جستجوی استاد راهنمای دانشگاهی

Richard Allenمشاهده پروفایل

استاد

• Seismology
• Earthquake Early Warning Systems
• Structural Health Monitoring
• +۹ مورد دیگر

Richard Allen is a Professor and the Class of 1954 Endowed Chair at the University of California, Berkeley, serving as Director of the Berkeley Seismological Laboratory. His work focuses on seismology, earthquake early warning systems, and seismic hazard mitigation. Research interests include earthquake rupture mechanisms, regional seismic structure and dynamics, mantle upwelling processes, fault interaction analysis, stress modeling in seismology, and machine learning applications in seismic data analysis. He pioneered smartphone-based seismic networks like MyShake and advanced technologies such as distributed acoustic sensing (DAS) for offshore monitoring. His recent publications highlight trends in earthquake early warning algorithms (EPIC, bEPIC), real-time ground-motion modeling, ShakeAlert system performance, and integration of multimodal data (e.g., social media, LLMs, DAS) for hazard mitigation. Collaborative efforts include global smartphone networks and cloud computing for seismic datasets. Allen leads the Berkeley Seismological Laboratory, driving innovations in seismic monitoring, structural health assessment, and public alerting systems to enhance disaster resilience.

Chris Russellمشاهده پروفایل

دانشیار

• Algorithmic Fairness
• Explainable AI
• Responsible AI Governance
• +۴ مورد دیگر

Chris Russell is the Dieter Schwarz Associate Professor of AI, Government & Policy at the Oxford Internet Institute (OII), University of Oxford. His work bridges computer vision, machine learning, and ethical AI governance. He leads the Governance of Emerging Technologies programme, focusing on algorithmic accountability, transparency, and fairness. Prior roles include Group Leader at the Alan Turing Institute and Reader at the University of Surrey. Russell’s research has been recognized with awards, including the ICRA Best Paper Prize for autonomous driving mapping work. Research interests span algorithmic fairness, explainable AI, and responsible AI design. Notable projects include collaborations with the British Antarctic Foundation on climate modeling and causal approaches to algorithmic fairness. His work with Sandra Wachter and Brent Mittelstadt informs GDPR guidelines and tools like TensorFlow’s 'What-if Tool.' Current projects include advancing medical machine learning for inflammatory arthritis prediction and governance frameworks for emerging tech. Russell advises PhD students on socio-technical AI evaluations and teaches courses in machine learning and AI ethics. Recent publications address deepfake proliferation, LLM regulation, and fairness in foundational models. He co-leads initiatives like the Digital Good Network to align tech development with societal benefit.

Jianjun Shiمشاهده پروفایل

استاد

• Manufacturing Systems
• Quality Control
• Cyber-Physical Systems
• +۳ مورد دیگر

Jianjun (Jan) Shi is the Carolyn J. Stewart Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and holds a joint appointment with the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. He previously served as the G. Lawton and Louise G. Johnson Chair Professor of Engineering at the University of Michigan. His research focuses on system informatics and control for manufacturing and service systems, with notable contributions to quality improvement, cyber-physical systems, and data-driven methodologies. B.S. & M.S. in Electrical Engineering, Beijing Institute of Technology (1984–1987) Ph.D. in Mechanical Engineering, University of Michigan (1992) Dr. Shi’s research interests include process modeling, control systems, and quality engineering. He pioneered methodologies for in-process quality improvement and developed advanced frameworks for high-dimensional data analysis in manufacturing. His work integrates statistical methods, machine learning, and system informatics to enhance operational efficiency and product quality. He has published over 150 peer-reviewed papers and secured $19 million+ in research grants from NSF, DOE, and industry partners. His lab, the System Informatics and Control Group, collaborates with automotive, aerospace, and pharmaceutical sectors. Shi leads initiatives such as the Quality Science Center at the Chinese Academy of Sciences and serves on editorial boards of journals like IIE Transactions and ASME Transactions . Recipient of the IIE Albert G. Holzman Distinguished Educator Award (2011) Fellow of INFORMS, ASME, and IIE Academician of the International Academy for Quality Shi advises 26 Ph.D. graduates, many of whom hold faculty positions or leadership roles in industry. His research group’s innovations have been implemented in global manufacturing systems, yielding significant economic impacts. Current work includes 4D printing, cyber-physical system resilience, and federated learning for industrial data.

