Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۶۱۶-۶۳۰ از ۷۰۴ استاد راهنما
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Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.
Dr. Steven W. McLaughlin serves as the Provost and Executive Vice President for Academic Affairs at Georgia Tech, overseeing all academic units. He is also a Professor in the School of Electrical and Computer Engineering. McLaughlin holds degrees from Northwestern University (B.S.E.E.), Princeton University (M.S.E.), and the University of Michigan (Ph.D.). His research focuses on communications and information theory, with contributions to error control coding, data security, and quantum key distribution. He co-founded CREATE-X, a program that has launched 72 student-led companies and engaged over 1,500 students in entrepreneurship. McLaughlin has authored over 250 publications, holds 36 U.S. patents, and is a Fellow of the IEEE. Awardees include the Chevalier dans l'Ordre National du Mérite (France’s second-highest civilian honor) and the Presidential Early Career Award for Scientists and Engineers (PECASE). His leadership roles include Dean of the College of Engineering and Vice Provost for International Initiatives. He has pioneered initiatives in global engagement and educational innovation, emphasizing interdisciplinary collaboration and technology-driven solutions.
Qinglong Tianمشاهده پروفایل
استادیار
Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.
Professor Troy Farrell is the Executive Dean of the Faculty of Science at Queensland University of Technology (QUT). He holds a PhD (QUT) and B.Sc (Hons) from the University of Newcastle. His expertise lies in applied mathematics and physical chemistry, focusing on industrial systems like batteries, solar cells, and biomass processing. He leads major research projects in electrochemical nano-diodes, metal-air batteries, and coal seam gas modeling. Professor Farrell has secured over $2.8M in external funding and led national initiatives such as the Mathematics in Industry Study Group (MISG) and the ATN Industry Doctoral Training Centre. He is a Fellow of the Queensland Academy of Arts and Sciences and has received prestigious awards for his research and teaching. His teaching focuses on mathematical modeling, partial differential equations, and calculus, emphasizing student-centered pedagogy and innovation. Key achievements include developing multiphase models for food drying, phase-field models for lithium-ion batteries, and population balance models for biomass pretreatment. Research Projects: Mathematical modeling of biofuel production from cellulosic materials Electrochemical nano-diodes using Poisson-Nernst-Planck models Optimization of lithium-air batteries for secondary power Multiscale modeling of porous materials with hybrid continuum/particle methods Agrochemical uptake in plant cuticles Awards: ANZIAM Mid-Career Research Award (2015) QUT Vice-Chancellor's Award for Partnerships (2013) Australian Government Citation for Teaching Excellence (2006) Teaching & Leadership: Professor Farrell pioneered innovative teaching methods recognized by the Carrick Institute, coordinating undergraduate and postgraduate programs in Mathematical Sciences. He has mentored over 13 doctoral students and actively bridges academia-industry collaboration through leadership roles in MISG and the ATN IDTC. His work emphasizes translating mathematical models into real-world solutions for energy, agriculture, and environmental sectors. Labs & Teams: He oversees interdisciplinary teams at QUT specializing in electrochemical systems, porous media modeling, and industrial mathematics. Current collaborations include projects with sugar cane industries, battery manufacturers, and coal seam gas operators to address challenges like biomass storage safety and energy storage optimization.
Ming Zhao is a Lecturer at the University of Gävle's Academy of Technology and Environment, teaching industrial economics, statistics, reliability engineering, and logistics simulation. Research focuses on reliability modeling, statistical methods, and quality management, with applications in software, manufacturing, and sensor networks. Key interests include masked data analysis for reliability estimation, copula-based modeling of dependent failures, and Bayesian methods for system reliability. Publications emphasize practical frameworks for software reliability and manufacturing system performance. Article trends show consistent innovation in reliability estimation techniques, particularly for systems with incomplete data. Recent work integrates statistical methods with industrial applications like reconfigurable manufacturing and wireless sensor networks. No awards, supervised students, or lab details are documented.
