McMaster UniversityCanada
Corrado Maurini is a Professor in Mechanics at Sorbonne University , Paris, France. He leads two international master programs: Mécanique des Solides (Solid Mechanics) and Computational Mechanics .
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۳۶-۷۵۰ از ۸٬۷۹۶ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Corrado Maurini is a Professor in Mechanics at Sorbonne University , Paris, France. He leads two international master programs: Mécanique des Solides (Solid Mechanics) and Computational Mechanics .
Privatdozent Dr. Marcela Suarez-Rubio is a Senior Scientist at the Institute of Zoology, University of Natural Resources and Life Sciences Vienna (BOKU) , specializing in urban ecology, biodiversity research, and wildlife monitoring . Her work integrates landscape ecology, remote sensing, and conservation biology to study how land-use changes affect birds and bats in temperate and tropical regions. She has led projects in Austria, Myanmar, and Bhutan, focusing on habitat connectivity, urban green spaces, and endangered species conservation . Education: PhD in Ecology, University of Maryland, USA (2011) MSc in Biology, University of Puerto Rico (2005) BSc in Biology, Universidad del Valle, Colombia (2000) Research Themes: Urbanization gradients and their nonlinear effects on avian and bat communities Biodiversity assessment in remote areas like Hkakabo Razi National Park Telemetric and acoustic methods for wildlife monitoring Conservation strategies for critically endangered species (e.g., White-bellied Heron) Ecosystem management in agricultural and forested landscapes Scientific Awards: ERASMUS+ Teaching Mobility (2022, 2018, 2017) National Parks Austria Research Award (2022) BOKU Teaching Award (2016) NASA-MSU Professional Enhancement Award (2011) Golden Key International Honour Society (2008) Advising and Grants: Supervised 14 theses and secured funding from National Parks Austria, Austrian Academy of Sciences, and international organizations . Active in scientific peer-review (Philosophical Transactions of the Royal Society B, Journal of Urban Ecology) and conservation policy for UNESCO World Heritage nominations.
Prof. Dr. Eda Taşçı is a faculty member at the Faculty of Engineering, Dumlupınar University, specializing in Metallurgical and Materials Engineering. With a career spanning over two decades, she has held positions including Research Assistant (2002-2010), Associate Professor (2011-2022), and Professor (2022-present). She served as Deputy Head of Department (2017-2018) and Vocational School Directorate (2018-2021). Education: PhD in Ceramic Engineering (2004-2010), Master's (2001-2004), and Bachelor's (1997-2001) from Anadolu University. Her research focuses on inorganic materials like ceramics and cement, emphasizing production processes, surface properties, and sustainable applications. Key projects include enhancing glaze chemical resistance, pozzolan-cement interactions, and industrial metallic glaze development. Her publications span topics from ancient mudbrick materials to modern ceramic processing, highlighting interdisciplinary work in material science, environmental engineering, and industrial chemistry. She received the Turkish Cement Manufacturers Association Trailblazers Scholarship in 2008. Email: eda.tasci@dpu.edu.tr
Victor M. Preciado is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research focuses on network science , control theory , and graph signal processing . Research Interests: Modeling and controlling spreading processes on complex networks Optimization algorithms for time-varying systems Applications in public health and cyber-physical security Selected Publications: Recent work includes machine learning for operator inference (2022), hybrid systems stability analysis (2021), and pandemic modeling frameworks (2021). Earlier contributions focus on spectral analysis of epidemics (2009-2016) and geometric optimization (2014).
