جست‌وجوی استاد و پژوهشگر

Eleonora Bottaمشاهده پروفایل

استادیار

• Space Debris Removal
• Tethered Systems Dynamics
• Spacecraft Dynamics and Control
• +۳ مورد دیگر

Eleonora Botta is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo (SUNY), affiliated with the School of Engineering and Applied Sciences. Her research focuses on space debris removal, tethered systems dynamics, spacecraft control, and flexible structure dynamics. She leads the UB Space System Dynamics and Control Lab. Education: PhD in Mechanical Engineering from McGill University (2017), MEng in Space Engineering from Politecnico di Milano (2013), and BEng in Aerospace Engineering from Politecnico di Milano (2010). Research Interests: Dynamics and control of space tethers Active debris removal strategies Collision detection and avoidance systems Spacecraft dynamics and control Publications Trends: Recent work emphasizes robust debris capture methods, tether system optimization, and real-time control algorithms for uncooperative debris. Key themes include inertia estimation, net deployment validation, and solar-electric propulsion trajectory design. Awards: 2024 NFS Young Professional of the Year 2023 SEAS Early Career Teacher of the Year 2015 Amelia Earhart Fellowship Grants & Advising: Active in grant-funded research on space debris mitigation. Supervises graduate students in robotics, control systems, and astrodynamics. Labs/Teams: Directs the UB Space System Dynamics and Control Lab, collaborating with industry and NASA on debris removal technologies.

Ersin Dasمشاهده پروفایل

استادیار

• Safety-critical control of autonomous systems
• Robust and adaptive control systems
• Data-driven modeling and system identification
• +۳ مورد دیگر

Ersin Das is an Assistant Professor in the Department of Mechanical, Materials, and Aerospace Engineering at the Illinois Institute of Technology (IIT), affiliated with the Armour College of Engineering. Previously, he was a Postdoctoral Researcher at Caltech's Burdick Group and a member of the Center for Autonomous Systems and Technologies (CAST). His research focuses on safety-critical control of autonomous systems, robust control, and machine learning applications in robotics, with collaborations at NASA's Jet Propulsion Laboratory (JPL) on space lander autonomy for ocean worlds like Europa and Enceladus. Education: Ph.D. (2019, Hacettepe University), M.S. (2014, Istanbul Technical University), and B.S. (2012, Kocaeli University), all in Mechatronics/Mechanical Engineering. Research interests include control barrier functions, data-driven modeling, and applications in robotics across ground, aerial, and space domains. He actively recruits Ph.D. students and collaborates with institutions such as the Tokyo Institute of Technology and the University of Bristol. Publications span journals like IEEE Transactions on Automatic Control and conferences including ICRA, ACC, and CDC. His work emphasizes safety, resilience, and adaptive control in dynamic environments.

Matthew Schneiderمشاهده پروفایل

دانشیار

• Data Privacy
• Statistical Methodology
• Time Series Forecasting
• +۷ مورد دیگر

Matthew Schneider is an Associate Professor in the Department of Decision Sciences and Management Information Systems at Drexel University's LeBow College of Business. He holds a PhD in Statistics from Cornell University and has previously served as an Assistant Professor at Northwestern University and a Visiting Scholar at Cornell. He teaches and conducts research at the intersection of data privacy, forecasting, and business analytics. PhD in Statistics, Cornell University MS in Statistics, Cornell University MS in Public Policy and Management, Carnegie Mellon University BS in Quantitative Economics, United States Naval Academy Dr. Schneider's research centers on data privacy and time series forecasting , with a focus on developing statistical methodologies that protect consumer privacy while preserving data utility for business decisions. His work enables organizations to balance regulatory compliance with data-driven profitability. He has applied his expertise in data anonymization and analytics frameworks for Fortune 500 companies in FinTech, pharma, insurance, and retail. His research has been published in leading journals including Harvard Business Review , Marketing Science , and Journal of the Royal Statistical Society . His recent publications demonstrate a consistent trend in privacy-preserving data analysis , particularly through synthetic data, differential privacy, and k-anonymity. He also explores forecasting using consumer reviews and robust methods for detecting demand shifts. These works span disciplines such as statistics, marketing science, information systems, and operations research. Dr. Schneider is actively engaged in executive education, teaching Data Privacy Strategy through eCornell. He has collaborated with prominent scholars such as Sachin Gupta and John Abowd. His industry experience includes roles at the RAND Corporation and Fort Rock Asset Management LLC. Scientific lead for pre-IPO validation of data science models in FinTech Director of Research at Fort Rock Asset Management LLC Used by major corporations to improve analytics and anonymization frameworks Teaches corporate executives through eCornell’s certificate program He has no listed scientific awards in the provided text. There is no mention of students he has advised. His work is supported by practical applications in industry rather than explicit grant funding in the source material.

