جست‌وجوی استاد و پژوهشگر

Albert H. Titusمشاهده پروفایل

استاد

• Wearable Sensors
• Biosensors
• Bioinstrumentation
• +۵ مورد دیگر

Albert H. Titus is a Professor in the Department of Biomedical Engineering and an Adjunct Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York. He serves as Associate Vice President for Regulatory Support in the Office of the Vice President for Research and Economic Development. His research focuses on analog VLSI design for neuromorphic visual processing, biosensors, wearable devices, optoelectronic systems, and neural networks. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology (1997) MS in Electrical Engineering, University at Buffalo (1991) BS in Electrical Engineering, University at Buffalo (1989) Research Interests: His work spans wearable and implantable sensors, bioinstrumentation, neural network-based visual processing, analog VLSI implementations, optoelectronics, and electronic packaging. He pioneered CMOS-based neuromorphic systems and developed patented technologies for glare sensing and RF power calorimetry. Publication Trends: His recent articles emphasize CMOS-integrated sensors, machine learning for bioimpedance analysis, implantable medical devices, and xerogel-based optical biosensors. These works bridge biomedical engineering and microelectronics. Scientific Recognition: He is a Fellow of the National Academy of Inventors and has received the SUNY Chancellor’s Award for Excellence in Service (2017), NSF CAREER award, and Western New York Inventor of the Year (2010). His inventions include a patented low-power glare sensor (U.S. Patent 7,586,079) featured in Popular Science’s 2011 Top Ten Inventions. Academic Leadership: As a faculty member, he has supervised nearly 20 PhD and over 40 MS students, while teaching courses in circuits, IC design, sensors, and signal processing across electrical and biomedical engineering disciplines.

Daniel Oropezaمشاهده پروفایل

استادیار

• Additive Manufacturing
• Materials Science
• Aerospace Engineering
• +۷ مورد دیگر

Dr. Daniel Oropeza is an Assistant Professor in the Materials Department at the University of California, Santa Barbara (UCSB), within the College of Engineering. His research focuses on advancing materials and manufacturing technologies for aerospace systems and extreme environments, emphasizing process-microstructure-property relationships. He leads the Materials and Manufacturing for Aerospace and Extremes (MMAX) Lab, which develops novel techniques for powder synthesis, additive manufacturing, and ceramic processing. Education: Ph.D. in Mechanical Engineering (MIT, 2021) M.S. in Aeronautics and Astronautics (Stanford, 2014) B.S. in Aerospace Engineering (UT Austin, 2012) Research Interests: His work spans powder synthesis (e.g., ultrasonic atomization of refractory alloys), additive manufacturing (porous materials, reactive binder jetting), and functional ceramics for applications in hypersonics, space propulsion, and robotics. The MMAX Lab integrates material science, mechanical engineering, and advanced manufacturing testbeds to enable responsive manufacturing solutions. Awards & Grants: LLNL Early Career UC Faculty Initiative Award (2024) Global Young Investigator Award (ACerS, 2025) ONR Grant for Ultrasonic Atomization Research (2024) CNSI Challenge Grant for UC M 2 ADE Consortium (2024) Advising & Labs: He mentors a team of graduate and undergraduate students in the MMAX Lab, focusing on projects like NASA-funded research on refractory metal alloys for space propulsion. The lab collaborates with national labs (e.g., LLNL) and industry partners to bridge fundamental research and applied technologies. Labs/Teams: MMAX Lab develops custom equipment for powder bed fusion, nanoparticle jetting, and reactive binder jetting systems. Current projects include ultra-high temperature ceramics (UHTCs) for extreme environments and multi-material manufacturing for defense and energy applications.

Bart J. Hengeveldمشاهده پروفایل

استادیار

• Internet of Things (IoT) Interaction Design
• Sound Design
• Tangible Interaction
• +۳ مورد دیگر

Bart J. Hengeveld is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Industrial Design department within the Faculty of Industrial Design. He leads the Future Everyday research cluster, focusing on the intersection of sound design and IoT systems. His work explores how embedded technologies can communicate through sound and tangible interaction in ways that enhance human experience. Education: BSc/MSc in Industrial Design Engineering, Delft University of Technology (2003) PhD in Industrial Design, TU/e (2011), with concurrent training in double bass at Codarts Conservatory Research Interests: Acoustic communication in IoT systems Tangible interaction design Social implications of ubiquitous computing Sound as a medium for human-technology interaction Recent Research Trends: Focus on multimodal interfaces combining sound/light/haptics Studies on sound design for healthcare and smart living environments Investigations into ethical and inclusive IoT soundscapes Awards: Two Teacher of the Year awards at TU/e, reflecting his commitment to pedagogical excellence. Advising & Projects: Supervised 113 student projects including interactive installations like 'Naktis' and 'Sonostapp' Leading the €1.5M 'Role of Sound in Hybrid Work Environments' project (2023–2026) Organized TEI 2016 conference and serves on its steering committee Teams/Labs: Core member of TU/e's Future Everyday cluster, collaborating with multidisciplinary teams on embodied interaction research.