استاد مدعو
Lee E. Frelich serves as an Adjunct Professor and Director of the Center for Forest Ecology at the University of Minnesota, where his work bridges academic research and applied forest management. His leadership in the Center drives interdisciplinary studies on ecosystem resilience and disturbance dynamics. His academic foundation includes a Ph.D. in Forest Ecology from the University of Wisconsin-Madison (1986), establishing decades of expertise in forest systems. Frelich's research examines boreal and temperate forests through the lens of climate change, invasive species, and disturbance interactions. He pioneered investigations into earthworm invasions as ecosystem engineers, demonstrating cascading effects on soil biota and plant communities. His work on fire-wind-deer disturbance synergies reveals complex legacies in forest regeneration, while recent studies quantify climate-driven shifts in species composition and carbon cycles. This integrative approach combines field experiments with large-scale modeling to address anthropogenic impacts on forest sustainability. Analysis of his 15 most recent publications (2024-2025) shows persistent focus on disturbance interactions (fire, wind, drought) and invasion ecology, with growing emphasis on socio-ecological linkages like outdoor recreation impacts. Methodologically, he increasingly employs structural equation modeling to unravel multi-driver systems, while maintaining strong empirical field components across North American and African ecosystems. His scientific recognition includes: Listing among the top 1% of all scientists globally in Ecology and Environment by Web of Science Frelich's applied work manifests through consulting contracts with the U.S. Army, Air Force, National Forest Service, and National Park Service, where he translates research into management strategies for fire-prone landscapes and invasive species control. Though specific grant histories aren't detailed, his 210+ publications with 332 international coauthors indicate sustained funding across collaborative projects. His media presence (570+ features including The New York Times and Washington Post ) amplifies policy relevance. As Director of the Center for Forest Ecology, he oversees research initiatives examining disturbance legacies and climate adaptation, fostering partnerships between university scientists and land management agencies to develop evidence-based conservation frameworks for North American forests.
پژوهشگر
B. B. Cael is an interdisciplinary climate and ocean scientist affiliated with the Department of the Geophysical Sciences at the University of Chicago and the Climate Systems Engineering Initiative . His work integrates data analysis and theoretical models to investigate global-scale questions about Earth's carbon cycle and climate system. Cael's research interests span climate mitigation , climate sensitivity , climatic extremes , ocean biogeochemistry , plankton ecology , remote sensing , and paleoclimate . His publications focus on ocean carbon fluxes, climate feedbacks, and marine ecosystem dynamics, with recent work applying machine learning to oceanographic data. His academic journey includes a PhD from the MIT-WHOI Joint Program , a Simons Foundation Postdoctoral Fellowship at the University of Hawai’i at Manoa, and a Principal Scientist role at the UK's National Oceanography Centre. Current research at UChicago addresses both carbon dioxide removal and solar geoengineering strategies. Scientific Awards Simons Foundation Postdoctoral Research Fellow
استادیار
Anna Korba is an Assistant Professor at École Polytechnique, specifically affiliated with ENSAE/CREST in the Statistics Department since September 2020. She is also a co-administrator of the Master Data Science program at École Polytechnique. Her academic journey has positioned her as a leading researcher in machine learning, with particular expertise in kernel methods, optimal transport, and statistical optimization. Dr. Korba received her PhD from Telecom ParisTech in 2018 under the supervision of Prof. Stephan Clémençon. Prior to her current position, she was a postdoctoral researcher at University College London's Gatsby Computational Neuroscience Unit working with Arthur Gretton from December 2018 to August 2020. Her academic foundation includes a Master's degree in Machine Learning and Computer Vision (MVA) from ENS Cachan and ENSAE in 2015. Anna Korba's research primarily focuses on machine learning with emphasis on kernel methods, optimal transport, optimization, particle systems, and preference learning. Her work bridges theoretical statistics with practical machine learning applications, particularly in developing novel sampling and optimization methods. She has made significant contributions to understanding Wasserstein gradient flows, density ratio estimation, and variational inference techniques. Her publication record demonstrates a strong trajectory in top-tier machine learning conferences including ICML, NeurIPS, AISTATS, and ICLR. Her research shows a clear evolution from foundational work on ranking and preference learning during her PhD to more recent contributions in Wasserstein-based optimization, sampling methods, and deep probabilistic modeling. The interdisciplinary nature of her work connects statistics, optimization theory, and practical machine learning applications. Top 10% Oral Presentation at AISTATS 2022 Top 15% Long Oral Presentation at ICML 2021 She actively mentors PhD students and postdoctoral researchers, currently advising seven PhD candidates and having successfully guided several alumni to prestigious positions. Dr. Korba also contributes to the academic community through her role in administering the Master Data Science program and collaborating with researchers across institutions worldwide. As part of the CREST research center, Dr. Korba works within a vibrant team of researchers focused on statistics, machine learning, and their applications to economic and social sciences. Her research group includes current PhD students and postdocs working on various aspects of her research interests, creating a dynamic environment for advancing the field of statistical machine learning.