• Structural Dynamics
• Aeroelasticity
• Uncertainty Modeling
• +۶ مورد دیگر

Professor Hamed Haddad Khodaparast is a faculty member in the School of Aerospace, Civil, Electrical and Mechanical Engineering at Swansea University, specializing in Aerospace Engineering. His roles include teaching and supervising postgraduate students in areas such as dynamics and advanced airframe structures. He has held academic positions from Lecturer to Professor since 2013, currently serving as a Professor since 2022. Research Interests: Structural Dynamics and Aeroelasticity Uncertainty Modeling and Propagation Vibration Control (Active/Passive) Composite Structures and Morphing Aircraft Reduced Order Modeling and Nonlinear Dynamics Recent Research Contributions: His work focuses on stochastic modeling, nonlinear system identification, and energy harvesting. Notable projects include gust load alleviation using hinged wingtips and nonlinear joint modeling for industrial applications. Collaborations involve aerodynamic optimization and experimental validation of piezoelectric systems. Grants and Supervision: Supervised PhD students on topics like nonlinear bolted joints and aeroelastic wing design. Active in interdisciplinary projects with industry partners, addressing challenges in morphing aircraft and structural health monitoring. Labs/Teams: Leads research in structural dynamics and aeroelasticity within the School’s Aerospace Engineering group, contributing to advanced composite materials and wind tunnel testing initiatives.

• Control Theory
• Optimization
• Cyber-Physical Systems
• +۷ مورد دیگر

Azwirman Gusrialdi is an Associate Professor and leads the Intelligent Networked Systems (IINES) group at the Faculty of Engineering and Natural Sciences, Tampere University, Finland. He holds a Dr.-Ing. in Control Engineering from Technische Universität München (TUM), Germany, and Bachelor/Master degrees from Tokyo Institute of Technology (Tokyo Tech). Previously, he was a research associate at the University of Central Florida (UCF), USA, and a visiting researcher at ANU. Education: PhD (Dr.-Ing) in Control Engineering, Technische Universität München (201?), Germany M.Eng in Mechanical & Control Engineering, Tokyo Institute of Technology, Japan B.Eng in Mechanical & Control Engineering, Tokyo Institute of Technology, Japan Research Focus: His work centers on resilient cooperative control and distributed optimization for cyber-physical-human systems, with applications to smart grids , intelligent transportation systems , and multi-robot networks . Key themes include data-driven control, network topology design, and cybersecurity resilience. Recent Contributions: Recent work emphasizes safe navigation via control barrier functions, resilient platooning for autonomous vehicles, and distributed algorithms for network connectivity verification. His 2025 papers address human-swarm interaction and eigen-analysis for multi-agent systems. Labs/Teams: Heads the IINES Group , focusing on networked systems and cyber-physical resilience. Active in doctoral training via the TTITO program. Funding: Current Academy of Finland grant on resilient distributed optimization (2020-2024).

