جست‌وجوی استاد و پژوهشگر

Pauliina Ilmonenمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Probability Theory
• Data Science
• +۳ مورد دیگر

Pauliina Ilmonen is an Associate Professor and Vice Dean in the Department of Mathematics and Systems Analysis at Aalto University, within the School of Science. Her research focuses on mathematical statistics, probability theory, and data science, with applications in healthcare and engineering. She holds academic positions in both the Department of Mathematics and Systems Analysis and Statistics and Mathematical Data Science. Her work spans statistical methodology, stochastic processes, and interdisciplinary collaborations in biomedicine. Notable research areas include extreme value analysis, functional data analysis, and the modeling of dependent data. She has contributed to studies on HIV transmission dynamics, prostate cancer genomics, and the statistical analysis of infectious diseases. Dr. Ilmonen’s recent publications (2020–2025) emphasize methodological advancements in statistical inference, computational statistics, and applications in healthcare. She leads projects on predictive modeling, cost-effectiveness analysis in cancer screening, and network traffic prediction. Her findings bridge theoretical statistics with practical challenges in medicine and technology. She advises 8 theses and collaborates on grants exploring mathematical data science, biostatistics, and stochastic systems. Active in academic service, she contributes to curriculum development and promotes ethical use of statistics in research.

• Earthquake Source Studies
• Strong Motion Seismology
• Seismic Wave Propagation
• +۴ مورد دیگر

Ralph Archuleta is a Professor of Earth Science at the University of California, Santa Barbara (UCSB), affiliated with the Earth Research Institute (ERI). His primary research focuses on Earthquake Source Studies and Strong Motion Seismology, emphasizing the dynamics of fault rupture, ground motion prediction, and seismic hazard assessment. He has advised numerous PhD students and collaborated with postdoctoral researchers, contributing to advancements in seismological modeling and field data analysis. Education: B.S., University of Wyoming, 1969 Ph.D., UC San Diego, 1976 Research Interests: Earthquake source mechanics and rupture dynamics Strong ground motion simulation and site effects Seismic wave propagation in heterogeneous media Development of numerical models for fault systems His work integrates field observations, laboratory experiments, and computational simulations to address seismic hazard challenges. Publications (Recent Trends): Archuleta’s recent work explores fault heterogeneity, nonlinear site response, and supershear earthquakes. Key themes include stochastic modeling of slip distributions, radiated seismic energy analysis, and improvements to ground motion prediction methods. Scientific Contributions: While no explicit awards are listed, his extensive publications and leadership in seismology reflect significant scholarly impact. He has mentored over 20 graduate students and supervised postdocs globally, advancing careers in academia and industry. Labs/Teams: Active in the ERI at UCSB, contributing to collaborative projects like the COSMOS Virtual Data Center for strong motion data dissemination.

Gianluca Aloiمشاهده پروفایل

دانشیار

• Wireless Networks
• Cellular Networks
• Sensor Networks
• +۷ مورد دیگر

Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.

Luis Ortizمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Game Theory
• +۱۲ مورد دیگر

Luis Ortiz is an Associate Professor at the University of Michigan - Dearborn in the Department of Computer and Information Science , College of Engineering and Computer Science. He previously held faculty positions at Stony Brook University (2008-2015), University of Puerto Rico, Mayaguez (2007-2008), and postdoctoral roles at MIT (2004-2006) and University of Pennsylvania (2001-2004). His research spans Artificial Intelligence , Machine Learning , Game Theory , and Graphical Models , with applications to networks, security, finance, and health. Education: Ph.D., Computer Science, Brown University (2002) B.S., Computer Science, University of Minnesota, Twin Cities His recent publications focus on graphical games, causal inference, and probabilistic modeling, with contributions to Nash equilibrium computation, strategic network analysis, and multi-agent systems. He has received multiple NSF awards , including a CAREER grant. Scientific Awards: 2019 NSF Research Award 2011 NSF CAREER Award 2008 NSF Faculty Diversity Award Ortiz has advised projects in computational game theory , machine learning , and network security . His research grants include NSF funding and industry support from Huawei/FutureWei Technologies. He has led projects on interdependent defense games, wireless localization, and causal strategic inference in networked systems. Labs & Teams: Collaborates with the Stony Brook Game Theory Summer Festival , University of Pennsylvania's Computer and Information Science Department , and industry partners in cellular networking and security.

