جست‌وجوی استاد و پژوهشگر

Edriss S. Titiمشاهده پروفایل

استاد

• Nonlinear Dynamical Systems
• Fluid Dynamics
• Mathematical Physics
• +۳ مورد دیگر

Edriss S. Titi is a Professor in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. His research focuses on nonlinear dynamical systems, fluid dynamics, and mathematical physics, with particular emphasis on geophysical fluid dynamics, partial differential equations, and data assimilation. Titi's work addresses fundamental questions in ocean and atmospheric modeling, turbulence, and climate systems. He has contributed extensively to the mathematical analysis of equations such as the Navier-Stokes, Euler, and primitive equations, exploring their well-posedness, regularity, and numerical treatment. His recent research includes studies on the hydrostatic approximation limit, energy conservation in fluid flows, and the application of machine learning to data assimilation in chaotic systems. Titi collaborates with international teams to develop advanced models for climate prediction and ocean dynamics, incorporating eddy parametrization and multiscale analysis techniques. Key areas of focus include: Global well-posedness of geophysical fluid models Non-uniqueness and admissibility of weak solutions Machine learning-enhanced data assimilation Mathematical analysis of turbulence and boundary layers

• Gravitational Waves
• Cosmology
• Particle Physics
• +۲ مورد دیگر

Daniel G. Figueroa is a Research Professor at CSIC's Instituto de Física Corpuscular, leading investigations in gravitational wave physics, cosmic inflation, and early universe phenomena. His research develops theoretical frameworks and computational tools to probe fundamental physics through cosmological signatures. Dr. Figueroa pioneered the CosmoLattice simulation code for modeling field dynamics in expanding spacetimes, enabling precision studies of reheating mechanisms, topological defect evolution, and gravitational wave generation. Current projects examine axion inflation dynamics, cosmic string signatures, and gravitational reheating scenarios. Recent publications analyze pulsar timing array data, nonlinear axion field behavior, and LISA mission science goals. His work supports next-generation gravitational wave observatories including LISA and Einstein Telescope through theoretical predictions and data interpretation frameworks. Dr. Figueroa maintains active collaboration within international consortia including the LISA Cosmology Working Group, advancing cosmic frontier research through interdisciplinary approaches.

• Econometrics
• Time Series Analysis
• Discrete Choice Models
• +۳ مورد دیگر

Prof. Dr. Dietmar Bauer holds the Chair of Econometrics at the Faculty of Business Administration and Economics at Bielefeld University. He maintains multiple affiliations including the Department of Empirical Methods, Center for Statistics, Institute for Technological Innovation, Market Development and Entrepreneurship, BIGSEM (Bielefeld Graduate School of Economics and Management), and the Bielefeld Graduate School in Theoretical Sciences. His academic credentials include a Habilitation in Econometrics (2007), Doctorate in Technical Mathematics (1998), and Diploma in Technical Mathematics (1995), all from TU Wien, Austria. Prior to his position at Bielefeld, he served as Senior Scientist at the Austrian Institute of Technology (2005-2014) and Assistant at TU Wien (1995-2005), with additional postdoctoral positions at Yale University, University of Linköping, and University of Newcastle. Prof. Bauer's research focuses on two primary areas: time series analysis and discrete choice models. His work in time series investigates subspace methods for state space models, particularly for integrated and seasonally integrated processes. In discrete choice modeling, he examines computational efficiency in Probit models and transportation mode choice, with special attention to the MACML approach. His publications demonstrate consistent contributions to both theoretical econometrics and applied transportation research. Multa Scripsit Award from Econometric Theory (2013) Best paper awards at UrbComp 2012 (with Jameson Toole, Marta Gonzalez, and Michael Ulm) Oskar Morgenstern Award of IHS, Vienna (with Martin Wagner) Prof. Bauer teaches numerous courses across four institutes including Software Applications for Economists, Data Analysis, Statistical Methods, and Econometrics. He supervises diploma theses, master's theses, and doctoral dissertations. His research has been supported by funding from DFG, FWF, FFG, and EU-level projects, reflecting his strong record in securing competitive research funding. He is actively involved with the Center for Statistics and the Bielefeld Center for Data Science, contributing to interdisciplinary research initiatives.

