جست‌وجوی استاد و پژوهشگر

James A. Ritterمشاهده پروفایل

استاد

• Gas Separation
• Adsorption Processes
• Hydrogen Storage
• +۳ مورد دیگر

James A. Ritter is a Carolina Distinguished Professor and the L. M. Weisiger Professor of Engineering in the Department of Chemical Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, where he also holds the Gibbons Teaching Professorship. With over 171 peer-reviewed articles and 8 U.S. patents, his work centers on advancing cyclic adsorption technologies for industrial applications. Education: Ph.D., State University of New York at Buffalo, 1989 M.S., State University of New York at Buffalo, 1985 B.S., State University of New York at Buffalo, 1983 Research Focus: Professor Ritter's pioneering work spans gas separation, hydrogen storage, and carbon capture through pressure swing adsorption (PSA) and temperature swing adsorption (TSA). His research integrates fundamental adsorption science with practical process development, emphasizing structured adsorbents, novel modeling techniques, and experimental characterization of mass transfer dynamics. This work addresses critical energy and environmental challenges including CO 2 capture and naval fuel production. Publication Trends: Recent publications (2023-2025) demonstrate consistent innovation in cyclic adsorption processes, with emphasis on structured adsorbent design, advanced modeling (particularly kinetically limited linear driving force approaches), and experimental techniques for dynamic condition analysis. Key application areas include carbon capture at national facilities, naval ammonia production, and fundamental gas separation mechanisms using carbon molecular sieves. Scientific Recognition: AIChE Institute Award for Excellence in Industrial Gases Technology (2016) Fellow of the American Chemical Society (2012) Fellow of the American Institute of Chemical Engineers (2013) Fellow of the International Adsorption Society (2023) Professional Impact: Professor Ritter has consulted for over 40 organizations including NASA MSFC, ExxonMobil, Shell, and DOE, with current research funded by ONR and DOE. As Editor-in-Chief of Adsorption (since July 2024) and former Associate Editor (2021-2024), he shapes the field's scholarly discourse while leading collaborative projects with national laboratories and industry partners. Research Infrastructure: His work leverages university facilities at USC and partnerships with the National Carbon Capture Center, focusing on bench-scale to pilot-scale validation of adsorption processes for energy and environmental applications.

Mikael Eklundمشاهده پروفایل

استاد

• Autonomous Systems
• Nonlinear System Identification and Control
• Health Informatics
• +۳ مورد دیگر

Dr. Mikael Eklund is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, within the Faculty of Engineering and Applied Science. He holds a PhD (2003), MSc (1996), and BSc (1989) in Electrical and Computer Engineering from Queen’s University. His research focuses on Autonomous Systems , Nonlinear System Identification and Control , Health Informatics , and Pervasive and Mobile Computing . He has expertise in medical image processing, robotic vehicles, and smart sensors for assisted living. Education and Work Experience : - PhD (Electrical and Computer Engineering), Queen’s University (2003) - MSc and BSc from Queen’s University (1996, 1989) - Visiting Postdoctoral Scholar at UC Berkeley (2003–2006) - Adjunct Assistant Professor at Queen’s University (2003) - Former Teaching Fellow at Queen’s University (1996–2001) - Industrial experience in flight control system engineering (CAE Inc., 1989–1996) Research Contributions : - Developed SensorNet, a wireless infrastructure for assisted living. - Advanced methods for nonlinear system identification in chemical processes and aerospace systems. - Pioneered real-time UAV safety protocols and model predictive control algorithms for aerial pursuit-evasion games. - Explored EMG signal processing for hand force estimation and MEG signal source localization in neuroscience. Teaching and Outreach : - Teaches courses like Medical Image Processing and Electric Circuits. - Active in interdisciplinary projects like the ITALH initiative for elder tech integration. - Presented at conferences such as IEEE EMBC, ACC, and CDC, emphasizing healthcare IT and autonomous systems.

