جست‌وجوی استاد و پژوهشگر

O. Burak Ozdoganlarمشاهده پروفایل

استاد

• Micro and Nano Manufacturing
• Mechanics of Material Removal
• Dynamics and Vibrations in Micro-scale Systems
• +۳ مورد دیگر

O. Burak Ozdoganlar is a Professor in the Departments of Mechanical Engineering and Biomedical Engineering at Carnegie Mellon University. His research focuses on multiscale (meso/micro/nano) manufacturing science, combining theoretical, numerical, and experimental analyses to advance three-dimensional device fabrication. He leads the Multiscale Manufacturing and Dynamics Laboratory (MMDL), with applications spanning medical, biomedical, energy, robotics, and aerospace fields. B.S., Istanbul Technical University, Turkey M.S., Ohio State University, Columbus Ph.D., University of Michigan, Ann Arbor Post-doc, University of Illinois at Urbana-Champaign Senior Member of Technical Staff, Sandia National Labs His work addresses mechanics of micro-scale material removal, dynamics of micro-scale structures, novel micro/nano-manufacturing techniques, and application-driven research. Key contributions include scalable fabrication of microneedle arrays, freeform 3D ice printing for vascular networks, and high-density soft-matter electronics. His research emphasizes predictability and precision in manufacturing processes. Recent publications highlight advancements in dissolvable microneedle arrays for transdermal delivery, freeform 3D printing of ice structures for biomimetic vascularization, and scalable methods for porous and soft-matter electronics. His work bridges fundamental mechanics with medical device innovation. Blackall Machine Tool and Gage Award, ASME, 2011 Russell V. Trader Career Faculty Fellow, CMU, 2009-2011 NSF CAREER award, 2006 Kuo K. Wang Outstanding Young Engineer, SME, 2007 Organizer, 'Manufacturing...The Future' symposium, NAE EU-American Frontiers Conference, 2011 Best paper award, NAMRI SME, 2007-2008 Struminger Teaching Fellow, CMU, 2007-2008 Ozdoganlar's Multiscale Manufacturing and Dynamics Laboratory (MMDL) develops cutting-edge manufacturing solutions for biomedical applications, including neural probes, cartilage implants, and biosensors. His research integrates mechanics, materials science, and process engineering to address challenges in device predictability and scalability.

Mahsa Ghasemiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Control Theory
• Active Perception
• +۳ مورد دیگر

Mahsa Ghasemi is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, leading the AKADEMI Group. Her research focuses on theoretical advancements in trustworthy sequential decision-making for autonomous systems, emphasizing human-aware collaboration and adaptation to dynamic environments. She is affiliated with the Institute for Control, Optimization and Networks (ICON). Education: PhD in Electrical and Computer Engineering from The University of Texas at Austin (2021), MSE in Mechanical Engineering (2017), and BSc in Mechanical Engineering from Sharif University of Technology (2014). Research Interests: Reinforcement learning, control theory, active perception, multi-agent systems, robotics, and online learning. Applications span disaster response, healthcare, and autonomous systems design. Key methodological directions include compositional learning, human-AI collaboration, and adaptive decision-making under uncertainty. Teaching: Courses include Reinforcement Learning Theory (ECE 59500), Introduction to Reinforcement Learning (ECE 49595), and Python for Data Science (ECE 20875). Awards: Finalist for Student Best Paper Award at the 2018 American Control Conference (ACC). Students: Current advisees include Maheed H. Ahmed, Jayanth Bhargav, and Somtochukwu Oguchienti. Past members include Lai Wei and Juan Sebastian Mateo Ruiz Bulla. Service: Editorial roles at ICRA, ICCPS, and IFAC workshops. Reviewer for top conferences (NeurIPS, ICML) and journals (Automatica, IEEE TAC). Labs/Teams: Leads the AKADEMI Group, focusing on algorithmic and theoretical research in autonomous decision-making systems.

