Find Academic Supervisors

Richard LarssonView profile

Researcher

• Climate Science
• Atmospheric Radiation
• Remote Sensing Technology
• +3 more

Dr. Richard Larsson is a Researcher at the University of Hamburg's Department of Earth System Sciences, affiliated with the Meteorological Institute. His work focuses on atmospheric radiation, climate science, and remote sensing technologies. He contributes to planetary atmosphere studies, particularly in Mars exploration and Earth's middle atmosphere dynamics. His research integrates advanced radiative transfer modeling, instrument development for space missions, and data assimilation techniques. Key research areas include: Zeeman effect analysis for magnetic field measurements Development of the Atmospheric Radiative Transfer Simulator (ARTS) Design of terahertz sensors for Martian atmospheric profiling Multi-source observation of electrojets and ionospheric phenomena His recent work emphasizes synergizing remote sensing data from satellites (e.g., EZIE mission) and ground-based systems. Projects like the Terahertz Radiometer for Mars and SMILES observation spectra demonstrate his expertise in instrument feasibility studies and atmospheric composition analysis. Research highlights include advancements in radiative transfer modeling for non-LTE conditions and halocarbon radiative forcing estimation. His contributions to climate science involve improving radiative calculation efficiency through sparse quadrature methods. Larsson collaborates on international missions like the Electojet Zeeman Imaging Explorer (EZIE) and contributes to Mars microsatellite sensor development. His work bridges computational modeling with practical instrumentation for planetary and terrestrial atmospheric studies.

• Structural Dynamics
• Railway Engineering
• Structural Health Monitoring
• +7 more

Anders Rønnquist is a Professor and Head of the Department of Structural Engineering at NTNU's Faculty of Engineering. His research focuses on structural dynamics, railway infrastructure, and timber structures, with emphasis on reliability, safety, and innovative monitoring techniques. Collaborations include KTH Stockholm, University of Porto, and industry partners like Elektromotus and Vilnius-based entities. Research Interests Dynamic behavior of railway catenary systems and bridges Structural health monitoring using advanced sensors and machine learning Timber frame dynamics and connection stiffness modeling Wind-structure interaction in long-span bridges Conceptual form-finding in architectural engineering Recent Research Trends Recent work emphasizes data-driven approaches: Kalman filters for crosswind load estimation, deep learning for structural defect detection, and surrogate models for pantograph-catenary systems. Studies also address long-term monitoring of timber buildings and fatigue reliability of railway bridges under evolving traffic conditions. Education & Teaching Coordinates the Teknostart Master's program in Civil & Environmental Engineering. Teaches advanced courses in structural dynamics, steel structures, and railway catenary systems at NTNU. Active in interdisciplinary education bridging structural engineering and architecture. Research Groups Structural Dynamics Group Conceptual Structural Design Group

He BaiView profile

Associate Professor

• Unmanned Systems
• Multi-Agent Systems
• Nonlinear Control
• +7 more

He Bai is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Oklahoma State University (OSU), part of the College of Engineering, Architecture and Technology (CEAT). His research focuses on unmanned systems in GPS-denied environments, multi-agent systems, nonlinear control, atmospheric turbulence modeling, and human-robot interaction. He advises OSU's Mercury Robotics Society and Automation Society and serves as a faculty adviser for graduate and undergraduate students. Education includes a Ph.D. (2009), M.S. (2007), and B.E. (2005) in Electrical Engineering and Automation from Rensselaer Polytechnic Institute and the University of Science and Technology of China. Research interests emphasize robust control strategies for autonomous systems, including cooperative navigation, adaptive control under disturbances, and machine learning applications in robotics. His work addresses challenges like wind estimation for unmanned aerial systems (UAS) and human-vehicle collaboration in mixed traffic environments. Professional roles include Associate Editor for IEEE Control System Letters and the IEEE Control System Society Conferences. Recent publications explore reinforcement learning for multi-agent coordination, decentralized Bayesian learning, and turbulence impact on UAS navigation. Advising and lab involvement includes directing the Mercury Robotics Society and collaborating on projects like the intelligent cooperative driving testbed. His research bridges theoretical control systems with practical applications in autonomous systems and robotics.