Raymond Holsapple is Associate Professor and Chair of Physical Sciences & Mathematics at West Liberty University's College of Sciences. His research applies mathematical modeling to solve complex problems in autonomous systems and control theory. Research domains include: Consensus algorithms and distributed optimization UAV assignment and mission planning Stochastic decision-making frameworks Numerical methods for differential equations Holsapple developed mathematical models for perimeter patrol optimization and cooperative control of unmanned aerial vehicles. His teaching covers core mathematics curricula and specialized topics in applied computation.
Prof. Doug S. English is an Associate Professor and Departmental Chair in the Department of Chemistry and Biochemistry at Wichita State University. His research focuses on nanomaterials, drug delivery systems, biophysical chemistry, and single-molecule spectroscopy. He has contributed to advancements in surfactant vesicles for biomedical applications, silica nanoparticles for drug encapsulation, and protein folding dynamics. His work integrates experimental techniques like fluorescence correlation spectroscopy, NMR, and single-molecule imaging to study biomolecular interactions and material properties. Key research themes include developing targeted drug delivery platforms using functionalized nanoparticles, investigating membrane interactions for antibacterial agents, and probing conformational changes in proteins. Over 20 years, his publications span nanotechnology, biophysics, and analytical chemistry, with notable contributions to understanding silica nanotube behavior, magnetic nanotube applications, and Lac repressor-DNA looping mechanisms. While no specific grants or awards are listed, his extensive publication record reflects sustained research excellence. His teaching and administrative roles emphasize undergraduate education in chemistry and biochemistry, with curricular innovations integrating soft nanomaterials into statistical learning.
- Nanomaterials
- Biophysics
- Self-Assembly
- +۷ مورد دیگر
Douglas S. English serves as the Department Chair and holds the academic rank of Associate Professor at Wichita State University. His work focuses on interdisciplinary research at the intersection of nanomaterials, biophysics, and analytical chemistry. Education: Ph.D., Iowa State University B.S., University of Missouri, Kansas City Research Interests: Douglas English explores nanomaterials self-assembly for drug delivery systems, particularly using catanionic surfactant vesicles and silica-based structures. He investigates biophysical phenomena such as protein folding dynamics and DNA-protein interactions using advanced techniques like fluorescence resonance energy transfer (FRET) and single-molecule spectroscopy. His studies also address fluorescence detection and spectroscopy challenges, including oxygen roles in photophysical processes and quantum dot characterization. These efforts bridge materials science , chemistry , and biology to develop functional nanomaterials and diagnostic tools. Publications: His recent works highlight trends in controlled drug release mechanisms (e.g., magnetic nanotubes for doxorubicin delivery), biomimetic systems (carbohydrate-modified vesicles), and single-molecule analysis (protein folding kinetics, lac repressor-DNA looping). The articles reflect a strong emphasis on nanoparticle synthesis , electrostatic sequestration , and optical characterization of novel materials. Awards: No scientific awards explicitly mentioned in the provided texts. Advising & Grants: No formally listed advisees or grant details available. His research group likely focuses on experimental nanotechnology and biophysical systems, given the technical nature of his publications. Office: MC 206B, Wichita State University.