• Control Systems Engineering
• Nonlinear Systems
• Observer Design
• +۷ مورد دیگر

Rajesh Rajamani is a Professor in the Department of Mechanical Engineering at the University of Minnesota, College of Science and Engineering. His research spans control systems, nonlinear dynamics, and applications in transportation and medical cyber-physical systems. He actively leads multiple funded research projects and contributes to interdisciplinary innovation. His research interests lie at the intersection of control theory and real-world engineering applications. Key areas include nonlinear observer design , actuator and sensor systems , vehicle dynamics and safety , and medical cyber-physical systems . He applies these to diverse domains such as autonomous vehicles, smart infrastructure, combustion engines, and closed-loop drug delivery. The recent publication trends reflect a strong focus on intelligent transportation (e.g., e-scooter safety, smart traffic signs), health monitoring using non-intrusive sensors, and control of pharmacological systems. His work combines deep theoretical control methods with practical implementation using real-time estimation, sensor fusion, and machine learning. Principal Investigator on multiple NSF and DOT-funded projects Expert commentator in media on transportation safety and automation Active collaborations with researchers in mechanical, biomedical, and electrical engineering Extensive publication record in top journals including IEEE Transactions and Mechanical Systems and Signal Processing Rajamani has secured significant grant funding, including recent awards from the National Science Foundation and the Minnesota Department of Transportation, supporting projects on smart traffic systems, non-contact actuator sensing, and medical cyber-physical systems. These grants highlight sustained research impact and leadership. He is involved in developing innovative technologies through university-industry partnerships and technology transfer initiatives, particularly in sensing and control for industrial and medical applications. His lab focuses on real-time estimation, feedback control, and system integration for safety-critical systems.

Yamir Morenoمشاهده پروفایل

استاد

• Complex Systems
• Network Science
• Disease Dynamics
• +۷ مورد دیگر

Prof. Yamir Moreno is a Professor of Physics at the Department of Theoretical Physics, Faculty of Sciences, University of Zaragoza, where he also serves as Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI) and leads the Complex Systems and Networks Lab (COSNET). He holds international affiliations as Research Director at CENTAI Institute (Turin, Italy) and External Professor at the Complexity Science Hub Vienna (Austria), and was previously a Principal Scientist and Area Coordinator at the ISI Foundation. PhD in Physics, University of Zaragoza (Summa Cum Laude, 2000) Ramón y Cajal Fellow (2005-2010) First-class Fellow, ISI Foundation Fellow, American Physical Society (2021) Fellow, Network Science Society (2022) His research focuses on complex systems , network science , epidemic modeling , and social dynamics . He investigates disease dynamics, diffusion processes, nonlinear systems, and higher-order network structures using tools from physics, mathematics, and data science. His work bridges theoretical foundations with real-world applications in public health, urban systems, and social behavior. Recent publications highlight trends in multilayer and higher-order networks , digital twins of cities , epidemic modeling with behavioral feedback , and social contagion in complex systems . His research integrates data-driven modeling, machine learning, and network theory to study urban resilience, information spread, and collective behavior. Complex Systems Society’s Senior Scientific Award (2019) Highly Cited Researcher (Web of Science, 2019) Service Award, Complex Systems Society (2022) Editor-in-Chief, Journal of Complex Networks Editor, PLoS Computational Biology, Chaos, Solitons & Fractals, Proceedings of the Royal Society A Divisional Associate Editor, Physical Review Letters (2014-2020) Prof. Moreno has supervised 23 PhD theses and over 30 master’s theses. He has led multiple EU-funded projects and has served on advisory boards for the EU Future and Emerging Technologies and the WHO Collaborative Center on Complexity Sciences for Health Systems. His collaborative network spans institutions in Europe, Latin America, and North America. He leads the Complex Systems and Networks Lab (COSNET) at BIFI, a multidisciplinary team focused on network theory, computational epidemiology, and social dynamics. The lab develops models for epidemic forecasting, urban resilience, and collective behavior using high-performance computing and data analytics.

