation, and random permutation patterns. He investigates the limiting behaviors of combinatorial systems, such as the statistical properties of large permutations and long-term dynamics of stochastic processes. Slivken has published extensively in top-tier journals like Random Structures and Algorithms , Probability Theory and Related Fields , and Electronic Journal of Probability . His work often explores fixed points in permutation structures, growth models on Hamming planes, and algorithmic complexity. He has presented invited talks at institutions including the University of Warwick, Dartmouth, and international conferences like the Vilnius Probability Conference. His research has been supported by collaborations with leading mathematicians such as Christopher Hoffman, Janko Gravner, and Jacopo Borga. He actively participates in organizing seminars and workshops, including the Davis-Warwick Probability Workshop and Bay Area Discrete Math Day.

• Applied Mathematics
• Partial Differential Equations
• Fluid Dynamics
• +۳ مورد دیگر

Jian-Guo Liu is a Professor in the Department of Mathematics and Department of Physics at Duke University. His research focuses on applied mathematics, partial differential equations, fluid dynamics, and kinetic theory. He has made significant contributions to areas including computational fluid dynamics, numerical analysis, and mathematical modeling of physical systems. His work often bridges theoretical analysis and practical computational methods, addressing challenges in fluid dynamics, plasma physics, and biomathematics. Key research interests include the analysis of Navier-Stokes equations, magnetohydrodynamics, chemotaxis models, and particle-based methods. He has developed novel numerical schemes for incompressible flows and contributed to the understanding of nonlinear dynamics in self-organized systems. Notable publications include studies on Hall-Magnetohydrodynamics, consensus-based optimization in machine learning, and random batch methods for particle systems. His work emphasizes both theoretical rigor and computational efficiency, with applications spanning fluid mechanics, materials science, and biological systems.

Tamar Keren-Portnoyمشاهده پروفایل

استاد

• Language Acquisition
• Infant Phonological Development
• Babbling and Early Vocalization
• +۷ مورد دیگر

Tamar Keren-Portnoy is a Professor in the Department of Language and Linguistic Science at the University of York, where she has held academic positions since 2006, progressing from Research Fellow to Professor in 2021. She is actively involved in research, teaching, and external collaborations, focusing on early language acquisition and developmental psycholinguistics. Her work bridges linguistics, psychology, and clinical applications, particularly in supporting infants at risk of language delay. Her research interests center on how low-level skills such as babbling and phonological perception contribute to higher-level language development. She investigates the role of infant vocalizations, parental speech input, sleep, and motor development in vocabulary growth. A significant focus is on interventions for children with Down syndrome, including the development and evaluation of the BabblePlay app. She also explores the psycholinguistics of Semitic languages like Hebrew and Arabic, examining root-and-pattern morphology in early language learners. The most recent publications reflect a strong trend in interdisciplinary research combining developmental science, technology, and clinical intervention. Topics include app-based babbling reinforcement, sleep’s role in word learning, cross-linguistic child-directed speech, and sound symbolism in maternal speech. Her work increasingly integrates experimental methods, systematic reviews, and randomized controlled trials to advance both theoretical understanding and practical applications. She is a member of the ESRC Peer Review College and leads multiple funded projects with collaborators across psychology, engineering, and health sciences. Her grants include support from the British Academy and the Economic and Social Research Council (ESRC), focusing on early intervention, vocalization reinforcement, and language outcomes in at-risk populations. Her laboratory, associated with the BabblePlay project and the York Baby Lab, engages in naturalistic observation and experimental studies with infants and toddlers. She collaborates internationally, including work with Ugandan populations and cross-linguistic studies of Danish, Arabic, and Hebrew. Her team includes researchers in linguistics, psychology, and health sciences, reflecting the interdisciplinary nature of her work.

