جست‌وجوی استاد و پژوهشگر

Aaron Osgood-Zimmermanمشاهده پروفایل

استادیار

• Spatial Statistics
• Spatiotemporal Modeling
• Public Health
• +۳ مورد دیگر

Aaron Osgood-Zimmerman serves as Assistant Professor of Statistics at Bucknell University's College of Arts and Sciences, based in Olin Science Building with contact email aeoz001@bucknell.edu. His academic credentials include: Ph.D. in Statistics, University of Washington M.S. in Statistics, University of Washington B.A. in Mathematics and B.S. in Engineering, Swarthmore College Specializing in spatial and spatiotemporal statistical modeling, his research bridges climate science and global public health. At the Institute for Health Metrics and Evaluation (IHME), he developed methodologies for mapping health burdens including child malnutrition in Africa, anemia prevalence in women, and cancer incidence across Europe. Current work focuses on climate-health interactions, specifically how environmental changes drive geographically variable health outcomes and disproportionately impact vulnerable communities. His approach integrates theoretical statistical innovation with practical policy applications. Publication analysis reveals consistent application of advanced spatial modeling to critical global health challenges, particularly in resource-limited settings. His high-impact work in Nature and Nature Medicine demonstrates expertise in translating complex statistical frameworks into actionable public health insights for low- and middle-income countries. During his five-year tenure at IHME, he contributed to large-scale collaborative initiatives including the Local Burden of Disease Child Growth Failure Collaborators. Teaching philosophy emphasizes data literacy, inclusive pedagogy, and real-world statistical application across diverse learner backgrounds.

Ming Xiangمشاهده پروفایل

استاد

• Psycholinguistics
• Neurolinguistics
• Syntax
• +۴ مورد دیگر

Ming Xiang is the David B. and Clara E. Stern Professor and Director of Graduate Studies in the Department of Linguistics at the University of Chicago. She holds a Ph.D. from Michigan State University (2005) and has been teaching at UChicago since 2010. Her research investigates the processing and neural mechanisms underlying real-time construction of linguistic representations, focusing on sentence processing across syntax, semantics, and discourse comprehension. Recent collaborative work explores how language processing influences language change. Her primary research interests include: Psycholinguistic and neurolinguistic mechanisms Syntax-semantics-pragmatics interfaces Chinese language processing Experimental methodologies (eye-tracking, ERP) Computational modeling of language Her recent publications demonstrate strong focus on: Real-time language processing mechanisms Interactions between syntax, semantics and pragmatics Cross-linguistic comparisons (Mandarin/English) Computational psycholinguistics approaches Neurocognitive correlates of language comprehension She directs the Language Processing Lab and teaches advanced courses including Structure of Chinese, Psycholinguistics, and Experimental Methods.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +۵ مورد دیگر

Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.

• Machine Learning
• Computer Vision
• Neural Networks
• +۵ مورد دیگر

Mohit Prabhushankar is a Research Fellow at the Georgia Institute of Technology's Department of Electrical and Computer Engineering, part of the College of Engineering. His work focuses on machine learning, computer vision, and explainable AI with applications in medical imaging, seismic interpretation, and uncertainty quantification. He has contributed to developing algorithms for active learning, self-supervised learning, and robust model evaluation. Research interests include hierarchical multimodal systems, counterfactual explanations, and domain adaptation in geoscience applications. His recent work addresses challenges in interpretability metrics, active learning strategies, and biomedical data analysis through projects like the OLIVES dataset for ophthalmic biomarkers and CRACKS for subsurface fault analysis. Publications span topics from action anticipation in robotics to neural network dynamics, emphasizing practical applications of AI in geoscience and healthcare. His research bridges theoretical advancements with real-world deployment considerations through frameworks like gradient-based anomaly detection and deployable clinical active learning systems. No scientific awards or grants are explicitly listed in available materials. Advising activities and student collaborations remain undocumented in the provided content.

