جستجوی استاد راهنمای دانشگاهی

Rudie P.J. Kunnenمشاهده پروفایل

دانشیار

• Turbulent Flow
• Multiphase Flow
• Heat Transfer
• +۷ مورد دیگر

Rudie P.J. Kunnen is an Associate Professor at the Faculty of Applied Physics and Science Education , Eindhoven University of Technology, leading the Turbulent and Multiphase Flows group. His research focuses on heat, mass, and particulate transport in turbulent flows, with applications in geophysics and industry. Active in UN Sustainable Development Goals related to environmental protection Collaborator in projects like Active Contamination Control for Equipment and SubstrateS Research Interests : Turbulent flow dynamics, rotating convection, vortex structures, thermophoresis, plasma-liquid interactions, and geostrophic turbulence. His work combines experimental and numerical approaches (e.g., direct numerical simulation, particle image velocimetry). Scientific Awards : NWO Vici Prize (2024) Advising and Collaborations : Supervised multiple BSc and MSc theses at TU/e. Collaborates with researchers like F. Toschi and H.J.H. Clercx on turbulence projects.

• Biomedical Informatics
• Public Health Informatics
• Clinical Research Informatics
• +۶ مورد دیگر

Professor Adam Dunn is a leading academic in Biomedical Informatics and Digital Health at The University of Sydney , where he established the Discipline of Biomedical Informatics and Digital Health in 2020. With nearly 20 years of experience, his work integrates machine learning , natural language processing , and computational social science to address challenges in public health , clinical epidemiology , and evidence synthesis . His research programs focus on: (1) improving health information access and trust, (2) analyzing misinformation uptake via digital traces, and (3) developing AI tools for systematic review efficiency. Current projects include generative AI applications in patient discharge instructions , fairness in multimodal health AI , and infodemic burden measurement toolkits for WHO. Recent publications span clinical NLP , vaccine credibility , and social media surveillance . Awards include global recognition in medical informatics and editorial leadership roles at npj Digital Public Health and npj Digital Medicine. He has supervised over 15 PhD scholars and served on NHMRC and MRFF grant review panels. Key Projects: WHO Infodemic Toolkit, NLM R01 grant on ClinicalTrials.gov integration Expertise: AI in health, systematic review methodology, health information trust

David Castañónمشاهده پروفایل

استاد

• Stochastic Control
• Estimation Theory
• Game Theory
• +۶ مورد دیگر

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

• Economics
• Game Theory
• Behavioral Economics
• +۳ مورد دیگر

Faruk Gul is the Susan Dod Brown Professor of Economics at Princeton University. His research focuses on decision theory, game theory, and behavioral economics, with a particular emphasis on topics such as preference dynamics, self-control models, and the integration of uncertainty into economic frameworks. He has collaborated extensively with Wolfgang Pesendorfer on foundational work in these areas. Gul's work explores the implications of ambiguous preferences, strategic interactions under uncertainty, and the design of economic mechanisms. His contributions include seminal studies on coarse competitive equilibrium, calibrated uncertainty, and the theory of random evolving lotteries. His research has been supported by grants from the National Science Foundation (SES-0010394, SES-0820101, SES-0550540, SES-0236882, SES-9911177). In policy-related areas, his work addresses strategic redistricting, partisan politics, and information warfare in economic systems. He has also contributed to foundational debates in economic methodology, including critiques of traditional rationality assumptions and the case for 'mindless economics.' Gul's articles span topics such as interdependent preferences, competitive equilibrium models, and the analysis of harmful addiction from an economic perspective. His interdisciplinary approach bridges microeconomic theory, decision science, and political economy.

