جست‌وجوی استاد و پژوهشگر

Qidi Pengمشاهده پروفایل

استاد پژوهشی

• Stochastic Processes
• Statistical Inference
• Machine Learning
• +۵ مورد دیگر

Qidi Peng is a Research Associate Professor at Claremont Graduate University (CGU) and Academic Director of the Master of Science in Financial Engineering (MSFE) program within the Institute of Mathematical Sciences. His academic career includes roles as Research Assistant Professor (2012–2021) and Senior Technical Expert at AIG (2018–2022). He holds a Ph.D. in Applied Mathematics from Lille 1 University (France), focusing on statistical inference for multifractional processes in stochastic volatility models, under Prof. Antoine Ayache. His research spans stochastic processes, statistical inference, machine learning, and financial modeling. Notable contributions include work on fractional Brownian motion, multifractional processes, and algorithmic regularization techniques. Peng is fluent in multiple programming languages (C++, MATLAB, R, Python) and has contributed to editorial boards, including the Operation Research and Applications: An International Journal since 2014. Prior to his current roles, he served as a teaching fellow in France and a consultant for SOFT SOLUTIONS Company. His work bridges theoretical mathematics with applied domains like finance, insurance, and wireless networks.

• Statistics
• Nonlinear Models
• Regression Analysis
• +۷ مورد دیگر

Prof. Müjgan Tez is a distinguished academic in the field of Statistics, currently serving as a Professor at Marmara University, Faculty of Arts and Sciences, Department of Statistics , and concurrently at Işık University, Faculty of Arts and Sciences, Department of Mathematics . Her career spans decades with significant contributions to statistical methodology and applications. Doctorate: Çukurova University, Mathematics (1985) Postgraduate: Ankara University, Mathematics (1982) Undergraduate: Selçuk University, Mathematics (1980) Her research interests include nonlinear models, regression analysis with measurement error, meta-analysis, survival analysis, robust statistics, and time series forecasting. She applies advanced statistical techniques to interdisciplinary problems in medicine, engineering, and public health. The recent publications reflect a strong trend in methodological statistics, particularly in nonlinear and semiparametric modeling, robust regression, and optimization techniques. Her work bridges theoretical statistics with real-world applications, including medical studies on neurosurgery career choices and pandemic forecasting. Scientific awards: Nato Bursları TUBITAK (1990–1991) Advising and grants: She has supervised over 20 graduate theses and led funded research projects such as "Genetic Algorithm and Jackknife Method Combined for Estimating Compartment Model Parameters" (BAP, 2016). Her academic leadership includes multiple terms as Head of Department in both Mathematics and Statistics at Marmara University. She has served on PhD thesis monitoring committees and contributed to numerous international conferences. Labs and teams: While no specific lab name is mentioned, her collaborative work with researchers like Taylan, Yerlikaya-Özkurt, İnan, and Türkşen suggests active participation in statistical research groups focusing on optimization, fuzzy systems, and functional data analysis.