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +۴ مورد دیگر

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

Young-Jin Chaمشاهده پروفایل

استاد

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +۵ مورد دیگر

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Fahim Khanمشاهده پروفایل

استادیار

• Computer Graphics
• Data Visualization
• Computer Vision
• +۵ مورد دیگر

Fahim Khan is an Assistant Professor in the Department of Computer Science and Software Engineering at California Polytechnic State University’s College of Engineering. He specializes in computer graphics, data visualization, computer vision, and machine learning, with a focus on applying these technologies to education, environmental monitoring, and public safety. His research emphasizes making complex data accessible through advanced tools, bridging the gap between raw data and actionable insights. He is deeply committed to inclusive education and fostering interdisciplinary collaboration. His work often integrates citizen science initiatives, empowering communities through mobile applications and machine learning. Notable projects include real-time rip current detection systems and platforms for high school students to engage in research. Khan advocates for equity in technology, designing inclusive learning environments and promoting diversity in STEM. He actively supports the university’s Learn by Doing philosophy, blending practical education with theoretical rigor. Professionally, he contributes to coastal observation networks and autonomous vehicle datasets while maintaining a balance through outdoor activities like exploring Pismo Beach. His research trends reflect a strong focus on mobile computing, environmental applications, and education technology, with recent efforts emphasizing citizen science and data-driven solutions. While no formal grants or advising records are detailed, his projects implicitly involve collaborative efforts. He is affiliated with labs focused on environmental monitoring and mobile technology development, though specific lab names are not mentioned.

Brian Surgenorمشاهده پروفایل

استاد

• Mechanical Engineering
• Machine Vision Systems
• Autonomous Vehicles
• +۳ مورد دیگر

Professor Brian Surgenor is a faculty member at Queen's University's Department of Mechanical and Materials Engineering, part of the Smith Engineering faculty. He holds a B.Sc. (1977), M.Eng. (AECL/Whiteshell), and Ph.D. (1983) in Mechanical Engineering from Queen's University. His research focuses on machine vision systems for automation, autonomous vehicle navigation, and mechatronic system design education. He has held key administrative roles including Department Head (1993-2002), Associate Dean (2008-2013), and Vice-Dean (2013-2016). His work emphasizes interdisciplinary innovation, such as the Mitchell Hall design project and contributions to Ingenuity Labs. Education: B.Sc. Mechanical Engineering, Queen's University (1977) M.Eng. Engineering Physics, McMaster University (AECL/Whiteshell) Ph.D. Mechanical Engineering, Queen's University (1983) Research interests include: - Pneumatic servosystems - Intelligent algorithms for machine vision - Off-road autonomous vehicle systems - Mechatronics education methodologies - Hybrid powertrain systems for vehicles His recent publications (2017–2024) explore autonomous systems, machine vision applications, and fuel cell hybrid technologies. Notable trends include advancements in UAV-based infrastructure inspection, terrain-adaptive autonomous driving, and low-cost machine vision solutions for small part sorting. His work bridges theoretical control systems with practical industrial automation challenges. He has contributed to laboratory design for CDIO curricula and pioneered mechatronics education through problem-based learning. His administrative leadership has shaped Queen's engineering graduate programs and research infrastructure. Currently involved in Ingenuity Labs, fostering cross-disciplinary innovation.

Melih Kandemirمشاهده پروفایل

دانشیار

• Machine Learning
• Reinforcement Learning
• Bayesian Inference
• +۳ مورد دیگر

Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.

Erisa Karafiliمشاهده پروفایل

دانشیار

• Cybersecurity
• Formal Methods in Security
• IoT Threat Models
• +۴ مورد دیگر

Erisa Karafili is an Associate Professor in Cybersecurity at the University of Southampton. She leads Teaching Methods Innovation at the GCHQ/EPSRC Academic Centre of Excellence for Cyber Security Education (ACE-CSE) and is a Champion in Security by Design at ACE-CSR. A Fellow of the Higher Education Academy, she joined the University in 2020 after roles including a Marie Curie Fellowship at Imperial College London, where she investigated cyber-attack attribution techniques. Her research focuses on formal methods applied to security, IoT threat models, and secure data sharing frameworks. Education: PhD in non-classical logics applied to multi-agent systems security from the University of Verona. Previous positions include PostDoc at Technical University of Denmark and Researcher at Imperial College London. Research Interests: Cyber-attack attribution, IoT security, formal methods in cybersecurity, data privacy, and argumentation-based reasoning for security. Awards: Higher Education Academy Fellowship. Current PhD Students: Betul Gokkaya, Mohammed Homaid Alquliti, Peter Geoffrey Williams, Steve Johnson. Active Projects: Heterogeneous Material Integrated MEMS/NEMS-Photonics Platform for Secure Communication (collaborative with Jize Yan and others).