• Power Systems Engineering
• Microgrid Technology
• Renewable Energy Integration
• +۳ مورد دیگر

Kumaraguru Prabakar is a researcher at the National Renewable Energy Laboratory (NREL) specializing in power systems engineering. His work focuses on microgrid technology, distributed energy resource integration, and grid support systems. Key collaborations include research with Hawaiian Electric and academic institutions on renewable energy solutions. Research Output: 115+ publications (2015–2025) Key Areas: Inverter engineering, microgrid stability, hydrogen energy systems, and electromagnetic transient simulations Recent research trends involve seamless integration of hydrogen electrolysis, wave energy, and photovoltaic inverters into distribution grids, with emphasis on advanced control algorithms and grid resilience. He actively contributes to hardware-in-the-loop testing methodologies for microgrid controllers. His experimental work includes validating grid-forming inverter interoperability, analyzing battery inverter performance, and developing data-driven voltage control strategies. Platforms like the Vulcan Test Platform and PHIL interfaces are central to his research.

Feng Wangمشاهده پروفایل

استاد

• Theoretical and Computational Chemistry
• Molecular Spectroscopy
• Sustainable Aviation Fuels
• +۷ مورد دیگر

Professor Feng Wang is a leading academic in theoretical and computational chemistry at the Department of Chemistry and Biotechnology, School of Science, Computing and Emerging Technologies, Swinburne University of Technology, Melbourne, Australia. She holds a PhD from the University of Newcastle and has held research positions at the University of Waterloo, University of Melbourne, and Swinburne. Education: PhD, The University of Newcastle, Australia MSc (Quantum Chemistry by Research), Sichuan University, China BSc (Physical Chemistry), Sichuan University, China Prof Cert (Teaching Practice, Higher Education), Swinburne University of Technology, Australia Her research spans molecular spectroscopy, sustainable aviation fuels, corrosion science, and computational drug design, with applications in energy, environment, and pharmaceuticals. She employs advanced methods like density functional theory (DFT), molecular dynamics, and machine learning to study molecular systems and materials. Her recent publications (2024–2025) reflect a strong focus on sustainable energy materials—particularly high-energy-density aviation fuels (e.g., JP-10, norbornyl derivatives), CO2 conversion, and battery aging—alongside computational drug discovery for SARS-CoV-2 and flavonoid-based antivirals. These works integrate quantum mechanics, NMR analysis, and AI-driven modeling. Scientific Awards and Honors: Fellow, Royal Australian Chemical Institute (RACI) Fellow, Australian Institute of Physics (AIP) Vice-Chancellor's Research Award, Swinburne University of Technology Professor Wang actively mentors PhD and Master’s students and leads nationally funded research projects. She serves on the Australian Research Council (ARC) College of Experts and has chaired the National Computational Merit Allocation Scheme (NCMAS). She evaluates proposals for international agencies including FRQNT (Canada), SFI (Ireland), FCT (Portugal), and SNSF (Switzerland). Her lab, the Digital Chemistry Research Laboratory, contributes to UN Sustainable Development Goals 7 and 13.

Hammou Elbarmiمشاهده پروفایل

استاد

• Order Restricted Inference
• Survival Analysis
• Categorical Data Analysis
• +۳ مورد دیگر

Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .

• Forest Growth and Yield Modeling
• Forest Biometrics
• Pine Plantation Management
• +۳ مورد دیگر

Mingliang Wang serves as a Researcher at the Plantation Management Research Cooperative (PMRC) within the Warnell School of Forestry and Natural Resources at the University of Georgia. His expertise centers on forest biometrics, with a specialization in growth and yield modeling for loblolly pine plantations across the southeastern United States, leveraging long-term experimental data to inform sustainable management practices. His research portfolio emphasizes: Forest Growth and Yield Modeling Pine Plantation Dynamics Forest Carbon Sequestration Silvicultural Treatment Impacts Stand Structure Analysis Statistical Biometrics He employs advanced regression techniques and longitudinal studies to quantify effects of thinning, fertilization, and site quality on plantation productivity. Analysis of his 15 most recent publications reveals consistent focus on loblolly pine ecosystems, with dominant themes including carbon stock dynamics, thinning efficacy, and species performance comparisons. Methodologically, he integrates nonlinear mixed-effects models and long-term experimental designs to address critical gaps in plantation forestry science. Dr. Wang actively contributes to the Plantation Management Research Cooperative (PMRC), a collaborative hub advancing science-based solutions for sustainable forest management through partnerships between academia, industry, and government stakeholders.

• Supply Chain Management
• Logistics
• Artificial Intelligence
• +۳ مورد دیگر

Batin Latif AYLAK serves as Associate Professor in the Department of Industrial Engineering at the Faculty of Engineering, Turkish-German University, Turkey. Previously, he held an Assistant Professor position at Hitit University starting in 2015. His academic career spans over a decade of research at the intersection of industrial engineering, logistics, and artificial intelligence. His educational foundation includes: Doctorate in Mechanical Engineering from University of Duisburg-Essen (2010-2015) Master's degree in Mechanical Engineering from University of Duisburg-Essen (2008-2010) Bachelor's degree in Industrial Engineering from Istanbul Kültür University (2001-2006) with full scholarship Dr. AYLAK's research pioneers the integration of artificial intelligence in supply chain optimization, with particular emphasis on sustainable logistics systems. His work bridges theoretical advancements with practical applications in blockchain implementation for supply chain transparency, machine learning-driven energy forecasting, and intelligent warehouse management. He has developed novel frameworks like SustAI-SCM for agentic AI in sustainable supply chains and contributed significantly to understanding digital transformation in Turkey's logistics sector. His publication trajectory (2021-2025) reveals a strategic evolution from foundational logistics research toward cutting-edge AI applications. Early work focused on optimization algorithms and blockchain integration, while recent publications demonstrate leadership in agentic AI systems for sustainability, advanced predictive modeling for renewable energy, and pandemic-responsive supply chain solutions. This progression highlights his adaptation to emerging technological paradigms while maintaining core focus on supply chain resilience. No scientific awards or fellowships were documented in the provided materials. Dr. AYLAK has secured competitive research funding including project BAP TAÜ - 2018BM0028 for digital trend analysis in Turkey's logistics sector. While his publications indicate collaborative research with multiple co-authors across institutions, no formal graduate student advisement relationships were specified in the available documentation.

• Financial Markets
• Mathematical Finance
• Derivatives
• +۶ مورد دیگر

Vedat Akgiray is a Professor at Bogazici University, holding a Ph.D. from Syracuse University. His teaching and research areas focus on Derivatives , Portfolio Management , Probability , and Financial Markets , with a particular emphasis on Mathematical Finance . He has published extensively on topics such as FinTech, corporate governance, pension systems, and risk management. Education : Ph.D., Syracuse University Email: akgirayv@bogazici.edu.tr