Loucas Pillaud-Vivienمشاهده پروفایل

پژوهشگر

• Machine Learning
• Stochastic Processes
• High-Dimensional Statistics
• +۳ مورد دیگر

Loucas Pillaud-Vivien is a researcher at the Applied Probability team within CERMICS (École des Ponts ParisTech), specializing in the high-dimensional stochastic dynamics underlying machine learning optimization algorithms. His work bridges statistical theory and computational methods, focusing on understanding algorithmic behavior in modern ML systems. Education : PhD from ENS/Inria Paris (2020) under Francis Bach; postdoctoral research at EPFL, New York University (Courant Institute), and the Simons Foundation (Flatiron Institute). Research interests include stochastic processes, optimization theory, and high-dimensional statistical learning. Key themes involve analyzing gradient-based methods, implicit regularization effects, and the interplay between computational efficiency and statistical accuracy. Recent publications emphasize topics such as gradient flow dynamics, variational inference, and spectral bias in neural networks. His work often addresses fundamental questions about algorithmic convergence and generalization in overparameterized models. Loucas is affiliated with the CERMICS laboratory, contributing to interdisciplinary projects at the intersection of applied mathematics and machine learning.

Elizabeth Stojanovskiمشاهده پروفایل

مدرس ارشد

• Biostatistics
• Longitudinal Modelling
• Multivariate Statistics
• +۶ مورد دیگر

Dr. Elizabeth Stojanovski is a Senior Lecturer at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA) . She leads the Mathematics Education Research Group and manages the statistical consulting unit, serving industry, academics, and postgraduate students. With a PhD in Statistics from the University of Newcastle, she specializes in Biostatistics , Longitudinal Modelling , and Multivariate Statistics . Education : PhD (University of Newcastle), Bachelor of Mathematics (Honours), Bachelor of Mathematics Professional Roles : Senior Lecturer (2007–present), Research Academic (2004–2006) Memberships : Member of the Australian Statistics Society Her research spans health and education , focusing on longitudinal relationships between life events and physical/mental health. She has developed Bayesian frameworks , longitudinal models , and meta-analytic techniques for applications in tobacco control, cardiovascular disease, cancer treatment, and mathematics education. Her work includes collaborations with Eric Beh on Nobel Prize data analysis, Manohar Garg on lipid-lowering studies, and Suzanne Snodgrass on physiotherapy biomechanics. Recent publications highlight her expertise in correspondence analysis for educational and scientific data, multivariate assessment of diabetes risk , and reinforcement learning for optimization. She has supervised 15 PhD/Masters students and peer-reviewed for journals and grants. Her grants total $496,207 , including projects on statistical literacy and phytosterol efficacy. Scientific Awards : 2001 "Temporal association between life events and health" Elizabeth's teaching spans theoretical and applied statistics, with a focus on psychology, health, and business applications. She has also contributed to forensic science through cut mark analysis on bone and policy research on tobacco investments in pension funds. Her leadership in CARMA and statistical consulting underscores her interdisciplinary impact.

Boris Levitمشاهده پروفایل

استاد

• Non-parametric estimation
• Semi-parametric models
• Optimal design in non-parametric regression
• +۵ مورد دیگر

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

Peter Sollichمشاهده پروفایل

استاد

• Statistical Mechanics of Disordered Systems
• Statistical Inference
• Machine Learning
• +۳ مورد دیگر

Professor Peter Sollich is a leading academic specializing in statistical mechanics of disordered systems and machine learning methodologies. His research bridges soft matter physics and advanced computational techniques, focusing on rheology, polydispersity, and non-parametric Bayesian inference. He holds a PhD from the University of Edinburgh (1995) and an MPhil from the University of Cambridge (1992). Research Contributions: His work addresses complex systems such as amorphous solids, glassy dynamics, and force chain prediction in jammed materials. He has contributed to theoretical frameworks like mean-field descriptions of aging linear response and oscillatory shear yielding mechanisms. Recent studies leverage graph neural networks for predictive modeling in materials science. Funding & Collaboration: Co-investigator on EPSRC-funded projects including SpeechWave (2018–2022) and Robust Speech Recognition initiatives. His interdisciplinary approach integrates statistical physics with AI, evidenced by collaborations in conferences like STATPHYS23 and editorial roles at journals such as Journal of Physics A . Impact: With 198+ research outputs and 5,818 citations, his work advances understanding of soft materials, disordered systems, and machine learning applications. His methodologies have implications for material design, computational modeling, and interdisciplinary problem-solving.

