Research Field Methods Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۰۶-۷۲۰ از ۳۸٬۳۶۸ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Shunde Yinمشاهده پروفایل

دانشیار

University of WyomingUnited States

Shunde Yin serves as an Associate Professor in the Department of Energy & Petroleum Engineering at the University of Wyoming's College of Engineering & Physical Sciences, based in Room 4019 of the Engineering Building. His research focuses on advanced computational methods in petroleum geomechanics with practical applications to reservoir engineering challenges. His educational background includes a Ph.D. in Geotechnical Engineering from the University of Waterloo (2008), an M.S. in Geotechnical Engineering from the Chinese Academy of Sciences (2003), and a B.E. in Civil Engineering from Shijiazhuang Railway Institute (1999). Dr. Yin specializes in Coupled thermal-hydraulic-mechanical-chemical (THMC) modeling and soft computing applications within petroleum geomechanics. His work integrates computational techniques to address subsidence, reservoir depletion, and seismic monitoring challenges, bridging theoretical geomechanics with field applications in energy extraction. Analysis of his 2002-2008 publications reveals consistent innovation in numerical methods for reservoir geomechanics, featuring displacement discontinuity techniques, finite element analysis, and machine learning applications across thermal, hydraulic, mechanical, and chemical domains. No scientific awards were documented in the source material. Information regarding student advising, research grants, and laboratory facilities was not provided in the available documentation.

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Mikkel N. Schmidtمشاهده پروفایل

دانشیار

Technical University of DenmarkDenmark

Mikkel N. Schmidt is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on statistical modeling, Bayesian methods, and their applications in science and industry. He has held visiting roles at Columbia University (2007) and Cambridge University (2008-2009). His work integrates probabilistic modeling with computational inference to address complex problems in diverse fields such as molecular discovery, optical communication, and brain connectivity analysis. Education highlights include visiting scholar and postdoctoral experiences at top-tier institutions. Research interests span statistical methodology development, machine learning applications, and interdisciplinary problem-solving. Current projects involve Bayesian neural networks for molecular discovery and federated learning optimization. Advising efforts include supervising multiple PhD students in areas like molecular discovery and denoising diffusion models. Notable collaborations involve work on materials science, quantum communication, and medical signal processing. His contributions bridge theoretical advancements with practical industrial applications, emphasizing interdisciplinary innovation.

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Jonathan Abbattمشاهده پروفایل

استاد

University of TorontoCanada

Jonathan Abbatt is a Professor of Chemistry at the University of Toronto, specializing in environmental chemistry with a focus on atmospheric processes. His research examines multiphase chemistry in indoor and outdoor environments, particularly aerosol particle interactions and their impacts on climate and air quality. He leads the Abbatt Group, which investigates topics such as Arctic chemistry, indoor chemical transformations, and brown carbon aging. Research interests span indoor/outdoor chemical reactions, aerosol physics, and environmental modeling. Notable projects include studies on ozone deposition on indoor surfaces, biomass burning emissions, and reactive chlorine sources in urban areas. His work integrates lab experiments, field measurements, and computational models. Recent studies highlight indoor surface reactivity, wildfire impacts on ozone, and multiphase oxidation mechanisms. Collaborations with institutions like Environment and Climate Change Canada ensure practical applications of his findings. Students and postdocs in his lab contribute to advancing knowledge in air quality and climate change mitigation.

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Wes Hartmannمشاهده پروفایل

استاد

Stanford UniversityUnited States

Wes Hartmann is the John G. McCoy-Banc One Professor of Marketing at the Stanford Graduate School of Business. His research focuses on the intersection of marketing, economics, and policy, with emphasis on pricing strategies, brand advertising, social interactions, and conservation. He has advised numerous PhD students who have secured academic and industry positions globally. Key research interests include understanding frequency in brand advertising, preference externality estimators, and the economics of harm reduction through conservation experiments. Hartmann’s work bridges theoretical econometric models with real-world applications, such as dynamic pricing in retail and the impact of digital cinema on market dynamics. His publications have been recognized with prestigious awards, including finalist nominations for the John D.C. Little Award and the Paul Green Award. Notable contributions include groundbreaking studies on Super Bowl advertising effectiveness and the implications of social networks on consumer behavior. Hartmann’s advising record spans over 15 students, many of whom have secured tenured positions or roles at top institutions like Harvard, Columbia, and NYU. His research collaborations include projects on conservation field experiments, internet vs. TV advertising efficacy, and the role of social interactions in targeted marketing.

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Hans-Georg Herzogمشاهده پروفایل

استاد

Technical University of MunichGermany

Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.