• Electrical Power Systems
• Smart Grids
• Power Quality
• +۱۲ مورد دیگر

Huilian Liao is currently a Lecturer in the Department of Electrical and Electronic Engineering at Sheffield Hallam University, where she has been employed since May 2016. Previously, she served as a Research Associate at the University of Manchester's School of Electrical and Electronic Engineering (2013–2016) and as a Lecturer in the School of Electric Power at South China University of Technology (2012–2013). Her educational background includes a Ph.D. in Electrical Engineering and Electronics from the University of Liverpool (2008–2011) and an M.Sc.(Eng) in Communication and Signal System from Shenzhen University (2005–2008). Huilian Liao's research is centered on modern electrical power systems, with a strong emphasis on smart grids, power quality, and data-driven methodologies. Her work spans distribution network optimization under uncertainty, state estimation in future grids, voltage sag and unbalance analysis, and the integration of renewable energy sources. She has made significant contributions to the application of FACTS devices for power quality improvement and has explored probabilistic and multi-objective optimization techniques for grid management. Her recent publications highlight a growing focus on big data analytics for smart energy systems, indicating a trajectory toward intelligent, data-rich power network solutions. The articles demonstrate expertise in both theoretical modeling and practical applications, often employing advanced computational methods such as deep learning and optimization algorithms. No scientific awards are listed in the provided text. Huilian Liao has contributed to numerous research projects, particularly during her tenure at the University of Manchester and Sheffield Hallam University. While specific grant details are not mentioned, her collaborative work with prominent researchers like Jovica V. Milanovic suggests involvement in significant research initiatives. She has co-authored multiple publications, indicating active research output, but no formal advisees or students are listed in the available information. There is no mention of specific laboratories, research teams, or centers she leads or participates in within the provided text.

Dmitry Ignatyevمشاهده پروفایل

مدرس ارشد

• Aeronautical Systems
• Autonomous Systems
• Computing, Simulation & Modelling
• +۷ مورد دیگر

Dr. Dmitry Ignatyev is a Senior Lecturer in Flight Dynamics and Control at Cranfield University's Centre for Autonomous and Cyberphysical Systems. With a PhD in Physics and Mathematics from Moscow Institute of Physics and Technology (2013), he previously spent eight years at Russia's Central Aerohydrodynamic Institute. His research spans aeronautical systems, autonomous vehicles, and structural dynamics. Education: PhD in Physics and Mathematics (2013) from Moscow Institute of Physics and Technology Affiliation: Cranfield University, School of Aerospace, Transport and Manufacturing Ignatyev's expertise includes Autonomous Systems , Flight Physics , and Computational Modeling . His work focuses on advanced control strategies and aerodynamic modeling to enhance vehicle performance and safety. Notable projects include developing autonomous emergency-responder aircraft with Cranfield's CraneAero team. His recent publications highlight trends in UAV control algorithms , Loewner framework applications for structural health monitoring, and Martian aerobot design . Awards include Fellowships in the Higher Education Academy (FHEA) and Royal Aeronautical Society (FRAeS). Scientific Recognition: Fellow of the Higher Education Academy (FHEA) Fellow of the Royal Aeronautical Society (FRAeS) Leadership: Deputy Course Director for the Autonomous Vehicle Dynamics and Control MSc program

• Nonlinear dynamic systems
• Graph theory
• Machine learning
• +۳ مورد دیگر

Dr Mengbang Zou is a Research Fellow in 6G Communications and Computing at Cranfield University's Centre for Autonomous and Cyberphysical Systems, supported by an Engineering and Physical Sciences Research Council (EPSRC) grant. He earned his Ph.D. from Cranfield's School of Aerospace, Transport and Manufacture, establishing his foundation in systems engineering and network analysis. His research expertise centers on nonlinear dynamic systems , graph theory , and machine learning , with rigorous investigations into stability, synchronization, and resilience quantification in complex networks. Applications span telecommunications (6G/O-RAN), industrial IoT systems, and social network dynamics, emphasizing data-driven control and optimization under uncertainty. Recent publications (2022-2025) demonstrate a cohesive trajectory in network stability analysis, leveraging reinforcement learning and graph neural networks for load balancing, cascade control, and topological rewiring. Key journals include IEEE Transactions on Network Science and Engineering, Information Sciences, and IEEE Systems Journal, reflecting interdisciplinary impact in engineering and computer science. Dr Zou operates within Cranfield's Centre for Autonomous and Cyberphysical Systems, focusing on next-generation network intelligence. His EPSRC-funded work targets 6G communications, with no documented advising roles or scientific awards in the provided materials.

