جستجوی استاد راهنمای دانشگاهی

• Structural Geology
• Seismology
• Tectonics
• +۵ مورد دیگر

Professor Ake Fagereng is a Professor in Structural Geology at the School of Earth and Environmental Sciences, Cardiff University. He has been with Cardiff University since 2014, initially as a Lecturer in Structural Geology (2014-2017), then as a Reader in Structural Geology (2017-present), and currently as a Professor. Prior to Cardiff, he was a Lecturer/Senior Lecturer in the Department of Geological Sciences at the University of Cape Town (2010-2014). He also holds an honorary position as a Research Fellow in the Department of Geological Sciences at the University of Cape Town (2014-present). Professor Fagereng's primary research focuses on how the Earth deforms and how this deformation is recorded in rocks. His work integrates field investigations of active and ancient deformation structures with microstructural analysis and geochemical techniques to understand: Seismic styles in subduction zones (from creep through slow slip events to giant earthquakes) Rift formation processes and the role of pre-existing structures Causes of large earthquakes far from plate boundaries Interplay between fluids (water and melt) and deformation processes His recent research has been significantly supported by an ERC-funded MICA project, which explores geological processes controlling fault slip speed and varied behavior of faults accommodating tectonic deformation in the Earth's crust. The project investigates why some faults experience slow earthquakes while others generate damaging seismic events, addressing critical questions of societal importance regarding earthquake hazard. Professor Fagereng has received numerous prestigious awards for his contributions to geoscience, including: Outstanding Young Scientist Award from the European Geosciences Union (2016) International Visiting Fellow at Kyoto University, Japan (2016) Excellent Reviewer Award from Earth, Planets, and Space (2015) Editor's Citation for Excellence in Refereeing from the American Geophysical Union (2013) President's Award from the National Research Foundation, South Africa (2013) As a dedicated educator and Fellow of the Higher Education Academy, Professor Fagereng teaches across undergraduate programs at Cardiff University, specializing in structural geology, tectonics, and geodynamics. He has served as Director of Fieldwork from 2015 to 2018 and regularly leads field courses in Cornwall, North Wales, Spain, and Cyprus. His research portfolio demonstrates consistent contributions to understanding earthquake processes, fault mechanics, and tectonic deformation through extensive international collaborations and fieldwork in diverse tectonic settings worldwide.

Venanzio Cichellaمشاهده پروفایل

استادیار

• Control Systems
• Robotics
• Autonomous Vehicles
• +۳ مورد دیگر

Venanzio Cichella serves as an Assistant Professor in the Department of Mechanical Engineering at the University of Iowa, specializing in advanced control systems and robotics research with applications spanning autonomous aerial vehicles, underwater systems, and soft robotics. His research portfolio prominently features Control Systems , Robotics , Autonomous Vehicles , Soft Robotics , Motion Planning , and Optimal Control . He develops theoretical frameworks for collision avoidance, coordinated path following, and motion planning, frequently employing Bernstein polynomials and Cosserat rod theory to address scalability challenges in multi-agent systems. His work on twisted and coiled artificial muscles (TCAMs) enables biomimetic soft robotic systems for underwater manipulation and rehabilitation applications. Analysis of his 2023-2025 publications reveals three dominant research threads: (1) Vision-based collision avoidance algorithms for intermittent measurement scenarios, (2) Physics-based modeling of TCAM-actuated soft robots using continuum mechanics, and (3) Bernstein polynomial-optimized trajectory generation for autonomous vehicles. These threads consistently bridge theoretical control innovations with practical implementations in UAV coordination, submarine maneuvering, and octopus-inspired robotics, demonstrating strong emphasis on environmental adaptation and computational efficiency.

Gian Pietro Piccoمشاهده پروفایل

استاد

• Building Automation
• Pervasive Computing
• Software Engineering
• +۱۲ مورد دیگر