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John Voightمشاهده پروفایل

استاد

Dartmouth CollegeUnited States

John Voight is a Professor of Mathematics at the University of Sydney, affiliated with the School of Mathematics and Statistics. He holds a Ph.D. from UC Berkeley (2005) and has held academic positions at the University of Sydney, University of Minnesota, University of Vermont, and Dartmouth College. His research focuses on arithmetic algebraic geometry, number theory, and computational aspects of these fields, including modular forms, elliptic curves, and quaternion algebras. Education: Ph.D. in Mathematics, UC Berkeley (2005); earlier studies include a focus on classical piano and liberal arts at Gonzaga University. Professional trajectory includes postdoctoral roles at the University of Sydney and University of Minnesota, followed by faculty appointments at the University of Vermont (2006–2013) and Dartmouth College (2013–present). Research Interests: Arithmetic algebraic geometry: modular curves, Shimura varieties, moduli spaces, and algorithmic methods Number theory: algebraic number theory, quadratic forms, cryptography, and coding theory Computational mathematics: modular forms, quaternion algebras, and database construction (e.g., LMFDB) Publications and Recognition: Over 90 peer-reviewed articles, including contributions to Contemp. Math. , Math. Comp. , and Res. Number Theory . Recipient of the Selfridge Prize in Number Theory and recognized for teaching excellence at Dartmouth. Leads projects on Hilbert modular forms and paramodular abelian surfaces. Teaching and Mentorship: Emphasizes student-centered learning and the integration of liberal arts with STEM. Advises students on topics ranging from elliptic curves to cryptography. Active in curriculum design and promoting computational tools in mathematics education. Labs/Teams: Collaborates with the L-Functions and Modular Forms Database (LMFDB) project, contributing to computational frameworks for algebraic geometry and number theory.

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Axel Brunsمشاهده پروفایل

استاد پژوهشی

Queensland University of TechnologyAustralia

Professor Axel Bruns is a Research Professor at Queensland University of Technology’s (QUT) Digital Media Research Centre (DMRC), an institution renowned for its leadership in media and communication studies. His work focuses on the digital transformation of media, the role of social media in public communication, and the dynamics of political polarization in online environments. Bruns is an internationally recognized innovator in computational methods for social media analysis, emphasizing interdisciplinary mixed-methods approaches to study complex societal phenomena. Research Interests: His research addresses critical challenges such as polarization’s threat to democracy, the role of algorithms in shaping public discourse, and the spread of disinformation. Notable areas include filter bubbles, platform governance, and the interplay between social media and political systems. He has pioneered concepts like 'gatewatching' to analyze news curation practices of digital intermediaries. Key Achievements: Bruns leads a prestigious Australian Laureate Fellowship project examining polarization drivers and dynamics. His work combines rigorous methodological innovation with policy relevance, evidenced by submissions to parliamentary committees on social media regulation. He has mentored numerous doctoral students who have become leading methodologists in their fields. Awards and Collaborations: Recipient of the Australian Laureate Fellowship (2023), Bruns collaborates widely with institutions globally, including Algorithm Watch, the Centre for Responsible Technology, and the Alexander von Humboldt Institute for Internet and Society. These partnerships enable cross-border research into digital media’s societal impacts.

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Gabi Laskeمشاهده پروفایل

استاد

University of California, San DiegoUnited States

Gabi Laske is a Professor at the Institute of Geophysics and Planetary Physics (IGPP), Scripps Institution of Oceanography (SIO), University of California, San Diego (UCSD). She is a leading researcher in seismology and geophysics, with a focus on Earth's internal structure, crustal and mantle modeling, and ocean-bottom seismology. Her work has significantly advanced global crustal models, including CRUST5.1, CRUST2.0, and CRUST1.0. Her research interests include seismology, geophysics, Earth's internal structure, surface wave tomography, normal mode analysis, crustal and lithospheric modeling, ocean bottom seismology, mantle plumes, inner core rotation, ambient noise seismology, and earthquake signal processing. She has led major projects such as the Hawaiian PLUME and SWELL experiments, utilizing ocean-bottom seismometers to study mantle dynamics and lithospheric rejuvenation. Her work on inner core differential rotation, particularly with Guy Masters, has been published in top journals like Nature and Science . The 15 most recent publications reflect a strong trend in ocean-bottom seismology, ambient noise analysis, instrument calibration, seismic signal quality, and imaging of crustal and mantle structure. Her work combines observational seismology with advanced signal processing and modeling techniques, often in collaboration with students and international teams. She has made significant contributions to understanding seismic anisotropy, normal modes, and the structure of volcanic and tectonic regions. Funded by NSF (OCE, EAR, CSEDI, MG&G) Collaborative projects with USGS, international institutions Advisor to PhD students, including Adrian Doran Lead developer of DLOPy for OBS orientation Contributor to global reference models (CRUST1.0, LITHO1.0) Gabi Laske has made enduring contributions to geophysics through her development of global crustal models, leadership in major seismic experiments, and mentorship of the next generation of seismologists. Her work continues to shape our understanding of Earth's deep interior and surface processes.