Hankui Zhangمشاهده پروفایل

دانشیار

• Remote Sensing
• Environmental Monitoring
• Deep Learning
• +۴ مورد دیگر

Hankui Zhang is an Associate Professor in the Department of Geography and Geospatial Sciences at South Dakota State University (SDSU), and a Research Scientist at the Geospatial Sciences Center of Excellence. He holds a Ph.D. from the Chinese University of Hong Kong (2013), specializing in satellite image fusion. His research focuses on developing algorithms for medium-resolution satellite data processing (e.g., Landsat and Sentinel-2), including cloud masking, BRDF correction, and compositing. He also explores AI applications in remote sensing for land cover mapping and environmental monitoring. As a Landsat Science Team member, he contributes to global remote sensing initiatives. Education: B.S. in Geographic Information Systems, Zhejiang University (2007) M.S. in Remote Sensing, Zhejiang University (2010) Ph.D. in Geography and Resource Management, Chinese University of Hong Kong (2013) Research Interests: Deep learning applications in remote sensing, land cover dynamics, analysis-ready data development, and geospatial data harmonization. His work emphasizes operationalizing satellite data for environmental decision-making. Grants & Awards: Over $2.5M in grants as PI/co-PI, including USDA and NASA-funded projects. Notable awards include the SDSU Wadsworth Research Award (2020-2021) and the Global Scholarship for Research Excellence from CUHK (2011-2012). Professional Roles: Editorial board member for Remote Sensing of Environment and Remote Sensing ; guest editor for special issues on deep learning in remote sensing. Top 20 reviewer for Remote Sensing of Environment (2020, 2022-2024). Key Contributions: Published over 70 SCI papers, developed cloud detection algorithms (e.g., LANA), and pioneered analysis-ready data workflows for global monitoring. His work bridges satellite data science with practical environmental applications.

Igor Jankovicمشاهده پروفایل

دانشیار

• Groundwater Hydrology
• Environmental Engineering
• Contaminant Transport
• +۲ مورد دیگر

Igor Jankovic is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on groundwater flow and contaminant transport in heterogeneous aquifers, with particular emphasis on the impact of aquifer heterogeneity on solute movement and transport modeling. Education: PhD in Civil Engineering, University of Minnesota (1997) MS in Civil Engineering, University of Minnesota (1993) BS in Civil Engineering, University of Split, Croatia (1990) His work addresses critical issues in groundwater hydrology including: Advective transport mechanisms in heterogeneous media Breakthrough curve prediction and analysis Effective hydraulic conductivity modeling Upscaling of flow and transport parameters Application of the Analytic Element Method (AEM) for complex aquifer simulations Comparison of transport models (CTRW, MRMT) in heterogeneous environments Research trends in his publications reveal a focus on: Three-dimensional heterogeneous aquifer modeling Non-Fickian and anomalous transport behavior Impact of spatial variability on contaminant migration Development of numerical algorithms for large-scale groundwater simulations Validation of stochastic transport theories against field experiments (e.g., MADE and Borden aquifers) Interaction between physical and chemical heterogeneity in reactive transport

Xiaoming Zhaiمشاهده پروفایل

دانشیار

• Oceanography
• Climate Dynamics
• Physical Oceanography
• +۳ مورد دیگر

Xiaoming Zhai is an Associate Professor in Ocean Modelling at the University of East Anglia's School of Environmental Sciences, and a member of the Centre for Ocean and Atmospheric Sciences. His career includes roles from Lecturer (2012) to Senior Lecturer (2017) before his current position (2019). He holds a PhD from Dalhousie University's Oceanography Department and has conducted postdoctoral work at the University of Oxford and IFM-GEOMAR in Germany. His research focuses on ocean physical processes, particularly the dynamics of ocean circulation, eddy interactions, near-inertial waves, and air-sea interactions. Key projects include investigating eddy energy dissipation timescales, Southern Ocean circulation impacts, and the annual cycle of eddy energy. He has secured funding from the Royal Society and Natural Environment Research Council for initiatives like the GEOMETRIC project on ocean eddy representation in climate models. Zhai has received prestigious awards including the Challenger Fellowship (2018) and Kan Tong Po Fellowship (2023). His recent publications explore topics like South China Sea eddies, internal tide generation under global warming, and zonal jet dynamics in geostrophic turbulence. He actively supervises PhD students and contributes to academic activities such as external doctoral examinations and peer review panels.

• Health Services Research
• Narrative Interventions
• Hypertension Control
• +۳ مورد دیگر

Dr. Jeroan Allison is Chair and Professor at UMass Chan Medical School's Department of Population and Quantitative Health Sciences. Trained as a primary care physician and epidemiologist (Harvard SPH), he leads research in narrative interventions for health behavior change, with continuous funding from NIH, AHRQ, and RWJF. His work spans hypertension control in Vietnam, AI-driven health communication, and implementation science for non-communicable diseases. Harvard School of Public Health: Master of Science in Epidemiology University of Alabama at Birmingham: MD Samford University: BS in Chemistry Research interests include narrative medicine , health disparities , mHealth , and health services research . He has published extensively on hypertension interventions in Vietnam, digital health tools, and tobacco cessation strategies using peer engagement. Recent publications focus on AI applications , global implementation science , and social determinants of health . His team has developed culturally adapted interventions for HPV vaccination and hypertension management using community storytelling methods. Scientific contributions include: Co-Editor-in-Chief of Medical Care Developing simulation-based training for research assistants Designing peer recruitment models for smoking cessation Dr. Allison holds multiple grants and leads the UMMS-Vietnam research collaboration , focusing on late-stage translation research in low-resource settings.

