جستجوی استاد راهنمای دانشگاهی

Archer Yangمشاهده پروفایل

دانشیار

• Statistical machine learning
• High-dimensional statistics
• Computational statistics
• +۸ مورد دیگر

Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning. Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare. His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory. ICML Spotlight Paper (top 2.6%, 313/12,107) Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields. His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.

Caroline Margaux Gevaertمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Caroline Margaux Gevaert serves as Associate Professor in the Department of Geo-information Processing at the University of Twente's Faculty of Geo-Information Science and Earth Observation (ITC), while also holding an appointment at the Digital Society Institute. Her academic career spans geospatial information systems, remote sensing, and the ethical applications of AI in geographical contexts. Dr. Gevaert earned her PhD in Informal Settlement Mapping with Unmanned Aerial Vehicles from the Faculty of Geo-Information Science and Earth Observation (ITC) in 2018, following Master's degrees in Geospatial Information Systems from Lund University (2014) and Remote Sensing from Universitat de Valencia (2013), and a Bachelor's in International Land and Water Management from Wageningen University & Research (2011). Her research focuses on the intersection of artificial intelligence and geospatial science, with particular emphasis on ethical dimensions of AI, algorithmic fairness in geo-intelligence workflows, and applications of machine learning for environmental monitoring. She has developed expertise in UAV applications for informal settlement mapping and flood vulnerability assessment, bridging technical innovation with societal impact. Analysis of her recent publications reveals a strong trend toward addressing ethical challenges in geospatial AI, with increasing focus on algorithmic fairness, explainable AI systems, and accountability frameworks. Her work demonstrates a consistent evolution from technical remote sensing applications toward socio-technical systems that consider both technological capabilities and societal implications. PhD Cum Laude (2018) Professor J.M. Tienstra Onderzoeksprijs 2020 Student Paper Competition Finalist (2017) Dr. Gevaert serves as Chair of De Jonge Akademie (2022-2027) and the International Society for Digital Earth (ISDE), while participating in the Frontier Technology Livestreaming Programme. Her external engagement includes consultancy for the World Bank and presentations on accountability in digital humanitarianism, demonstrating her commitment to applying geospatial intelligence for global development challenges. Her research activities center around geo-intelligence workflows, with particular focus on developing accountable and fair AI systems for spatial analysis. Current projects investigate causality frameworks for bias detection in flood vulnerability assessments and explainable workflows for ecological monitoring, positioning her at the forefront of ethical geospatial AI research.

Danesh Taftiمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Fluid Mechanics
• Heat Transfer
• +۷ مورد دیگر

Danesh Tafti is the William S. Cross Professor and Associate Department Head for Graduate Studies in the Department of Mechanical Engineering at Virginia Tech. He holds a PhD from Pennsylvania State University (1989) and has held roles at institutions including the University of Illinois at Urbana-Champaign and West Virginia Institute of Technology. His research focuses on computational fluid dynamics (CFD) and heat transfer, with applications in gas turbines, biomedical systems, and renewable energy. Key research areas include turbulence modeling, fluid-structure interaction, and biofluid mechanics, leveraging high-performance computing. His work spans aerodynamics of flapping flight, cardiovascular flows, and particle-laden flows. Tafti leads the High Performance Computational Fluid-Thermal Science and Engineering Lab, developing tools like GenIDLEST software for complex fluid-thermal systems analysis. Education: PhD (Penn State, 1989), MS (Texas Tech, 1983), BE (Bombay University, 1980) Awards: ASME Fellow (2014), Virginia Tech Dean’s Research Award (2012) Labs: High Performance Computational Fluid-Thermal Science and Engineering Lab Publications emphasize CFD advancements, machine learning integration, and multiphase flow modeling. Tafti’s work bridges computational methods with real-world applications in energy systems, aerospace, and biomedicine.

