جست‌وجوی استاد و پژوهشگر

Michael Fuمشاهده پروفایل

استاد

• Simulation Modeling
• Operations Research
• Financial Engineering
• +۵ مورد دیگر

Michael Fu is the Smith Chair of Management Science at the Robert H. Smith School of Business, University of Maryland, with joint appointments in the Institute for Systems Research and the Department of Electrical and Computer Engineering. He holds a Professor rank and specializes in simulation optimization, stochastic processes, and financial engineering. His research bridges theoretical advancements and practical applications in supply chain management, healthcare systems, and artificial intelligence. His academic accolades include INFORMS College on Simulation's Outstanding Publication Award (1998, 2019), IEEE Fellow (2007), and INFORMS Simulation Society's Distinguished Service Award (2018). He has held editorial roles at journals such as Management Science and Operations Research , and served as Program Director at the National Science Foundation (2010–2012, 2015). Research Focus: Simulation optimization, stochastic control, and Monte Carlo methods applied to healthcare, finance, and manufacturing. Key Contributions: Co-author of seminal works like Conditional Monte Carlo and Simulation-Based Algorithms for Markov Decision Processes . Recent Trends: Integrating AI techniques (e.g., Monte Carlo Tree Search) with classical stochastic optimization frameworks. Fu's interdisciplinary work spans multiple domains, including kidney transplantation logistics, financial risk modeling, and pandemic response strategies (e.g., real-time COVID-19 decision support systems). He has advised numerous research initiatives funded by NSF, SEMATECH, and Air Force Office of Scientific Research, emphasizing practical applications of stochastic methods.

Andy Leighمشاهده پروفایل

استاد

• Plant Ecology
• Climate Change
• Ecophysiology
• +۴ مورد دیگر

Professor Andy Leigh is a plant ecologist at the University of Technology Sydney (UTS), specializing in plant responses to extreme environmental conditions, particularly thermal stress and climate change impacts. She holds a professorial position in the School of Life Sciences and has led interdisciplinary research on thermal tolerance, resilience of plant communities, and Indigenous knowledge integration. Her work bridges ecology, physiology, and climate science, with a focus on arid and alpine ecosystems. Education and Roles: PhD in Botany (Australian National University, 2007) BSc Honours and BA (Visual Arts) from ANU Past leadership roles: Head of School (Life Sciences), UTS; Associate Dean for Teaching and Learning (2015–2021) Research Focus: Her research investigates how plants acclimate to heatwaves, drought, and fire, with emphasis on thermal thresholds, genetic adaptation, and ecosystem resilience. Collaborations include Indigenous custodians at Uluru-Kata Tjuta National Park and global networks like the Leaf Temperature Working Group. Awards and Recognition: Senior Fellow, Higher Education Academy UTS Learning and Teaching Award (2013) Three UTS Faculty of Science Excellence Awards Grants and Leadership: Current grants include ARC-funded projects on thermal tolerance and alpine resilience. She co-leads the AusTraits plant trait database and IDEAcology expert elicitation tool. Over 50 peer-reviewed publications span plant physiology, climate adaptation, and ecological modeling. Labs and Teams: Leigh Lab members include 8 PhD/Honours students and co-supervised researchers. Partnerships with ANU, CSIRO, and Parks Australia drive field-based and genomic studies. Public engagement includes media appearances, podcasts, and blogs on plant science and climate change.

Alan Aitkenمشاهده پروفایل

دانشیار

• Potential field geophysics
• Tectonics and lithospheric processes
• Subglacial Surface Processes
• +۴ مورد دیگر

Associate Professor Alan Aitken is affiliated with the School of Earth and Oceans at The University of Western Australia. His research focuses on geophysical and numerical techniques applied to Earth systems, with expertise in Antarctic ice dynamics, tectonics, and mineral systems. He leads projects like BEDMAP3, a comprehensive Antarctic ice bed dataset, and investigates crustal evolution in Western Australia and Antarctica. His work bridges geology and climate science, addressing global sea-level change and resource exploration. Research Interests: Potential field geophysics, tectonic processes, subglacial dynamics, and mineral system modeling. Key projects include Antarctic lithosphere studies and the evolution of cratonic Australia. Collaborations span international teams analyzing ice sheet stability and sedimentary basins. Grants and Collaborations: Principal investigator in ARC-funded projects such as the Australian Centre for Excellence in Antarctic Science and studies on Proterozoic rift basins. Active in global initiatives like BEDMAP3, involving 60+ international researchers. Labs/Teams: Part of interdisciplinary teams at UWA and international Antarctic research networks, contributing to ice-sheet modeling and geological data integration.