Gian Pietro Picco is a Full Professor at the Department of Information Engineering and Computer Science (DISI) at the University of Trento, Italy. His research focuses on wireless networks, particularly ultra-wideband (UWB) technology, wireless sensor networks, and cyber-physical systems. He teaches courses including Distributed Systems, Low-power wireless networking for the Internet of Things, and Programming 2. Professor Picco's research interests span several interconnected domains in pervasive computing and networking: Ultra-wideband (UWB) technology for precise localization and communication Wireless sensor networks and Internet of Things (IoT) systems Cyber-physical systems and networked control Energy-efficient networking protocols Distributed systems and middleware Software engineering approaches for networked embedded systems His recent publications demonstrate a strong focus on ultra-wideband technology applications, particularly in localization, ranging, and concurrent transmissions. His work bridges theoretical foundations with practical implementations, often addressing real-world challenges in human-robot interaction, contact tracing, and infrastructure monitoring. A notable trend is the increasing application of UWB technology for precise positioning in complex environments, with significant contributions to understanding and mitigating human occlusion effects on ranging accuracy. Professor Picco has received numerous prestigious awards for his research contributions: Best Paper Award at IPSN'23 for "Network On or Off? Instant Global Binary Decisions over UWB with Flick" Best Paper Award at IPIN 2019 for "TALLA: Large-scale TDoA Localization with Ultra-wideband Radios" Best Paper Award at EWSN 2018 for "Concurrent Ranging in Ultra-wideband Radios" Best Paper Award at IPSN 2015 for "Geo-referenced Proximity Detection of Wildlife" Best Paper Award at IPSN 2011 for "Wireless Sensor Networks for Adaptive Lighting in Road Tunnels" Best Paper Award at IPSN 2009 for "Monitoring Heritage Buildings with Wireless Sensor Networks" Mark Weiser Best Paper Award at PerCom 2012 Professor Picco actively advises numerous PhD and Master's students, with several of his advisees becoming prominent researchers in wireless networking. His laboratory has secured significant funding for research projects in wireless sensor networks, IoT systems, and cyber-physical systems. His work has practical applications in heritage building monitoring, wildlife tracking, road tunnel lighting systems, and pandemic contact tracing. His research group maintains the Cloves large-scale ultra-wideband testbed and has developed several middleware systems for wireless sensor networks, including Lime and TeenyLIME. The group collaborates extensively with international research institutions and has made significant contributions to standardization efforts in IoT networking protocols.

Zhongguo Liمشاهده پروفایل

مدرس

• Distributed Control
• Optimization
• Robotics
• +۵ مورد دیگر

Zhongguo Li is a Lecturer in Robotics, Control, Communication & AI at the University of Manchester. He holds a B.Eng. (2017) and Ph.D. (2021) in Electrical and Electronic Engineering from the University of Manchester. Prior to his current role, he was a Lecturer at University College London (2022-2023) and a Research Associate at Loughborough University (2020-2022). His research focuses on distributed control, optimization, and reinforcement learning, particularly in robotics and autonomous systems. Key areas include multi-agent coordination, networked systems, and applications in autonomous vehicles. He has authored over 40 papers in top journals/conferences and co-authored a book on Distributed Optimization and Learning (2024). Teaching responsibilities include courses such as Control Systems II, Nonlinear and Adaptive Control, and Embedded Systems Project. He serves as an Associate Editor for Drones and Autonomous Vehicles and Guest Editor for Machines and Frontiers in Control Engineering. Dr. Li actively mentors PhD students, offering guidance on funding opportunities and research projects in distributed algorithms, robotics, and control systems. His work aligns with UN Sustainable Development Goals related to innovation and infrastructure.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +۵ مورد دیگر

Nisar Ahmed is an Associate Professor at the University of Colorado within the Aerospace Engineering Sciences department. His research focuses on the intersection of Artificial Intelligence , Robotics , and Autonomous Systems , emphasizing decision-making under uncertainty, sensor fusion, and human-machine collaboration. Key research interests include: Active Inference for autonomous planning Decentralized Data Fusion in multi-robot systems Machine Self-Confidence and competency assessment Reinforcement Learning for spacecraft and robotic guidance Uncertainty Quantification in dynamic environments Recent publications highlight trends in Pareto-optimal decision-making , Bayesian optimization , contextual bandits , and trust calibration for UAS and planetary rovers. His work integrates probabilistic modeling with real-time autonomy , ensuring robustness in applications like search-and-rescue missions and lunar exploration. Contact: Nisar.Ahmed@Colorado.EDU

Zhong-Ping Jiangمشاهده پروفایل

استاد

• Nonlinear Control Theory
• Dynamical Networks
• Data-Driven Optimization
• +۷ مورد دیگر

