جستجوی استاد راهنمای دانشگاهی

Mecit Cetinمشاهده پروفایل

استاد

• Intelligent Transportation Systems
• Traffic Flow Theory
• Connected and Automated Vehicles
• +۴ مورد دیگر

Dr. Mecit Cetin is a Professor at Old Dominion University (ODU) and Director of the Transportation Research Institute (TRI). He specializes in Intelligent Transportation Systems (ITS), connected/automated vehicles, traffic flow theory, and infrastructure resilience. His academic background includes a Ph.D. in Transportation Engineering from Rensselaer Polytechnic Institute (2002), an M.S. in Civil Engineering (1999), and a B.S. from Boğaziçi University (1995). Research focuses on big data analytics for traffic prediction, congestion pricing, and floodwater detection using LIDAR and computer vision. He has secured over $3M in grants including Federal funding for SMARTc systems and state projects on truck turnaround reduction. Notable awards include the 2011 Best Paper Award at the World Congress on Intelligent Transport Systems and multiple Shining Star Awards from ODU. Key projects include developing auction-based tolling systems for connected vehicles, enhancing traffic signal control with probe data, and modeling secondary incident impacts. His work bridges transportation engineering with emerging technologies like IoT and AI. Dr. Cetin collaborates with state agencies (VDOT) and institutions like EVMS. His lab develops tools for urban flood monitoring, eco-routing algorithms, and safety service patrol optimization using discrete-event simulation.

• Control Engineering
• Automation
• Model Predictive Control
• +۵ مورد دیگر

Martin Steinberger is an Associate Professor at the Institute of Control and Automation (IRT) at Graz University of Technology (TU Graz). His work focuses on advanced control systems, networked control, model predictive control (MPC), and automation in manufacturing and autonomous systems. He leads research into real-time optimization, fault diagnosis, and safety-critical applications in industries like pharmaceuticals and automotive. Research interests include: Networked Control Systems Model-Based Control Autonomous Vehicle Trajectory Planning Process Automation Robotics and Industrial Automation His work bridges theoretical control engineering with practical applications in manufacturing lines, chemical processes, and autonomous driving. Recent studies emphasize digital real-time release testing for pharmaceuticals, universal control concepts for manufacturing systems, and safety-aware trajectory optimization for automated vehicles. His publications frequently address challenges in time-varying delays, packet loss mitigation, and robust observer design for nonlinear systems. Steinberger collaborates on EU-funded projects and regularly contributes to conferences like the International Workshop on Variable Structure Systems. His research often involves experimental validation, as seen in work with compact pharmaceutical manufacturing setups and small-scale autonomous vehicle testing platforms.

• Circadian Rhythm
• Dynamics
• Neuroscience
• +۳ مورد دیگر

Casey Diekman is a Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on mathematical and computational modeling of circadian rhythms, neural dynamics, and physiological systems. He has led NSF-funded projects, including studies on circadian clock mechanisms, neuronal data assimilation, and hybrid modeling of Alzheimer’s disease. His work integrates biophysical models with machine learning to study systems like cardiac electrophysiology and respiratory control. Roles: Professor, PI of multiple NSF grants Key Collaborations: Projects with researchers in neuroscience, cardiology, and computational biology Education & Background: While specific educational details are not provided, his academic position implies advanced training in applied mathematics or systems biology. Research Interests: Circadian rhythms, entrainment dynamics, computational modeling of biological systems, and applications in health (e.g., arrhythmias, neurodegenerative diseases). His work bridges mathematical theory and experimental data to address clinical problems like drug timing and respiratory failure. Grant Highlights: NSF GOALI (2022–2026): Integrates deep learning and mechanistic models for circadian clock neurons and Alzheimer’s disease NSF Career (2016–2021): Developed data assimilation tools for circadian rhythm studies NSF (2014–2017): Modeled circadian clock mechanisms from synapse to gene Recent Research Trends: Publications since 2022 focus on circadian impacts on drug safety, respiratory control during SARS-CoV-2 infection, and Alzheimer’s disease modeling using deep learning. His work often emphasizes interdisciplinary approaches to understand biological timing and its disruptions. Awards & Recognition: While specific prizes are not listed, his extensive grant portfolio and citation count (792) reflect scholarly impact. Advising & Grants: Oversees research teams in circadian biology and computational neuroscience. Media coverage highlights breakthroughs like explaining ‘happy hypoxia’ in COVID-19 patients and antiarrhythmic drug timing risks. Labs/Teams: Engaged in collaborative projects with institutions like NIH and other universities, focusing on systems-level biological modeling.