استادیار
Dr. Amir K. Miri is an Assistant Professor in the Department of Biomedical Engineering at New Jersey Institute of Technology (NJIT) and Director of the Advanced Biofabrication Lab. His work focuses on additive manufacturing for biomedical applications, particularly bioprinting technologies for tissue regeneration and disease modeling. After receiving his PhD in Mechanical Engineering from McGill University (2013) and completing postdoctoral training at the MIT-Harvard Division of Health Sciences and Technology, he began his academic career at Rowan University before joining NJIT. PhD, Mechanical Engineering, McGill University (2013) MSc, Mechanical Engineering, Sharif University of Technology (2007) BSc, Mechanical Engineering, Iran University of Science and Technology (2005) Dr. Miri's research spans advanced bioprinting platforms, including multi-axial extrusion, handheld printers, and digital light projection systems. His work emphasizes the development of biomimetic models for cancer, vocal fold tissue, and vascular systems, with a particular focus on microfluidic integration and material optimization for bioprinting. He has pioneered low-cost prototyping solutions for resource-limited settings and explored the role of extracellular matrix mechanics in cellular behavior. Key trends in his publications include 3D bioprinting for tumor modeling, microfluidic device applications in drug screening, and the use of hydrogels like GelMA in cancer research. His group has also advanced acoustic metasurface technology for biomedical wave manipulation and investigated the interplay between biomaterial rheology and bioprinting resolution. Dr. Miri leads a research team at NJIT focused on biofabrication and microfluidics, though specific student advisees are not listed in the provided information. His lab emphasizes interdisciplinary collaboration, particularly in the development of multi-material and multi-scale tissue constructs.
Kévin Bailly is a Lecturer at Sorbonne University, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and part of the Machine Learning and Artificial Intelligence (MLIA) team. His research focuses on computer vision, deep learning, and their applications in facial expression recognition, neural network optimization, and medical imaging. Dr. Bailly's research interests span multiple areas including: Computer Vision and Image Analysis Deep Learning and Neural Network Optimization Facial Expression and Action Unit Recognition Model Compression and Quantization Techniques Medical Applications of Artificial Intelligence His recent publications demonstrate a strong focus on neural network optimization, with particular emphasis on quantization, pruning, and compression techniques that maintain model performance while reducing computational requirements. His work spans both theoretical advancements in deep learning and practical applications in healthcare, human-computer interaction, and affective computing. He has developed novel approaches like PowerQuant for non-uniform quantization, RULe for real-time face alignment in degraded conditions, and RED++ for data-free pruning of deep neural networks. Dr. Bailly has published extensively in top-tier venues including ICLR, NeurIPS, IEEE TPAMI, and IEEE TAC, with a consistent output of high-impact research from 2022-2024. His work bridges theoretical computer vision with practical applications, particularly in medical diagnostics and human-computer interaction systems. He actively collaborates with researchers across multiple institutions, including Arnaud Dapogny, Edouard Yvinec, and Matthieu Cord, and has contributed to interdisciplinary projects that apply AI techniques to medical domains such as fracture classification and obstetrics.