Dr. Zhong-Ping Jiang is a Professor in the Department of Electrical and Computer Engineering and an Affiliate Professor in the Department of Civil and Urban Engineering at the Tandon School of Engineering, New York University. He has been with NYU since 2007, previously serving as Associate Professor (2002-2007) and Assistant Professor (1999-2002) at Polytechnic University (now part of NYU). Dr. Jiang received his MSc degree in Statistics from the University of Paris XI in 1989 and his PhD degree in Automatic Control and Mathematics from the Ecole des Mines de Paris in 1993. He has maintained strong international collaborations with researchers across Europe throughout his career. Internationally recognized as a key contributor to nonlinear small-gain theory, Dr. Jiang is also well-known for his seminal work in robust adaptive dynamic programming and robust reinforcement learning for real-time safety-critical control applications. His research spans dynamical networks, nonlinear control theory, data-driven optimization, and reinforcement learning with applications to mechanical, information and biological systems. Dr. Jiang's publications have had significant impact in the field, with over 500 peer-reviewed journal and conference papers, more than 34,300 citations, and an h-index of 90 on Google Scholar. He has also published six research monographs since 2011. His notable awards and honors include: Election to European Academy of Sciences and Arts (2023) Stanford's Top 2% Most Highly Cited Scientists (2023) Election to Academia Europaea (2021) Highly Cited Researchers by Clarivate Analytics (2018) Fellow of Chinese Association of Automation (2017) Fellow of International Federation of Automatic Control (2013) Fellow of IEEE (2008) National Distinguished Professor, Chinese Ministry of Education (2009) Dr. Jiang serves as Deputy Editor-in-Chief for the IEEE/CAA Journal of Automatica Sinica and has held editorial positions for numerous prestigious journals including IEEE Transactions on Automatic Control. He has delivered more than 20 plenary, semi-plenary and keynote speeches at major international conferences and workshops.

Haibo Heمشاهده پروفایل

استاد

• Computational Intelligence
• Neural Networks
• Reinforcement Learning
• +۵ مورد دیگر

Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.

Sarah K. Carmichaelمشاهده پروفایل

استاد

• Fluid-rock reactions
• Paleoenvironmental reconstruction
• Manganese geomicrobiology
• +۲ مورد دیگر

Dr. Sarah K. Carmichael is a Professor in the Department of Geological and Environmental Sciences at Appalachian State University , where she also serves in the Appalachian Studies Faculty . Her interdisciplinary research focuses on fluid-rock reactions , manganese geomicrobiology , and paleoenvironmental reconstruction of mass extinction events through Devonian period studies . B.A. from Smith College M.A. and Ph.D. from Johns Hopkins University Her research combines geochemistry of mass extinctions , microbe-mineral interactions in caves , and Appalachian manganese ore formation through fieldwork from Mongolian mountains to Vietnamese basins . She leads the DAGGER (Devonian Anoxia, Geochemistry, Geochronology, and Extinction Research) Team and collaborates with Dr. Suzanna Bräuer on cave geomicrobiology projects. Recent publications focus on Devonian-Mississippian stratigraphy in Mongolia , mercury isotope evidence for Late Devonian ocean anoxia , and human impact assessment in Appalachian caves . Her work has been recognized with National Geographic Explorer status, inclusion in the Explorers Club 50 , and the UNC Board of Governors Teaching Award . Scientific awards include National Geographic Explorer (2018, 2022) Explorers Club 50 (2022) Board of Governors Teaching Award (2020) Academy of Outstanding Teachers induction (2022) KWI Distinguished Service Award (2020) William C. Strickland Outstanding Junior Faculty Award (2014) She mentors numerous student researchers and has secured over $1.2 million in external funding from organizations like the National Science Foundation and National Geographic Society . Her laboratory work utilizes advanced techniques such as electron microscopy and isotope analysis to decode micron-scale mineral textures that reveal Earth's ancient environmental changes.

Haoyu Wangمشاهده پروفایل

پژوهشگر

• Event-Centric NLP/NLU
• LLM Reasoning and Planning
• Knowledge Graph
• +۵ مورد دیگر

Haoyu Wang is a Researcher in the Computer and Information Science department at the University of Pennsylvania . He previously held research positions at Shanghai Jiao Tong University and interned at Google DeepMind , Amazon AWS , ByteDance , AI2 , Tencent AI Lab , and Goldman Sachs . Education : PhD in Computer and Information Science (2021–Present), MS in Computer and Information Science (2019–2021), BS in Computer Science (2015–2019). His research focuses on Event-Centric NLP/NLU , LLM Reasoning and Planning , Knowledge Graph , and Pose Estimation in Computer Vision . His work includes event causality identification, semantic classification in context, and synthetic control for temporal reasoning. He has contributed to multimodal hallucination analysis and safety in reasoning models through projects like RESIN-11 and Devil's Advocate . His publications span venues like EMNLP , EACL , and ACL . His recent articles analyze LLM limitations in NP-hard problems , clinical trial prediction , event causality , and hallucination in vision-language models . He has served as PC Member for conferences including ACL , NAACL , NeurIPS , and EMNLP since 2019.