• Machine Learning
• Reinforcement Learning
• Bandit Problems
• +۷ مورد دیگر

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. Her research focuses on sequential decision making and theoretical reinforcement learning (RL), particularly in non-stationary environments, bandit problems, and principled learning algorithms. She has received prestigious awards including the Emmy Noether Award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , respectively. Claire has previously worked as a Research Scientist at DeepMind (London) and as a part-time Applied Scientist at Amazon (Berlin). Education : PhD in Machine Learning from Telecom ParisTech (2017), under Prof. Olivier Cappé. Research Interests include: Sequential Decision Making Bandit Problems (Combinatorial, Delayed Feedback, Sparse Actions) Reinforcement Learning Theory Meta-Learning and Lifelong Learning Optimization Algorithms Game-Theoretic Approaches to PCA Publications highlight trends in non-stationary environments, contextual bandits, and theoretical foundations of RL and bandit algorithms. Her work spans applications in scientific discovery, statistical testing, and optimization. Scientific Awards : Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award at ICLR 2021 Advising and Grants : Claire leads a research group with ongoing PhD and postdoc opportunities through IMPRS-IS and ELLIS doctoral programs. Her projects receive funding from the European Research Council and DFG, with focus on continual learning and adaptive AI systems. Labs/Teams : She coordinates the Tübingen Women in Machine Learning (TWiML) initiative and co-leads the Women in Learning Theory (WiML-T) website. Her group emphasizes diversity, inclusivity, and collaborative research in theoretical machine learning.

Dongda Zhangمشاهده پروفایل

مدرس

• Process Systems Engineering
• Machine Learning
• Reaction Engineering
• +۹ مورد دیگر

Dr. Dongda Zhang is a University Lecturer in Process Systems Engineering and Machine Learning at the University of Manchester's Department of Chemical Engineering, with an Honorary Research Fellow position at Imperial College London. His research focuses on Digital Chemical Engineering, combining Process Systems Engineering, Machine Learning, Reaction Engineering, and Industrial Data Analytics. Key areas include hybrid model-based bioprocess predictive modeling, machine learning for quality control, and interpretable AI frameworks for reaction engineering. Education: BSc (Tianjin University, 2011), MSc (Imperial College London, 2013), PhD (University of Cambridge, 2016). Awards include the Leverhulme Early Career Fellowship and Honorary Research Fellow recognition. He holds editorial roles for Digital Chemical Engineering and Biochemical Engineering Journal , and serves on the BBSRC Pool of Experts and CPACT Industrial Management Board. Research emphasizes multidisciplinary approaches, including kinetic modeling of catalytic systems, real-time process monitoring, and data-driven strategies for biomanufacturing. His work contributes to UN Sustainable Development Goals related to affordable and clean energy and industry innovation. The group welcomes PhD students and interns, with fully sponsored programs.