Niyousha Hosseinichimehمشاهده پروفایل

دانشیار

• Health Systems Engineering
• Public Health Policy
• System Dynamics Modeling
• +۴ مورد دیگر

Niyousha Hosseinichimeh is an Associate Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech's College of Engineering. Her research focuses on improving health and healthcare systems through system dynamics modeling and simulation. She holds a Ph.D. in Public Policy from SUNY Albany (2012) and a B.S. in Mechanical Engineering from Sharif University of Technology (2001). Her work addresses complex issues like adolescent drinking/driving behaviors, major depressive disorder, and infant mortality. Methodologically, she advances calibration techniques for dynamic models and group model-building approaches. Her research has been funded by NIH, NSF, and the Ohio Department of Health. Notable contributions include modeling the hypothalamus-pituitary-adrenal axis and developing tools for rapid parameter estimation in system dynamics. She advises students such as Alba Rojas-Cordova (winner of multiple awards) and Arash Baghaei Lakeh. Her grants include projects on depression dynamics and emergency department utilization. Her work intersects systems engineering, public health, and computational methods, emphasizing practical policy and clinical applications.

Alain Hecqمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Financial Economics
• +۵ مورد دیگر

Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.

Saria Awadallaمشاهده پروفایل

استادیار

• Epidemiology
• Biostatistics
• Pharmacoepidemiology
• +۴ مورد دیگر

Saria Awadalla is an Assistant Professor in the Department of Epidemiology and Biostatistics at the School of Public Health, University of Illinois Chicago. Her academic work bridges advanced biostatistical methodology with critical public health applications, focusing on health disparities and evidence-based interventions across diverse populations. Her research spans pharmacoepidemiology, women's health, and social determinants of health, with particular expertise in case-crossover study designs, medication adherence analysis, and health services research. She investigates bias in observational studies, cervical cancer screening disparities across sexual orientation and racial groups, and the impact of food insecurity on oral health outcomes. Her methodological rigor extends to developing predictive tools for emergency department triage and analyzing longitudinal pain trajectories. Analysis of her recent publications reveals a consistent emphasis on real-world evidence generation using national datasets, with strong attention to vulnerable populations including older adults, cisgender women, and urban underserved communities. Her work demonstrates sophisticated integration of epidemiological methods with clinical questions, particularly in chronic disease management and preventive health services. Key thematic areas include LGBTQ+ health equity, environmental exposures, and the intersection of social factors with biological outcomes.

Petros Dellaportasمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Financial Econometrics
• +۷ مورد دیگر

Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.

• Information Management
• Data Security
• Scientific Databases
• +۵ مورد دیگر

Marianne Winslett is a Professor at the University of Illinois' Siebel School of Computing and Data Science, affiliated with the Department of Computer Science since 1987. Her research focuses on data security, information management, and privacy in cyber-physical systems. She co-led the TrustBuilder project, advancing access control and authentication in open computing environments, and directed the Advanced Digital Sciences Center (ADSC) in Singapore from 2009–2013, addressing challenges in data analytics and smart grids. Her work includes pioneering methods to ensure privacy in biomedical data analysis. Education: Earned her doctorate in Computer Science from Stanford University and worked at Bell Labs before joining Illinois. Awards: ACM Fellow (2006), NSF Presidential Young Investigator (1989), University Scholar, and Stanley H. Pierce Award for advising. She has supervised 24 PhD theses and mentored numerous graduate students, particularly supporting female scholars. Research Interests Secure data management in distributed systems Privacy-preserving techniques for biomedical data Adversarial attack detection in cyber-physical systems like smart grids Elastic resource scheduling in cloud environments Query optimization under differential privacy constraints Key Contributions Developed frameworks for self-supervised learning in smart grid cybersecurity Pioneered causal mechanism transfer networks for mechanical system domain adaptation Advanced auto-scaling strategies for real-time stream processing (DRS/Elasticutor systems) Labs & Teams Former Director of the Advanced Digital Sciences Center (ADSC), a University of Illinois research outpost in Singapore focusing on data analytics and IoT applications.

• Causal inference
• Reproductive Toxicology
• Clinical and Translational Research
• +۴ مورد دیگر