Pardis Pishdadمشاهده پروفایل

دانشیار

• Sustainable Built Environments
• Integrated Project Delivery (IPD)
• Digital Twin Technology
• +۹ مورد دیگر

Pardis Pishdad is an Associate Professor and Graduate Program Director in the School of Building Construction at Georgia Institute of Technology’s College of Design. She directs the Smart Built Environment Eco-System (Smart Bees) Laboratory, focusing on integrating cyber-physical systems, digital twins, and innovative project delivery methods (e.g., IPD, Flash Tracking) for sustainable built environments. Her research bridges technology adoption, trust-building in construction contracts, and supply chain optimization. Education: PhD, Environmental Design and Planning (Virginia Tech) Master’s Degrees: Civil Engineering (Virginia Tech), Design Studies in Project Management (Harvard), Architecture (University of Tehran) Bachelor’s in Architectural Engineering (Azad University of Shiraz) Research Interests: Her work emphasizes sustainable construction practices using IoT, BIM, and blockchain. Key areas include lifecycle cost analysis, lean construction, and smart building technologies. She explores trust dynamics and collaboration in construction projects through game theory and process optimization. Recognition: 2018 ENR Top 20 Under 40 Professionals 2016 CII National Outstanding Researcher Award 2020-2022 Georgia Tech Provost Teaching Learning Fellow Advisory Roles: Academic Advisor for CII’s Supply Chain Management Community, Vice Chair of BuildingSMART’s BIM Forum 5D Taskforce. Formerly advised the Construction Management Association of America’s Board (2016–2018). Industry Collaboration: Partnerships with Turner Construction, GDOT, and VDOT. Research on Flash Tracking and blockchain has been integrated into industry practices. Labs & Teams: The Smart Bees Lab pioneers cyber-physical systems for smart buildings, exploring AI-driven solutions and sustainable construction frameworks.

Youngchan Kimمشاهده پروفایل

مدرس

• Quantum Biology
• Biophotonics
• Bioimaging
• +۳ مورد دیگر

Dr. Youngchan Kim is a Lecturer in Quantum Biology at the University of Surrey , serving as Director of the Quantum Biology Doctoral Training Centre (QB-DTC). He is affiliated with multiple departments including the School of Biosciences, Advanced Technology Institute, and Quantum Sciences Group. PhD in Physics (2011), Korea Advanced Institute of Science and Technology MSc in Physics (2008), KAIST BSc in Physics (2006), Chung-Ang University Graduate Certificate in Learning and Teaching (2022), Advance HE His research focuses on quantum phenomena in biological systems at physiological temperatures, particularly using femtosecond optical spectroscopy and genetically engineered fluorescent proteins to explore evolutionary adaptations and develop quantum-bio-inspired technologies like room-temperature single-photon sources. The 15 most recent publications span quantum biology, biophotonics, and optical spectroscopy, with particular emphasis on quantum coherence in biological systems , terahertz birefringence , fluorescent protein dynamics , and biomedical imaging innovations . These works demonstrate his interdisciplinary approach bridging physics, biology, and medical applications. As QB-DTC Director, he leads transdisciplinary initiatives fostering collaboration between quantum physics and biosciences. His technical expertise includes time-correlated single-photon counting , common-path interferometry , and ultrafast fluorescence depolarization techniques.

João Paulo Costeiraمشاهده پروفایل

دانشیار

• Computer Vision
• 3D Reconstruction
• Structure from Motion
• +۳ مورد دیگر

João Paulo Costeira is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (1995) and was a Visiting Scientist at Carnegie Mellon University's Robotics Institute (1991–1995). His research focuses on Computer Vision, 3D Reconstruction, and Structure from Motion, with contributions to object recognition, robotics, and multimedia analysis. Education: PhD in Electrical and Computer Engineering, IST (1995); Visiting Scientist, CMU Robotics Institute (1991–1995). Roles: Coordinator of the Signal and Image Processing Group (SIPg), Co-director of the Carnegie Mellon|Portugal Dual PhD Program in ECE and Robotics (2007–2018), and Scientific Director of Carnegie Mellon|Portugal (2014–2018). Research Interests: João's work emphasizes 3D reconstruction from video, rigid and non-rigid motion analysis, and applications in robotics and urban surveillance. He has pioneered methods for motion segmentation and robust correspondence problems in computer vision. Publications: His recent work includes advancements in apple counting systems, rotation averaging for robotics, and domain adaptation for traffic density estimation. These contributions highlight his expertise in real-world computer vision challenges. Awards: None explicitly listed. However, his extensive publication record and academic leadership reflect significant scholarly impact. Advising & Grants: Supervised 13 PhD students, many co-advised with CMU faculty. Active in projects like CityCam (vehicle counting) and MultiDrone (robotics collaboration). Funded by FCT, EU, and industry partnerships. Labs/Teams: Leader of the Signal and Image Processing Group (SIPg) at ISR. Involved in NETSyS program for networked systems and robotics.