Alireza Ramezaniمشاهده پروفایل

دانشیار

• Robotics
• Control Theory
• Nonlinear Systems
• +۳ مورد دیگر

Alireza Ramezani is an Associate Professor of Electrical and Computer Engineering at Northeastern University, leading the SiliconSynapse Lab. He focuses on bio-inspired robotics, nonlinear systems, and robot locomotion, with a particular emphasis on morphological design and control inspired by biological systems. His work integrates control theory and experimental robotics to develop robots capable of navigating confined spaces, such as caves and ducts, using mechanisms derived from bat movements. Education : PhD, Mechanical Engineering, University of Michigan (2014) MS, Mechanical Engineering, ETH Zurich (2010) BSc, Mechanical Engineering, Iran University of Science and Technology (2007) Research Interests : Design of robots with non-traditional morphologies Nonlinear feedback control systems Legged and fluidic-based locomotion Bio-inspired robotics and biology-driven engineering Awards : 2024 ASME Rising Star Award 2024 NSF CAREER Award 2022 NASA Game Changing Program Award Science Magazine Top 5% Research Output (2020) Advising & Labs : Ramezani mentors students in projects like the NASA-funded “Crater Observing Bio-inspired Rolling Articulator” and oversees the SiliconSynapse Lab, which develops robots for space exploration and confined environments. Notable advisees include Henry Noyes, a NASA Space Technology Fellow. Labs/Teams : His lab collaborates with institutions like NASA’s Jet Propulsion Lab (JPL) on projects such as the Mars Multi-modal Morphing (M4) Rover and bio-inspired snake robots for lunar crater exploration.

Kais Atallahمشاهده پروفایل

استاد

• Electromechanical Energy Conversion
• Magnetic Torque/Force Transmission Systems
• Renewable Energy Systems
• +۴ مورد دیگر

Professor Kais Atallah is a Professor in Electrical Engineering at the University of Sheffield's School of Electrical and Electronic Engineering, with a secondary role as a Lecturer in Aerospace Engineering. He joined the University in 1989 as a PhD student and became academic staff in 2000. His research focuses on electromechanical energy conversion, magnetic torque systems, and applications in renewable energy, electric vehicles, and aerospace actuation. He is a member of the Centre for Research Into Electrical Energy Storage and Applications (CREESA) and leads the Electrical Machines and Drives Research Group. His research interests include novel drivetrain designs for wind turbines, flywheel systems, and fault-tolerant machine systems for safety-critical aerospace applications. He has advised multiple PhD students and contributed to over 150 publications, with recent work emphasizing magnetic gear design, high-performance ferrite machines, and modular multilevel converters. His articles explore themes like high-efficiency power transmission, magnetic gear optimization for wind turbines, and traction machine control systems. Collaborations include work on subsea ROV propulsion and flywheel energy storage. He oversees research labs focused on advanced electrical machine design and energy storage solutions.

• Digital Circuits
• Digital Signal Processing
• Machine Learning
• +۱۰ مورد دیگر

Maurizio Martina is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He is a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center and serves as an Associate Editor for the IEEE Transactions on Circuits and Systems I (2018-2023). His research focuses include: Digital circuits and signal processing Machine learning hardware architectures RISC-V extensions and post-quantum cryptography VLSI design for edge computing and IoT Recent publications emphasize cryptographic hardware implementations (CHIMERA, Keccak co-processors), RISC-V integration methodologies, and privacy-preserving neural network frameworks. His work spans VLSI architectures for video processing, bio-inspired electronics, and error correcting codes, with applications in cybersecurity, robotics, and biomedical systems. Scientific Recognition : Premio Nazionale Innovazione (2013) Premio dei Premi (2014) He supervises 12 PhD students across cycles 35-40 in Electrical, Electronics and Communications Engineering, including: Valeria Piscopo (2024-in progress) Alessandra Dolmeta (2022-in progress) Luigi Giuffrida (2022-in progress) Walid Walid (2019-2023) As part of the VLSILAB Group , his research explores hardware accelerators for machine learning, post-quantum cryptography on RISC-V, and bio-inspired embedded systems. Teaching activities include courses on Integrated Systems Architecture and Hardware & Wireless Security at Politecnico di Torino and Università di Pavia.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Huy T Tranمشاهده پروفایل