Dr. Ronghui Xu is a Professor at the Herbert Wertheim School of Public Health & Human Longevity Science at the University of California, San Diego (UCSD). She holds affiliations with the Department of Family Medicine and Public Health and previously with the Department of Mathematics. Her research focuses on biostatistics, clinical and translational research, and reproductive toxicology. Dr. Xu leads the Biostatistics unit at UCSD's Clinical and Translational Research Institute (CTRI) and serves as lead statistician for the OTIS Collaborative Research Center, investigating topics like fetal alcohol spectrum disorders and pregnancy medication safety. Education: Bachelor of Science in Mathematics, Nankai University, China (1990) Master of Arts in Applied Mathematics, UCSD (1995) Ph.D. in Mathematics, UCSD (1996) Research Interests: Dr. Xu specializes in causal inference, high-dimensional data analysis, and survival analysis. Her work spans drug repurposing for neurodegenerative diseases, radiation therapy optimization for cancer, and maternal-fetal health outcomes. She develops statistical methodologies for competing risks and observational studies. Publications: Her recent work includes studies on ulcerative colitis treatment strategies, Alzheimer's drug candidates, and biomarkers for autism spectrum disorders. Key themes include clinical trial analysis, pharmacotherapy evaluation, and statistical modeling in epidemiology. Awards: David P. Byar Young Investigator Award (1998) Fellow of the American Statistical Association (2013) Grants & Teams: She directs the CTRI Biostatistics Unit, supported by NIH CTSA grants, and collaborates on projects like the Vaccine and Medication in Pregnancy Surveillance System. Her team addresses complex health questions through interdisciplinary approaches, including machine learning and large-scale clinical data analysis.

• Risk Management
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Joshua Hunte is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on applying Bayesian network methodologies to product safety, risk assessment, and medical device risk management. He specializes in causal modeling for safety protocols and consumer risk communication. Key research interests include developing causal Bayesian network frameworks for evaluating risks in smart technologies, medical devices, and consumer products. His work bridges artificial intelligence with practical safety standards, aiming to enhance decision-making processes in risk management. Recent publications highlight advancements in hybrid Bayesian networks for medical device safety and methodologies for building causal idioms in product safety analysis. His research emphasizes both theoretical causal modeling and real-world applications in industry and healthcare. No academic awards or grants are explicitly listed in the provided materials. He contributes to the School's research initiatives in electronic engineering and computer science, particularly in risk-related computational methods.

Ken Kitsonمشاهده پروفایل

استاد مهمان

• Causal Discovery
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Ken Kitson is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on causal discovery, Bayesian networks, and machine learning methodologies. He specializes in developing algorithms that infer causal relationships from data, particularly through active learning frameworks that integrate human expert knowledge. Kitson's work emphasizes probabilistic graphical models and their applications in diverse fields such as healthcare, sports analytics, and epidemiology. He has contributed to improving the stability and accuracy of Bayesian network structure learning algorithms, addressing challenges like variable ordering instability and noisy data. His recent research trends highlight advancements in causal machine learning, including the use of large language models like GPT-4 to enhance causal discovery processes. His studies on sepsis and COVID-19 demonstrate practical applications of causal modeling in health informatics. Kitson's publications span algorithmic improvements, surveys of Bayesian network learning techniques, and empirical validations of methods in real-world scenarios. He holds a focus on bridging theoretical advancements with practical implementations in data-driven decision-making contexts.

• Pharmacoepidemiology
• Epidemiology
• Observational Research
• +۹ مورد دیگر

D.L. (Daniala) Weir is an Assistant Professor at Utrecht University's Science college, specifically within the Pharmacoepidemiology and Clinical Pharmacology department. Her research bridges clinical pharmacology , epidemiology , and applied data science to enhance medication safety for patients with multimorbidity. Education: B.Sc. in Biological Sciences (University of Alberta, 2012) M.Sc. in Epidemiology (University of Alberta, 2014) Ph.D. in Epidemiology (McGill University, 2019) Postdoctoral Fellowship (University of Toronto, 2021) Her work combines causal inference frameworks with machine learning to address clinical challenges using real-world data from Canada, the UK, and the Netherlands. She collaborates across disciplines with biostatisticians , data scientists , and healthcare professionals , emphasizing patient-centered approaches through engagement with patients and caregivers. Key research areas include: Dynamic treatment regimens Medication-related harm prevention Adverse drug event detection mHealth applications for adherence Her publications demonstrate expertise in applied data science methodologies for time-to-event analysis , predictive modeling , and multimorbidity treatment optimization .