Luis Lehnerمشاهده پروفایل

استاد پژوهشی

• Strong Gravity
• Gravitational Wave Astronomy
• General Relativity
• +۷ مورد دیگر

Luis Lehner holds the Carlo Fidani Rainer Weiss Chair in Theoretical Physics at the Perimeter Institute for Theoretical Physics and is an Associate Professor in the Department of Physics at the University of Guelph. He also holds adjunct roles at the University of Wisconsin-Milwaukee and Louisiana State University. His research focuses on strong gravity regimes, testing alternative theories of gravity using gravitational wave detectors, and exploring higher-dimensional scenarios and AdS/CFT correspondence. He is a prominent figure in gravitational wave astronomy, working on multimessenger observations and developing frameworks to probe General Relativity's validity. Lehner has held leadership positions including Deputy Faculty Chair at Perimeter Institute and advisory roles at KITP and ICTP-SAIFR. His work bridges theoretical physics with observational astrophysics, emphasizing black hole dynamics, relativistic hydrodynamics, and turbulence. Recent publications highlight advancements in gravitational wave signal analysis, black hole mimicker studies, and nonlinear gravity effects. Education : Not explicitly listed in text, but his roles imply PhD-level training in Physics. His awards include membership in the National Academy of Sciences of Argentina, the Buchalter Cosmology Prize, and fellowships from APS and the Institute of Physics. He actively participates in international collaborations like the Gravitational Wave International Committee and contributes to detector networks like LIGO-Virgo.

Aimilia Savvaمشاهده پروفایل

مدرس

• Adaptive Bayesian non-parametric inference
• Asymptotic performance of posterior distributions
• Hierarchical Bayes procedures
• +۳ مورد دیگر

Dr Aimilia Savva is a Lecturer in Mathematics and Statistics at the School of Sciences within the University of Central Lancashire Cyprus (UCLan Cyprus). She holds a PhD in Statistics (University of Cyprus, 2024), an MSc in Statistics (University of Sheffield, 2017), and a BSc in Mathematics and Statistics (University of Cyprus, 2016). Her academic roles include teaching Linear Models and Computational Statistics and Data Analysis at UCLan Cyprus, alongside serving as a Risk Consultant at EY Cyprus. Her research focuses on Adaptive Bayesian non-parametric inference , asymptotic performance of posterior distributions , and hierarchical/empirical Bayes procedures . She has contributed to peer-reviewed publications, including a 2024 article in Bernoulli on Bayesian methodology in white noise models. Her work bridges theoretical statistics with practical applications, emphasizing spatially inhomogeneous unknowns and p -exponential priors. Prior to her current roles, Dr Savva served as a Teaching Assistant at the University of Cyprus, instructing courses like Probabilities II and Statistical Methods . She actively engages with academic communities, presenting research at events such as the European Young Statisticians Meeting (2021) and the Pancyprian Statistics Day (2022). She is a member of the Cyprus Statistical Society.