Edward Johnsمشاهده پروفایل

دانشیار

• Robot Learning
• Robot Manipulation
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Edward Johns is an Associate Professor in the Department of Computing at Imperial College London and Director of the Robot Learning Lab. He specializes in robot learning, focusing on enabling robots to learn tasks through imitation and language-based reasoning. His expertise spans robotics, machine learning, and computer vision, with a particular emphasis on manipulation tasks requiring physical interaction with objects. He holds a BA and MEng from the University of Cambridge and a PhD from Imperial College London. Prior to his current role, he was a postdoc at UCL, a founding member of the Dyson Robotics Lab, and led the robot manipulation team there. He also served as Head of Robot Learning at Dyson (part-time, 2021–2022). His research has produced state-of-the-art capabilities such as one-shot imitation learning and language-driven task execution. Key areas of interest include sim-to-real transfer, self-supervised learning, and adaptive robotic systems. His work bridges foundational AI research with practical robotics applications, emphasizing real-world deployment and human-robot collaboration. Dr. Johns has published over 60 peer-reviewed papers, with over 4,000 citations, and has received prestigious awards including the UK-RAS Early Career Award (2023) and the Best Conference Paper Award at ICRA (2024). He is also actively involved in industry through advisory roles for robotics and AI startups. His teaching includes graduate courses on reinforcement learning and robot learning, and he collaborates extensively with labs such as the Robotics Forum and the Artificial Intelligence Network at Imperial College.

Constantin A. Rothkopfمشاهده پروفایل

استاد

• Perception and Action
• Computational Neuroscience
• Machine Learning
• +۳ مورد دیگر

Prof. Constantin A. Rothkopf is a W3 Professor at the Department of Psychology, Technische Universität Darmstadt, and a secondary member of the Department of Computer Science. He serves as Founding Director of the Centre for Cognitive Science and founding member of the Hessisches Zentrum für Künstliche Intelligenz (hessian.ai). He is also part of the European Laboratory for Learning and Intelligent Systems (ELLIS) and the DAAD Konrad Zuse Schools of Excellence in Artificial Intelligence (ELIZA). His research focuses on the interplay between perception and action, using computational models and experimental studies in humans. Current work includes eye-tracking studies in naturalistic environments, inverse optimal control models, and developing algorithms for virtual agents. Education: Ph.D. in Neuroscience and Informatics from the University of Rochester, followed by postdoctoral research at Frankfurt Institute for Advanced Studies (FIAS). He has held visiting professorships at Central European University (2017) and Columbia University (2023). Awards include an ERC Consolidator Grant (2022) and SCENE Project Funding (2025). Research Interests: Active vision, decision-making under uncertainty, sensorimotor control, and computational modeling. Key themes include how humans use sensory input to form beliefs, make decisions, and act in dynamic environments. Grants/Awards: ERC Consolidator Grant (2022), SCENE Funding (2025) Labs/Teams: Centre for Cognitive Science, hessian.ai, ELLIS Unit Darmstadt