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Domenec Puig is a Professor at the Department of Computer Science and Mathematics, Rovira i Virgili University, Spain. His research focuses on machine learning, medical imaging, and computer vision, with applications in healthcare and biomedical informatics. He collaborates extensively with researchers in AI-driven medical diagnostics, including fundus image analysis, MRI segmentation, and neonatal birth weight prediction using multimodal data. Key research areas include deep learning architectures for semantic segmentation, generative adversarial networks (GANs), and interpretable AI models. His work bridges theoretical advancements in neural networks with practical solutions for challenges in medical image analysis, such as crack detection in materials, tumor classification, and survival prediction in oncology. Recent publications highlight contributions to transformer-based models, hybrid architectures (e.g., CoAtUNet), and domain adaptation techniques for cross-site generalization in MRI segmentation. His methodologies often emphasize explainability and efficiency in resource-constrained settings.

Holger Caesarمشاهده پروفایل

استادیار

• Autonomous Vehicle Perception
• Autonomous Vehicle Prediction
• Scalability of Learning Systems
• +۴ مورد دیگر

Dr. Holger Caesar is an Assistant Professor in the Intelligent Vehicles group at TU Delft. His research focuses on scalable learning and annotation approaches for autonomous vehicle perception and prediction. He previously led Motional's Data Annotation, Autolabeling, and Data Mining teams. He holds a PhD in Computer Vision from the University of Edinburgh. Education: PhD in Computer Vision (University of Edinburgh), studies at KIT Karlsruhe, EPFL Lausanne, ETH Zurich Research emphasizes datasets like nuScenes/nuPlan and methods like PointPillars. His work addresses safety, sensor fusion, and closed-loop testing in autonomous systems. Collaborations include projects on 2050 mobility scenarios and photorealistic safety testing frameworks.

Andrew Gelmanمشاهده پروفایل

استاد

• Bayesian Statistics
• Political Methodology
• Statistical Inference
• +۶ مورد دیگر

Andrew Gelman is the Higgins Professor of Statistics and Professor of Political Science at Columbia University, where he also serves as director of the Applied Statistics Center. His dual appointments reflect his interdisciplinary approach to research and teaching, bridging statistical methodology with political science applications. Gelman earned his Ph.D. from Harvard University in 1990. His educational background established the foundation for his influential career that combines rigorous statistical methodology with substantive social science inquiry. He has maintained a strong presence in both academic communities throughout his career, contributing to the development of statistical methods while applying them to pressing questions in political science and public policy. Gelman's research spans an exceptionally wide range of topics at the intersection of statistics and social science. His work addresses fundamental questions in voting behavior, electoral systems, and political representation, while simultaneously advancing methodological innovations in Bayesian statistics, multilevel modeling, and data visualization. He has made significant contributions to understanding why it is rational to vote, why campaign polls are variable despite predictable elections, and how redistricting affects democracy. His methodological work spans statistical inference challenges in diverse contexts from toxicology to medical imaging, from arsenic exposure in Bangladesh to radon levels in homes, and from police practices to social network analysis. His development of multilevel regression and poststratification (MRP) has become a standard technique in survey analysis and small-area estimation. Analysis of Gelman's recent publications reveals a continued focus on foundational statistical methodology with applications across multiple domains. His work demonstrates consistent attention to practical implementation challenges in Bayesian computation, causal inference, and survey methodology. A strong theme throughout his recent work is addressing the replication crisis through improved statistical practice, with particular emphasis on model checking, transparent reporting of uncertainty, and the integration of Bayesian methods with machine learning approaches. His research increasingly focuses on the practical implementation challenges of advanced statistical methods in real-world settings. Outstanding Statistical Application award from the American Statistical Association Best article published in the American Political Science Review Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of forty As director of Columbia's Applied Statistics Center, Gelman oversees a hub for interdisciplinary statistical research and collaboration. He has mentored numerous students and researchers through the Center's activities, fostering a community that applies advanced statistical methods to real-world problems across various domains. His teaching includes graduate courses in quantitative political research and applied regression methods, reflecting his commitment to training the next generation of researchers in robust statistical practice. Gelman has taught courses including Principles of Quantitative Political Research, Quantitative Political Research, and Quantitative Methods II: Applied Regression. Gelman leads research teams focused on developing and applying advanced statistical methods to social science questions. His work often involves collaboration across disciplines, bringing statistical expertise to address substantive questions in political science, public health, and social policy. The Applied Statistics Center under his direction serves as a nexus for methodological innovation and application, connecting statisticians with domain experts to tackle complex data challenges while promoting best practices in statistical analysis and communication.