• Control Engineering
• Robotics
• Mechatronics
• +3 more

Stephen Mascaro is an Associate Professor (Lecturer) in the Department of Mechanical Engineering at the University of Utah, where he has served since 2005. He currently holds the role of Director of Robotics Studies, effective January 2024. His academic journey includes degrees from Clarkson University (BS in Mechanical Engineering and BA in Physics, 1995), and advanced degrees from MIT (MS 1997, PhD 2002 in Mechanical Engineering). His research focuses on control engineering, mechatronics, robotics, and sensor systems. Notable contributions include fingernail imaging for tactile force sensing, robotic tendon modeling, and biomimetic actuators. He has secured grants such as the NRI-Small grant for grasp force research. His work bridges robotics, biomedical engineering, and haptics, with applications in surgical robotics, human-robot interaction, and sensor development. Mascaro teaches courses in Mechatronics and System Dynamics, emphasizing hands-on learning. He actively mentors FIRST Robotics teams and engages in community outreach, including mentoring high school robotics competitions. His awards include the 2024 Top 15% Instructor Award from the University of Utah. His lab develops innovative sensors and robotic systems, integrating principles from biomechanics and materials science. Current projects include real-time tactile sensing via deep learning and robotic tendon repair simulation. His work is published in IEEE Transactions, Journal of Dynamic Systems, and other leading journals, with over 40 peer-reviewed articles since 1998.

• Robotics
• Machine Learning
• Artificial Intelligence
• +7 more

Nguyen Tran is a Doctoral Researcher affiliated with the School of Business and the Department of Information and Service Management . Their work focuses on advancing robotic grasping and manipulation techniques through data-driven and physics-based approaches. Research Interests: Robotics, Soft Robotics, Machine Learning, Human-Robot Interaction, Haptics, and physical property modeling for robotic systems. Recent Work: Developments in deformable object manipulation, 6-DOF grasp sampling, and multi-fingered hand designs inspired by human anatomy. Technical Contributions: Novel algorithms like SPONGE for contact prediction, force control systems for safe grasping, and simulation-based quality metrics for deformable object interactions.

Michael GevelberView profile

Associate Professor

• Materials Processing
• Energy Efficiency
• Building Systems
• +4 more

Michael Gevelber is an Associate Professor in the Department of Mechanical Engineering at Boston University’s College of Engineering, and an affiliated faculty member of the Institute for Global Sustainability (IGS). He holds dual affiliations with the Division of Materials Science and Engineering and the Division of Systems Engineering. His work bridges advanced materials processing and sustainability, focusing on real-time control systems for manufacturing processes such as plasma spray, electrospinning, and crystal growth. He is also a leader in energy efficiency research, particularly in optimizing building HVAC systems and reducing energy consumption in commercial and university buildings. Gevelber earned his PhD, MS, and BS in Mechanical Engineering and Physics from MIT and Brown University, respectively. His research lab, the Advanced Materials Process Control Laboratory, develops control solutions to enhance manufacturing precision and sustainability. He co-founded Aeolus Building Efficiency (winner of the 2013 MIT Clean Energy Contest) and Cyber Materials LLC, which commercialize his innovations in control systems. His sustainability efforts include leading Boston University’s first campus energy audit, serving on the Sustainability Committee, and co-chairing the Energy Working Group. His research integrates interdisciplinary approaches, combining sensor development, computational modeling, and system design to address challenges in both industrial manufacturing and urban energy systems.

Boyang LiView profile

Lecturer

• Uncrewed Aerial Vehicle (UAV)
• Flight Dynamics
• Model Predictive Control
• +3 more

Dr. Boyang Li is a Lecturer in Aerospace Systems Engineering at the University of Newcastle, Australia. His career includes roles as Research Assistant Professor at Hong Kong Polytechnic University (2020–2023), where he founded the Autonomous Aerial Systems Laboratory (AASL), and postdoctoral research at Nanyang Technological University (Singapore) and the University of Edinburgh (UK). He holds a PhD from Hong Kong Polytechnic University and B.Eng./M.Eng. degrees from Northwestern Polytechnical University, China. Education: Doctor of Philosophy, Hong Kong Polytechnic University (2018) M.Eng., Northwestern Polytechnical University (2015) B.Eng., Northwestern Polytechnical University (2012) Research Interests: Dr. Li's work focuses on advancing uncrewed aerial vehicle (UAV) technology, including nonconventional UAV configurations (VTOL tail-sitters), flight dynamics integration with advanced control methods (e.g., Model Predictive Control), and trajectory optimization. His empirical research involves field experiments with aerial and underwater robotic systems. Key themes include autonomous navigation, sensor integration, and real-world applications in infrastructure inspection and environmental monitoring. Grants & Funding: NSW Space Research Network Project ($14,922, 2025) RuralAI Sensor Cluster Development ($15,000, 2023) Lab2Field RuralAI Kit ($15,000, 2023) AU$22.5K Start-up Fund (2023–2024) Supervision: Dr. Li has supervised/co-supervised 6 completed and 1 ongoing HDR student, focusing on UAV control systems, underwater robotics, and reinforcement learning applications. Notable advisees include Li-yu Lo (MPhil), Yefeng Yang (PhD), and Haochen Hu (MSc). Labs & Teams: He leads the Autonomous Aerial Systems Group at the University of Newcastle and collaborates with the High-speed Thermo-fluid and MAV/UAV Lab (HK PolyU). His lab develops UAV systems for applications like infrastructure inspection and advanced air mobility.