Andrea Rapisardaمشاهده پروفایل

استاد

• Complex Systems
• Statistical Mechanics
• Deterministic Chaos
• +۷ مورد دیگر

Andrea Rapisarda is a Full Professor of Theoretical Physics at the University of Catania, Italy, affiliated with the Department of Physics and Astronomy "Ettore Majorana" and INFN Catania. He is the coordinator of the PhD program in Complex Systems for Physical, Socio-economic and Life Sciences and serves as co-director of the International School on Complexity at the Ettore Majorana Foundation in Erice. Additionally, he is an External Faculty member at the Complexity Science Hub Vienna, reflecting his international academic engagement. His research spans Complex Systems, Statistical Mechanics, Deterministic Chaos, Nonlinear Dynamics, Complex Networks, and Agent-Based Models , with a strong emphasis on applications to socio-economic systems. He investigates the role of randomness, luck, and inefficiencies in hierarchical organizations, drawing from interdisciplinary methods in physics and computational modeling. His work has demonstrated how random strategies can outperform merit-based ones in organizational efficiency and democratic design. The 15 most recent publications highlight a consistent focus on modeling randomness in success, inequality, and decision-making across domains such as sports, economics, politics, and public health. His work integrates agent-based simulations, network analysis, and statistical physics to understand complex phenomena, often with policy-relevant implications. Ig Nobel Prize for Management (2010) Ig Nobel Prize for Economics (2022) Rapisarda is an active advisor and researcher, though specific student names are not listed in the provided texts. He has contributed to numerous editorial boards, including Physica A, Entropy, Frontiers in Physics (Social Physics) , and Complexity . He has also been involved in public outreach, policy discussions on democratic reform via sortition, and science communication through media interviews and public lectures. He leads research projects on improving organizational and societal systems using insights from complexity science.

Sean H Riceمشاهده پروفایل

استاد

• Evolutionary Biology
• Theoretical Biology
• Mathematical Biology
• +۹ مورد دیگر

Sean H. Rice is a Professor in the Department of Biological Sciences at Texas Tech University, where he conducts research and teaches in evolutionary theory and mathematical biology. His work spans theoretical and empirical domains, focusing on the conceptual and mathematical foundations of evolution, developmental modeling, and morphological evolution across diverse taxa. University: Texas Tech University School: College of Arts and Sciences Department: Department of Biological Sciences Academic Rank: Professor Email: sean.h.rice@ttu.edu Education: B.A. in Biology, University of California, Santa Cruz (1984) Ph.D. in Ecology & Evolutionary Biology, University of Arizona (1991) Sean H. Rice's research interests lie at the intersection of theoretical biology and empirical evolutionary studies. He investigates the mathematical underpinnings of evolutionary processes, particularly the role of developmental constraints, epistasis, and stochasticity in shaping evolutionary trajectories. His lab explores key conceptual issues through systems such as molluscan shell development, heterochrony in primate brain evolution, and speciation mechanisms in rotifers. He emphasizes analytical models over simulations, arguing that mathematical rigor provides deeper biological insight. The trends in his publications reflect a consistent focus on foundational evolutionary theory. His work integrates population genetics, quantitative genetics, developmental biology, and macroevolution, with recurring themes including the Price equation, G-matrix evolution, canalization, and species selection. He is particularly known for developing axiomatic frameworks that derive evolutionary laws from first principles, bridging philosophy and formal theory. Scientific Awards: No specific awards are mentioned in the provided texts. Advising and Grants: Dr. Rice mentors graduate students who develop independent research projects aligned with broad evolutionary questions. His former and current students include Lisa Suatoni, Heinrich Zu Dohna, David Ralph, Morgan Baker, John Harting, Ben Qin, and Sarah Fumagalli. While specific grant funding is not listed, his sustained research output and lab activity suggest active external support. He values deep conceptual engagement and encourages students to build strong theoretical foundations. Labs and Research Groups: Dr. Rice leads a research group within the Systematics & Evolution research cluster at Texas Tech. His lab combines theoretical modeling with empirical data to address fundamental questions in evolutionary biology. Key projects include an axiomatic theory of evolution, modeling developmental evolution with epistasis, studying heterochrony, and analyzing molluscan shell morphogenesis. The lab fosters independent thinking, with students designing their own dissertation projects under his guidance.