Aditya Mahajanمشاهده پروفایل

استاد

• Control Theory
• Stochastic Processes
• Reinforcement Learning
• +۴ مورد دیگر

Aditya Mahajan is a Professor in the Department of Electrical and Computer Engineering at McGill University, Montreal, Canada. He holds affiliations with prominent research institutes including Mila (Quebec Artificial Intelligence Institute), CIM (Center for Intelligent Machines), GERAD (Group for Research in Decision Analysis), and ILLS (International Laboratory for Low Temperature Science). His research focuses on stochastic control, reinforcement learning, decentralized systems, and multi-agent decision-making. Key themes include analysis of Markov decision processes (MDPs), robust control strategies, and optimization in partially observable environments. He explores theoretical foundations of actor-critic algorithms, human-automation collaboration, and networked control systems with applications to robotics and autonomous systems. Recent work emphasizes scalable methods for complex systems, including approximation techniques for MDPs with unbounded costs, delay-resistant stochastic approximation, and policy design in POMDPs. His contributions span both algorithmic innovations and rigorous theoretical analysis, bridging gaps between control theory and modern machine learning paradigms. Major research directions include: Reinforcement learning in decentralized and partially observable settings Optimal control for human-machine teams Robustness and convergence properties of stochastic algorithms Multi-agent systems and mean-field games His articles highlight advancements in control theory, with recent attention to: Decision-making under cognitive load Convergence guarantees for Q-learning variants Structural results for restless bandits and resource allocation He participates in interdisciplinary collaborations through his affiliations, contributing to AI applications in robotics, energy systems, and networked control architectures.

Peng Zhangمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +۳ مورد دیگر

Peng Zhang is an Assistant Professor at Rutgers, The State University of New Jersey, affiliated with the School of Arts and Sciences. His research focuses on theoretical computer science, algorithms, computational complexity, and optimization. He leads the Theory of Computing research group and has received the NSF CAREER Award for his work. His research explores advanced topics such as discrepancy theory, Laplacian solvers, and algorithmic hardness results. Key research interests include developing efficient algorithms for structured numerical problems, analyzing computational complexity, and advancing methods in combinatorial optimization. His recent work addresses challenges in randomized experiments, covariate balancing, and parallel algorithm design. Awards: NSF CAREER Award Lab/Group: Theory of Computing Research Group Office: Hill 444 His publications span topics from discrepancy minimization to linear programming equivalence, reflecting a deep engagement with foundational computational theory and its applications.

• Computer Vision
• Deep Learning
• Robotics
• +۵ مورد دیگر

Hao Yang is a Professor at Southeast University's School of Computer Science and Engineering, specializing in computer vision, deep learning, and robotics. His research spans multiple domains including medical imaging, infrastructure inspection, and multimodal AI systems. His research interests focus on advancing computer vision techniques for practical applications. He develops innovative deep learning architectures for object detection, image segmentation, and quality assessment, with particular emphasis on solving challenges in complex real-world scenarios including rotated object detection, ancient character recognition, and 3D point cloud analysis. His work often integrates novel transformer variants and Mamba architectures to address domain-specific challenges. His publication record shows significant contributions across multiple IEEE and ACM journals, with a strong emphasis on practical applications of AI in engineering contexts. His recent work demonstrates expertise in adapting cutting-edge neural network architectures to solve domain-specific problems in civil engineering, medical imaging, and transportation systems. Hao Yang actively collaborates across disciplines, with publications spanning computer science, electrical engineering, civil engineering, and biomedical applications. His work frequently appears in top-tier conferences including CVPR, AAAI, and ACL, demonstrating his broad impact across multiple AI subfields.