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Samuel Jean Bassettoمشاهده پروفایل

دانشیار

Polytechnic School of MontrealCanada

Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.

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David A. Fosterمشاهده پروفایل

استاد

University of FloridaUnited States

David A. Foster is a Professor in the Department of Geological Sciences at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research integrates thermochronology, structural geology, and petrology to investigate tectonic and magmatic processes, with a recent focus on enhanced weathering of basaltic rocks for carbon sequestration. He leads the Thermochronology Lab, providing analytical services for mineral separation, Ar/Ar dating, and thermal modeling. Research interests span: Tectonics & Geodynamics : Orogenic collapse, terrane accretion, and supercontinent cycles. Thermochronology : Application of temperature-sensitive isotopic systems to crustal evolution. Surface Processes : Links between tectonic uplift, erosion, and carbon capture via rock weathering. His publications emphasize regional tectonics (e.g., Andes, Appalachians, Cordillera), utilizing geochronology, isotope geochemistry, and structural analysis. Recent work explores collisional orogens, basin provenance, and magmatic arcs, with recurring themes of extensional collapse and paleogeographic reconstructions. Educational contributions include co-authoring the comprehensive textbook Geology of National Parks , now in its 8th edition, which synthesizes geological features of U.S. national parks in the context of plate tectonics and landscape formation.

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Chongmin Songمشاهده پروفایل

استاد

University of New South WalesAustralia

Professor Chongmin Song is a faculty member at the University of New South Wales (UNSW), affiliated with the School of Civil and Environmental Engineering. His academic rank is Professor, and he specializes in computational mechanics with a focus on innovative numerical methods. He holds a BE and ME from Tsinghua University and a DEng from the University of Tokyo. His research explores computational mechanics, fracture analysis, wave propagation, and soil-structure interactions. Key methodologies include the Scaled Boundary Finite Element Method (SBFEM), image-based modeling, and dynamic simulations of infrastructure systems. He leads significant ARC-funded projects like 'A scaled boundary framework for nonlinear dynamic analysis of structures' (DP250100955) and 'Developing sustainable graded porous cementitious structures' (LP240100123), totaling over $1M in recent grants. Recent publications emphasize adaptive modeling techniques, multiphysics simulations, and high-performance computing applications. Trends include topology optimization for structural dynamics, phase-field fracture modeling for brittle materials, and GPU-accelerated elastodynamics. His work integrates computational efficiency with real-world engineering challenges, particularly in geomechanics and material failure analysis. Professor Song collaborates extensively on projects involving computational fracture mechanics and maintains laboratories focused on numerical simulation advancements. Future work targets scalable algorithms for 3D crack propagation and multiphysics coupling in infrastructure systems.

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Mark Crowleyمشاهده پروفایل

دانشیار

University of WaterlooCanada

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo , with a cross-appointment in the Cheriton School of Computer Science . He is actively involved in the Waterloo Artificial Intelligence Institute (WAII) , the Waterloo Institute for Complexity and Innovation (WICI) , and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC) , coordinating the Canadian Conference on AI . Research interests span the theoretical and applied aspects of Reinforcement Learning , Deep Learning , Manifold Learning , and Ensemble Methods . His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving , Medical Imaging , and Material Design . Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications. Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray. Teaching includes graduate and undergraduate courses in Algorithm Design , Computational Intelligence , Reinforcement Learning , and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.

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Daniel Readمشاهده پروفایل

مدرس ارشد

University of LondonUnited Kingdom

Dr. Daniel Read is a Senior Lecturer at the Institute for Sport Business , Loughborough University London. His interdisciplinary research addresses critical integrity issues in sport through qualitative and quantitative methodologies, focusing on athlete welfare, governance, sponsorship, fan behavior, and digital media. PhD in Sport Business (Loughborough University London) MRes in Psychology BSc in Sports Science and Physiology Dan's current projects examine: Athlete welfare (care-seeking behavior, medicine misuse, education) Commercial innovation vs. sporting tradition Digital divide impacts on sport access 21st-century sport governance He collaborates with organizations such as the World Anti-Doping Agency , FIFA , World Rugby , and the Professional Footballers’ Association . His teaching expertise spans research methods, data analytics, sport integrity, and digital media, alongside PhD supervision.