Kaiming Biمشاهده پروفایل

استادیار

• Infectious Disease Modeling
• Epidemic Forecasting
• Public Health Decision-Making
• +۱۲ مورد دیگر

Kaiming Bi, Ph.D., is an Assistant Professor in the Department of Management, Policy & Community Health at the University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health . As an affiliated member of the Center for Health Care Data , his research bridges quantitative methods and public health, focusing on infectious disease modeling , epidemic forecasting , and data-driven health policy . B.S. in Mathematics from Northeastern University (2015) Ph.D. in Industrial Engineering from Kansas State University (2020) Postdoctoral training at University of California San Diego School of Medicine (2020-2021) and University of Texas at Austin (2021-2024) His methodological expertise spans mathematical modeling , machine learning , and optimization , applied to diverse public health challenges including respiratory infections , vector-borne diseases , STIs , and the opioid epidemic . Recent work includes modeling SARS-CoV-2 Omicron subvariants , population immunity dynamics , and multi-pathogen burden projections for the 2023-2024 US winter season. Dr. Bi has received prestigious accolades such as the Pencis Best Researcher Award (2021) and IISE Best Paper (2018). He previously taught graduate courses in Integer Programming , Information Systems , and Industrial Simulation at Kansas State University. Currently leading the Big-data and Infectious Disease Modeling Lab (BI Lab) , he seeks STEM-motivated PhD students to develop computational solutions for epidemic control.

Susan Solomonمشاهده پروفایل

استاد

• Atmospheric Chemistry
• Climate Change
• Ozone Depletion
• +۳ مورد دیگر

Susan Solomon is the Lee and Geraldine Martin Professor of Environmental Studies at MIT, holding joint appointments in Earth, Atmospheric and Planetary Sciences and Chemistry. She previously served as a scientist at NOAA and adjunct professor at the University of Colorado (1982–2011). Her research focuses on atmospheric chemistry, climate change, and ozone depletion mechanisms. Key contributions include identifying chlorofluorocarbons (CFCs) as the cause of the Antarctic ozone hole and advancing understanding of climate-chemistry interactions. Dr. Solomon’s work underpins the Montreal Protocol, which phased out ozone-depleting substances and averted significant global warming. She has led expeditions to Antarctica, discovered the irreversibility of CO₂-driven warming, and pioneered studies on wildfire impacts on stratospheric chemistry. Her honors include the National Medal of Science (2000), the Crafoord Prize (2018), and the 2023 VinFuture Award for Female Innovators. Affiliations: MIT Department of Earth, Atmospheric and Planetary Sciences; MIT Chemistry Department Research Labs: The Solomon Group at MIT, focusing on poles and tropics, with projects like ozone hole healing, CFC emissions tracking, and gravity wave impacts Her recent studies address emerging challenges, such as HFC-23 emissions and climate policy loopholes. She advocates for global cooperation to address climate change, drawing parallels to the Montreal Protocol’s success.

Donald C. Wunschمشاهده پروفایل

استاد

• Neural Networks
• Reinforcement Learning
• Adaptive Resonance Theory
• +۶ مورد دیگر