James Tuckمشاهده پروفایل

استاد

• Computer Architecture
• Compiler Design
• Chip Multiprocessors
• +۳ مورد دیگر

James Tuck is a Professor and Senior Associate Department Head for Undergraduate Affairs in the Department of Electrical and Computer Engineering at NC State University. He holds a BE from Vanderbilt University, and MS and PhD from the University of Illinois at Urbana-Champaign. His research focuses on computer architecture, compiler design, and DNA-based data storage, with notable contributions to chip multiprocessors and speculative execution. He has been recognized with two IEEE Micro Top Picks Paper Awards and the William F. Lane Outstanding Teaching Award. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2007) MS in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (2003) BE in Computer Engineering, Vanderbilt University (1999) Research Interests: Computer Architecture and Systems Compiler Design for Multiprocessors Hardware Support for Speculative Execution Advances in DNA Data Storage Non-Volatile Memory Systems Recent work emphasizes DNA storage scalability and security, including frameworks like FrameD and innovations in nanopore decoding. His articles span hardware optimization, persistent memory security, and biochemical storage solutions. Awards highlight both technical and pedagogical excellence. Advising and Grants: Leadership in Undergraduate Engineering Affairs NSF grants for DNA storage and memory systems Labs/Teams: Member of Undergraduate Affairs Team in NC State ECE Collaborations with Chemical and Biomolecular Engineering

Milica Orlandicمشاهده پروفایل

دانشیار

• Hyperspectral Imaging
• Remote Sensing
• Digital Design
• +۷ مورد دیگر

Milica Orlandic is an Associate Professor in the Department of Electronic Systems at NTNU. She holds an MSc from the University of Montenegro (2009) and a PhD from NTNU (2015). Her research focuses on hyperspectral imaging, remote sensing, FPGA-based systems, and embedded computing for aerospace applications. She is actively involved in the HYPSO CubeSat mission, developing onboard processing systems for Earth observation. Education: MSc in Electrical Engineering, University of Montenegro (2009) PhD in Electronics, NTNU (2015) Research Interests: Her work spans hyperspectral data processing , including compression, anomaly detection, and onboard computing for satellites. She also explores reconfigurable hardware (FPGAs) for real-time signal processing, cyber-physical systems, and spaceborne sensor systems. Publications Trends: Recent work emphasizes lightweight machine learning for anomaly detection, FPGA acceleration of hyperspectral compression (CCSDS 123), and algorithm co-design for CubeSat missions. Key contributions include robust onboard processing frameworks for HYPSO-1 and adaptive hardware-software systems. Advising & Teams: She supervises a dynamic team of over 40 PhD and MSc students working on FPGA implementations, satellite systems, and hyperspectral algorithms. Notable collaborations include the HYPSO CubeSat project, which aims to deliver high-resolution Earth observation data with low latency. Labs & Infrastructure: Her research leverages NTNU’s facilities for embedded systems prototyping, FPGA development, and CubeSat payload testing. The HYPSO mission integrates her team’s hardware-software co-design innovations for space applications.

• Computational Chemistry
• Molecular Simulation
• Path Sampling Methods
• +۳ مورد دیگر

Professor Titus Sebastiaan van Erp is affiliated with the Department of Chemistry at the Norwegian University of Science and Technology (NTNU), where he has worked since 2016. His research focuses on advancing molecular simulation techniques to study complex biological and industrial processes without approximations, particularly through path sampling methods for rare events. 2016 – Present: Professor, NTNU 2012 – 2016: Associate Professor, NTNU 2006: Centre-of-Excellence Fellow, Leuven 2004: Marie Curie Fellow His research develops innovative methodologies like RETIS and REPPTIS to enhance simulation accuracy and expand accessible time/system scales. He has supervised students in DNA denaturation, electron transfer reactions, and protein folding studies. Recent publications analyze NaCl dissociation, ABL-imatinib kinetics, and permeation mechanisms. His work involves Python-based PyRETIS software development and collaborations across computational chemistry, biophysics, and materials science. 2025: NaCl Dissociation via Predictive Power Path Sampling 2025: RETIS/REPPTIS for Biomolecular Kinetics 2024: PyRETIS 3 for Boundary-Free Rare Events Scientific recognitions include: Centre-of-Excellence Fellowship (2006) Marie Curie Fellowship (2004) He has advised multiple students in masters theses on molecular simulation, including projects on DNA unwinding, electron transfer, and protein folding. His lab integrates algorithm development with applications in chemical reactions, biomolecular systems, and nanoscale materials.