• Approximation Theory
• Numerical Analysis
• Machine Learning
• +۱۲ مورد دیگر

Emma Perracchione is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at Politecnico di Torino, where she conducts research at the intersection of approximation theory, machine learning, and scientific computing. Her work focuses on kernel-based methods, inverse problems, and applications in space weather and solar physics. She is actively involved in teaching and research leadership, including PhD supervision and national projects. PhD in Mathematics, University of Turin (2017, cum laude) M.Sc. and B.Sc. in Mathematics, University of Turin (2013, 2011) Her research interests center on approximation theory and its applications, particularly greedy methods and two-layered kernel machines used for feature reduction in geomagnetic storm forecasting and optimal sampling for solar nanosatellites. These efforts contribute significantly to advancements in inverse problems and scientific computing . Her expertise spans machine learning , imaging , and data-driven modeling , with applications in climate action and space weather. The most recent publications highlight a strong trend in developing and analyzing variably scaled kernels , feature selection via greedy algorithms, and machine learning applications in solar and astrophysical contexts. These works integrate numerical analysis with real-world data from solar wind and imaging instruments, demonstrating a blend of theoretical rigor and practical relevance. Scientific awards received include: GNCS Young Researchers Funding (2020) GNCS Young Researchers Funding (2016) "Luciana Picco Botta" Study Award (2015) COST Short Term Scientific Mission (STSM) grant (2015) Emma Perracchione supervises PhD student Matteo Trombini in the Mathematical Sciences program (40th cycle, 2025–ongoing) and leads the PRIN-funded project GOSSIP – Greedy Optimal Sampling for Solar Inverse Problems (2025–2027). She has taught various courses including Linear Algebra and Geometry , Numerical Methods and Scientific Computing , and advanced topics on Kernels for Machine Learning in aerospace, automotive, and computer science engineering programs. She is a member of the Space Weather Italian Community (SWICo) and the National Scientific Computing Group (GNCS-INdAM) , and serves as Guest Editor for Dolomites Research Notes on Approximation . She has also participated in organizing major conferences such as DWCAA24 and GIMC-SIMAI Young.

Pengcheng Liuمشاهده پروفایل

مدرس ارشد

• Robotics
• Bio-inspired Robotics
• Soft Robotics
• +۱۴ مورد دیگر

Pengcheng Liu is an Associate Professor (Senior Lecturer) in Robotics and Applied Control at the Department of Computer Science, University of York, UK. He has previously held academic positions at Cardiff Metropolitan University, University of Lincoln, Bournemouth University, and visiting roles in China. He is a member of IEEE and IFAC, and serves on several IEEE Technical Committees related to robotics and automation. PhD in Robotics and Control MSc in Control Theory and Control Engineering BEng in Measurement Control and Instrumentation His research focuses on the convergence of robotics and biology, particularly in bio-inspired design, soft robotics, human-robot collaboration, rehabilitation, and embodied AI. He explores how biological systems can inform adaptive, resilient, and energy-efficient robotic systems. His work spans modeling animal/human motion, sensory-motor learning, morphological design, and control optimization. The 15 most recent publications reflect a strong interdisciplinary trend combining robotics, control theory, machine learning, and biomedical applications. Key themes include soft robotics, bio-inspired actuators, motion planning, rehabilitation systems, and wireless communication for robotic systems. The research integrates simulation, hardware prototyping, and intelligent control, often using ROS and Gazebo. Global Peer Review Awards — Top 1% in Cross-Field (2019) Global Peer Review Awards — Top 1% in Engineering (2019) Outstanding Associate Editor of 2020, IEEE Access Outstanding Contribution Awards, Elsevier (2017) Dr Liu has extensive experience in securing and managing research grants from EPSRC, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, HEIF, NHS I4I, and NSFC. He serves as a regular reviewer for around 30 journals and 8 flagship conferences in robotics, AI, and control. He is an Associate Editor for IEEE Access and Academic Editor for PeerJ Computer Science, and has contributed to special issues on computational learning for robotics. He leads research in bio-inspired robotics and advises PhD students in areas such as lifelong learning machine intelligence, soft robotics, motion planning, and cyber-physical systems. He is actively recruiting motivated PhD candidates with backgrounds in engineering, computer science, or applied mathematics.

V. Radu Craiuمشاهده پروفایل

استاد

• Computational Statistics
• Markov Chain Monte Carlo
• Bayesian Inference
• +۵ مورد دیگر