Zoltán Hatóمشاهده پروفایل
پژوهشگر ارشد
Zoltán Ható is a Senior Research Fellow at the Faculty of Engineering, University of Pannonia, specializing in computational modeling of molecular systems. His work focuses on molecular dynamics simulations of membrane diffusion processes, ion channel behavior, and clay mineral transformations, with significant contributions to understanding transport phenomena in confined geometries. Research Focus Dr. Ható's research centers on computational nanoscience with emphasis on: Molecular dynamics of membrane diffusion in biological ion channels and synthetic membranes Kaolinite clay intercalation, delamination, and nanoscroll formation mechanisms Ion transport selectivity in nanopores and zeolite membranes Multiscale modeling bridging atomistic simulations with continuum theories His work integrates advanced simulation techniques to address fundamental questions in materials science and separation technology. Publication Trends Analysis of Dr. Ható's publications (2017-2025) reveals a consistent focus on computational characterization of confined systems. Key trends include: Progressive shift toward multiscale approaches connecting molecular dynamics with Poisson-Nernst-Planck modeling Increasing emphasis on practical applications in water purification and energy storage Systematic investigation of interlayer interactions in clay minerals for nanocomposite development Rigorous examination of scaling laws governing ion selectivity in nanopores
Andrea Braides is a Full Professor of Mathematical Analysis at the University of Rome Tor Vergata. He holds a dual affiliation with SISSA, Trieste. His research focuses on Calculus of Variations, Homogenization, and Discrete Systems, with applications to materials science and fracture mechanics. He has authored influential books like Gamma-convergence for Beginners and contributed to over 200 publications. His work bridges discrete and continuous models, addressing problems in phase transitions, lattice systems, and geometric measure theory. Affiliations: University of Rome Tor Vergata (Department of Mathematics), SISSA, Trieste. Education: Laurea in Mathematics (University of Pisa, 1983), PhD in Mathematics under Ennio De Giorgi at Scuola Normale Superiore. Research Interests: Braides' work emphasizes variational methods, Γ-convergence, and homogenization. He explores energy relaxation in discrete systems, phase transitions in Ising models, and microstructure analysis in materials. His recent studies include long-range interactions, anti-phase boundaries, and stochastic homogenization. Publications: His 150+ articles span topics like non-local energies, discrete-to-continuum limits, and ferromagnetic systems. Notable recent work includes studies on modulated phases in Ising systems (2025) and closure theorems for Γ-convergence (2024). Awards: Tullio Levi Civita Prize (2015), Timoshenko Fellowship (2004), Invited Speaker at ICM 2014. Advising & Leadership: Supervised 11 PhD theses, including those by R. Alicandro and M. Cicalese. Organized major conferences like the School on Multiple-scale Problems (2001) and the EMS Summer School (2018). Served on editorial boards of Archive for Rational Mechanics and Analysis and Networks and Heterogeneous Media . Labs/Teams: Coordinates research groups on variational problems, homogenization, and discrete systems at the University of Rome Tor Vergata and SISSA. Collaborates internationally, including with Oxford and TU Munich on multiscale phenomena.
Elia Baluganiمشاهده پروفایل
دانشیار
Elia Balugani is an Associate Professor at the Department of Engineering Sciences and Methods of the University of Modena and Reggio Emilia. His research focuses on operations research, logistics optimization, inventory management, renewable energy systems, and multi-criteria decision analysis. He holds positions at both the Reggio Emilia Technopole and Morselli Pavilion campuses. His work integrates advanced methodologies like robust ordinal regression, machine learning, and sensor technologies for industrial applications. Key research areas include: Development of decision support systems for sustainable manufacturing and logistics Ergonomic risk assessment using Kinect technology Optimization of supply chain processes through Six Sigma and combinatorial algorithms Circular economy strategies for waste management and additive manufacturing adoption Recent publications emphasize innovative approaches in: Multicriteria decision analysis with FITradeoff and PROMETHEE methods Smart city projects integrating tourism and urban infrastructure Energy efficiency in industrial retrofitting through data-driven modeling He maintains active collaborations in: Industrial safety and ergonomic assessment Environmental quality evaluation for buildings Big data applications in healthcare and urban planning
Mahdi Imaniمشاهده پروفایل
استادیار
- Machine Learning
- Control Theory
- Learning Theory
- +۳ مورد دیگر
Mahdi Imani is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. He holds a PhD in Electrical Engineering from Texas A&M University (2019), and MSc and BSc degrees in Electrical and Mechanical Engineering from the University of Tehran (2014 and 2012, respectively). His research focuses on machine learning, control theory, Bayesian statistics, and signal processing, with applications in gene regulatory networks, network security, and human-AI collaboration. Dr. Imani has received prestigious awards, including the NIH NIBIB Trailblazer Award (2022), the NSF CISE Career Award (2020), and the Outstanding Associate Editor Award from IEEE Transactions on Neural Networks and Learning Systems (2023 and 2024). He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Vehicular Technology, and is a Senior Member of IEEE. His research projects include DARPA-funded work on verified probabilistic reasoning in mixed reality systems, NSF-funded statistical inference methods, and ONR-funded studies on human-AI team synergy. He leads a lab focused on developing scalable Bayesian methods and reinforcement learning techniques for complex systems.