Dmitriy Drusvyatskiyمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science Applications
• Convex Optimization
• +۵ مورد دیگر

Dmitriy Drusvyatskiy is a Professor in the Department of Mathematics at the University of Washington and holds the Paul Tseng Faculty Fellow title. He also serves as an Adjunct Professor in the Department of Statistics. His research focuses on continuous optimization, particularly in large-scale problems arising in data science, leveraging tools from convex/nonlinear optimization, variational analysis, semi-algebraic geometry, and high-dimensional probability/statistics. Education: PhD in Operations Research from Cornell University (2013). Research Interests: His work emphasizes optimization theory and applications, including stochastic optimization, nonsmooth analysis, matrix recovery, and statistical learning. He explores foundational aspects like convergence guarantees, algorithm design, and applications in machine learning and data-driven decision-making. Notable Trends in Publications: Recent work highlights advancements in stochastic algorithms with geometric step decay, low-rank matrix recovery, and generalization properties of flat minima. His studies often bridge theoretical guarantees with computational efficiency in high-dimensional settings. Awards: Paul Tseng Faculty Fellow (2020+). Advising & Grants: No explicit student advisee list provided. His research is supported by grants focusing on nonsmooth optimization’s structure, complexity, and conditioning. Collaborations span mathematical theory and interdisciplinary applications in data science.

• Mathematical Foundation of Deep Learning
• Deep Learning Theory
• Optimization
• +۳ مورد دیگر

Chaoyue Liu is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, located in West Lafayette. His research focuses on the mathematical foundations of deep learning, deep learning theory, and optimization techniques. He holds a position in the Department of Electrical and Computer Engineering and contributes to advancing theoretical and practical aspects of machine learning systems. Research Interests: Mathematical underpinnings of deep learning architectures and their theoretical guarantees Optimization algorithms for large-scale machine learning models Federated learning and privacy-preserving techniques Analysis of neural network training dynamics and loss landscapes Key Research Trends in Publications: Recent work emphasizes optimization challenges in over-parameterized systems, federated learning frameworks, and the dynamics of neural network training. His articles explore topics like SGD batch saturation, differential privacy integration, and the emergence of linear behavior in wide neural networks. Grants & Advising: No specific grants or student advisement information is listed. Collaborations may involve Purdue's research groups in machine learning and electrical engineering. Labs/Teams: Affiliated with Purdue's Electrical and Computer Engineering research clusters, likely contributing to interdisciplinary projects in AI and signal processing.

• Heat Transfer
• Fluid Mechanics
• Energy Systems
• +۹ مورد دیگر

Anthony Jacobi is the Richard W. Kritzer Distinguished Professor and Department Head of Mechanical Science and Engineering at the University of Illinois, Urbana-Champaign. He holds leadership roles including Co-Director of the Air Conditioning and Refrigeration Center (ACRC), a nationally leading research hub with over 30 industrial sponsors. His academic journey includes a PhD from Purdue University (1989) and prior faculty positions at Johns Hopkins University and UIUC since 1992. Research focuses on heat and mass transfer, two-phase flows, and energy systems. Notable contributions include modeling evaporation in microchannels and developing next-generation energy-efficient technologies. His work addresses environmental impact through advanced refrigeration and heat exchanger designs. Professional accolades include ASME and ASHRAE Fellowships, the E.K. Campbell Award (2004), and teaching excellence awards spanning decades. He oversees multidisciplinary ACRC projects involving faculty, students, and global industry partners. Education: PhD (1989), MS (1986), BS (1984), A.S. (1981) in Mechanical Engineering, Purdue University Labs/Teams: Convection Heat Transfer Lab, ACRC (30+ sponsors, 60+ researchers) Grants/Awards: NSF Research Initiation Award (1991), Kritzer Faculty Scholar (2003–2006)