• Event Cognition
• Human-Robot Interaction
• Visual Working Memory
• +۴ مورد دیگر

Frank Papenmeier is a Professor in the Department of Psychology at the University of Tübingen, within the Faculty of Science. His research focuses on event cognition, human-robot interaction, visual working memory, and visual attention. He coordinates the 'Coordination Cognitive Psychology and Research Methods' research group. His work explores how people perceive and interact with dynamic environments, including studies on event segmentation, cognitive offloading, and aesthetic judgments. He has contributed to over 100 peer-reviewed articles, with recent work addressing topics like the impact of framing on art perception and the role of AI in education. Papenmeier's research integrates experimental methods with interdisciplinary approaches, including collaborations on teleoperation systems and AI-based tutoring. He has presented at major conferences such as the European Society for Cognitive Psychology and the Psychonomic Society. His lab emphasizes methodological rigor, evidenced by contributions to replication databases and open science initiatives. Education: Not explicitly stated in the text, but his titles include Dr. rer. nat. (Doctor of Natural Sciences) and Diplom-Psychologe (Psychology Diploma). Research Interests: His primary areas include event cognition, human-robot interaction (e.g., helping behavior toward robots), visual working memory (e.g., spatial configuration processing), and cognitive offloading (e.g., impact on memory and performance). He also investigates aesthetic judgments and narrative comprehension through eye-tracking and experimental paradigms. Articles Trends: Recent work addresses applied topics like cookie consent interfaces, AI in education (e.g., R programming tutors), and perceptual effects in 3D cinema. His studies often bridge cognitive theory with real-world applications, such as usability design and social robotics. Labs/Teams: Leads the research group 'Coordination Cognitive Psychology and Research Methods' at the University of Tübingen. Collaborates with interdisciplinary teams on projects involving robotics, AI, and human-computer interaction.

• Statistical Relational Learning
• Machine Learning
• Probabilistic Graphical Models
• +۵ مورد دیگر

Lise Getoor is a Professor in the Department of Computer Science at the University of California, Santa Cruz, within the Baskin School of Engineering. She previously held positions at the University of Maryland, College Park and has established herself as a leading researcher in statistical relational learning and neuro-symbolic artificial intelligence. Her research focuses on developing principled approaches to reasoning and learning with rich, relational data. She has made significant contributions to probabilistic soft logic, statistical relational learning, knowledge graph construction, and the integration of symbolic and neural approaches to AI. Her work bridges theoretical foundations with practical applications in areas including social network analysis, sustainable recommendations, cyberbullying detection, and fair machine learning systems. Analysis of her recent publications reveals a strong trend toward neuro-symbolic integration, with increasing focus on combining neural networks with logical reasoning frameworks. Her research demonstrates consistent innovation in developing scalable algorithms for structured prediction across diverse application domains while addressing critical issues of fairness and interpretability in AI systems. Professor Getoor has advised numerous PhD students who have become active contributors to the field, including Connor Pryor, Charles Dickens, Eriq Augustine, and Varun Embar. Her research has been supported by multiple grants from major funding agencies, though specific details aren't provided in the current data. She leads research in the Statistical Relational AI (StaRAI) Lab at UC Santa Cruz, where her team develops frameworks that combine logical reasoning with probabilistic modeling to address complex real-world problems requiring both structured knowledge and statistical learning.

Jesús Arroyoمشاهده پروفایل

استادیار

• statistical network analysis
• machine learning
• high-dimensional data analysis
• +۱ مورد دیگر

Jesús Arroyo is an Assistant Professor in the Department of Statistics at Texas A&M University, specializing in statistical network analysis, machine learning, and neuroimaging applications. He holds a PhD in Statistics from the University of Michigan (2018), advised by Professor Liza Levina, and dual B.Sc. degrees in Applied Mathematics and Computer Engineering from ITAM, Mexico. Education : PhD in Statistics (University of Michigan, 2018); B.Sc. in Applied Mathematics and Computer Engineering (ITAM). Postdoctoral Experience : Center for Imaging Science at Johns Hopkins University and Department of Mathematics at University of Maryland, College Park (2018–2021). His research focuses on developing statistical methods for analyzing network data, particularly in neuroimaging and high-dimensional settings. Recent work spans multilayer community detection, anomaly detection in dynamic networks, and integrating covariates into network structure learning. He has secured NSF funding (DMS-2413553) for projects on statistical network integration and also explores causal inference and prediction in network data. Key trends in his publications include applications of network analysis to neuroscience, methodological advancements in high-dimensional statistics, and leveraging machine learning for complex network structures. Collaborators include prominent researchers like Carey E. Priebe, Joshua T. Vogelstein, and Vince Lyzinski. Jesús actively participates in academic conferences, including the International Indian Statistical Association 2025 Conference and workshops on causal inference and network learning at IMPA and other institutions. His work bridges theoretical statistics, computational methods, and real-world neuroimaging challenges.