Mikhail Tammمشاهده پروفایل

پژوهشگر ارشد

• Statistical Physics
• Complex Networks
• Soft Condensed Matter
• +۵ مورد دیگر

Mikhail Tamm is a Senior Research Fellow at Tallinn University's School of Digital Technologies, where he applies statistical physics and complex network theories to human-generated data. His academic journey includes positions as Associate Professor at Moscow State University and Higher School of Economics, Lecturer at Moscow Physical Technical Institute, and postdoctoral research at Université Paris-Sud. He holds a PhD in Physics from Moscow State University (2002), supervised by Igor Erukhimovich. Research interests focus on interdisciplinary applications of physics methodologies: Statistical physics approaches to social/cultural data analysis Complex network theory for transport patterns and psycholinguistics Dimensional reduction techniques for human activity patterns Modeling abrupt transitions in social systems Publication analysis reveals consistent focus on network science applications across physics, biology, and cultural studies, with recent emphasis on hyperbolic networks, language coexistence models, and computational cultural analytics. Methodological innovations in statistical mechanics form the core of his interdisciplinary work. Current research projects include: Principal Investigator: 'Learning Processes in Language Dynamics' (Estonian Research Council, 2024-2025) Team member: 'Cultural Data Analytics Open Lab' (Tallinn University, 2024-2027) Team member: 'Learning Processes in Language Dynamics' (PRG1059, 2021-2025) Previous projects include the EU-funded 'Cultural Data Analytics' (2019-2024) and 'Kinokroonika Exploration Project' (2021-2022).

Neena Modiمشاهده پروفایل

استاد

• Neonatal Medicine
• Pediatrics
• Public Health
• +۵ مورد دیگر

Neena Modi is a Professor of Neonatal Medicine and Vice-Dean (International) at Imperial College London's Faculty of Medicine. She is an Honorary Consultant in Neonatal Medicine at Chelsea and Westminster NHS Foundation Trust. Her roles include leading the Neonatal Data Analysis Unit, managing the UK National Neonatal Research Database (a国家级信息资产), and directing the National Institute for Health Research Imperial Biomedical Research Centre's 'Pregnancy and Prematurity' theme. She chairs the Scientific Committee of the International Paediatric Association and previously held leadership positions in the Royal College of Paediatrics and Child Health, British Medical Association, and UK Medical Women's Federation. Her research focuses on perinatal determinants of metabolic health, neonatal nutrition, and medical ethics. Key initiatives include establishing the UK Neonatal Collaborative and advocating for child health equity, publicly funded healthcare, and youth suffrage. She has authored influential reports like the 'Turning the Tide' review on strengthening UK child health research. Awards include Fellowship of the Academy of Medical Sciences and multiple honorary fellowships. Her work spans global health advocacy, clinical trial leadership (e.g., PREMFOOD, OPTI-PREM), and policy development. Current projects include the COLLABORATE precision-medicine trial and the Neonatal Update conference series. She also chairs NHS England's Infant, Children and Young People Patient Safety Expert Group and serves on ethics committees.

• Mathematical Physics
• Probability Theory
• Stochastic Processes
• +۳ مورد دیگر

Zhengye Zhou is an Assistant Professor in the Department of Mathematics at the University of Southern California Dornsife College of Letters, Arts and Sciences. Their research focuses on advanced mathematical frameworks for stochastic processes and quantum algebra. Research Interests: Interacting particle systems, orthogonal polynomial duality, asymptotic analysis, quantum groups, integrable systems, and non-commutative random surface growth. Their recent work explores asymptotics and duality in dynamic stochastic models, including multi-species asymmetric exclusion processes and higher-spin vertex models. This research intersects probability theory, mathematical physics, and algebraic structures. Zhengye Zhou contributes to theoretical developments in probability, particularly through rigorous analysis of Markov duality, unitary symmetries, and Gaussian fluctuations in stochastic systems. Notable publications span topics like dynamic ASEP, quantum group bialgebras, and boundary-driven exclusion processes.