• Internet of Things Security
• Lightweight Security Systems
• Authentication Protocols
• +2 more

Dr. Sye Loong Keoh is an Associate Professor in the School of Computing Science at the University of Glasgow (Singapore campus) and Director of Research Programmes. He holds a PhD in Computing from Imperial College London and previously served as Senior Scientist at Philips Research Eindhoven. Research focuses on cyber-security for Internet of Things, including lightweight authentication protocols, key management schemes, and end-to-end communication security. Recent work explores data provenance in IoT environments and security for building management systems. Recent publications demonstrate strong focus on machine learning applications in transportation (38%), IoT security (25%), and educational technology (25%), with emerging work in adversarial robustness and formal methods. Research consistently bridges theoretical security concepts with practical implementations in smart cities and industrial systems. Leads cybersecurity research activities at University of Glasgow Singapore where his team has developed multiple authentication protocols for IoT and industrial control systems. Research funded by National Cyber Security Centre, EPSRC, Meta Reality Labs, and PETRAS National Centre.

Saraju P. MohantyView profile

Professor

• Smart Electronic Systems
• Internet of Things (IoT)
• VLSI Design
• +7 more

Dr. Saraju P. Mohanty is a Professor in the Department of Computer Science and Engineering at the University of North Texas (UNT), where he leads the Smart Electronic Systems Laboratory (SESL). He holds honorary and adjunct positions at IIIT-Naya Raipur, MNIT Jaipur, and Oriental University, Indore, in India. Education: Ph.D. in Computer Science and Engineering, University of South Florida (USF), 2003 Masters in Systems Science and Automation (AI), Indian Institute of Science (IISc), 1999 B.E. in Electrical Engineering (Honors), College of Engineering and Technology, Bhubaneswar (OUAT), 1995 Dr. Mohanty's research is centered on Smart Electronic Systems, with a strong focus on IoT, VLSI, hardware security, and healthcare applications. His work integrates machine learning, embedded systems, and nanoelectronics to develop secure and efficient solutions for smart cities, agriculture, and medical devices. He has authored over 550 peer-reviewed publications and five books, including a PROSE Award-winning textbook. His recent publications reflect a growing emphasis on AI-driven solutions for synthetic media detection, smart farming, driver monitoring, and personalized health. These works demonstrate a trend toward intelligent, edge-based systems that leverage sensor data and lightweight AI for real-time decision-making. Scientific Awards and Honors: Fulbright Specialist Award (2021) IEEE Consumer Electronics Society Outstanding Service Award (2020) IEEE-CS-TCVLSI Distinguished Leadership Award (2018) PROSE Award for Best Textbook (2016) Top 2% Scientist globally (PLOS Biology, 2019–2022) Multiple Best Paper and Best Poster Awards UNT Toulouse Scholars Award (2016–2017) President’s Scout Award, India (1988) Dr. Mohanty has supervised 3 postdocs, 18 Ph.D. students, 29 M.S. theses, and over 40 undergraduate research projects. Eleven of his advisees have received outstanding student awards. He has received multiple UNT Provost’s Thank a Teacher and Honors Day recognitions. His research has been funded by NSF, SRC, US Air Force, NIDILRR, and Mission Innovation. He has held key editorial roles, including Editor-in-Chief of IEEE Consumer Electronics Magazine and founding EiC of IEEE VLSI Circuits and Systems Letter. He is actively involved in IEEE leadership and conference organization, serving on steering committees for IEEE-iSES, ISVLSI, and OCIT. Laboratories and Teams: He directs the Smart Electronic Systems Laboratory (SESL) at UNT, which focuses on cutting-edge research in IoT, edge computing, hardware security, and smart healthcare. The lab fosters interdisciplinary collaboration and has produced numerous award-winning student projects and publications.

Daniele ZambonView profile

Researcher

• Graph Representation Learning
• Machine Learning
• Learning in Non-Stationary Environments
• +7 more

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.