Steven Collinsمشاهده پروفایل

عضو هیئت علمی

• Timber Engineering
• Structural Reliability
• Sustainability
• +۷ مورد دیگر

Steven Collins is a Professor of Practice in Civil Engineering at Aalto University , specializing in industrial wood construction. His work bridges scientific research and industry applications to enhance timber's role in sustainable structural engineering. Research Interests : Species-specific timber behavior (particularly birch), focusing on modeling, testing, and variance analysis. Non-destructive testing techniques like laser scattering, grain orientation analysis, and digital image correlation. Statistical and Bayesian approaches for probabilistic modeling of material properties and uncertainty quantification. Timber finger joint mechanics and wood processing techniques (e.g., densification, chemical treatment). Scientific Contributions : His recent publications (2022–2025) emphasize mechanical behavior of timber , predictive modeling , and innovative processing methods . Key trends include optimizing birch timber for structural applications, advancing Bayesian statistical models for wood mechanics, and improving non-destructive testing protocols using laser technologies and digital image correlation. Laboratory & Collaborations : Actively involved in experimental testing (destructive and non-destructive) and statistical analysis of wood properties. Collaborates with researchers like Gerhard Fink , Lauri Rautkari , and Farid Vafadar on timber mechanics and reliability studies. Part of the Structures – Structural Engineering, Mechanics and Computation research group at Aalto University.

• Fluid Dynamics
• Hydrodynamic Instability
• Turbulence
• +۵ مورد دیگر

Devesh Ranjan is the Grainger Dean of the College of Engineering at the University of Wisconsin-Madison, appointed in June 2025. He oversees one of the nation's top engineering institutions with 8 departments, 220 faculty, and $120M+ annual research expenditures. Previously, he was Professor and School Chair at Georgia Tech (2014-2025) and held faculty roles at Texas A&M University (2009-2014). His research centers on fluid dynamics, turbulent mixing, and energy systems in extreme environments. Education: Ph.D. in Mechanical Engineering, University of Wisconsin-Madison (2007) M.S. in Mechanical Engineering, University of Wisconsin-Madison (2005) B.E. in Mechanical Engineering, National Institute of Technology-Trichy, India (2003) Research Focus: Ranjan's interdisciplinary work explores power conversion, supersonic/hypersonic flows, hydrodynamic instabilities, and granular thermal energy transport. His lab employs advanced experimental diagnostics and numerical modeling to study turbulence, mixing phenomena, and renewable energy applications. Publications: Recent articles (2023-2025) emphasize turbulent flow analysis, granular dynamics for solar storage, Rayleigh-Taylor instabilities, and supercritical fluid behavior. Methodologies combine high-fidelity simulations with cutting-edge experimental techniques like multi-tracer PLIF and shock tube studies. Awards & Honors: 2023: ASME Gustus L. Larson Memorial Award, USG Executive Leadership Fellow 2021: Inaugural Ring Family Chair (Georgia Tech) 2020: Diversity & Inclusion Fellow 2019: NAE Global Grand Challenges Summit Invitee 2018: Provost Teaching Fellow, Markstein Paper Award 2016: DOE Early Career Award, NAE Frontiers Symposium 2013: NSF CAREER, AFOSR Young Investigator Fellow: ASME, Governor’s Teaching Fellows Leadership: Mentored award-winning students (e.g., 2016 Best M.S. Thesis advisor) and secured major grants including DOE/NSF awards. Directed interdisciplinary initiatives at Georgia Tech spanning pediatric tech, brain imaging, and advanced manufacturing.

Qianqian Tongمشاهده پروفایل

استادیار

• Stochastic Optimization
• Sparse Learning
• Federated Learning
• +۴ مورد دیگر

Qianqian Tong is an Assistant Professor in the Computer Science department at the University of North Carolina at Greensboro. Her research focuses on stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning algorithms. She has developed novel methods for efficient optimization in deep learning and federated learning frameworks, including communication-efficient distributed algorithms and decentralized systems. Education: Ph.D. Computer Science and Engineering, University of Connecticut M.S. Computational Mathematics, Zhengzhou University B.S. Mathematics, Zhengzhou University Research interests include designing algorithms for sparse learning, federated learning with privacy guarantees, and applying deep graph learning to drug discovery. Recent projects involve tensor-based models for multidimensional data analysis and improving convergence in ADAM optimization. Her publications span optimization theory, federated learning systems, and molecular modeling applications. Though no awards are listed, her work demonstrates significant contributions to efficient machine learning methodologies. Teaching includes advanced courses on data science and computer science foundations. No lab affiliations or grant details are explicitly mentioned in the provided text.