• Nonparametric function and density estimation
• Shape-restricted inference
• Constrained regression models
• +۲ مورد دیگر

Mary C. Meyer is a Professor in the Department of Statistics at Colorado State University. Her research focuses on nonparametric function and density estimation, particularly under shape restrictions, with applications across ecological modeling, survey methodology, and statistical computing. Education: Ph.D. in Statistics from the University of Michigan (1996) Research Interests: Nonparametric function and density estimation Shape-restricted inference Constrained regression models Bayesian nonparametric methods Penalized isotonic regression Statistical software development Recent Publications show a focus on constrained regression splines, domain mean estimation, and applications in forest dynamics and survey methodology. She has developed several R packages including cgam and tools for cone projections. Grants: Funded by NSF grant DMS-0905656 for research on constrained optimization algorithms. Email Contacts: meyer@stat.colostate.edu and mmeyer@stat.uga.edu

Yue Guoمشاهده پروفایل

استاد

• Energy Storage Systems
• Automotive Engineering
• Lithium-Ion Batteries
• +۴ مورد دیگر

Yue Guo is a Professor of Battery Systems at Coventry University, affiliated with the Institute of Future Transport and Cities and the Centre for E-Mobility and Clean Growth. He holds a BEng in Electrical Engineering and Automation from Harbin Institute of Technology (China), an MSc in Management of Information Technology from the University of Nottingham, a PhD in Model-Based Design for Automotive Electronic Systems from the University of Warwick, and an MBA in Global Energy Industry from Warwick Business School. His research focuses on energy storage systems, battery thermal management, and automotive electronics. He has collaborated extensively with the automotive industry on projects involving system-of-systems modeling, Li-ion battery testing, and low-carbon transport technologies. Notable roles include former Project Manager and Deputy Head of the Energy Innovation Centre at WMG, University of Warwick, and current Chair of Battery Systems at C-ALPS. Professional memberships include Chartered Engineer, IEEE Senior Member, and SAE International Battery Thermal Management Committee. Key research trends in his publications include predictive battery diagnostics, thermal runaway prevention, and LCA analysis of battery production. His work emphasizes real-time monitoring technologies and sustainable energy storage solutions. Projects address challenges in battery aging, environmental impacts, and integration into low-carbon vehicle systems. Awards and recognitions include his academic appointments and industry collaborations, though no specific scientific awards are listed. He has advised on numerous research initiatives and contributed to advancing battery technologies through innovative thermal management systems and model-based testing frameworks. Labs and teams: Active in the Centre for E-Mobility and Clean Growth, leading interdisciplinary projects at Coventry University's Institute of Future Transport and Cities. Collaborations span academic and industrial partners globally, focusing on next-generation energy storage solutions.

Prasad Tadepalliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

Tesfaye Kidaneمشاهده پروفایل

استاد

• Structural Geology
• Tectonics
• Paleomagnetism
• +۶ مورد دیگر

Dr. Tesfaye Kidane is a tenured Professor in the Department of Environmental Science and Geology at Wayne State University (WSU), College of Liberal Arts and Sciences. Previously, he held academic leadership roles at Addis Ababa University (AAU) and the University of KwaZulu Natal, South Africa. His career includes postdoctoral research fellowships in Hawaii, Japan, Germany, and the United States, along with visiting professorships in Japan. He serves on the American Geophysical Union (AGU) publication committee and as an associate editor for Tectonics since 2017. **Education**: Ph.D. in Internal Geophysics, Institut de Physique du Globe de Paris, France (1999) M.Sc. in Structural Geology, Addis Ababa University (1993) B.Sc. in Geology, Addis Ababa University (1989) **Research Interests**: Dr. Kidane’s work focuses on structural geology, tectonics, and paleomagnetism. He uses paleomagnetic and rock-magnetic techniques to study continental rifting (e.g., the Afar Depression and East African Rift), Pangea/Gondwana reconstructions, and sedimentary processes in extreme environments. His recent projects include the Afar Dallol Drilling (ADD-ON) initiative to explore rift basin dynamics and hydrothermal systems. **Grants & Awards**: Notable awards include the African Fulbright Scholarship (2012–2013), Alexander von Humboldt Fellowship (2012–2013), and JSPS Fellowship (2005–2008). He has secured over $1.4 million USD in grants for equipment and field research, including projects on geothermal exploration and continental rifting mechanisms. **Teaching & Mentoring**: Current courses include Structural Geology , Sedimentology and Stratigraphy , and Geomorphology . While no named advisees are listed, his research collaborations and grant leadership imply extensive mentorship of students and junior researchers. **Labs & Collaborations**: Involved in international initiatives such as the AGU committees and the ADD-ON drilling project. His work integrates field studies, laboratory analyses, and numerical modeling to address tectonic and environmental questions in Africa’s major rift systems.