• Humanoid Robotics
• Biped Locomotion
• Tactile Feedback Control
• +4 more

Dr. Julio Rogelio Guadarrama Olvera is a researcher and leader of the Humanoid Robotics Group at the Chair of Cognitive Systems, Technical University of Munich (Prof. Gordon Cheng). He holds a Dr.-Ing. (Engineering Doctorate) from TUM, awarded with honors in 2021 for his thesis on humanoid robot whole-body control and biped locomotion. His current role includes postdoctoral research focusing on tactile feedback control, object manipulation, and bipedal locomotion. Education: Bachelor's in Mechatronic Engineering (2011), National Polytechnic Institute, Mexico City Master's in Engineering (2013), Center for Research and Advanced Studies, National Polytechnic Institute, Mexico City PhD in Robotics (2021), Technical University of Munich Research Interests: His work centers on advanced robotics systems, particularly tactile-based control for humanoid robots, bipedal locomotion stability, and human-robot interaction. Key areas include: Development of artificial robot skin for sensory feedback Real-time control algorithms for dynamic environments Ethical and safety frameworks for collaborative robots Applications in healthcare robotics and neuroengineering Teaching: Active in teaching advanced robotics courses such as ' Modelling and Control of Legged Robots ' and ' Practical Course RoboCup@Home ' at TUM. Labs/Teams: Leads the Humanoid Robotics Group within ICS, collaborating with international teams on projects like tactile feedback systems and robot skin technology. Involved in EU and industry-funded robotics initiatives.

Aaron JohnsonView profile

Professor

• Robotics
• Legged and Aerial Robotics
• Bioinspired Robotics
• +12 more

Aaron Johnson is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering, where he directs the Robomechanics Lab . He also holds courtesy appointments in the Robotics Institute and the Department of Electrical & Computer Engineering. His research focuses on enabling robots to operate robustly in complex, real-world environments through innovations in robot design, control, and interaction dynamics. Ph.D., Electrical & Systems Engineering, University of Pennsylvania (2014) B.S., Electrical & Computer Engineering, Carnegie Mellon University (2008) Johnson’s research interests lie at the intersection of legged robotics, adaptive control, bioinspired design, and physics-based planning . He investigates how robots can intelligently interact with unstructured environments—such as rocky terrain, cluttered homes, or industrial sites—by integrating mechanical design, sensor feedback, and intelligent control. His lab develops platforms like Zippy (the world’s smallest bipedal robot) and Picotaur , and works on dynamic behaviors including climbing, jumping, and navigating entanglements. The most recent publications highlight a strong trend in hybrid dynamical systems, robust state estimation, terrain-aware navigation, and ethical considerations in robotics . His group advances techniques in contact-implicit control, MPC, Kalman filtering for hybrid systems, and field deployment of autonomous robots for environmental monitoring. Themes of scalability, energy efficiency, and bioinspiration recur across the work. Johnson has received several prestigious awards: NSF CAREER Award (2020) Army Research Office Young Investigator Award (2019) Best Workshop Paper Award at ICRA 2022 (Quad-SDK) David Thuma Laboratory Project Award (2008) Honorable mention, CRA Outstanding Undergraduate Award (2008) He actively mentors students through his graduate course 24-775 Robot Design & Experimentation and lab outreach programs, including partnerships with Gwen’s Girls. He has secured grants for fielding legged robots in real-world applications, such as soil contamination sampling and hill climbing. Johnson co-organizes the CMU Locomotion Seminar and is committed to diversity, equity, and inclusion in robotics, co-authoring the Black in Robotics Reading List and advocating for ethical research practices. The Robomechanics Lab emphasizes ethical research, academic reform, equitable access, and community support. It develops full-stack frameworks like Quad-SDK and conducts field experiments in diverse environments—from deserts to power plants—pushing the boundaries of where robots can go and what they can do.

Bahram RavaniView profile

Professor

• Kinematics
• Dynamics
• Highway Safety
• +7 more

Bahram Ravani is a Distinguished Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis, affiliated with the College of Engineering. He serves as Co-Director of the AHMCT Research Center and Campus Director of CITRIS. His research focuses on kinematics, dynamics, impact biomechanics, highway safety, intelligent transportation systems, design and manufacturing, robotics, and computational geometry. He has contributed to advanced manufacturing processes like additive manufacturing, digital twins, and factory layout optimization using AI techniques. His work spans interdisciplinary areas including 5G-enabled manufacturing systems, phase field simulations for material fatigue, and the application of reinforcement learning in production systems. He has been recognized with the College of Engineering Outstanding Faculty Awards and honored by the American Society of Mechanical Engineers (ASME). Research Highlights: Developed physics-based digital twin models for machine tools and material flows. Advanced 5G performance evaluation for industrial applications. Pioneered simulation frameworks for brownfield factory planning and additive manufacturing defect prediction. Contributed to highway safety innovations like wrong-way driving mitigation systems. His collaborative projects include partnerships with Caltrans on transportation safety and federal agencies on advanced manufacturing systems. He maintains active involvement in both foundational engineering research and applied technologies.