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Ananya Choudhuryمشاهده پروفایل

استاد

The University of ManchesterUnited Kingdom

Professor Ananya Choudhury serves as Chair and Honorary Consultant in Clinical Oncology at the University of Manchester, where she is also Co-Group Leader of the Translational Radiobiology Group within the Division of Cancer Sciences. She joined The Christie NHS Foundation Trust in 2008, specializing in urology and sarcoma, and has since focused on radiotherapy-related research in prostate and bladder cancers. Professor Choudhury is clinical lead for advanced radiotherapy, including the groundbreaking MRLinac project, and plays a key role in national radiotherapy research initiatives. Professor Choudhury earned her BA (Hons) in 1993, MB. BChir (Cantab) in 1995, and MA (Cantab) in 1997 from Trinity College, Cambridge. She completed her Clinical Oncology training at the Yorkshire Deanery from 2000-2008, during which she earned her MRCP in 2000 and F.R.C.R in 2004. She completed her PhD in 2008 through the University of Leeds and Princess Margaret Hospital in Toronto, Canada, where she studied the molecular epidemiology of DNA double strand break repair in bladder cancer. Professor Choudhury's research program focuses on optimizing and personalizing radiotherapy using advanced imaging technology to deliver high doses while minimizing side effects. Her work centers on prostate and bladder cancers, with particular interest in predictive biomarkers, hypoxia, and the integration of magnetic resonance imaging to improve treatment precision. She has pioneered research in radiotherapy dose optimization, biomarker development, and the identification of patients who would benefit most from different treatment approaches. Her extensive publication record demonstrates a strong focus on radiation therapy, particularly in genitourinary cancers. Recent work explores MRI-guided radiotherapy, hypoxia biomarkers, and personalized treatment approaches across multiple cancer types. She has made significant contributions to understanding how imaging technology can improve radiotherapy precision and effectiveness while reducing side effects, with several publications appearing in top journals through 2025. Professor Choudhury has received multiple prestigious awards recognizing her contributions to the field: Cancer Research-UK/Royal College of Radiologists Clinical Training Fellowship (2005) Fellowship for the 10th ECCO-AACR-ASCO Workshop on Methods in Clinical Cancer Research (2007) Outstanding Contribution, Greater Manchester Clinical Research Awards (2017) RCR Research Fellowship (2005) Research Fellowship, Princess Margaret Hospital, Toronto (2004) Professor Choudhury has supervised numerous doctoral and master's students across multiple cancer types, with current students expected to complete through 2024. She is Principal Investigator on multiple research grants, including 'Measuring tumour radioresistance to improve radiotherapy outcomes' and the 'MAESTRO Programme' as part of CRUK RadNet. Her research program is supported by significant funding from NIHR Manchester Biomedical Research Centre and other major funding bodies. As Co-Group Leader of the Translational Radiobiology Group, Professor Choudhury collaborates extensively with leading researchers including Peter Hoskin, Catharine West, Corinne Faivre-Finn, and Marcel van Herk. Her team is at the forefront of integrating advanced imaging with radiotherapy to improve cancer treatment outcomes, with active projects spanning from basic radiobiology to clinical implementation of novel radiotherapy techniques.

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Justin Sirignanoمشاهده پروفایل

استاد

University of OxfordUnited Kingdom

Justin Sirignano is a Professor of Mathematics at the University of Oxford, affiliated with the Mathematical Institute. His research bridges Applied Mathematics, Machine Learning, and Financial Mathematics, developing novel mathematical frameworks and computational methods. Education: B.A. in Mathematics, Princeton University PhD in Mathematics, Stanford University Chapman Fellow, Imperial College London His research focuses on theoretical and applied aspects of machine learning, particularly in mean-field analysis of neural networks , deep learning for PDEs/SDEs , and scientific machine learning . He has pioneered methods for solving complex financial and scientific problems using data-driven approaches. His recent publications emphasize recurrent neural networks, reinforcement learning, and PDE closure models with applications in turbulence simulation and hypersonic flows. These works span numerical methods, optimization, and stochastic processes. Scientific Awards: 2014 SIAM Financial Mathematics and Engineering Conference Paper Prize Grants & Collaborations: He has secured over $16.5 million in funding from agencies like ONR, NSF-EPSRC, and DoE. His PhD students hold positions at J.P. Morgan, Bank of America, and other institutions. Labs & Teams: He leads research groups in Machine Learning and Mathematical Finance at Oxford, collaborating with institutions like Notre Dame, Boston University, and UIUC.

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