• Fluid Dynamics
• Hydrogen Engines
• Combustion
• +۴ مورد دیگر

Elia Distaso is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research focuses on fluid dynamics, hydrogen combustion, and computational modeling. University: Politecnico di Bari Department: Mechanics, Mathematics & Management Academic Rank: Assistant Professor Email: elia.distaso@poliba.it His work spans hydrogen engines , CFD simulations , and hydraulic systems , with recent publications addressing auto-ignition mechanisms, cavitation phenomena, and sustainable aviation technologies. He specializes in leveraging numerical methods for combustion and fluid flow analysis. His 15 most recent publications highlight trends in computational fluid dynamics, including boundary condition modeling, pressure-velocity coupling, and OpenFOAM® applications. Key subfields include hydrogen combustion dynamics , lubricant oil reactivity , cryogenic heat exchanger design , and piezohydraulic pump analysis .

• Bioengineering
• Microfluidics
• Organ-on-Chip Technology
• +۵ مورد دیگر

Matthias Meier is a Full Professor at the Institute of Biochemistry, University of Leipzig, and Principal Investigator at Helmholtz Pioneer Campus, Helmholtz Zentrum München. His research focuses on advancing microfluidic organ-on-chip technology for single-cell and whole-organ disease modeling. Education: PhD in Biophysics (University of Basel, 2006) Research Interests: Dr. Meier's work bridges bioengineering and metabolic disorders, using organ-on-chip platforms to study stem cell differentiation, pancreatic/adipose tissue interactions, and dynamic microenvironmental signals. His lab integrates microfluidics with hiPSC-derived organoids for obesity and diabetes research. Publication Trends: Recent studies emphasize organ-on-chip systems, single-cell analysis , and stem cell engineering , with applications in cardiovascular disease modeling, spatial transcriptomics, and bioelectronic monitoring. Scientific Awards: Feodor-Lynen Postdoctoral Fellowship (2008) Emmy-Noether Fellowship (2012-2018) ERC Consolidator Grant (2017) Advising & Grants: He has led independent research groups with major grants, focusing on energy imbalance mechanisms and patient-specific organoid models for metabolic disease therapies. Labs & Teams: The Matthias Meier Lab develops microfluidic platforms to control chemical, architectural, and mechanical cues for hiPSC differentiation, emphasizing spatial protein profiling and organoid assembly.

• Control Engineering
• Process Control
• Optimal Control
• +۱۱ مورد دیگر

Torsten Wik is a Professor in Control Engineering at Chalmers University of Technology. He leads the Control Engineering research group and focuses on process control with theoretical and applied methodologies. Institution: Chalmers University of Technology Department: Control Engineering His research spans optimal control, model reduction, and systems with model uncertainties. Applications include energy-saving systems, environmental improvement, biological systems (water purification, recirculating fish farms, LED greenhouse lighting), and battery estimation/modeling/control. Recent work emphasizes battery degradation diagnosis, state estimation, fast charging, and reconfigurable systems. Key methodologies include physics-informed frameworks, machine learning integration, entropy-based predictive algorithms, and hypergraph modeling. Applications extend to electric vehicles, photovoltaic systems, fuel cells, and biofilm reactors. Publications highlight collaborations across engineering domains, focusing on control theory, electrochemical modeling, and real-time optimization. His work bridges theoretical advancements with industrial applications in energy systems, transportation, and sustainable agriculture.