Professor V. Radu Craiu is a distinguished faculty member in the Department of Statistical Sciences within the Faculty of Arts and Science at the University of Toronto. He has served as Chair of the Department for 5 years (2018-2022 and 2023-2024) after joining as an Assistant Professor in 2001, being promoted to Associate Professor in 2006 and to Full Professor in 2013. Ph.D. in Statistics (2001) - University of Chicago M.S. in Mathematics (1996) - University of Bucharest B.S. in Mathematics (1995) - University of Bucharest Professor Craiu's research spans multiple domains of statistics with particular expertise in computational methods. His work has evolved from foundational research on Markov chain Monte Carlo samplers to broader applications in Bayesian statistics, copula models, statistical genetics, and more recently, astronomy. His research demonstrates both theoretical depth and practical applications across diverse fields including genetics, ecology, and astrophysics. His recent publications show a strong focus on advancing computational methodologies while addressing complex real-world problems. The research trends reveal increasing interdisciplinary collaboration, particularly with astronomers working on radio transients and stellar flares, while maintaining strong contributions to core statistical methodology in areas like copula modeling, MCMC algorithms, and dimension reduction. Fellow of the American Statistical Association (2022) Fellow of the Institute of Mathematical Statistics (2020) Faculty Affiliate of the Vector Institute (2020) CJS Award for 'Likelihood Inflating Sampling Algorithm' (2019) CRM-SSC prize from Centre de Recherches Mathematiques and Statistical Society of Canada (2016) Elected Member of the International Statistical Institute (2015) Professor Craiu has supervised numerous doctoral students whose work spans statistical genetics, computational methods, and copula modeling. His editorial service includes positions as Contributing Editor for the IMS Bulletin and Associate Editor for multiple prestigious journals including Harvard Data Science Review, Journal of Computational and Graphical Statistics, Statistics Surveys, The Canadian Journal of Statistics, and Statistical Methods and Applications. His research has been supported by various grants that have enabled extensive collaborations across disciplines.

Filippo Piermaria Ferroniمشاهده پروفایل

استاد

• Empirical Macroeconomics
• Applied Time Series Analysis
• Monetary Economics
• +۳ مورد دیگر

Filippo Piermaria Ferroni is a Full Professor in the Department of Economic Sciences at the University of Bologna. He specializes in Empirical Macroeconomics, Applied Time Series Analysis, and Monetary Economics. Before joining the University of Bologna in 2024, he held positions at the Federal Reserve Bank of Chicago (currently on leave), Banque de France, and the European Commission. His research focuses on structural vector autoregression (SVAR) models, DSGE modeling, and the impacts of monetary policy on labor markets and inequality. Dr. Ferroni holds a B.A. from Bocconi University, Milan, and an M.Sc. and Ph.D. in Economics from Pompeu Fabra University, Barcelona. His work emphasizes bridging theoretical models with empirical evidence, particularly in analyzing policy shocks, labor supply dynamics, and financial market interdependencies. Recent research explores how unusual economic shocks challenge conventional models and the distributional consequences of monetary policy. His articles span topics like inflation dynamics, central bank policy frameworks, and cryptocurrency risk measurement. Notable contributions include developing the Chicago Fed DSGE Model and analyzing the labor share response to monetary policy. Despite no explicit mentions of awards, his extensive publication record reflects recognition in academic and policy circles. Dr. Ferroni’s advising and grant activities are not detailed here, but his professional trajectory underscores sustained engagement with central banks and academic institutions. His research often involves collaborative projects, though specific teams or labs are not explicitly mentioned.

• Constraint Programming
• Algorithm Selection
• String Constraint Solving
• +۴ مورد دیگر

Roberto Amadini is an Associate Professor in the Department of Computer Science and Engineering at the University of Bologna. His work focuses on constraint programming, algorithm selection, and string constraint solving, with applications in cloud-edge computing and IoT systems. He leads research in deploying microservices over hybrid infrastructures and optimizing solver portfolios for constraint satisfaction problems. Research interests include advanced constraint solving techniques, formal methods for software analysis, and energy-efficient resource allocation. Amadini’s contributions span the design of frameworks like FREEDA and SUNNY-as2, which enhance deployment resilience and algorithm selection efficiency. His work bridges theoretical foundations with practical implementations in programming languages and verification tools. Recent publications explore sustainable cloud-edge applications, failure-resilient systems, and dynamic symbolic execution. He actively contributes to open-source projects like JSetL and participates in international solver challenges. No awards are explicitly listed in the provided materials. Amadini’s research group addresses cutting-edge challenges in distributed computing and constraint-based methodologies. Ongoing projects focus on optimizing solver portfolios for real-world computational tasks and advancing string analysis techniques in programming paradigms.