Sharique Hasanمشاهده پروفایل

دانشیار

• Entrepreneurial Strategy
• Digitization
• Field Experiments
• +۵ مورد دیگر

Sharique Hasan is an Associate Professor of Strategy at Duke University’s Fuqua School of Business and holds a courtesy appointment as Associate Professor of Sociology. His research focuses on entrepreneurship, innovation, digitization, and their social implications. He earned a B.S. in Computer Science and Philosophy from Rutgers College, an M.S. in Public Policy from Carnegie Mellon University, and a Ph.D. from Carnegie Mellon’s Heinz College in 2010. Previously, he was on the faculty at Stanford University’s Graduate School of Business. His work examines entrepreneurial processes, organizational decision-making, and labor market dynamics. Notable contributions include studies on A/B testing’s impact on startup performance, racial distrust in gentrifying communities, and the societal effects of online school ratings. Hasan is Deputy Editor of Organization Science and sits on editorial boards for Strategic Management Journal and Journal of Management . He co-founded the Conference on Field Experiments in Strategy (CFXS) and co-developed Scientifiq.AI, a tool to identify commercially viable scientific research. Teaching focuses on strategy implementation and core strategy courses across Duke’s MBA programs. His 2018 Excellence in Teaching Award reflects his pedagogical impact. He also serves as Co-Scientific Director of the Innovation Growth Lab, a global think-tank addressing innovation policy. Education: Ph.D., Carnegie Mellon University (2010); M.S., Carnegie Mellon (2005); B.S., Rutgers College (2003) Awards: Excellence in Teaching Award (Duke University, 2018) Editorial Roles: Deputy Editor ( Organization Science ), Editorial Board Member ( Strategic Management Journal , Journal of Management ) Research highlights include analyzing firm-driven labor market search strategies, the role of experimentation in organizational decision-making, and the societal impacts of gentrification. His work bridges strategy, sociology, and policy, with applications in education, technology, and urban development.

Frances Rossمشاهده پروفایل

استاد

• Materials Science
• Nanotechnology
• Electron Microscopy
• +۸ مورد دیگر

Frances Ross serves as the TDK Professor in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology, where she leads cutting-edge research in nanoscale materials characterization and growth mechanisms. Education: Ph.D. in Materials Science, University of Cambridge B.A. in Physics, University of Cambridge Her research pioneers in situ transmission electron microscopy and scanning tunneling microscopy to observe real-time crystal growth during epitaxy, electrochemical deposition, and catalysis. This work drives innovations in microelectronics and energy storage through atomic-level understanding of nanomaterial self-assembly, with recent focus on developing next-generation microscopy instrumentation for liquid-phase studies. Her publication portfolio demonstrates convergence of materials science with computing hardware, exemplified by nanosecond-scale programmable resistors for analog deep learning. This reflects broader trends in designing energy-efficient neuromorphic devices using novel nanomaterials. Award highlights: Joseph F. Keithley Award (2024) Gerhard Ertl Lecture Award (2019) Hatsujiro Hashimoto Medal (2018) IBM Outstanding Accomplishment Award (2017) She maintains fellowships in six major scientific societies including the American Physical Society and Materials Research Society. Professor Ross actively mentors graduate researchers including Serin and Shu Fen, with her lab supported by grants focused on advanced microscopy development. Her team operates within MIT.nano facilities, collaborating across materials science and electrical engineering disciplines to pioneer new characterization methodologies. The Microscopy@MIT laboratory under her direction specializes in real-time observation of nanomaterial synthesis, maintaining strong partnerships with semiconductor industry leaders and national laboratories for applied research in energy storage solutions.

Christian Ghiglinoمشاهده پروفایل

استاد

• Economic Theory
• Growth
• Networks
• +۹ مورد دیگر

Professor Christian Ghiglino is a faculty member at the University of Essex , serving as Head of the Department of Economics since 2019. His research spans Economic Theory , Growth , Social Networks , Wealth Inequality , and Fiscal Policy . He has extensive editorial experience as Associate Editor of leading journals. Education: PhD in Economics (University of Geneva, 1993), PhD in Particle Physics (CERN, 1988) Research Interests focus on the intersection of economic theory, network analysis, and inequality dynamics. Key themes include wealth distribution's impact on macroeconomic stability, social identity in fiscal policy formation, and network-based approaches to growth and innovation. Publication Trends show a consistent emphasis on network structures (2013-2024), inequality dynamics (2007-2021), and growth theory (1993-2024). His work combines general equilibrium modeling with modern network analysis techniques. Supervision includes 14 PhD/MRes students (2010-2023) working on topics like innovation networks, productivity analysis, and fiscal policy in oil economies. Current supervisee: Lu Liu.