• Structural identifiability
• Observability analysis
• Nonlinear systems
• +۳ مورد دیگر

Alejandro F. Villaverde is a Ramón y Cajal research fellow at the Department of Systems & Control Engineering, Universidade de Vigo. His research focuses on structural properties of nonlinear systems, particularly structural identifiability and observability analysis. His primary research interests include: Structural identifiability of dynamic models Observability analysis of nonlinear systems Control theory and systems engineering Development of computational tools for model analysis Applications in systems biology and pharmacokinetics Villaverde's work centers on developing mathematical methods and software tools to analyze structural properties of nonlinear models. His most notable contributions include the STRIKE-GOLDD MATLAB toolbox and its Python counterpart StrikePy, which implement the FISPO algorithm for simultaneous assessment of state observability and parameter identifiability. His research has practical applications in systems biology, biochemical network analysis, and pharmacokinetic modeling where model validation and parameter estimation are critical. Among his scientific recognitions, he holds the prestigious Ramón y Cajal research fellowship, a competitive program in Spain supporting experienced researchers. Villaverde actively supervises students, with David Rey Rostro having developed the StrikePy toolbox under his supervision. His work demonstrates strong connections between theoretical control engineering and practical computational implementations, making structural analysis methods more accessible to researchers across various disciplines.

• Digital Signal Processing
• Parallel Computing Architectures
• Nonlinear Dynamics
• +۳ مورد دیگر

Prof. Victor Grigoras is a faculty member at the Technical University of Iași, holding the rank of Professor. He specializes in electrical engineering and computer science, focusing on signal processing, parallel architectures, and nonlinear dynamics in power systems. His research interests include smart grid technologies, renewable energy integration, and data-driven methodologies for grid optimization. He teaches courses such as 'Semnale, Circuite și Sisteme' and 'Algoritmi și Structuri Paralele de Calcul.' His research spans over 15 recent articles (2021–2025), emphasizing advancements in smart grid automation, machine learning applications in voltage quality analysis, and optimal power flow solutions for renewable integration. Notable trends include SCADA system improvements, energy storage strategies for prosumer grids, and IoT-based energy management. His work addresses challenges in grid reliability, power quality, and future urban grid resilience under high EV adoption scenarios. Prof. Grigoras has contributed to frameworks for electric vehicle charging station placement, hydropower plant optimization via data mining, and demand response mechanisms using smart metering. His methodologies often combine clustering techniques with fuzzy logic or metaheuristic algorithms to solve complex grid problems.

Sigurd Skogestadمشاهده پروفایل

استاد

• Process Control
• Systems Engineering
• Optimization
• +۲ مورد دیگر

Sigurd Skogestad is Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). He serves as Program Leader for the 5-year Master program in Chemical and Biochemical Engineering. His research develops simple yet rigorous methods to solve engineering problems, focusing on self-optimizing control structures that enable near-optimal operation under varying conditions. His approach moves economic optimization from slow real-time optimization to faster control layers. Key research areas: Self-optimizing control structures Process systems engineering Multivariable feedback control Plantwide process control Distillation modeling and control He has received significant recognition including the AIChE Computing in Chemical Engineering Award and IFAC Fellow status. He leads research groups in Process Control and contributes to the SUBPRO center on subsea production.

Anuj Sharmaمشاهده پروفایل

استاد

• Big Data Analytics in Transportation
• Data-Driven Decision Making
• Traffic Signal Systems
• +۳ مورد دیگر

Dr. Anuj Sharma is a Professor in the Department of Civil, Construction, and Environmental Engineering at Iowa State University, serving as Co-Director of the REACTOR Lab and Director of FAST-TrAC. He holds affiliations with InTrans, CTRE, and REACTOR, and is CEO of ETALYC Inc. His research focuses on big data analytics, traffic signal systems, and real-time transportation decision-making. Education: PhD in Civil Engineering, Purdue University, 2008 MS in Civil Engineering, Texas A&M University, 2004 BE in Civil Engineering, Regional Engineering College, Rourkela, India, 2001 Research Interests: Dr. Sharma’s work leverages big data to improve transportation systems, including real-time analytics, traffic signal optimization, and smart rural infrastructure. His lab, REACTOR, integrates multiple data streams for policy and operational decisions. Publications: His recent work spans driver behavior analysis, autonomous systems, and traffic safety, with a focus on data-driven solutions. Notable projects include congestion modeling and pandemic-era traffic adaptations. Awards: Social Analytics Strategic Network (SASNet) Fellow, 2016 Outstanding Reviewer (Journal of Transportation Engineering), 2014 Pikarsky Award for PhD Dissertation, 2008 Grants & Projects: Secured over $13M in funding for 32+ projects, including work on adaptive traffic control, AI-driven data analysis, and rural safety. Active roles in TRB committees and journal reviewing highlight his leadership in the field. Labs & Teams: Leadership at REACTOR Lab drives innovations in transportation data management and real-time systems, collaborating with agencies like Iowa DOT.