Matthew Shumمشاهده پروفایل

دانشیار مهمان

• Econometrics
• Industrial Organization
• Empirical Microeconomics
• +۳ مورد دیگر

Matthew Shum is the William D. Hacker Professor of Economics in the Division of Humanities and Social Sciences at the California Institute of Technology (Caltech). He holds a B.A. from Columbia University (1992) and a Ph.D. from Stanford University (1998). His academic appointments at Caltech include Professor (2008-2016), Johnson Professor (2016-2022), Hacker Professor (2023-2024), and currently Visiting Associate (2024-2027). His research focuses on econometrics, industrial organization, and empirical microeconomics, with specific interests in discrete choice modeling, dynamic decision processes, market design, and behavioral economics. He teaches courses on Industrial Organization, Econometrics, and Empirical Methods. Shum's extensive publication record demonstrates a consistent focus on developing and applying innovative econometric methods to real-world market behaviors. His recent work explores dynamic discrete choice models, rational inattention, empirical industrial organization, and behavioral phenomena in markets ranging from pharmaceuticals to online gaming. While no specific awards are mentioned in the provided materials, Shum maintains an active research program with ongoing work on bounded rationality, two-sided platforms, and empirical auction theory. He leads econometrics courses and supervises graduate research at Caltech.

• Medical Image Processing
• Computational Anatomy
• Deep Learning
• +۵ مورد دیگر

HONTANI Hidekata is a Professor at Nagoya Institute of Technology's Department of Information Engineering, Media Informatics Field, and Graduate School of Engineering, Media Informatics Program. He is also affiliated with the Advanced Medical Physics and IT Research Center and the Center for Research and Development in Higher Engineering-Education. His career includes academic roles at institutions like Yamagata University and The University of Tokyo. Doctorate in Engineering from The University of Tokyo (2000) Professional memberships in IEEE, SICE, IEICE, and IPSJ Research focuses on Medical Image Processing and Computational Anatomy , with recent advancements in deep learning applications for pathology image analysis, TMS electromagnetic modeling, and spatiotemporal cancer dynamics. He pioneered techniques for counterfactual image generation in lymphoma pathology and adaptive sparse regularization for signal processing. Notable trends in his publications (2017-2024) include AI-driven histopathology , generative models for medical imaging, and tumor microenvironment analysis using multi-scale MRI-pathology fusion. His work bridges machine learning , computational anatomy , and clinical applications . Scientific Awards : Multiple Japan Society of Medical Imaging and Information Sciences awards (2017-2024), Cum Laude Poster Award at SPIE Medical Imaging (2018) Grants : Principal Investigator for JSPS KAKENHI projects on lymphoma subtyping (2022-2025), 3D tumor modeling (2018-2021), and computational anatomy (2014-2019) He leads the Advanced Medical Physics and IT Research Center and contributes to academic societies as a committee member in organizations including IEICE and Japan Society of Medical Imaging and Information Sciences.

• Bioinformatics
• Computational Biology
• Evolutionary Biology
• +۵ مورد دیگر

Benjamin J Ridenhour is an Associate Professor in Mathematics and Statistical Science at the University of Idaho's College of Science. His research bridges computational biology, evolutionary theory, and public health, with affiliations to the Initiative for Bioinformatics and Evolutionary Studies, Institute for Interdisciplinary Data Sciences, and the Institute for Modeling Collaboration and Innovation. He applies mathematical models to microbiome studies, epidemiology, and bacterial evolution. Education: BA in Environmental Biology (1998) from Utah State University, PhD in Evolution, Ecology, and Behavior (2004) from Indiana University Bloomington His work focuses on microbial community dynamics, including Lotka-Volterra models for microbiome resilience, phenotypic heterogeneity in bacterial adaptation, and plasmid-driven evolution . Recent studies analyze SARS-CoV-2 wastewater surveillance, sociodemographic risk factors for diseases, and geroscience applications in primate models. He employs advanced statistical methods like elastic net regression and Hi-C technology for rare plasmid detection. His affiliations highlight interdisciplinary collaboration between data sciences, bioinformatics, and modeling institutions. Research trends emphasize translational approaches to aging, microbial network analysis, and public health policy implications.