مدرس ارشد
Dr. Paul Brindley is a Senior Lecturer at the School of Architecture and Landscape , University of Sheffield, specializing in geospatial analysis of human-landscape interactions. His work combines GIS, statistics, and digital data to address urban greenspace equity, rural-urban classification, and health-environment linkages. Senior Lecturer in GIS and Spatial Analysis Key researcher in the NERC-funded Improving Wellbeing through Urban Nature (IWUN) project Co-author of official Rural-Urban Classification for England and Wales Research focuses on: Urban Greenspace Accessibility using GPS and social media data Health Inequality Mapping through epidemiological GIS studies Vague Geographic Objects modeling of subjective spatial boundaries Computational Neighborhood Mapping via web data mining Project leadership includes: 2016-2019: IWUN Work Package 1 (Sheffield health-green space analysis) 2013-2014: Rural-Urban Classification updates for 2011 Census 2015: Real-Time Bus Data Mapping system development Teaching modules: LSC119: The Changing Landscape LSC336: Landscape Planning Toolkits LSC5020: Rural Landscape Planning GIS Workshops Professional contributions: Co-developer of Crime Map Analyst toolkit for non-GIS experts Joint Director of Learning and Teaching Links Member of Creative Spatial Practices initiative
Anders Forsgren is a Professor of Optimization and Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology since 2003. His research focuses on nonlinear programming, particularly Newton-type methods for smooth optimization, with applications in radiation therapy, cell biology, and telecommunications. PhD in Optimization and Systems Theory (KTH, 1990) MS in Operations Research (Stanford, 1987) MSc in Engineering Physics (KTH, 1985) Research Interests: Anders develops methods for constrained optimization and applies them to intensity-modulated radiation therapy, metabolic networks, and wireless communication systems. His work bridges algorithmic innovation with real-world clinical and engineering challenges. Recent Publications: Focus on robust optimization for radiation therapy under uncertainty, quasi-Newton methods, and applications in medical physics. His 2025 papers address interplay-robust optimization and scenario positioning in proton therapy. Scientific Leadership: Co-chair, 8th SIAM Conference on Optimization (2005) Editorial board member, Computational Optimization and Applications (since 1998) Member, Mathematical Optimization Society and SIAM Mentorship: Supervises PhD students in optimization and systems theory, with former advisees working on radiation therapy robustness, metabolic modeling, and network design.
Christopher J. Cooper, M.D., is Distinguished University Professor of Medicine at the University of Toledo, Ohio, where he also served as Dean of the College of Medicine (2014-2024), CEO of University of Toledo Physicians, and Executive Vice-President for Clinical Affairs. Triple-board-certified in Internal Medicine, Cardiovascular Disease, and Interventional Cardiology, he directs the UT Clinical Coordinating Center and has led national randomized trials on renal-artery stenosis. Education & Training: B.A. magna cum laude in Biology (Philosophy minor), Wittenberg University, 1985 M.D., University of Cincinnati College of Medicine, 1988 — Valedictorian, Alpha Omega Alpha Internship & Residency, Brigham and Women’s Hospital / Harvard Medical School, 1988-1991 Cardiology Fellowship, Brigham and Women’s Hospital, 1991-1994 Harvard School of Public Health, Clinical Effectiveness Program, 1992 Research Focus: Cooper’s group investigates renovascular hypertension, renal-artery stenosis, and cell-based therapies for vascular disease. As PI of the NIH-funded CORAL trial and multiple industry grants, he has published pivotal papers defining when renal stenting benefits patients and elucidating inflammatory and signaling pathways that couple renal artery narrowing to hypertension and renal failure. Recent work integrates large-scale clinical outcomes databases with mechanistic studies on Na/K-ATPase signaling, microRNA regulation of cardiac fibrosis, and biomarkers such as CD40-ligand in renovascular disease. Honors & Awards: Best Doctors in America — multiple years (2002-2014) America’s Top Doctors, Castle Connolly — consecutive listings (2006-2015) College of Medicine Award for Excellence in Clinical Research, UT (2008) Elected Fellow, American Heart Association Council on High Blood Pressure Research (2004) Valedictorian & Alpha Omega Alpha, Univ. of Cincinnati (1988) Leadership & Grants: Cooper has held continuous NIH and industry funding for multicenter trials; he founded and directs the UT Clinical Coordinating Center, overseeing regulatory, data, and biostatistical cores for national studies. His administrative leadership spans Department Chair, Heart & Vascular Center Director, and Dean of the College of Medicine, while maintaining an active interventional cardiology practice. Laboratory & Teams: He leads the Cooper Renal & Vascular Research Group within the UT Cardiovascular Research Institute, mentoring faculty, fellows, and graduate students in translational protocols that bridge molecular cardiology, interventional imaging, and large pragmatic trials.