Jackson Kulikمشاهده پروفایل

استادیار

• Astrodynamics
• Guidance, Navigation, and Control
• State Estimation
• +۶ مورد دیگر

Jackson Kulik is an Assistant Professor in the Mechanical and Aerospace Engineering Department at Utah State University's College of Engineering. He leads the Dynamics, Control, and Estimation (DyCE) Lab and is part of the Autonomy, Robotics, Control, and Optimization (ARCO) Group, focusing on cislunar astrodynamics, rendezvous operations, and space situational awareness. Education: PhD in Applied Mathematics, Cornell University, 2024. Dissertation: "Linear and Multilinear Methods for Guidance, Navigation, and Control of Satellite Formation Flight". BS in Mathematics, Texas Tech University, 2020. Dr. Kulik's research centers on advanced guidance, navigation, and control systems for space applications, leveraging higher-order tensor methods for partial derivatives and statistical moments. His work spans astrodynamics, state estimation, and trajectory optimization with emphasis on cislunar navigation, spacecraft proximity operations, and orbital uncertainty modeling. Recent publications (2024-2025) reveal a strong focus on linear covariance analysis fidelity, non-Gaussian uncertainty propagation, and reachable set theory for spacecraft dynamics. These works integrate tensor mathematics with practical space mission challenges, particularly in satellite formation flight and space domain awareness. Scientific Awards: DOD NDSEG Fellowship (2021) Dr. Kulik mentors five graduate students including PhD candidates Alex Nelson (RPO/Cislunar GN&C), Braden Hastings (Situational Awareness), and Allan Lyon (stochastic navigation), plus Master's student Zack Fraser (GN&C engineering). His research is supported by Department of Defense funding, building on his NDSEG Fellowship background. The DyCE Lab operates within USU's ARCO Group, developing novel mathematical frameworks for space system dynamics and control. Current projects address cislunar navigation challenges, spacecraft rendezvous algorithms, and space object tracking using tensor-based statistical methods.

• Diabetes Epidemiology
• Implementation Science
• Global Health
• +۳ مورد دیگر

Anna C Rivara is an Assistant Professor in the Peter O' Donnell School of Public Health at the University of Texas Southwestern Medical Center, specializing in diabetes research and implementation science for underserved populations. Her work bridges anthropological perspectives with public health interventions to improve chronic disease outcomes. Her educational background includes: Bachelor's in Anthropology from the University of Oregon Master of Arts in African Studies from the University of Illinois, Urbana-Champaign MPH (Infection Control) and PhD in Applied Anthropology from the University of South Florida Global Health Equity Scholars Fellowship at Yale School of Public Health (NIH Fogarty/NIDDK-funded) Postdoctoral Associate and Associate Research Scientist roles at Yale School of Public Health Dr Rivara's research employs mixed-methods and implementation science to address diabetes care delivery gaps, particularly among Samoan populations. She investigates type 2 diabetes risk factors, care preferences via discrete choice experiments, and illness experiences through longitudinal studies. Her current NIH NIDDK K01-funded project examines diabetes care evolution in Samoa, while local initiatives focus on MASLD and diabetes comorbidities in Dallas-area underserved communities. Her anthropological training informs culturally grounded intervention design. Analysis of her 15 most recent publications (2020-2024) reveals consistent focus on Pacific Islander health with strong emphasis on implementation frameworks. Key trends include care cascade analysis for diabetes/hypertension, genetic variant impacts on metabolic outcomes, and adaptation of research methods (e.g., accelerometry, discrete choice experiments) to Samoan contexts. Her work increasingly integrates patient preference data and addresses implementation barriers in resource-limited settings. Her major scientific recognition includes: NIH NIDDK Mentored Research Scientist Career Development Award (K01) Global Health Equity Scholars Fellowship (NIH Fogarty International Center/NIDDK) Dr Rivara leads active grant-funded research including her K01 project assessing diabetes care preferences among Samoans and protocols for adolescent-mediated diabetes interventions. While specific advisees aren't documented, her work involves training postdoctoral associates and international collaborators. She contributes to the Soifua Manuia cohort study and collaborates with Samoan health authorities on non-communicable disease programs. Her research operates through the Soifua Manuia observational cohort study framework with the Samoan Ministry of Health, Yale collaborators, and UTSW-based teams. Current efforts focus on scaling implementation strategies for diabetes care cascades and adapting interventions to local cultural contexts in both Samoa and Dallas.