Christian Pansch-Hattichمشاهده پروفایل

استاد

• Marine Ecology
• Ocean Acidification
• Climate Change
• +۷ مورد دیگر

Christian Pansch-Hattich is a Professor of Marine Ecology at Åbo Akademi University's Faculty of Natural Sciences and Engineering, specializing in Environmental and Marine Biology. His research explores eco-physiological responses in marine organisms, focusing on interactions across environmental scales from microhabitats to large-scale climate shifts. Expertise : Marine heatwaves, ocean acidification, salinity gradients, and microbial ecology Projects : Active contributor to the SOS: Center for Sustainable Marine Science (2024-2028) Collaborations : Regular collaborator with Baltic Sea Research Institute, Leibniz-Institute, and EU Biodiversa project BaltVib Current work examines: Climate change impacts on Vibrio pathogens Acclimatization mechanisms in benthic ecosystems Interspecific competition in phytoplankton communities Thermal habitat variability effects on marine vegetation Recent publications highlight: 2025: Vibrio prevalence in Baltic salinity gradients 2024: Heatwave acclimation in temperate ecosystems 2024: Eutrophication controls on Vibrio vulnificus 2022: Eco-evolutionary dynamics in microalgal adaptation

Lan Wangمشاهده پروفایل

استاد

• high-dimensional statistical learning
• quantile regression
• reinforcement learning
• +۳ مورد دیگر

Lan Wang is a Centennial endowed chair professor and Department Chair of the Department of Management Science at the Miami Herbert Business School, University of Miami. She holds secondary appointments as Professor in the Department of Health Management and Policy within the Miami Herbert Business School and as Professor in the Department of Public Health Sciences at the Miller School of Medicine. Dr. Wang earned her Ph.D. in Statistics from Pennsylvania State University and her Bachelor's degree in Applied Mathematics from Tsinghua University, China. Prior to joining the University of Miami, she was a Professor of Statistics at the School of Statistics, University of Minnesota. Dr. Wang's research spans several interrelated areas including high-dimensional statistical learning, quantile regression, reinforcement learning, optimal personalized decision recommendation, survival analysis, and business analytics. Her work is characterized by strong methodological development with applications in business, economics, healthcare, and other domains. She is particularly interested in interdisciplinary collaboration that addresses real-world problems through innovative statistical approaches. Her research has significant implications for precision medicine, where she develops methods to identify optimal individualized decision rules to improve patient outcomes. Dr. Wang's recent publications demonstrate a consistent focus on advancing statistical methodology for high-dimensional data analysis and personalized decision making. Her work bridges theoretical statistics with practical applications, particularly in healthcare analytics. She has made significant contributions to quantile regression theory, high-dimensional regression techniques, optimal treatment rules, and statistical learning frameworks. A notable theme across her publications is the development of robust methods that maintain performance even with heavy-tailed error distributions. Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Member of the International Statistical Institute Dr. Wang has served as Co-Editor for Annals of Statistics (2022-2024) and as associate editor for several leading statistical journals including Journal of the American Statistical Association, Annals of Statistics, Journal of the Royal Statistics Society, and Biometrics. Her editorial leadership reflects her standing in the statistical community and her commitment to advancing methodological research. While specific grant details aren't provided, her extensive publication record in top-tier journals suggests substantial research funding supporting her work.