Ning Ningمشاهده پروفایل

استادیار

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +۴ مورد دیگر

Ning Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and the Institute for Quantum Science and Engineering. She co-founded the Stochastic Processes Seminar in the Department of Mathematics. Her research focuses on stochastic processes, Markov chains, machine learning, and quantum computing. Prior to her academic career, she worked as a software developer and database architect in Los Angeles for four years, specializing in data analysis and parallel computing. Education: B.S. in Math from Shandong University (China Math Base), M.S. in Math from the University of Southern California, and Ph.D. in Statistics and Applied Probability from UCSB. She held postdoctoral positions at the University of Washington (Applied Math) and the University of Michigan (Statistics). Research interests include stochastic algorithms, Monte Carlo methods, hidden Markov models, and quantum counterparts of classical methods. She is an Associate Editor for Bayesian Analysis (since 2025), Journal of Computational and Graphical Statistics (since 2024), and Statistics and Computing (since 2023). Recent talks and contributions include invited sessions at the Bayes Comp 2025 conference and the KAUST Stochastic Numerics Workshop. Her work spans theoretical advancements and computational innovations in statistical methods, with applications to large-scale data analysis and machine learning.

• Reinforcement Learning
• Stochastic Dynamic Optimization
• Sequential Learning
• +۶ مورد دیگر

Somayeh Moazeni is an Associate Professor (Tenured) at the School of Business, Stevens Institute of Technology since September 2021, and also holds a Visiting Associate Professor position at the Department of Industrial Engineering & Management Sciences, Northwestern University since September 2023. Her academic journey includes previous positions as Assistant Professor at Stevens Institute of Technology in both the School of Business (2017-2021) and School of Systems and Enterprises (2014-2016), as well as a Postdoctoral Research Associate at Princeton University (2012-2014). She earned her PhD in Computer Science from the University of Waterloo in 2012, supervised by Professors Thomas F. Coleman and Yuying Li. Her educational background also includes an MMath in Combinatorics and Optimization (2006), a Certificate in University Teaching (2010), and a BSc and MSc in Mathematics (2005) from Amirkabir University of Technology in Iran. Research Interests: Dr. Moazeni's research focuses on Reinforcement Learning , Stochastic Dynamic Optimization , Sequential Learning and Information Collection , Bayesian Optimization , and Mathematical Optimization . Her work spans various application areas including Inventory Management , Logistics , Algorithmic Trading , and Energy Markets . Her recent publications demonstrate a strong trajectory in applying optimization techniques to complex decision-making problems under uncertainty across multiple domains. Her research has been supported by significant funding from organizations including the National Science Foundation (NSF), Natural Sciences and Engineering Research Council of Canada (NSERC), PSEG Foundation, and Accenture. She is an affiliated faculty member of the Stevens Institute for Artificial Intelligence (SIAI) and a faculty member of the Stevens Energy Efficiency cluster. IEEE Senior Member (2019) Vice-provost for Academics Teaching Excellence commendation (2017) Anita Borg Institute GHC Faculty Scholar (2017) Ranked First for NSERC Postdoctoral Fellowship-Mathematical Sciences in Canada (2013) INFORMS Teaching Effectiveness Colloquium (2012) First Place at MITACS Annual Conference Poster Presentation Competition (2009) Dr. Moazeni has successfully supervised multiple PhD and Master's students, including Ruijing Yang (PhD in progress), Rodrigo Andrade (PhD graduated 2020), and several Master's students who completed theses on topics related to energy markets and financial engineering. She serves as a Dissertation Advisory Committee Member for numerous PhD candidates across multiple departments at Stevens. Her industry experience includes roles as Senior Risk Analyst at Royal Bank of Canada and Risk Analyst at Bank of Montreal, providing practical grounding for her academic research.

• Mathematical Statistics
• Machine Learning
• Kernel Methods
• +۸ مورد دیگر

Dr Dalia Chakrabarty is a Reader in Statistical Data Science at the Department of Mathematics, University of York. Previously, she held positions as Senior Lecturer at Newcastle University and Lecturer at Lancaster University. Her research focuses on probabilistic methods, Bayesian inference, and machine learning applications in fields such as medicine, astronomy, and materials science. She specializes in kernel methods, random graph analysis, and causal forecasting. Notable contributions include developing methodologies for uncertainty quantification and non-parametric learning. Her academic career includes a Royal Society Dorothy Hodgkin Fellowship and supervision of students like Kane Warrior. Dr Chakrabarty's work bridges theoretical statistics and real-world challenges, with publications spanning journals like Plos One and Artificial Intelligence in Medicine . She also authored the textbook Supervised Learning: Mathematical Foundations & Real-world Applications (2024, CRC Press). Research Highlights: Inter-graph distance metrics for medical data analysis Bayesian state-space modeling of galactic dynamics High-dimensional data applications in oncology and materials science Collaborations: Maintains ties with Brunel Mathematics for PhD supervision and international research networks. Contact: Email dalia.chakrabarty@york.ac.uk , Tel: +44 (0)1904 32 1486