• Nanowire Materials Science
• Semiconductor Optoelectronics
• Solar Cell Technology
• +۳ مورد دیگر

Tuomas Haggren is a Researcher at the Research School of Physics, Australian National University (ANU), affiliated with the ARC Centre of Excellence for Transformative Meta-Optical Systems. He focuses on nanowire-based optoelectronics, semiconductor materials, and photovoltaic technologies. His work emphasizes high-efficiency solar cells, flexible electronics, and metasurface engineering, with applications in energy conversion and quantum photonics. Research interests include nanowire growth techniques, photoluminescence dynamics, and material passivation strategies. He explores novel semiconductors such as GaAs, InP, and AlGaAs for solar cells, photodetectors, and laser systems. His studies address challenges in scalability, cost-efficiency, and environmental stability of III-V materials. Recent publications highlight advancements like 21.2% efficient GaAs solar cells using bilayer contacts and tunable quantum entanglement in metasurfaces. Haggren collaborates on projects like nonlinear imaging via sum-frequency generation, contributing to ANU's interdisciplinary research networks. He supervises research students in experimental and theoretical studies of nanowire optoelectronics. Key achievements include flexible GaAs UV photodetectors and strain-engineered epitaxial lift-off techniques for photovoltaics. His work bridges fundamental material science with applied technologies for next-generation optoelectronic systems.

• Probability Theory
• Mathematical Analysis
• Stochastic Analysis
• +۲ مورد دیگر

Ilya Chevyrev serves as a Reader (equivalent to Associate Professor) in the School of Mathematics at the University of Edinburgh, where he applies probability theory and mathematical analysis to model physical phenomena including crystal growth dynamics and quantum field theory processes. His academic journey spans three continents: Bachelor of Science in Mathematics from the University of Auckland, specializing in algebraic number theory Master's degree in Probability Theory from Université Pierre-et-Marie-Curie (Paris), completed entirely in French PhD in Rough Path Theory from the University of Oxford Chevyrev's research bridges abstract mathematics and real-world applications, focusing on stochastic differential equations and renormalization techniques. His work over the past decade has contributed to establishing rigorous mathematical foundations for quantum field theory processes, particularly in eliminating non-physical infinities through advanced probabilistic methods. He investigates how randomness influences physical systems, using crystal growth as a key case study for developing predictive mathematical frameworks.

• Statistical Methods
• Machine Learning
• Materials Science
• +۵ مورد دیگر

Matthias Neumann is an Assistant Professor at the Institute of Statistics, Graz University of Technology . He completed his PhD in 2020 at Ulm University under Prof. Volker Schmidt, earning the PhD prize of Ulm University . His research focuses on stochastic 3D modeling and statistical analysis of micro- and nanostructures for functional materials, including battery electrodes , fuel cells , and paper-based materials . He has received start-up funding from ProTrainU (2020-2022) and served as principal investigator in the POLiS Cluster of Excellence (2022-2023). Research Interests: His work integrates mathematical morphology , machine learning , and spatial statistics to develop methods for microstructure quantification , estimation of geometrical descriptors (e.g., tortuosity, constrictivity), and data-driven models linking morphology to effective physical properties . He utilizes random fields , point processes , and copulas for virtual microstructure generation and parameter estimation. Teaching: He lectures on Applied Statistics , Statistical Modeling , and Mathematical Statistics at Graz University of Technology, with prior teaching experience at Ulm University in Multivariate Stochastic Modeling , Point Processes , and Spatial Statistics . Scientific Achievements: PhD prize of Ulm University (2020) ProTrainU start-up funding (2020-2022) POLiS Cluster of Excellence grant (2022-2023) Publications: His 15 most recent articles (2023-2025) emphasize machine learning techniques for microstructure segmentation , stochastic 3D modeling of nanoporous materials , and data-driven quantification of transport-property relationships . Key topics include random forests , neural networks , and R-vine copulas applied to fuel cells , sodium-ion batteries , and polymer electrolytes .