Christopher KittsView profile

Professor

• Robotics
• Multi-robot Systems
• Control Systems
• +7 more

Christopher Kitts is the William and Janice Terry Professor of Mechanical Engineering at Santa Clara University's School of Engineering, where he also serves as Associate Dean of Research and Faculty Development and Faculty Director of the Ciocca Center for Innovation and Entrepreneurship. His external appointments include Associate Researcher at the Monterey Bay Aquarium Research Institute (MBARI) and Visiting Researcher at Lawrence Livermore National Laboratory, reflecting his interdisciplinary work in field robotics. He earned his Ph.D. and M.S. in Mechanical and Aerospace Engineering from Stanford University, M.P.A. in International & Defense Policy from the University of Colorado, and B.S.E. in Mechanical & Aerospace Engineering from Princeton University. Prof. Kitts' research centers on field robotics for scientific discovery and education, with key focus areas: Multi-robot systems design and coordination Collaborative Human/Cobot Control architectures Model-Based Anomaly Management for spacecraft Advanced robotic platforms for underwater, terrestrial, aerial, and space environments His recent publications (2022-2025) demonstrate growing emphasis on human-robot interaction through transformer networks and emotion detection, alongside continued innovation in marine robotics and adaptive navigation for environmental monitoring. Scientific recognition includes: ASME Fellow and AIAA Associate Fellow Multiple Santa Clara University awards across teaching, research, and service NASA Group Achievement Awards (8 total) Industry honors including Edison Innovation Award and AIAA National Award His program has secured tens of millions in funding from government agencies and industry partners for student-operated NASA spacecraft missions and MBARI marine robotics projects, resulting in documented scientific discoveries like tsunami wave evidence in Lake Tahoe. He directs the Robotic Systems Laboratory and Santa Clara's Maker Lab, creating hands-on educational frameworks that integrate entrepreneurial thinking with engineering practice.

Timo BetzView profile

Professor

• cell mechanics
• tissue mechanics
• biophysics
• +10 more

Prof. Dr. Timo Betz is a Professor of Biophysics at the Third Institute of Physics, Georg August University Göttingen, leading the Betz-Lab located at Friedrich-Hund-Platz 1, Room F.03.123, 37077 Göttingen, Germany. His research focuses on deciphering the fundamental physical processes that confer stability and robustness to living systems despite their complexity, non-equilibrium nature, and non-linear dynamics. The Betz-Lab investigates how mechanics influences biological function across multiple scales, from intracellular processes to tissue-level phenomena. Their work spans cell mechanics, tissue mechanics, and the development of novel biophysical measurement techniques. A central theme is understanding the intricate dependencies between biochemical signaling, mechanical forces, and viscoelastic properties in living systems. The lab develops advanced instrumentation including optical tweezers-based microrheology systems and custom microscopy approaches to quantify forces and tension in 3D biological environments. Analysis of Dr. Betz's recent publications (2023-2025) reveals a strong emphasis on intracellular mechanics, particularly through the development of the 'Mean Back Relaxation' (MBR) method for characterizing active processes from spontaneous fluctuations. His research spans diverse biological systems including zebrafish embryogenesis, cancer cell migration, and skeletal muscle mechanics, consistently bridging fundamental physics with biological applications. Notable methodological contributions include BeadBuddy software for analyzing elastic stress sensors and advanced traction force microscopy techniques for nonlinear materials. Dr. Betz leads a vibrant research team comprising multiple PhD students, postdoctoral researchers, and technical staff. His laboratory has developed significant software tools including BeadBuddy for analyzing fluorescent force sensors in biological tissue and collagen deformation analysis tools. The lab receives funding from multiple sources as indicated by their acknowledgments of support. The Betz-Lab maintains several active research projects: Intracellular passive and active microrheology to study organelle distribution and intracellular forces Collective cell migration in development, using zebrafish epiboly as an in vivo model Collective cancer cell migration in structured 3D environments Investigating mechanical niche cues in skeletal muscle stem cell activation Developing specialized tissue chambers for high-resolution imaging of living muscle and connective tissue Designing new instrumentation and analysis software for biophysical measurements