Lenka Zdeborováمشاهده پروفایل

دانشیار

• Statistical Physics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Lenka Zdeborová is an Associate Professor at EPFL, jointly affiliated with the School of Basic Sciences and School of Computer and Communication Sciences. She leads the Laboratory of Statistical Physics of Computational Systems, where her research bridges statistical physics, machine learning, and computational biology. Education: PhD in Physics, Université Paris-Cité (2012) MSc in Fundamental Physics, École Normale Supérieure (2009) BSc in Physics, École Normale Supérieure de Lyon (2007) Her work focuses on phase transitions in learning algorithms, high-dimensional statistics, and neural network theory. Current projects investigate fundamental limits of machine learning, dynamics of graph neural networks, and applications to biological systems. Recent publications explore attention mechanisms in transformers, neural network depth advantages, and Bayes-optimal learning. Methodological innovations include cavity methods for hypergraphs and analysis of high-dimensional inference problems. Supervises doctoral students researching statistical physics approaches to machine learning and optimization. Teaches graduate courses in data science and machine learning for physicists.

Sisi Zhouمشاهده پروفایل

پژوهشگر

• Quantum Metrology
• Quantum Error Correction
• Quantum Learning
• +۲ مورد دیگر

Sisi Zhou is a Research Faculty member at the Perimeter Institute for Theoretical Physics, specializing in quantum information science. Her work focuses on quantum metrology, quantum error correction, and quantum learning, with a particular emphasis on advancing methodologies for noisy quantum systems. Her research explores theoretical frameworks to enhance precision in quantum measurements, optimize error-correcting codes, and apply machine learning techniques to photonic platforms. Recent contributions include studies on non-Markovian metrology, Gaussian measurement limitations, and entanglement-enabled protocols for bosonic channels. Zhou has delivered seminars at institutions such as the Banff International Research Station and the University of Calgary, addressing topics like quantum metrological limits in noisy environments and error mitigation strategies for near-term quantum processors.

• Machine Learning
• Theoretical Computer Science
• Data Science
• +۳ مورد دیگر

Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago (UIC) and Director of the IDEAL Institute. He specializes in the theory of machine learning, data science, and artificial intelligence, with affiliations to theoretical computer science and mathematical foundations. Prior roles include a Simons Postdoctoral Fellowship at Georgia Tech and an NSF Computing Innovation Fellowship at Yahoo! Research. He earned his Ph.D. from Yale University (NSF doctoral fellowship) and a bachelor’s degree from Princeton University. Research interests focus on algorithmic learning theory, computational complexity, and applications of machine learning to real-world problems. Notable work includes contributions to statistical learning algorithms, adversarial bandits, and graph theory. His research has been funded by NSF grants (e.g., ECCS-2217023, CCF-2307106), DOD programs, and others since 2015. He has received awards at leading conferences (ICML, COLT, AISTATS). Reyzin holds editorial roles, including Editor-in-Chief of Mathematics of Data, Learning, and Intelligence and Chair positions in major conferences like FOCS 2024 and ALT 2017. His leadership in academic organizations highlights his influence in shaping theoretical computer science and machine learning research.

Caterina Ida Zeppieriمشاهده پروفایل

دانشیار

• Calculus of Variations
• Partial Differential Equations
• Material Science
• +۳ مورد دیگر

Professor Caterina Ida Zeppieri is an Associate Professor at the Institute for Numerical and Applied Mathematics , University of Münster, Germany. Her research focuses on homogenization theory, calculus of variations, and applications in material science. She leads the Analysis and Modelling Research Group , which explores variational methods and their computational implications. Research Interests: Her work centers on stochastic and deterministic homogenization of free-discontinuity problems, Γ-convergence, and the analysis of heterogeneous materials. She investigates mathematical models for fracture mechanics, phase transitions, and plasticity, often employing advanced techniques from calculus of variations and partial differential equations. Recent Contributions: Recent articles address topics such as strong approximation of SBV functions (2025), stochastic homogenization in perforated domains (2024), and gradient damage models (2022). These studies emphasize rigorous mathematical analysis with applications to real-world material behavior. Grants & Collaborations: Zeppieri’s research is supported by grants from the German Research Foundation (DFG), including projects within the Cluster of Excellence 2044 (e.g., C1: Evolution and Asymptotics). She collaborates with institutions globally, advancing interdisciplinary approaches to material modeling. Teaching: She teaches advanced courses like Applied Functional Analysis and Calculus of Variations , and mentors students through seminars and thesis supervision. Her group hosts regular colloquia and workshops, fostering academic exchange in applied mathematics.