John H. Lienhard Vمشاهده پروفایل

استاد

• Desalination
• Thermodynamics
• Heat and Mass Transfer
• +۵ مورد دیگر

John H. Lienhard V is the Abdul Latif Jameel Professor of Water and Mechanical Engineering at MIT. He holds academic and administrative roles, including Director of the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) and Director of the Rohsenow Kendall Heat Transfer Laboratory. His research focuses on desalination, thermodynamics, and heat and mass transfer, emphasizing energy-efficient water purification and sustainable resource recovery. Education: BS and MS from UCLA (Thermal Engineering), PhD from UC San Diego (Fluid Dynamics). Over 90 PhD/Master’s students advised, 300+ peer-reviewed papers, and 40+ patents. Awards include the ASME Heat Transfer Memorial Award and lifetime achievement recognition in desalination. Research interests include solar-driven desalination, membrane technologies, and energy-water nexus challenges. He led J-WAFS, awarding $25M+ in grants and fostering spin-off companies. His textbooks on heat transfer and measurements are globally used, with over 130,000 copies sold. Key contributions: Pioneering nanofiltration for lithium recovery, solvent-driven brine treatment, and monovalent-selective electrodialysis. Editorial roles in Desalination , Journal of Heat Transfer , and others. Served on MIT’s Mechanical Engineering faculty since 1988.

Vitalii Konarovskyiمشاهده پروفایل

استاد

• Stochastic Analysis
• Interacting Particle Systems
• Stochastic Partial Differential Equations
• +۳ مورد دیگر

Prof. Dr. Vitalii Konarovskyi is a Professor of Stochastic Processes and their Applications at the University of Hamburg's Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences. He holds a W2 professorship and has held academic roles at institutions such as Universität Leipzig, Bielefeld, and Jena. His research focuses on stochastic analysis, interacting particle systems, stochastic PDEs, measure-valued diffusions, random graphs, and machine learning. He actively teaches advanced courses in stochastic processes, mathematical statistics, and related fields, including recent modules like 'Stochastic Differential Equations' and 'Large Deviations.' Education & Career: PhD in Mathematics (2010-2015), Institute of Mathematics of NAS of Ukraine W2 Professor, University of Hamburg (2023–present) Postdoc roles at Universität Bielefeld, Leipzig, and the Max Planck Institute Research Interests: His work bridges stochastic processes with applications in physics and machine learning, emphasizing systems with singular interactions, measure-valued dynamics, and coalescing/fragmenting particle systems. Recent focus includes the Dean-Kawasaki equation, stochastic gradient descent dynamics, and stochastic block models. Awards & Recognition: No specific awards mentioned, but his contributions to stochastic analysis and coalescing particle systems are internationally recognized through collaborative research centers like SFB 1283 at Bielefeld. Teaching & Supervision: Extensive teaching portfolio includes stochastic calculus, large deviations, and mathematical methods for physicists. Active in supervising PhD students through research groups and seminars on topics like machine learning and stochastic flows. Labs/Teams: Collaborates with groups like Benjamin Gess's team at Bielefeld and Max von Renesse's group, contributing to projects on fluctuating hydrodynamics and mean-field limits.

Linkan Bianمشاهده پروفایل

استاد

• Additive Manufacturing
• AI/ML in Manufacturing
• Predictive Maintenance
• +۴ مورد دیگر

Dr. Linkan Bian is the Thomas B. & Terri L. Nusz Endowed Professor in Industrial and Systems Engineering at Mississippi State University (MSU) and currently serves as a Program Director in the Advanced Manufacturing (AM) cluster at the National Science Foundation (NSF). He holds a Ph.D. from Georgia Institute of Technology and a B.S. in Applied Mathematics from Beijing University. His primary research focuses on additive manufacturing process modeling, AI/ML integration into manufacturing systems, and healthcare analytics. Key areas include process-structure-property relationships, defect detection via thermal imaging, and medical imaging analysis for cancer diagnostics. Dr. Bian has published over 60 peer-reviewed papers and secured federal funding from NSF, NIH, DoD, and industry partners. He received the IISE Outstanding Young Investigator Award and multiple Best Paper Awards. He serves as Associate Editor for IISE and ASME journals and previously led the IISE Quality Control and Reliability Engineering Division. His research lab, the CAVS Additive Lab, pioneers innovations in additive manufacturing quality control and AI-driven medical applications. Current NSF duties involve advancing advanced manufacturing research initiatives while maintaining MSU academic responsibilities. His work bridges manufacturing engineering with interdisciplinary applications in cybersecurity, predictive maintenance, and healthcare decision-support systems.