Mesut Baranمشاهده پروفایل

استاد

• Power Systems
• Smart Grid Technology
• Distributed Energy Resources
• +۳ مورد دیگر

Mesut Baran is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He focuses on applying computer, control, and system analysis techniques to power system operation and planning, particularly in smart power distribution systems and distributed energy resource integration. His research emphasizes grid resilience, renewable energy integration, and advanced grid technologies. Education: Ph.D. in Electrical Engineering, University of California, Berkeley (1988) Master's in Electrical Engineering, Middle East Technical University, Turkey (1981) Bachelor's in Electrical Engineering, Middle East Technical University, Turkey (1979) Research interests include power electronics, smart grid technologies, distributed energy resource management, and grid resilience. His recent work addresses challenges in EV charging impacts, fault tolerance, and microgrid coordination. Baran has contributed to IEEE standards and received prestigious awards, including the IEEE Fellow designation (2010) and the William F. Lane Outstanding Teaching Award (2017). Publications highlight advancements in distribution system state estimation, fault mitigation, and microgrid management. Collaborations with industry (e.g., Strata Solar) and federal grants ($3.1M DOE award) underscore his applied research impact. He teaches foundational courses like ECE200 and advises on FREEDM Systems Center projects.

Zeljko Panticمشاهده پروفایل

دانشیار

• Electrified Transportation
• Power Converters Design and Control
• Wireless Power Transfer Systems
• +۵ مورد دیگر

Zeljko Pantic is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from NC State (2013) and B.S./M.S. degrees from the University of Belgrade (1998/2007). Before joining NC State in 2019, he served as an Assistant Professor and Associate Director of the Electric Vehicle and Roadway Research Facility at Utah State University. He is actively involved in editorial roles for IEEE Transactions on Transportation Electrification and serves on the IEEE IAS Transportation Systems Committee. Education: Ph.D., Electrical Engineering, North Carolina State University (2013) M.S., Electrical Engineering, University of Belgrade (2007) B.S., Electrical Engineering, University of Belgrade (1998) Research: Dr. Pantic specializes in electrified transportation systems, wireless power transfer (WPT), power converter design, and DC microgrid technologies. His work addresses challenges in EV charging infrastructure, magnetic circuit optimization, and energy conversion principles for transportation electrification. Recent projects include autonomous wireless charging systems for UAVs, marine DC microgrids, and road-embedded DWPT solutions. Awards & Recognition: 2019 IEEE JESTPE Second Prize Paper Award 2017 Outstanding Teacher of the Year (USU) 2012 NC State Mentored Teaching Assistantship Award Advisees & Grants: While specific student names are not listed, Dr. Pantic has advised graduate students on projects spanning WPT systems, EV infrastructure, and battery management. His work has been supported by grants focusing on dynamic charging, magnetic materials, and autonomous observatory nodes. Labs & Facilities: He leads research at NC State's Electric Vehicle and Roadway facility, focusing on roadway-integrated wireless charging and high-power WPT systems. Collaborations include ocean observatory development and autonomous system integration.

Kyle DeMarsمشاهده پروفایل

دانشیار

• Space situational awareness
• Navigation, guidance, and control
• Bayesian filtering
• +۶ مورد دیگر

Kyle DeMars is an Associate Professor and Associate Department Head for Theoretical and Computational Research in the Department of Aerospace Engineering at Texas A&M University. He holds a Ph.D. from The University of Texas at Austin (2010) and has expertise in space situational awareness, navigation systems, Bayesian filtering, and information theory. His work focuses on advanced estimation techniques for spacecraft autonomy and space surveillance. Dr. DeMars' research emphasizes robust nonlinear filtering, multitarget tracking, and information-theoretic approaches to orbital dynamics. He has developed innovative methods for spacecraft navigation, including terrain-relative systems and anonymous feature processing. His contributions address challenges in uncertainty quantification, sensor fusion, and cislunar space domain awareness. Education: Ph.D./M.S.E./B.S. in Aerospace Engineering (UT Austin, 2004–2010) Awards: AIAA Young Professional Award (2017), NASA Innovation Award (2014), and multiple teaching/research recognitions Labs/Teams: Active in space situational awareness, guidance & control, and probabilistic navigation systems Key trends in his publications include: Advances in particle flow and Gaussian mixture methods for nonlinear estimation Cislunar trajectory analysis and resonance-based surveillance strategies Development of fault-resistant and anonymous navigation frameworks Integration of information theory into sensor tasking and uncertainty management His work bridges theoretical developments with practical applications in planetary landing navigation, space traffic management, and autonomous spacecraft systems.