استادیار

• Autonomy
• Reinforcement Learning
• Artificial Intelligence
• +۵ مورد دیگر

Huy T Tran is an Assistant Professor in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign's College of Engineering, with additional appointments at the Applied Research Institute. His research focuses on the intersection of robotics, artificial intelligence, and multi-agent systems, with applications spanning autonomous navigation, critical infrastructure resilience, and intelligent transportation. Dr. Tran earned his Ph.D. in Aerospace Engineering from Georgia Institute of Technology in 2015, following advanced degrees from Georgia Tech and University of Wisconsin-Madison. His academic journey includes research assistant professor positions before achieving his current assistant professor role in 2021. He previously worked as a Senior Multi-Disciplinary Systems Engineer at The MITRE Corporation and served as a Visiting Scholar at the Air Force Institute of Technology. His research interests encompass Autonomy, Reinforcement Learning, Artificial Intelligence, Machine Learning, Robotics, Multiagent Systems, Intelligent Transportation Systems, and Critical Infrastructure Resilience. As director of the Lab for Intelligent Robots and Agents (LIRA), he leads cutting-edge research in autonomous systems that interact with humans and other robots. His work has evolved from foundational resilience modeling in aerospace systems toward increasingly sophisticated AI applications in multi-robot coordination and explainable decision-making. Dr. Tran's publication record demonstrates a clear trajectory toward explainable AI and human-AI collaboration, with recent work focusing on generating explanations for reinforcement learning policies, coordination in ad hoc teams, and neuro-symbolic approaches to robot policy interpretation. His research bridges theoretical advances with practical applications in air traffic control, field robotics, and critical infrastructure management. Best Paper Award: Theoretical (2016 Complex Adaptive Systems Conference) Selected for oral presentation at IROS 2023 Workshop 27% full paper acceptance rate at AAMAS 2022 44% acceptance rate at ICRA 2020 As an educator, Dr. Tran teaches core aerospace courses including Computational Systems Engineering, Aerospace Numerical Methods, and Reinforcement Learning. He has secured significant research funding from NASA's Transformational Tools and Technologies program, ARL A2I2 program, ONR Science of AI program, and DARPA. His current projects span ad hoc teaming in multi-robot systems, collective autonomous air mobility, hierarchical reinforcement learning, and interpretable AI agents.

• Lean Construction
• Real-time Production Control
• Location Based Management Systems
• +۷ مورد دیگر

Olli Seppänen serves as Associate Professor in Civil Engineering at Aalto University's School of Engineering, specializing in operations management for construction productivity improvement. He coordinates the Vision 2030 consortium—comprising 13 Finnish construction and design firms—to develop industrialized building methods for 2030, while leading multiple Business Finland-funded research initiatives focused on digital construction workflows and real-time monitoring. His research centers on lean construction principles, location-based management systems, and digital transformation through IoT, AI, and robotic vision. Key focus areas include prefabrication optimization, construction logistics, and shifting work off-site to industrialize processes. He aims to solve industry-wide productivity challenges by creating real-time situational awareness and implementing takt production systems for workflow stability. Recent publications (2024-2025) reveal strong emphasis on digital twin frameworks, semantic modeling for quality assurance, and AI applications in risk management. His work bridges theoretical lean construction concepts with practical implementations, particularly in real-time resource tracking, waste reduction in MEP work, and cross-sector learning from high-performing teams. Seppänen has received significant recognition including: School of Engineering doctoral dissertation award (2024) Best paper at IEEE Wireless Sensors Conference (2019) Nordic Conference best paper award for PhD research (2019) DSc dissertation award (2010) As principal investigator, he manages: Vision 2030 consortium projects (2-3 annually; PI for two current projects) iCONS: Real-time resource flow monitoring via indoor positioning RECAP: Deep learning analysis of progress/quality from images/point clouds DiCtion: Integrated data systems for real-time stakeholder situation pictures He actively contributes to the "Performance in Building Design and Construction" research group and leverages the Vision 2030 consortium as a collaborative platform for industry transformation, driving adoption of digitalized, industrialized construction methods through academic-industry partnerships.