Jochen Trumpfمشاهده پروفایل

استاد

• Control Theory
• Observer Theory
• Optimization on Manifolds
• +۳ مورد دیگر

Prof Jochen Trumpf is a Professor in the School of Engineering at the Australian National University (ANU). He holds an ORCID identifier and has an h-index of 22 with over 2,500 citations. His research focuses on control theory, observer theory, optimization on manifolds, and applications in robotics, computer vision, and wireless communication. He completed his PhD in Mathematics at the University of Würzburg (2002) and held postdoctoral positions at Ben-Gurion University of the Negev and the University of Notre Dame prior to joining ANU in 2003. His research interests emphasize geometric approaches to nonlinear systems, including equivariant filter design, attitude estimation, and SLAM (Simultaneous Localization and Mapping). He has led or co-investigated multiple projects, including the National Facility for Electricity Grid Security and Resilience Research (2023–2025) and studies on distributed collaborative localization and control. He has collaborated widely, with notable contributions to sensor fusion, inertial navigation, and observer-based control strategies. Prof Trumpf’s work integrates mathematical rigor with practical engineering challenges, with over 90 publications in peer-reviewed journals and conferences. His projects often involve cross-disciplinary teams addressing issues in autonomous systems, navigation, and sensor technology. Despite no explicit awards listed, his citation metrics and project leadership reflect significant academic impact. He supervises research students and has been involved in doctoral training programs, such as the Defence Staff PhD Agreement with Joyce Mau (2018–2022). His research extends to applications in robotics, environmental sensing, and smart grid systems, reflecting a balance between theoretical innovation and real-world problem-solving.

Alanwar Amrمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Formal Verification
• Data-Driven Reachability Analysis
• +۷ مورد دیگر

Prof. Alanwar Amr is a Professor at the Chair of Cyber-Physical Systems within the TUM School of Computation, Information and Technology at Technical University of Munich (TUM). His research focuses on data-driven reachability analysis, security-assured reinforcement learning, and privacy-preserving estimation techniques using advanced mathematical frameworks like zonotopes. He holds a PhD from TUM and has held prior positions as an Assistant Professor at Jacobs University Bremen and a postdoctoral researcher at KTH Royal Institute of Technology. His work bridges formal verification, control theory, and cybersecurity in cyber-physical systems. Education: PhD in Automation and Control from TUM (under Prof. Althoff) Research Associate at UCLA and Engineer at Siemens/Morpho Research Interests: Data-driven formal verification methods Secure and resilient control systems Privacy-preserving set-based estimation Adversarial attack defense mechanisms His recent publications emphasize advancements in zonotope-based techniques for safety verification, privacy in distributed systems, and AI-driven control systems with formal guarantees. Key contributions include logical zonotopes for boolean function analysis and diffusion-based observers for multi-agent systems. Notable awards include the KTH Postdoctoral Fellowship, Qualcomm Innovation Fellowship finalist distinctions (2017/2018), and a best demonstration award at IPSN 2017. His work addresses critical challenges in autonomous systems, cybersecurity, and privacy in IoT environments.

Julien Bectمشاهده پروفایل

دانشیار

• Bayesian Optimization
• Gaussian Processes
• Uncertainty Quantification
• +۴ مورد دیگر

Julien Bect is an Associate Professor at CentraleSupélec, affiliated with the Laboratoire des Signaux et Systèmes (L2S). His primary research focuses on Bayesian optimization, Gaussian processes, uncertainty quantification, and sequential design of experiments. He has collaborated extensively with researchers such as Emmanuel Vazquez and Paul Feliot, contributing to advancements in statistical modeling and computational methods for engineering and scientific applications. His work emphasizes the development of efficient algorithms for estimating probabilities of failure, excursion sets, and quantile-based inversion in complex systems. Notable contributions include methodologies for multi-fidelity computer experiments, Bayesian subset simulation, and adaptive experimental design. His research bridges theoretical foundations in statistics with practical applications in fields like reliability engineering, food safety, and electrical systems. Bect has published numerous articles in prestigious journals such as Technometrics , SIAM/ASA Journal on Uncertainty Quantification , and Bernoulli , showcasing his expertise in statistical inference and optimization. His recent work explores novel approaches in quantile set inversion and complex-valued frequency response modeling, reflecting his commitment to advancing methodologies for real-world stochastic systems.