Pulin Liمشاهده پروفایل

استاد

• Tissue Engineering
• Synthetic Biology
• Developmental Biology
• +۵ مورد دیگر

Pulin Li is the Eugene Bell Career Development Professor of Tissue Engineering at the Massachusetts Institute of Technology (MIT) and a Core Member at the Whitehead Institute for Biomedical Research. His research focuses on understanding how genetic circuits drive multicellular behavior in natural and engineered systems, leveraging synthetic biology, mathematical modeling, and experimental approaches like mouse genetics and stem cell engineering. He earned a PhD in Chemical Biology from Harvard University (2012) and a BS in Life Sciences from Peking University (2006). Key research themes include the spatial patterning of tissues, particularly in the embryonic and adult lung, and the functional roles of morphogen gradients. His lab employs bottom-up strategies to reconstitute multicellular systems, combining synthetic biology with quantitative imaging and machine learning. Notable achievements include uncovering design principles of Hedgehog signaling gradients and developing novel methods for studying single-cell signaling histories. Awards: Teaching Prize for Undergraduate Education, MIT School of Science (2023) Allen Distinguished Investigator (2021) NIH New Innovator Award (2021) R.R. Bensley Award in Cell Biology (2021) His work bridges foundational discoveries and translational applications in tissue engineering, with a focus on creating scalable strategies for biomedical innovation. The Li Lab collaborates broadly, integrating computational and experimental methods to address complex biological systems.

Florent Di Meglioمشاهده پروفایل

دانشیار

• Control Theory
• Partial Differential Equations
• Fluid Dynamics
• +۳ مورد دیگر

Florent Di Meglio is an Associate Professor at MINES ParisTech, working in the Centre Automatique et Systèmes (Center for Automatic Control and Systems). His research focuses on control theory, particularly for partial differential equations and their applications in various engineering domains. Education: Ph.D. in Mathematics and Control at MINES Paristech in 2011 Graduated from MINES Paristech (Grande Ecole) in 2008 Postdoctoral researcher at UC San Diego under Miroslav Krstic Professor Di Meglio's research spans several areas of control theory with applications in oil and gas drilling, robotics, and fluid dynamics. His work primarily focuses on the control of partial differential equations, particularly hyperbolic systems, using methods like backstepping and boundary control. He has made significant contributions to the stabilization of coupled PDE-ODE systems, estimation techniques for drilling processes, and the application of control theory to thermoacoustic systems and robotics. His recent publications demonstrate a strong focus on robust control design for coupled hyperbolic PDEs, state estimation for complex systems, and applications in drilling technology. His work often bridges theoretical control methods with practical engineering problems, particularly in the oil and gas industry. Scientific Awards: 2014 France-Berkeley Fund Award for the project "Analysis and Control of Grid-Integrated Plug-in Electric Vehicle Fleets" with Professor Scott MOURA Professor Di Meglio has advised numerous Ph.D. students working on diverse topics including deep learning approaches to combustion/acoustic coupling, parameter estimation for thermoacoustic instabilities, and state estimation for flexible exoskeletons. His research has been supported by several major projects including the H2020 Marie-Curie EID – ITN project MAGISTER (2017-2021), the H2020 Marie-Curie EID - ITN project HYDRA (2016-2020), and the ANR project MACSDRILL (2016-2020). He is actively involved in the academic community, serving as the animator of the PDE Workgroup (Groupe de Travail EDP) of the GDR MACS.

Alexander von Rohrمشاهده پروفایل

پژوهشگر

• Robotics
• Machine Learning
• Bayesian Optimization
• +۴ مورد دیگر

Alexander von Rohr is a postdoctoral researcher at the Technical University of Munich , affiliated with the Learning Systems and Robotics Lab . Previously, he was a doctoral researcher at the Max Planck Institute for Intelligent Systems and RWTH Aachen University , supported by IAV . His educational background spans Computer Science (RWTH Aachen), Electrical Engineering (BHT Berlin), and University of Lübeck . Research Focus : Embodied AI, Bayesian optimization, risk-aware reinforcement learning, robust control, probabilistic models, data-driven controller synthesis. His recent publications (2024-2025) emphasize event-triggered learning for time-varying systems, diffusion models with constraints , and robust safety via entropy regularization in RL. These works demonstrate applications in robot manipulators , digital twins , and underactuated systems . Awards include the Best Reviewer Award at AISTATS 2025 . Collaborations include Prof. Sebastian Trimpe and Angela P. Schoellig , with co-authorships on 15 recent articles. His lab focuses on probabilistic control for dynamical systems with formal guarantees.