• Computer Science
• Multidimensional Data Visualization
• Visualization Quality Assessment
• +۴ مورد دیگر

Dr. Rasa Karbauskaitė is a Researcher at the Cognitive Computing Group within Vilnius University's Institute of Data Science and Digital Technologies . She holds a Doctor of Computer Science degree (2010) and specializes in multidimensional data visualization, dimensionality reduction, and intrinsic dimension estimation. Her work combines geometric and statistical methods to analyze high-dimensional datasets. PhD: Computer Science (2010), focusing on local structure preservation in multidimensional data visualization Advanced Training: B2.1 English language course (2016) Research interests include: Fractal dimension analysis for speech emotion classification Manifold learning and topological preservation Optimization of maximum likelihood estimators for dimensionality reduction Geodesic distance applications in data structure analysis Nonlinear data projection algorithms Scientific contributions show a focus on Developing visualization quality assessment frameworks Advancing dimensionality reduction techniques Fractal-based feature selection for emotion recognition Comparative analysis of intrinsic dimension estimation methods Parameter optimization in manifold learning algorithms Awards : Lithuanian Academy of Sciences Young Scientists' Research Prize (2011) Professional Roles : Managing Editor of the Informatica journal Participant in international conferences like Data Analysis Methods for Program Systems (2011-2015) Contributor to IEEE proceedings and specialized workshops

• Statistics
• Biostatistics
• Survival Analysis
• +۳ مورد دیگر

Takumi Saegusa is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, specializing in Statistics and Biostatistics. His work bridges theoretical and applied statistical methods. Rank: Associate Professor Department: Department of Mathematics University: University of Maryland, College Park His research focuses on advanced statistical techniques for survival analysis, two-phase sampling, and data integration. He develops methods to handle longitudinal covariates, heterogeneous populations, and merged datasets from multiple sources. Recent publications address confidence bands for survival curves, variable selection in threshold regression, and computational approaches for statistical packages like osDesign and tpsDesign . These works span biostatistical applications, computational methods, and theoretical foundations. He contributes to statistical software development, including R packages for two-phase study design and analysis. His work has applications in epidemiology, clinical research, and precision medicine.

Sylvain Delagrangeمشاهده پروفایل

استاد

• Functional ecology
• Remote sensing and modeling
• Intensive forestry
• +۷ مورد دیگر

Sylvain Delagrange is a Professor in the Department of Natural Sciences at the University of Quebec in Outaouais (UQO) and a Professor, Researcher, and Project Manager at the Institute of Temperate Hardwood Forest Sciences (ISFORT). He is also recognized as a Regular Researcher in Applied Forest Ecology and Ecophysiology at the Center for Forest Research (CEF). His academic journey includes a dual PhD in Biological Sciences (2004, UQAM) and Forest Ecology and Ecophysiology (2004, UHP-Nancy-I), along with postdoctoral training in sugar maple tree modeling and tropical tree ecophysiology. Dr. Delagrange's research primarily focuses on plant functional traits and their adaptive capacities, with expertise spanning plant ecophysiology and phenology, 3D tree architecture analysis, silvicultural impacts on biodiversity, and urban forest ecosystem services. His recent work has increasingly examined plant-animal relationships in biodiversity conservation contexts. His research employs a combination of field studies, remote sensing, and modeling approaches to address pressing questions in forest ecology and climate change adaptation. Analysis of his recent publications (2022-2025) reveals a strong emphasis on sugar maple phenology under climate change, urban forest resilience, and the physiological mechanisms underlying tree responses to environmental stressors. His work bridges fundamental ecological questions with practical applications for sustainable forest management and urban forestry. René-Pomerleau Award for Excellence in Research (SIAQ), 2025 Canadian emblem of the coronation of King Charles III (Federal Government), 2023 Dr. Delagrange actively advises graduate students and has supervised numerous Master's and PhD candidates to completion. His research group includes current postdocs, Master's students, and collaborations with researchers across Canada and internationally. His lab focuses on applied forest ecology research with strong connections to practical forestry and urban tree management challenges.