Donald C. Wunsch is the Mary K. Finley Missouri Distinguished Professor in Electrical and Computer Engineering at Missouri University of Science & Technology (Missouri S&T), where he directs the Applied Computational Intelligence Laboratory. He previously served as Interim Director of S&T’s Intelligent Systems Center and holds courtesy appointments in Computer Science, Systems Engineering, and Engineering Management. His research focuses on neural networks, reinforcement learning, adaptive resonance theory, bioinformatics, and robotics. Wunsch is an IEEE Fellow, INNS Fellow, and recipient of the 2015 INNS Gabor Award and 2019 Ada Lovelace Service Award. He has led numerous professional organizations, including the INNS Board of Governors and IJCNN conferences. Education: Executive MBA, Washington University in St. Louis (2006) Ph.D. in Electrical Engineering, University of Washington (1991) M.S. in Applied Mathematics, University of Washington (1987) B.S. in Applied Mathematics, University of New Mexico (1984) Research Interests: Donald’s work spans adaptive resonance systems, reinforcement learning architectures, neurofuzzy regression, robotic swarms, and bioinformatics. He emphasizes practical applications in energy systems (e.g., pumped storage hydro optimization), cybersecurity, and medical data analysis. Recent projects include Hamiltonian-driven adaptive dynamic programming and cluster validity indices for streaming data. Awards & Recognition: 2015 INNS Gabor Award INNS Senior Fellow (2007–2013) NSF CAREER Award 2019 INNS Ada Lovelace Service Award Labs & Leadership: As director of the Applied Computational Intelligence Lab, he oversees research in AI/AS applications for critical infrastructure and smart grids. He also chairs the Missouri S&T Information Technology and Computing Committee and actively contributes to interdisciplinary initiatives like the University of Missouri Bioinformatics Consortium.

Bin Huمشاهده پروفایل

استادیار

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +۵ مورد دیگر

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

• Chemical Engineering
• Pharmaceutical Processing
• Catalytic Combustion
• +۳ مورد دیگر

Antonello Barresi is a Full Professor of Transport phenomena at the Department of Applied Science and Technology (DISAT), Politecnico di Torino. He leads the Process development and design course and serves on the PhD board for Chemical Engineering. His research focuses on drying, combustion processes, and pharmaceutical freeze-drying optimization. He has authored over 300 papers and holds patents for freeze-drying monitoring systems. His awards include the AFSIA Award (2018) and Arun S. Mujumdar Medal (2021). Roles: Full Professor, PhD Program Member, Course Coordinator Research Interests: Pharmaceutical Freeze-Drying, Catalytic Combustion, Process Scale-Up, Nanotechnology Key Projects: Multi-hazard industrial infrastructure vulnerability modeling, SMART VIAL freeze-drying monitoring Labs: Lyophilization Lab, Molecular Engineering Lab (MolE), Process System Engineering Recent work includes multi-risk vulnerability frameworks for industrial systems and advancements in freeze-drying process analytical technology (PAT). He actively contributes to editorial boards like Pharmaceutics and Drying Technology.

Marta Zanioloمشاهده پروفایل

استادیار

• Water Resources Planning and Management
• Drought Modeling
• Climate Change Adaptation
• +۵ مورد دیگر

Marta Zaniolo serves as an Assistant Professor in Civil and Environmental Engineering at Duke University since January 2024, addressing critical water sustainability challenges through integrated hydrology, machine learning, and systems modeling approaches. Her work focuses on ensuring equitable water access amid climate change, scarcity, and competing demands across diverse spatial scales. Her academic foundation includes: B.S. in Environmental Engineering from Politecnico di Milano (2014) M.S. in Environmental Engineering from Politecnico di Milano (2017) Ph.D. in Information Technology (Environmental Intelligence Lab) from Politecnico di Milano (2020) Postdoctoral Research at Stanford University (2020-2023) Dr. Zaniolo's research integrates water resources planning , drought modeling , and climate adaptation with cutting-edge machine learning techniques including reinforcement learning and feature extraction. The ZEDD Lab develops computational models that synthesize climate, hydrological, and socio-economic data to enhance decision-making for water security. Her work emphasizes real-world applicability through stakeholder engagement and participatory design, particularly in vulnerable regions like the Omo-Turkana basin where mismanaged infrastructure operations can trigger socio-ecological conflicts. Analysis of her 2021-2025 publications reveals three dominant research trajectories: (1) development of synthetic weather generators (e.g., FIND model) for precise drought characterization, (2) neuro-evolutionary algorithms for multi-objective reservoir control under climate uncertainty, and (3) quantification of flexibility value in water infrastructure planning. These studies consistently bridge technical innovation with equity considerations, particularly in transboundary water systems. She leads the Zaniolo Lab for Environmental Data and Decisions (ZEDD Lab), which operates at the intersection of hydrology, systems modeling, and machine learning. The lab's framework combines computational water system dynamics with stakeholder-inclusive approaches to develop decision support tools for extreme event preparedness. Current projects focus on urban drought resilience, dam reoperation strategies for climate extremes, and heat-substance use risk intersections in rural communities, all grounded in case studies spanning California, Ethiopia, and the American South.