Timo Hytönen is a Professor in the Department of Agricultural Sciences at the University of Helsinki's Faculty of Agriculture and Forestry. He holds a Docentship in the same department and is an integral member of the Viikki Plant Science Centre (ViPS). His academic leadership extends to supervising doctoral students across three major programs: Sustainable Use of Renewable Natural Resources, Integrative Life Science, and Plant Sciences. Additionally, he maintains an external position as Principal Research Scientist at NIAB EMR in Genetic Genomics & Breeding since November 2018. Professor Hytönen's research interests span plant genetics, molecular biology, and crop production sciences with a specialized focus on berry crops, particularly strawberries. His work investigates fundamental plant processes including flowering time regulation, environmental adaptation mechanisms, fruit development, and disease resistance. His research integrates genomic, transcriptomic, and epigenetic approaches to understand how plants respond and adapt to environmental challenges. His extensive publication record (88 publications) reveals a strong focus on woodland strawberry (Fragaria vesca) as a model system for studying economically important traits in berry crops. Recent work demonstrates expertise in QTL mapping, genome-wide association studies, circadian rhythm adaptation, and the genetic basis of climate adaptation. His research shows consistent emphasis on translating basic science into practical applications for crop improvement. Professor Hytönen leads significant research projects including 'Mansikan vihreä vallankumous' (Strawberry Green Revolution) funded by the Academy of Finland and 'ASTROS: How meristems guide plant reproductive development' supported by the Jane and Aatos Erkko Foundation. He is also a Principal Investigator in the Viikki Plant Science Centre (ViPS), a major collaborative research infrastructure. His academic service includes extensive peer review activities (37 manuscript reviews), hosting academic visitors (32 instances), and participation in numerous conferences and seminars. He has provided guidance to graduate students and supervised doctoral candidates, contributing significantly to the training of next-generation plant scientists.
Geoffrey Goodhill is Professor of Neuroscience and Professor of Developmental Biology at Washington University School of Medicine, where he directs the Center for Theoretical & Computational Neuroscience. His laboratory bridges experimental and theoretical approaches to study brain development. Goodhill earned his BSc in Mathematics and Physics from the University of Bristol (1986), MSc in Artificial Intelligence from the University of Edinburgh (1988), and PhD in Cognitive Science from the University of Sussex (1992). His postdoctoral training included a Medical Research Council Fellowship and a Sloan Theoretical Neuroscience Fellowship at the Salk Institute. His research focuses on computational principles of brain development, particularly using larval zebrafish to investigate neural coding development, behavioral emergence, and alterations in Autism Spectrum Disorders. Key projects examine neural coding and spontaneous activity patterns zebrafish behavioral development autism-related circuit dysfunction calcium imaging analysis methods historical work on axon guidance mechanisms His recent publications show a clear trajectory from molecular gradient studies toward complex systems neuroscience using zebrafish models. Scientific recognition includes: Paxinos-Watson Prize (2012) Elspeth McLachlan Plenary Lecture (2019) Keynote at Computational Neuroscience Meeting (2020) Sloan Theoretical Neuroscience Fellowship (1995) The Goodhill Lab maintains an interdisciplinary team with backgrounds in biology, mathematics, physics and engineering. Current research analyzes human video data for early autism detection while continuing zebrafish neural circuit investigations. The lab has received consistent funding for its innovative approaches to developmental neuroscience questions.
Csaba Szepesvári is a Professor and Canada CIFAR AI Chair in the Department of Computing Science at the University of Alberta, affiliated with Amii. Since 2017, he has been on partial leave, leading the Foundations team at DeepMind in Edmonton. His work bridges theoretical machine learning and practical reinforcement learning systems. His research interests lie at the intersection of reinforcement learning, sequential decision making, bandit algorithms, and theoretical machine learning . He develops and analyzes algorithms for efficient learning in complex environments, focusing on sample efficiency, function approximation, and optimization. His recent work explores policy gradient methods, offline and online RL, uncertainty estimation, and foundational limits of learning algorithms. The trends in his recent publications (2023–2024) emphasize theoretical advances in RL and bandits , particularly in q π -realizability, natural policy gradient, ensemble sampling, and lower bounds. The work spans NeurIPS, ICML, COLT, and AISTATS, reflecting deep theoretical engagement with practical implications. He is recognized as a Canada CIFAR AI Chair , a prestigious award supporting leading researchers in artificial intelligence. Csaba Szepesvári mentors students and collaborates extensively, often with researchers like András György, Tor Lattimore, Gellért Weisz, and Dale Schuurmans. He has co-organized RL theory seminars and workshops and co-authored the influential book Bandit Algorithms (2020). His leadership in both academic and industrial research (DeepMind) underscores his impact on the field. He is actively involved in the machine learning community through research, mentorship, and event organization, contributing to both foundational theory and real-world applications.
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