Luca Schenatoمشاهده پروفایل

استاد

• Control Systems
• Distributed Optimization
• Federated Learning
• +۵ مورد دیگر

Luca Schenato is a Full Professor in the Department of Information Engineering at the University of Padova. His research focuses on distributed control systems, federated learning, multi-agent optimization, and wireless communication protocols. He has extensive experience in developing algorithms for cyber-physical systems, with applications in robotics, smart grids, and sensor networks. Education and Appointments section lists his academic journey but lacks explicit details. He has held positions related to control systems and information engineering throughout his career. Research interests include: Design of resilient wireless control systems Federated learning architectures for edge computing Distributed optimization under communication constraints Robotics and multi-agent coordination Smart energy management systems His recent publications (2021–2025) demonstrate a strong focus on: Over-the-air federated learning innovations High-speed wireless control systems (e.g., 1 kHz Wi-Fi control) Resilient distributed optimization algorithms Human-centric building automation He has contributed to numerous projects related to networked control systems and has organized conferences like ECC13. His work emphasizes bridging theoretical control principles with practical industrial applications.

• Numerical Analysis
• Uncertainty Quantification
• Inverse Problems
• +۳ مورد دیگر

Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.

• Fluid Mechanics
• Computational Fluid Dynamics
• Multiphase Flows
• +۶ مورد دیگر

Dr Sergii Veremieiev is an Associate Professor in the Department of Engineering at Durham University. His research focuses on fluid mechanics modeling, particularly in free-surface and multiphase flows, capillary phenomena, and computational fluid dynamics. He holds a PhD in Mechanical Engineering from the University of Leeds and has held academic positions at Liverpool John Moores University and Durham University since 2013. Education: Bachelor of Physics, Donetsk National University (2006) Master of Physics, Donetsk National University (2007) PhD in Mechanical Engineering, University of Leeds (2011) Research Interests: Dr Veremieiev’s work integrates computational and experimental approaches to study complex fluid dynamics phenomena, including droplet impact dynamics, rivulet instabilities, and high-performance numerical simulations using finite element and finite volume methods. His research addresses challenges in renewable energy systems, agricultural pesticide applications, and industrial flow optimization. Teaching: He teaches modules in fluid mechanics, nuclear engineering, and computational fluid dynamics (CFD) analysis at both undergraduate and postgraduate levels, including coursework in thermal renewables and design practicum. Advising & Grants: Supervised 4 completed PhD students and currently guides 5 active researchers. Develops numerical tools such as a C++/MPI free-surface solver and MATLAB subroutines for fluid dynamics analysis.

Anika Großمشاهده پروفایل

استاد

• Database Systems
• Knowledge Graphs
• Ontologies
• +۷ مورد دیگر

Prof. Anika Groß is a Professor for Database Systems and Programming at the Department of Computer Science and Languages at Anhalt University of Applied Sciences in Köthen, Germany. She holds a PhD from the University of Leipzig (2014) and has held roles as a PostDoc in strategic research at Daimler AG and as a research assistant at the Interdisciplinary Centre for Bioinformatics (IZBI). She is actively involved in academic governance, serving on the faculty council, board of examiners, and various sustainability and research data management committees. Her research focuses on database systems, knowledge graphs, and ontology engineering, with applications in environmental science, medicine, and material sciences. Education - PhD (Dr. rer. nat.) in Computer Science, University of Leipzig (2014) - Diploma in Bioinformatics, MLU Halle-Wittenberg (2007) - Postdoctoral research at University of Leipzig's Database Group (2008–2013) Research Interests Her work emphasizes temporal graphs, ontology evolution, and semantic data integration. She develops tools like Region Evolution eXplorer (REX) and contributes to projects such as AgriRestore (ecosystem restoration) and MeineWaldKI (AI-based forest monitoring). Her recent focus includes wearable device applications in healthcare and digital twins for lithium value chains. Recent Research Trends Recent publications highlight interdisciplinary applications of knowledge graphs in agriculture and environmental science, leveraging temporal analysis and machine learning for tasks like chemical transformation prediction and bladder monitoring. She also explores cross-lingual semantic annotations in medical forms and holistic clustering of linked data. Professional Roles - Executive Committee Member, GI Database Systems Group - Fellow, Institute for Technologies and Economics of Lithium (ITEL) - Co-Chair of multiple workshops including FGDB-Workshop @ LWDA 2018 and BigDS@BTW 2017 Labs & Collaborations She leads research teams in projects funded by DFG, European Union (EFRE), and ITEL, collaborating with organizations like Daimler AG and ANU. Her lab focuses on advancing database systems and interdisciplinary data science applications.