Cynthia Sungمشاهده پروفایل

دانشیار

• Computational Robotics
• Origami Robotics
• Soft Robotics
• +۲ مورد دیگر

Cynthia Sung is an Associate Professor in the Mechanical Engineering and Applied Mechanics department at the University of Pennsylvania's School of Engineering and Applied Science, with secondary appointments in Computer and Information Science and Electrical and Systems Engineering. She directs the Sung Robotics Lab, focusing on computational methods for robot design, origami robotics, planning for distributed systems, and fabrication of reconfigurable systems. Her lab is funded by NSF, ONR, ARO, NASA, and Penn Health-Tech. Her research spans four key areas: Computational co-design integrating mechanical, electronic, and software components Soft/origami robotics leveraging compliance for adaptable systems Distributed planning for multi-robot coordination Novel fabrication techniques for deployable structures Publications show consistent focus on robotic mechanisms with recent trends in magnetic origami reconfiguration (2025), underwater jet coordination (2025), educational robotics kits (2025), and tunable-stiffness actuators (2024). Article keywords predominantly fall in Robotics, Material Science, and Control Systems. Awards & Recognition ONR Young Investigator Award (2023) NSF CAREER Award (2019) Johnson & Johnson Women in STEM2D Scholars Award (2020) Popular Mechanics Breakthrough Award (2017) Research Teams & Advising Leads the GRASP Lab-affiliated Sung Robotics group. Current doctoral students include Zhiyuan Yang (jet propulsion), Daniel Feshbach (kinematic design), and Gabriel Unger (reconfigurable structures). Recent graduates include Yipeng Zhang (MSc, SALP robotics) and Christopher Kim (PhD, self-sensing actuators).

Qiongxiu Liمشاهده پروفایل

استادیار

• Cybersecurity
• Distributed Optimization
• Privacy-Preserving Technologies
• +۵ مورد دیگر

Qiongxiu Li is a Tenure-Track Assistant Professor in the Cyber Security group at Aalborg University's Copenhagen campus, part of the Technical Faculty of IT and Design. Her research focuses on cybersecurity, distributed optimization, privacy/security, and federated learning. She has authored/co-authored 38 papers in top-tier venues including IEEE Transactions on Information Forensics and Security, ICLR, and EUSIPCO. Education: PhD in Privacy and Security from Aalborg University (2018-2021). Notable achievements include winning the EUSIPCO 2020 3MT Contest and co-delivering a tutorial on privacy-preserving distributed optimization at EUSIPCO 2024. She actively reviews for conferences like NeurIPS, ICLR, and journals such as TPAMI and TIFS. Research Themes: Privacy-preserving distributed algorithms, federated learning security, differential privacy, and adversarial machine learning. Recent Trends: Focus on securing AI systems (e.g., LLM vulnerabilities, federated clustering privacy), quantization for privacy, and theoretical bounds in decentralized learning. Awards: 2020 EUSIPCO 3MT Winner (outstanding finalist in EURASIP's annual doctoral research competition). Grants/Projects: Co-PI of the AI:SECURITY project (2025-2029) addressing AI security threats like phishing and malicious actors. Labs/Teams: Leads the Cyber Security group at Aalborg's Copenhagen campus, focusing on theoretical and applied research in secure distributed systems.

Stéphane Doncieuxمشاهده پروفایل

استاد

• Cognitive Robotics
• Developmental Robotics
• Evolutionary Robotics
• +۱۶ مورد دیگر

Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +۶ مورد دیگر

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

• Aeronautics
• Mechanical Engineering
• Fluid Dynamics
• +۵ مورد دیگر

Marco Raiola is an Associate Professor at the Department of Aerospace Engineering , Universidad Carlos III de Madrid (UC3M). His research focuses on fluid dynamics, turbulence, and aerodynamics, with applications in flow diagnostics, heat transfer, and control systems. Research Interests: Turbulent flows, data-driven modeling, particle image velocimetry (PIV), convective heat transfer, and bio-inspired aerodynamics. Projects: Principal researcher in INFLUENTIA-CM-UC3M (2024-2026) and Diagnóstico del ruido de chorro (2022-2025). Collaborator in EU-funded initiatives like HumanIC and ODE4HERA . Contact: Email mraiola@ing.uc3m.es | ORCID: 0000-0003-2744-6347

Karl-Erik Årzénمشاهده پروفایل

استاد

• Control Engineering
• Cyber-Physical Systems
• Real-Time Systems
• +۹ مورد دیگر

Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.