• Particle physics
• High-energy physics
• Beyond Standard Model
• +۷ مورد دیگر

Jørgen Beck Hansen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen, specializing in Experimental Subatomic Physics . His career spans roles at CERN and NBI, focusing on particle physics detectors, high-energy collisions, and computational methods. Education: Ph.D. in Particle Physics (1996) and M.Sc. in Physics (1993) from the University of Copenhagen. Research interests include two-boson physics at the ATLAS detector, precision measurements within the Standard Model, effective Lagrangian densities, and searches for new physics beyond the Standard Model (e.g., Higgsless theories, extra dimensions). He also works on GRID computing and distributed data analysis. Recent trends in his research involve Higgs boson studies, vectorlike top quarks, photonuclear collisions, and detector trigger optimization. Collaborative efforts with the ATLAS Collaboration and Danish NORDUGRID team highlight his interdisciplinary approach. Scientific awards : Skou Stipend (Assistant Professor) from the Danish Natural Science Council Teaching and supervision include mentoring 5 summer students, 2 Ph.D., 4 Master's, and 10 Bachelor's students. He has contributed to popular science through Danish Cosmic Rays at Schools and public lectures. Labs and teams : Actively involved in the ATLAS Collaboration and the Danish NORDUGRID team for distributed computing infrastructure.

• Gravitational Physics
• Cosmology
• Astrophysics
• +۵ مورد دیگر

Jose Maria Ezquiaga is an Associate Professor at the Theoretical high energy, astroparticle and gravitational physics section of the Niels Bohr Institute (NBI), University of Copenhagen's Faculty of Science. He joined NBI as an Assistant Professor in 2022 and was subsequently promoted to Associate Professor. Prior to his position at Copenhagen, he held a prestigious NASA Einstein Fellowship at the University of Chicago. His work focuses on exploiting gravitational wave data to explore fundamental physics questions at the intersection of cosmology and astrophysics. PhD from Universidad Autonoma de Madrid (2019) NASA Einstein Fellow at University of Chicago Associate Professor at Niels Bohr Institute (2022-present) Dr. Ezquiaga's research centers on probing gravity and unveiling the nature of dark energy and dark matter through gravitational wave observations. His work spans gravitational lensing of gravitational waves, black hole formation mechanisms, primordial black holes as potential dark matter candidates, and cosmological applications of gravitational wave data. He actively investigates how gravitational wave observations can test general relativity and alternative theories of gravity in extreme regimes. His recent publications (2023-2025) demonstrate a strong focus on gravitational lensing effects in gravitational wave astronomy, with particular attention to waveform distortions, identification techniques, and cosmological applications. His work on spectral sirens and cosmology from gravitational wave data represents an innovative approach to measuring cosmic expansion without traditional astrophysical assumptions. NASA Einstein Fellow Member of LIGO Scientific Collaboration Member of LISA Consortium Member of Cosmic Explorer Collaboration As a member of multiple major international collaborations including LIGO, LISA, and Cosmic Explorer, Dr. Ezquiaga contributes to both theoretical development and observational analysis in gravitational wave astronomy. His research group focuses on developing novel techniques for extracting cosmological information from gravitational wave signals and testing fundamental physics through multi-messenger observations. He has been involved in numerous significant discoveries related to gravitational wave lensing and its implications for cosmology and fundamental physics.

• Control Systems
• Signal Processing
• Automation Engineering
• +۱ مورد دیگر

Professor Minyue Fu is an Honorary Professor in the School of Engineering at the University of Newcastle, Australia, specializing in Electrical and Computer Engineering. With over 30 years of research experience, he has established himself as a leading expert in control systems and signal processing, having published over 500 research papers with an H-index of 55. His academic journey began with a Bachelor's degree from the University of Science and Technology of China, followed by M.S. and Ph.D. degrees from the University of Wisconsin-Madison. Prof. Fu's research interests span a broad range of topics in control theory and signal processing. His work consistently focuses on fundamental theoretical problems with practical applications in diverse fields including power systems, sensor networks, multi-agent systems, and cyber-physical systems. He has made significant contributions to distributed control algorithms, stochastic systems, quantization effects in control, and networked systems. His recent publications (2021-2024) demonstrate continued productivity and relevance in the field, with research spanning decentralized optimal control, anomaly detection in cyber-physical systems, cart-pole control systems, and mean-field games. These works reflect his ability to bridge theoretical control concepts with practical engineering challenges, particularly in the context of modern networked and distributed systems. Fellow of IEEE (2004) Fellow of IFAC (2022) Fellow of Engineers Australia Fellow of Chinese Association of Automation (2018) Throughout his career, Prof. Fu has held significant editorial positions including Editor of IEEE Transactions on Signal Processing (2010-2014) and Associate Editor for several prestigious journals. His research has been supported by numerous grants, though specific details aren't provided in the current text. His laboratory work has focused on practical implementations of control algorithms in various systems, demonstrating the real-world applicability of his theoretical contributions. Prof. Fu has also supervised numerous students throughout his career, though specific names aren't listed in the available information.