• Fractional Calculus
• Anomalous Diffusion
• Probability Theory
• +۵ مورد دیگر

Mark Meerschaert is a University Distinguished Professor in the Department of Statistics and Probability at Michigan State University. His career spans academia and industry, with teaching appointments at institutions including the University of Michigan, Michigan State University, and the University of Otago. He holds a doctorate in Mathematics from the University of Michigan (1984). His research focuses on fractional calculus, anomalous diffusion, probability models, and applications to environmental science and engineering. Key research areas include stochastic models for fractional calculus, continuous time random walks, and parameter estimation for heavy-tailed distributions. His work extends to medical imaging, fractional PDEs, and groundwater flow modeling. Collaborations involve interdisciplinary teams in computational mathematics, mechanical engineering, and climate science. Recent publications emphasize spectral methods for fractional PDEs, stochastic phase-field modeling, and time series analysis for climate data. His contributions bridge theoretical developments and practical applications in fields like turbulence, contaminant transport, and material science. Professional recognitions include his distinguished professor title at MSU. He advises across multiple disciplines and collaborates internationally on projects funded by grants from agencies like NSF and DOE.

Karoline Disserمشاهده پروفایل

استاد

• Applied Analysis
• Theory of PDEs
• Mathematical Fluid Dynamics
• +۶ مورد دیگر

Prof. Dr. Karoline Disser is a Professor of Analysis at the Institute of Mathematics, Universität Kassel, within Faculty 10: Mathematics and Natural Sciences. Her research focuses on applied analysis and partial differential equations, particularly in fluid dynamics, fluid-structure interaction, and complex flow systems. She holds a PhD from TU Darmstadt (2009) and habilitation from Humboldt-Universität zu Berlin (2017). Her teaching includes advanced courses in calculus of variations, function spaces, and mathematical fluid dynamics, spanning institutions like TU Darmstadt, HHU Düsseldorf, and TU Berlin. Research interests encompass reaction-diffusion systems, elliptic/parabolic regularity, and variational methods for evolution equations. Recent publications (2016–2022) address fluid-structure interaction, semiconductor equations, and multiscale chemical reaction modeling. Her work emphasizes rigorous mathematical analysis with applications in engineering and geophysical systems. No awards are listed, but her contributions reflect interdisciplinary engagement in applied mathematics and continuum mechanics.

Sean Humbertمشاهده پروفایل

استاد

• Flight Dynamics and Control
• Bio-inspired Perception and Estimation
• Autonomous Robotics
• +۵ مورد دیگر

J. Sean Humbert is a Professor at the University of Colorado Boulder, holding a courtesy appointment in the College of Engineering and Applied Science (AES). He serves as Director of the Robotics Program and Faculty Director for the Aerospace and Defense Western Colorado University Partnership Program. His research focuses on bio-inspired robotics, autonomous systems, and advanced control methodologies. Key research areas include flight dynamics, bio-inspired perception, micro-robotics, and soft robotics. He leads the Robotics and Systems Design group, affiliated with the Hypersonic Vehicles IRT. His work integrates bio-mimetic principles with engineering challenges, emphasizing robust control in unstructured environments. Recent publications span topics like soft robotic actuators, distributed sensing, and neural dynamics in robotics. His lab develops cutting-edge technologies for subterranean exploration, UAV navigation, and bio-inspired sensor systems. Collaborations include industry partnerships and interdisciplinary projects at the intersection of robotics, biology, and control theory. Awards and recognitions are not explicitly mentioned in the provided texts. Sean Humbert’s lab is located at ECES 1B14, with an office in ECES 146. His academic contributions bridge theory and application, addressing real-world challenges in autonomous systems and robotics innovation.

• Biomedical Signal Processing
• Computational Neuroscience
• Brain-Computer Interfaces (BCI)
• +۷ مورد دیگر

David B. Grayden is a Professor at The University of Melbourne, affiliated with the Melbourne School of Engineering and the Department of Electrical and Electronic Engineering . His work spans Biomedical Signal Processing , Computational Neuroscience , and Brain-Computer Interfaces (BCI) , focusing on applications in Epilepsy Research and Cochlear Implants . Melbourne Neural Engineering Laboratory member Collaborator in multidisciplinary biomedical research Key research interests include: Developing Seizure Prediction Algorithms using long-term EEG/iEEG data Neural mass modeling for Epilepsy and Inhibitory Network Behavior Optimizing Cochlear Implants via computational models Advancing Endovascular BCI Systems and Neural Stimulation Recent publications highlight trends in Machine Learning , Path Signatures , and Multi-Frequency Stimulation for SSVEP-based BCIs . His work integrates Computational Modeling with Biomedical Engineering to address clinical challenges in neuroprosthetics and sensory processing. Grayden leads projects on Neural Network Dynamics , Biomedical Signal Analysis , and Neurostimulation , often collaborating with institutions like Monash University and Royal Melbourne Hospital .