Elliot Hawkesمشاهده پروفایل

دانشیار

• Dynamic Systems
• Control & Robotics
• Micro & Nano Technology
• +۵ مورد دیگر

Elliot Hawkes is an Associate Professor in the Department of Mechanical Engineering at the University of California, Santa Barbara (UCSB). His research bridges design, mechanics, and non-traditional materials to develop robust, adaptable, human-safe robots for uncertain environments. He leads the Hawkes Lab, focusing on bio-inspired microstructured adhesives, nonlinear compliant mechanisms, soft actuators, exoskeletons, and growing robots. PhD from Stanford University, 2015 Postdoctoral Scholar at Stanford's CHARM Lab, 2015-2016 Assistant Professor at UCSB since 2016 Current projects include: Material-like robotic collectives with spatiotemporal control Variable friction shoe for locomotor therapy High-force soft actuators for industrial applications Vine-inspired robots for search and rescue Growing robots for biomedical and environmental use Recent publications in Science and Nature highlight breakthroughs in soft robotics and human-safe actuation. His team has received multiple NSF GRFP awards and a UCSB Regents Fellowship. The lab holds patents in adhesive gripping, soft actuation, and reconfigurable robotics.

Kaidi Yangمشاهده پروفایل

استادیار

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +۵ مورد دیگر

Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.

Manoj Karkeeمشاهده پروفایل

استاد

• AI in Agriculture
• Agricultural Robotics
• Precision Agriculture
• +۳ مورد دیگر

Dr. Manoj Karkee is the Norman R. and Sharon R. Scott Professor of Agriculture and Life Sciences at Cornell University's Department of Biological and Environmental Engineering. He leads the #AgRobotics Lab, focusing on AI, robotics, and automation for precision agriculture. His research includes robotic systems for crop monitoring, harvesting, and sustainable farming practices. Education: PhD in Agricultural Engineering and Human-Computer Interaction, Iowa State University (2009) ME in Remote Sensing and GIS, Asian Institute of Technology (2005) BE in Computer Engineering, Tribhuvan University (2002) Associate Degree in Civil Engineering, Tribhuvan University (1997) Research Interests: AI-driven robotic solutions for fruit harvesting and crop management Soft robotics for orchard operations Autonomous crop monitoring with sensor integration Modular robotic systems for small-scale and urban farming Awards: 2020 Rain Bird Engineering Concept of the Year 2019 Pioneer in AI and IoT (Connected World) CIGR Next Generation Leader (2015) Advising & Collaboration: Dr. Karkee leads a multidisciplinary lab collaborating with industry partners and global institutions to translate research into scalable technologies. He previously directed the Washington State University Center for Precision and Automated Agricultural Systems. Labs/Teams: The #AgRobotics Lab at Cornell develops AI and robotics solutions for labor-intensive agricultural tasks, emphasizing precision, sustainability, and economic viability.

• Sensor technology
• Electrical measurement technology
• Robotics
• +۲ مورد دیگر

Hubert Zangl is a Professor at the University of Klagenfurt and Head of the Institute for Intelligent System Technologies . He serves as Chairman of the Information Technology Curricular Commission and participates in the Faculty Conference of the Faculty of Technical Sciences. Key research areas include: Sensor technology Electrical measurement technology Robotics Signal processing Electronics Recent research trends focus on: High-fidelity FMCW radar simulation frameworks Energy-efficient sensor systems Printed electronics for structural health monitoring Uncertainty propagation in measurement science Modular robotics with secure transducer identification Capacitive tactile sensing for robotic grasping Contact: Hubert.Zangl@aau.at

Jie Xuمشاهده پروفایل

پژوهشگر

• Polymer Electronics
• Flexible Electronics
• Sustainable Materials
• +۴ مورد دیگر

Jie Xu is a Scientist at Argonne National Laboratory and a CASE Affiliated Scientist at the University of Chicago, Pritzker School of Molecular Engineering . Her research focuses on engineering durable, scalable, and sustainable polymer semiconductors for skin-like electronics and autonomous material discovery. Education : PhD in Chemistry (Nanjing University), Postdoctoral Fellow (Stanford University) Her research bridges polymer physics , self-driving laboratories , and AI-guided material synthesis to address challenges in stretchable electronics, recyclable polymers, and energy-efficient manufacturing. She pioneered polymer circuits that remain conductive under extreme deformation and developed the first roll-to-roll mass-production method for stretchable semiconductors. Her 15 most recent articles highlight advancements in AI-driven polymer discovery , biodegradable electronics , and multi-modal energy dissipation . Key themes include autonomous experimentation , hydrogen-bonded polymer systems , and machine learning for conjugated polymers , with applications in wearable medical sensors , soft robotics , and human-computer interfaces . Scientific accolades include the Materials Research Society Postdoctoral Award , MIT Technology Review’s Innovators Under 35 , and recognition as a Scialog Fellow . She serves on editorial boards for APL Machine Learning and Flexible Electronics , and her team at Argonne includes postdocs and students working on self-driving labs and degradable polymers .

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.