Elliot Wright Hawkesمشاهده پروفایل

دانشیار

• Robotics
• Soft Robotics
• Mechanical Engineering
• +۳ مورد دیگر

Elliot Wright Hawkes is an Associate Professor at the Department of Mechanical Engineering , University of California, Santa Barbara . He leads the Hawkes Group, focusing on robotics innovation through design, mechanics, and non-traditional materials. His research enables robust, adaptable, human-safe robots for unstructured environments. Hawkes holds a PhD in Mechanical Engineering (2015) and MS in Mechanical Engineering (2014) from Stanford University, and a AB in Mechanical Engineering (2011) from Harvard. Research Areas : Solid Mechanics, Materials & Structures, Dynamic Systems and Control, Robotics, Biomechanics, Micro/Nanoscale Engineering Key Projects : Bio-inspired microstructured adhesives, Non-linear compliant mechanisms, High-power soft actuators, Soft exoskeletons, Growing robots His work has produced 20+ patents and 15+ recent publications spanning vine robots, soft actuators, rehabilitation devices, and extreme mobility systems. Notable awards include: Presidential Early Career Award for Scientists and Engineers (PECASE) National Science Foundation CAREER Award NASA Early Career Faculty Award 2020 Packard Fellow Northrop Grumman Excellence in Teaching Award Student Advisees (PhD/Master's): Matthew Devlin, William Heap, Sophia Paul, Nathan Usevitch, Charles Xiao, Nicholas Naclerio, David Haggerty, Lucia Gan, Patrick Slade, Cole Simpson, Mengjia Zhu, Anna Alvarez, Mijail Mendoza Flores, Laura Blumenschein, Tania Morimoto, Zachary Hammond, Muhammad Usman, Hao Jiang, Inrak Choi, and others. Laboratory Focus : The Hawkes Lab explores the intersection of design, mechanics, and materials to solve challenges in robotics, medicine, and biomechanics. Notable projects include: Material-like robotic collectives with spatiotemporal control Variable friction shoe for rehabilitation (US Patent 12,082,641) Soft robotic burrowing devices for NASA applications Light-controlled actuator arrays for extreme environments Plant root-inspired anchoring systems for granular media

• Software Engineering
• Industrial Automation
• Cloud Computing
• +۷ مورد دیگر

David Hästbacka is an Associate Professor (tenure track) at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences at Tampere University. His research focuses on software engineering, industrial automation, and energy systems, emphasizing system architecture, interoperability frameworks, and dependable IoT solutions. He leads a research group exploring edge and cloud computing, semantic integration, and smart energy systems. Education & Professional Background : While specific educational details are not provided, his academic career includes roles such as Postdoctoral Researcher in the SEMIS project (2017-2020) and extensive involvement in EU-funded initiatives like COCOP (EU H2020) and Horizon Europe projects. Research Projects : Active in high-impact projects like Hedge-IoT (Horizon Europe, 2024-2027), TwinfFlow (Business Finland), and TRINEFLEX (Horizon Europe), with a focus on industrial automation, distributed systems, and energy grids. Past projects include FEMMa (Business Finland), DisMa (Academy of Finland), and Arrowhead (ECSEL). Teaching & Supervision : Specializes in Web/Cloud architectures, IoT systems, and dependable automation technologies. Supervises students in topics like edge computing frameworks and MLOps pipelines. Technical Contributions : Develops frameworks for industrial interoperability (e.g., OPC UA PubSub integration), edge-cloud toolchains, and MLOps methodologies. His work addresses challenges in microservices, Kubernetes distributions, and semantic data integration. Labs & Teams : Leads a research group advancing automation technologies through interdisciplinary collaboration, with partnerships in industry and academia to bridge theory and practice in smart systems.

Roman Kucمشاهده پروفایل

استاد

• Robotics
• Biomimetic Sensors
• Digital Signal Processing
• +۴ مورد دیگر

Roman Kuc is a Professor of Electrical Engineering at Yale University, affiliated with the School of Engineering & Applied Science. He directs the Intelligent Sensors Laboratory, focusing on biomimetic sensors for robotics and bioengineering. His research explores brain-based devices (BBDs), sonar sensing, and neuromorphic processing inspired by biological systems. He holds a BSEE from Illinois Institute of Technology and a PhD from Columbia University. Dr. Kuc’s work bridges signal processing, robotics, and bioengineering, with applications in autonomous systems and clinical diagnostics. He has published over 200 papers and authored textbooks like Electrical Engineering in Context and The Digital Information Age . Notable honors include an honorary doctorate from the Glushkov Institute of Cybernetics and the Yale Sheffield Distinguished Teaching Award. His research themes include cognitive mapping via sonar echoes, neural network-based classification of environmental features, and biomimetic approaches to echolocation. Recent work emphasizes sensorimotor integration and robust performance in uncertain environments. Scientific awards highlight his contributions to robotics, signal processing, and education. His lab develops systems that emulate biological sensory mechanisms, aiming to advance robotics, medical applications, and assistive technologies.