Hailiang Liu is a Professor of Mathematics at Iowa State University, with a courtesy appointment in Computer Science and membership in the Translational AI Center (TrAC). He also serves as the Data Science/AI Coordinator in the Department of Mathematics. Currently, he is on leave as a Program Director in the Applied Mathematics Program at the National Science Foundation (NSF). Dr. Liu holds a Ph.D. in Applied Mathematics from the Chinese Academy of Sciences (1995), an M.Sc. from Tsinghua University (1988), and a B.Sc. in Math Education from Henan Normal University (1984). His research focuses on computational and applied mathematics, including mathematical modeling via PDEs, numerical methods, scientific computing, and their intersections with deep learning and data-driven approaches. Key areas include: Deep Learning: Neural ODEs, selection dynamics, and optimal transport-based algorithms Mathematical Biology: Population dynamics, ecological dispersal, and aggregation modeling Computational Methods: Discontinuous Galerkin (DG) schemes, energy-stable numerical methods, and Gaussian beam methods Kinetic Theory: Photon scattering, Bose-Einstein condensation, and hyperbolic balance laws His work has been supported by the NSF and DOE. Notable contributions include structure-preserving numerical schemes, critical threshold analysis in hyperbolic systems, and data-driven optimal control frameworks.
Volodymyr Minin is a Professor in the Department of Statistics at the University of California, Irvine, where he also serves as Associate Director of the Infectious Disease Initiative and Honors Program Director for the School of Information & Computer Sciences. Additionally, he is Co-Director of the Irvine Summer Institute in Biostatistics and Undergraduate Data Science (ISI-BUDS), which recently welcomed its fourth cohort of 16 undergraduate students from across the country. Professor Minin leads an active research group focused on developing and applying statistical methods for infectious disease modeling, phylogenetics, and population dynamics. His group holds weekly journal club meetings on Fridays where members present and discuss recent research papers related to stochastic and statistical modeling approaches in epidemiology and evolutionary biology. His research interests span Biostatistics , Infectious Disease Modeling , Phylogenetics , Bayesian Statistics , Population Dynamics , Epidemiological Modeling , and Stochastic Processes . Minin's recent publications demonstrate a strong methodological focus with practical public health applications, particularly in infectious disease dynamics. His work integrates multiple data sources including genetic sequences, case reports, and wastewater surveillance to improve epidemiological inference. Key themes include addressing reporting delays in real-time analyses, developing more accurate methods for estimating the effective reproduction number, and investigating transmission dynamics of pathogens like SARS-CoV-2 and tuberculosis. Professor Minin has mentored numerous students through their academic careers, including Catalina Medina who recently defended her dissertation and is starting as an Assistant Professor at CSU Channel Islands, and students like Jessalyn Sebastian and Christian Bernal Zelaya who have received recognition at conferences like WNAR 2025 where Christian won the best student oral presentation award. His former students include Andy Magee, Jon Fintzi, Michael Karcher, Jason Xu, Jane Lange, Peter Chi, Julia Palacios, and Soyoung Ryu. His research group actively collaborates with public health organizations and participates in initiatives like the Infectious Disease Initiative at UCI. The group maintains an active GitHub presence with repositories documenting their journal club activities and sharing resources for stochastic and statistical modeling. The group's work has important implications for understanding and controlling infectious disease outbreaks through advanced statistical modeling approaches.