Thomas Le Barbanchonمشاهده پروفایل

استاد

• Labor Economics
• Public Finance
• Econometrics
• +۵ مورد دیگر

Thomas Le Barbanchon is a Full Professor of Economics at Bocconi University since 2025 and holds the Rodolfo Debenedetti Chair in Labor Economics. He received his PhD from Ecole Polytechnique (CREST-ENSAE) in 2012 and has been affiliated with institutions like CEPR, CREST, J-PAL, LEAP, BIDSA, IGIER, IZA, and IFS. His research focuses on labor economics, particularly job search mechanisms, unemployment insurance effects, gender disparities, and labor market policy evaluation. Educational background: PhD in Economics (Ecole Polytechnique), MSc in Economics (Universitat Pompeu Fabra), Diplôme d'ingénieur statisticien-économiste (ENSAE), and Diplôme d'ingénieur (Ecole Polytechnique) Research highlights include: Quantifying the impact of unemployment insurance reforms in France Analyzing gender differences in job search behavior Studying the effectiveness of hiring credits during economic crises Investigating how traditional AI improves job matching Examining migrant-native job search segregation patterns Scientific contributions appear in top journals like American Economic Journal: Applied Economics , Quarterly Journal of Economics , and Review of Economic Studies . Awards include two ERC grants (2017 & 2024) and the Bocconi Impact Award (2022). He mentors PhD students in economics and labor policy, currently serves as Director of IGIER research center, and maintains active editorial roles at Review of Economic Studies and Journal of the European Economic Association .

Martin Ostoja-Starzewskiمشاهده پروفایل

استاد

• Continuum Mechanics
• Stochastic Mechanics
• Fractal Media
• +۷ مورد دیگر

Martin Ostoja-Starzewski , Ph.D. (McGill), is a Professor of Mechanical Science & Engineering at the University of Illinois at Urbana-Champaign , with affiliate appointments at the Beckman Institute and National Center for Supercomputing Applications . His research spans continuum mechanics , stochastic wave propagation , and fractal media , focusing on scaling laws and the statistical-to-representative volume element transition. 2006–Present: Professor, UIUC 2001–2005: Canada Research Chair, McGill University 2023: Rothschild Distinguished Visiting Fellow, University of Cambridge 2022: Member, European Academy of Sciences and Arts 2024: Academia Europaea Foreign Member His work pioneered continuum mechanics with spontaneous second law violations and tensor random fields for stochastic PDEs. He authored four foundational books, including Tensor-Valued Random Fields for Continuum Physics (2019), and edited 16 special issues. Recent 2025–2023 articles explore odd elasticity , fractal thermodynamics , and stochastic wavefronts , bridging non-equilibrium thermodynamics to granular Couette systems . Scientific accolades include the Worcester Reed Warner Medal (2018) and ASME , AAM , SES fellowships. As part-time faculty at Beckman Institute (2008–Present), he integrates bioimaging with mechanics across electrosurgery and traumatic brain injury modeling.

Nils-Christian Deteringمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Energy Markets
• +۴ مورد دیگر

Nils-Christian Detering is an Associate Professor in the Department of Statistics & Applied Probability at the University of California, Santa Barbara (UCSB). He also serves as the Undergraduate Diversity, Equity, and Inclusion Officer. His research focuses on financial mathematics, probability theory, and their applications in systemic risk analysis, energy markets, and machine learning. Detering has contributed to understanding default contagion in financial systems, stochastic processes in energy derivatives, and neural network applications in functional data analysis. Research Interests : Financial systemic risk: Analyzing default contagion using random graphs and systemic stability metrics Infinite-dimensional stochastic analysis: Modeling energy markets via stochastic PDEs and forward curves Machine learning: Developing neural network frameworks for functional spaces and financial applications Teaching includes courses on stochastic processes, mathematical finance, and probability theory at both undergraduate and graduate levels. His work has been recognized with awards such as the Best Paper Award at the ACM International Conference on AI in Finance (2023). Key Publications address topics like reinforcement learning in banking networks, neural network calibration of energy curves, and integrated fire sales models. His research bridges theoretical probability with applied financial engineering challenges.