Fu-Kuo Changمشاهده پروفایل

استاد

• Multi-functional materials
• Intelligent structures
• Structural health monitoring
• +۷ مورد دیگر

Fu-Kuo Chang is a Professor in the Department of Aeronautics and Astronautics at Stanford University, with a secondary affiliation in the Bio-X program. His research focuses on multifunctional materials, intelligent structures, and structural health monitoring (SHM), emphasizing applications in aerospace, robotics, and medical devices. He has pioneered work on embedded sensors, self-diagnostic systems, and energy storage composites. Academic Appointments: Professor (Stanford), Editor-in-Chief of International Journal of Structural Health Monitoring (since 2012), and Chair of the International Workshop on Structural Health Monitoring (since 1997). Honors: Multiple lifetime achievement awards in SHM, AIAA and ASME Fellowships, and the NSF Presidential Young Investigator Award (1988). Research interests include bio-inspired sensory materials, autonomous systems (e.g., 'fly-by-feel' vehicles), and multidisciplinary integration of structural mechanics, electrical engineering, and materials science. His recent work addresses challenges in smart skins for robotics, thermoplastic composites, and predictive modeling of material degradation. Publications span structural health monitoring, advanced composites, and robotics, reflecting expertise in both theoretical and applied domains. His lab, the Structures and Composites (SACL) laboratory, drives innovation in smart materials and system integration. Advising: Supervises doctoral and master’s students in aeronautics and materials science. Grants/Contributions: Active in industry and government collaborations, including roles on the US Army Research Laboratories Advisory Board and leadership in SHM industry initiatives.

Karthik Dantuمشاهده پروفایل

دانشیار

• Mobile Sensor Networks
• Robot Networks
• Networked Embedded Systems
• +۸ مورد دیگر

Karthik Dantu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, within the School of Engineering and Applied Sciences. His research focuses on mobile sensor networks, robot networks, networked embedded systems, mobile computing, wireless networks, and embedded operating systems. He leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab and has received significant funding including an NSF CAREER Award. Dr. Dantu's educational background includes: PhD in Computer Science from University of Southern California (2009) BE in Computer Science from Sri Jayachamarajendra College of Engineering (1999) His research interests center on algorithmic and systems challenges in Edge Computing Systems, with particular focus on enabling seamless vision sensing in cloud-edge environments. Dantu's work bridges mobile systems and robotics, developing novel approaches for UAV software, visual SLAM, and distributed sensing. His research addresses critical challenges in resource-constrained environments, security, and real-time performance for mobile and robotic systems, with emphasis on practical implementations that solve real-world problems in autonomous systems. Dr. Dantu's publication record shows a strong trajectory in mobile systems and robotics research, with increasing focus on edge computing applications for visual sensing. His recent work demonstrates expertise in adapting visual SLAM to edge environments, securing mobile systems through technologies like Rushmore, and developing novel approaches for UAV software reliability and depth sensing. The research spans theoretical algorithms and practical system implementations, with particular strength in bringing academic research to practical applications in robotics and mobile computing. Dr. Dantu has received several scientific honors: NSF CAREER Award on Enabling Seamless Vision Sensing in Cloud-Edge Systems Outstanding service award from the Office of International Services NSF Travel Grant for SenSys 2005 Conference Travel Grant for SIGCOMM 2002 As an advisor, Dr. Dantu has mentored numerous PhD students to completion, with graduates now working at companies like Samsung Research and Zoox Inc., or continuing academic careers as Assistant Professors. His research is supported by substantial grants including a DARPA OFFSET Sprint 4 award ($470k), an NSF CAREER award ($550k), and multiple NSF collaborative grants totaling over $1.5 million. He serves on numerous conference committees including Mobicom, MobiSys, and ICRA, demonstrating leadership in the mobile systems and robotics research communities. Dr. Dantu leads the Distributed Robotics and Networked Embedded Sensing (DRONES) Lab at UB, which focuses on developing algorithms and systems for mobile sensor networks, robot networks, and embedded sensing applications. The lab's work spans theoretical foundations to practical implementations, with particular expertise in UAV systems, visual SLAM, and edge computing for robotics, maintaining strong collaborations with industry partners and other academic institutions to advance the state of the art in mobile and robotic systems.