• Mathematical Optimization
• Conic Optimization
• Interior Point Methods
• +۳ مورد دیگر

Prof. Dr. Florian Jarre is affiliated with the Mathematical Institute at Heinrich-Heine-Universität Düsseldorf, specializing in mathematical optimization. His research spans conic optimization, interior point methods, semidefinite programming, and nonlinear optimization with applications in computational mathematics. Research interests focus on theoretical and applied aspects of optimization algorithms, including convergence analysis of iterative methods, complexity theory for convex problems, and development of efficient computational techniques for large-scale optimization challenges. Work extends to applications in machine learning and systems biology. Publications demonstrate consistent focus on optimization theory advancements, particularly in interior-point methods and convex programming. Recent work explores connections between optimization and machine learning, including SVM training methods and stochastic gradient descent variants.

• Nonlinear optimization
• Stochastic approximation
• Algorithm design for big data analytics
• +۳ مورد دیگر

Yuyuan Ouyang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University. He holds a Ph.D. in Mathematics from the University of Florida (2013). His research focuses on nonlinear optimization, stochastic approximation, and algorithm design for big data analytics. His work bridges theoretical foundations with practical applications in machine learning, network flow programming, and convex optimization. Dr. Ouyang teaches advanced courses including Machine Learning I/II, Network Flow Programming, and Nonlinear Programming. His recent publications emphasize gradient sliding methods, decentralized optimization, and saddle-point problem analysis. He has contributed to SIAM Journal on Optimization, Mathematical Programming, and Operations Research Letters. His research trends show a strong emphasis on algorithmic innovation for large-scale systems, with notable work on complexity bounds, convex reformulations, and statistical estimation techniques. He has collaborated widely on topics ranging from variational inequalities to MRI image reconstruction.

John Kaszubaمشاهده پروفایل

استاد

• Geochemistry
• Fluid-Rock Interactions
• Carbon Storage Systems
• +۳ مورد دیگر

Dr. John Kaszuba is a Professor at the University of Wyoming with joint appointments in the Department of Geology and Geophysics and the School of Energy Resources. He holds the John and Jane Wold Centennial Chair in Energy and directs research on geochemical fluid-rock interactions across diverse geological systems. His research integrates laboratory experiments, computational modeling, and field studies to examine: Carbon storage systems and CO 2 -fluid-rock reactions Unconventional petroleum reservoirs and enhanced recovery Critical mineral mobilization in sedimentary basins Geothermal energy applications Geologic hydrogen systems Dr. Kaszuba's publication record demonstrates sustained focus on energy-relevant geochemistry, with recent works emphasizing CO 2 utilization, critical mineral resources, and advanced characterization techniques. His articles consistently feature experimental validation of geochemical processes across multiple scales. Honors include the endowed Wold Centennial Chair recognizing his contributions to energy geoscience. He actively advises graduate students on fundamental and applied research projects, with current NSF and DOE-funded investigations into carbon storage assurance, critical minerals recovery, and geologic hydrogen systems. He established and directs an experimental hydrothermal laboratory capable of simulating crustal conditions. His research group collaborates extensively with industry partners and national laboratories to address energy transition challenges.

Adrian Sescuمشاهده پروفایل

استاد

• Fluid Mechanics
• Aeroacoustics
• Turbulent Flows
• +۲ مورد دیگر

Adrian Sescu serves as Airbus Helicopters Professor, Graduate Coordinator, and Interim Associate Dean at Mississippi State University's Bagley College of Engineering. He holds a Ph.D. in Mechanical Engineering (2011), M.S. in Aerospace Engineering (2005), and B.S. in Aerospace Engineering (2004). Research focuses on fluid dynamics and aerodynamics: Boundary layer stability and control Theoretical and computational aeroacoustics Turbulence modeling and LES simulations Wind energy applications High-speed flow phenomena His 15 most recent publications demonstrate sustained focus on computational methods, aeroacoustics, and wind energy, with emerging work on vortex dynamics and flow control. Teaching covers fluid mechanics, aerodynamics, and CFD courses at both undergraduate and graduate levels.