Weiwei Huمشاهده پروفایل

استاد

• Applied Mathematics
• Partial Differential Equations
• Mathematical Control Theory
• +۳ مورد دیگر

Weiwei Hu is a Professor in the Department of Mathematics at the University of Georgia, specializing in applied mathematics with a focus on control theory and fluid dynamics. Her research bridges theoretical analysis and computational methods to address complex problems in partial differential equations and optimal control systems. She received her Ph.D. in Mathematics from Virginia Tech, establishing the foundation for her expertise in mathematical modeling and analysis. Her educational background directly informs her innovative approaches to fluid dynamics and control problems. Professor Hu's research spans approximation and mathematical control theory of partial differential equations, optimal control of transport and mixing via fluid flows, well-posedness and long-time behavior of mathematical fluid dynamics, data-driven optimal control for network dynamics, computational methods for model reduction, and reliability analysis of renewable systems. She develops advanced theoretical frameworks and numerical algorithms to solve boundary control problems in Stokes and Navier-Stokes flows, with applications in engineering and environmental systems. Her publication record from 2018-2023 reveals consistent advancement in fluid flow control, particularly in optimal mixing and transport phenomena. She has pioneered numerical methods for boundary control of fluid systems while expanding into data-driven approaches for network dynamics and renewable energy applications. Her collaborative work with institutions including Kansas State University, Missouri S&T, and Carnegie Mellon University demonstrates the interdisciplinary impact of her research. Her scientific contributions have been recognized with the prestigious Humboldt Research Fellowship for Experienced Researchers in 2024. Professor Hu has secured over $1.5 million in research funding as principal investigator from NSF, AFOSR, and DARPA, including a 2023-2026 AFOSR grant on hybrid control of semi-dissipative systems and multiple NSF collaborative projects addressing deep-learning-enabled optimization for power systems and computational methods for optimal transport. Her grant portfolio reflects leadership in securing competitive funding for high-impact mathematical research. She actively collaborates with researchers across the United States and Europe on interdisciplinary projects integrating PDE modeling, machine learning, and topology analysis for applications in MRI analysis and renewable energy systems, demonstrating strong leadership in collaborative mathematical research.

Tomislav Plesaمشاهده پروفایل

مدرس

• Synthetic Biology
• Mathematical Biology
• Biochemical Systems
• +۴ مورد دیگر

Dr. Tomislav Plesa is a Lecturer and Fellow at Peterhouse, University of Cambridge. His research bridges mathematics and biology, focusing on synthetic biology and the construction of deterministic/stochastic dynamical systems for biological substrates. He completed his PhD at the University of Oxford (2018) and postdoctoral work at Imperial College London (2021). Education: Doctoral Research, Mathematical Institute, University of Oxford (2018) Postdoctoral Research, Department of Bioengineering, Imperial College London (2021) Research Interests: Dr. Plesa investigates design principles of living systems and challenges in biochemical system design. His work includes integral feedback mechanisms, stochastic approximations, noise utilization in molecular computing, and chemical reaction network dynamics. He explores both theoretical and applied aspects of synthetic biology, with a focus on deterministic/stochastic modeling and mathematical approximations. Publication Trends: Recent works (2023–2024) emphasize chemical reaction networks, integral feedback, and stochastic control. Earlier publications (2016–2021) address noise dynamics, bistability, and homoclinic bifurcations. His research spans nonlinear dynamics, biomathematics, and computational biology, often integrating synthetic biology with theoretical frameworks.