• Human-Computer Interaction
• Mobile Computing
• Educational Technology
• +۳ مورد دیگر

Alistair Morrison is a Lecturer in Computing Science at the University of Glasgow, specializing in Human-Computer Interaction (HCI), mobile computing, and educational technology. His research focuses on app usage analysis, user behavior modeling, and ethics in data-driven systems. He has contributed to studies on work-based learning in software engineering, digital phenotyping, and large-scale mobile HCI deployments. His work bridges academia and industry, addressing challenges in educational technology such as skill transfer between university and workplace environments. He has explored topics like student engagement in computing science, ethical guidelines for mobile app trials, and screen time tracking. Morrison's research often involves collaborations with international conferences like CHI and IEEE, resulting in over 40 peer-reviewed publications. Key areas of interest include improving consent processes for data collection, analyzing smartphone usage patterns, and designing systems that foster professional competency development. His recent projects emphasize practical applications in software engineering apprenticeships and the ethical implementation of digital tracking technologies.

Vilda Purutçuoğluمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• High Dimensional Data
• +۲ مورد دیگر

Prof. Vilda Purutçuoğlu is a Professor in the Department of Statistics at Middle East Technical University (METU). Her academic career includes roles as Assistant Professor (2010–2012), Associate Professor (2012–2018), and Professor since 2018. She holds a Ph.D. from Lancaster University (2007) and has affiliations with the Department of Biomedical Engineering, Institute of Applied Mathematics, and Informatics Institute at METU. Her research focuses on bioinformatics, systems biology, high-dimensional data analysis, neuroscience, and social statistics . Key professional roles include Head of the Department of Statistics (since 2025), Chair of the Award Fund Committee at the International Biometric Society (since 2022), and Editorial roles in journals like BMC Bioinformatics. She has led projects on topics ranging from family dynamics to outlier detection in biomedical data, funded by TÜBİTAK and METU. Prof. Purutçuoğlu has been recognized with multiple Academic Performance Awards (2011–2022 excluding 2020) and was a finalist for the Sabri Ülker Science Award (2015). She collaborates internationally, including a Global Engagement Fellowship (2024–2025) at University College London and research at Università Della Svizzera Italiana (2024). Her publications span books on operations research and bioinformatics, and she actively supervises projects and students in interdisciplinary areas like biomedical engineering and computational statistics.

Christopher Stapenhurstمشاهده پروفایل

پژوهشگر ارشد

• Economics
• Game Theory
• Environmental Policy
• +۴ مورد دیگر

Christopher Stapenhurst is a Research Fellow with expertise in Economics and Game Theory. He holds a PhD in Economics, an MSc in Economics, and an MSc in Applicable Mathematics. His professional experience includes serving as an Assistant Economist at the Office for National Statistics. Stapenhurst's research focuses on applying game-theoretic mechanisms to policy design, inequality analysis, and environmental economics. Education PhD Economics MSc Economics MSc Applicable Mathematics Research Interests Game Theory applications in industrial policy and environmental governance Inequality measurement through ordinal data analysis Anti-corruption mechanisms and principal-agent frameworks International cooperation dynamics for pollution reduction Mathematical modeling of economic stability Statistical inference for economic indices Article Trends Stapenhurst's publications demonstrate an interdisciplinary focus combining economics, mathematics, and environmental policy. His recent work (2024-2025) centers on international cooperation mechanisms for marine pollution reduction and industrial policy optimization using social risk preferences. Earlier studies (2021-2023) explore corruption deterrence through strategic information control and inequality analysis via median-preserving spreads. The research spans theoretical game theory, empirical economic analysis, and environmental economics applications.