• Fluid Dynamics
• Turbulence
• Mechanical Engineering
• +۳ مورد دیگر

Wen Wu is an Assistant Professor of Mechanical Engineering at the University of Mississippi, affiliated with the Department of Mechanical Engineering within the College of Engineering. His research focuses on fluid dynamics, turbulence, and boundary layer behavior, with emphasis on flow control mechanisms and bio-inspired designs. He holds a Ph.D. from Queen's University at Kingston (2016), an M.Sc. from the University of Science & Techno. of China (2010), and a B.S. from the University of Science & Technology (2007). Education Background: B.S. Mechanical Engineering, University of Science & Technology, 2007 M.Sc Mechanical Engineering, University of Science & Techno. of China, 2010 Ph.D. Mechanical Engineering, Queen's University at Kingston, 2016 Research Interests: Dr. Wu's work explores laminar and turbulent boundary layer dynamics, flow separation mechanisms, and applications of bio-inspired surfaces (e.g., shark denticles) for flow control. His studies integrate experimental techniques like particle image velocimetry with numerical simulations such as large-eddy and Reynolds-averaged Navier-Stokes models. Recent investigations include pressure-induced separation, rotating channel flows, and turbulence model validation in separated flows. Publications Trend: His recent articles (2022–2025) emphasize experimental analysis of passive flow control structures, turbulence modulation in bio-inspired systems, and Reynolds-number effects in rotating flows. Key themes include boundary layer separation dynamics, roughness impacts on turbulent flows, and computational modeling of complex flow phenomena. Lab & Teams: His research group focuses on aerodynamics, fluid mechanics, and computational fluid dynamics, with active projects on bio-inspired flow control and turbulence modeling. He collaborates with institutions globally on fluid dynamics challenges in engineering and biological systems.

Joao Pomboمشاهده پروفایل

استاد

• Railway Engineering
• Railway Technology
• Vehicle-Track Interaction
• +۷ مورد دیگر

Joao Pombo serves as Professor of Railway Technology at the Institute of Railway Research, University of Huddersfield, UK. He holds dual appointments in the Department of Engineering within the School of Computing and Engineering. With a PhD and Post-Doc in Mechanical Engineering from the University of Lisbon, Portugal, he brings over 24 years of specialized experience in railway engineering and technology. His academic leadership extends to serving as editor-in-chief of the International Journal of Railway Technology and chairman of the International Conference on Railway Technology series. Professor Pombo's research expertise spans railway vehicle dynamics, pantograph-catenary interaction, structural analysis, and condition monitoring. His work addresses critical challenges in high-speed rail, maintenance engineering, and predictive maintenance systems. He employs advanced methodologies including multibody dynamics, machine learning algorithms, and hardware-in-the-loop testing to solve complex railway engineering problems. His research directly contributes to UN Sustainable Development Goals related to sustainable transport infrastructure and clean energy systems. Analysis of his recent publications reveals a strong focus on advancing railway electrification systems through improved pantograph-catenary interaction models, while increasingly incorporating machine learning techniques for infrastructure optimization and condition monitoring. His work demonstrates consistent innovation in modeling complex railway dynamics, with particular emphasis on real-world applications that enhance safety, efficiency, and sustainability in rail transport systems. Professor Pombo's scientific recognition includes four innovation prizes for his R&D work. He serves on editorial boards for prestigious journals including Multibody System Dynamics and Energies - Electric Vehicles , and has established himself as a leading authority in international railway research circles. Editor-in-chief of International Journal of Railway Technology Chairman of International Conference on Railway Technology series Editorial board member of Multibody System Dynamics Editorial board member of Energies - Electric Vehicles With over 170 publications in refereed journals and international conferences, Professor Pombo actively supervises PhD students and collaborates with leading international research centers and industry partners. His prior three years of industry experience at BOMBARDIER and ALSTOM provides valuable practical context for his academic research. Current projects include developing advanced condition monitoring systems, optimizing railway infrastructure performance, and creating novel analysis tools for railway electrification systems. As a core member of the Institute of Railway Research at the University of Huddersfield, Professor Pombo contributes to one of the world's leading railway research centers. His work with the UKRRIN Centre of Excellence in Rolling Stock demonstrates his commitment to translating academic research into practical industry applications that support railway electrification and infrastructure modernization projects worldwide.