Find Academic Supervisors

Christopher SmithView profile

Associate Professor

• Computer Vision
• Robotics
• Wireless Sensor Networks
• +3 more

Christopher Smith is an Associate Professor in the School of Computer Science & Mathematics at Lake Superior State University, within the College of Business, Engineering, Computer Science, & Mathematics. His research focuses on computer vision, robotics, and wireless sensor networks, with applications in precision agriculture, medical imaging, and human-robot interaction. He holds a B.S. from Lake Superior State University, an M.S. from the University of Michigan, and a Ph.D. from the University of Minnesota. Education: Bachelor of Science, Lake Superior State University Master of Science, University of Michigan Doctor of Philosophy, University of Minnesota Research Interests: Dr. Smith’s work spans robotics, sensor networks, and vision-based systems. Recent projects include: Developing robust algorithms for plant phenotyping in 3D point clouds Advancing fractal-based models for image segmentation and tracking Leveraging wireless sensor networks for accessibility solutions Exploring statistical methods for dynamic system modeling Publications reflect a focus on interdisciplinary applications of computer vision and robotics, with contributions to precision agriculture, medical imaging, and autonomous systems. His work often bridges theoretical foundations and real-world implementations, emphasizing practical solutions in unstructured environments. He has no listed scientific awards or grants in the provided data, and no advisees are mentioned. His office is located in CAS 206 F.

Mushu LiView profile

Assistant Professor

• Machine Learning
• Wireless Communications
• Network Engineering
• +5 more

Mushu Li is an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University, specializing in machine learning for communications and networking, intelligent IoT, and next-generation wireless networks. She holds a Ph.D. in Electrical and Computer Engineering from the University of Waterloo (2021), an M.A.Sc. from Toronto Metropolitan University, and a B.Eng. from the University of Ontario Institute of Technology. Education : Ph.D., Electrical and Computer Engineering, University of Waterloo M.A.Sc., Electrical and Computer Engineering, Toronto Metropolitan University B.Eng., Electrical Engineering, University of Ontario Institute of Technology Research Interests : Her work focuses on AI-driven networking, edge computing, 6G systems, and digital twin technology. Key areas include resource management in cooperative networks, quality-of-experience optimization for XR services, and secure-edge AI frameworks. Her contributions address challenges in wireless communication efficiency, IoT integration, and autonomous systems. Publications Trends : Recent work emphasizes digital twin applications for resource allocation, edge AI for real-time systems, and 6G infrastructure innovations. Over 15 articles (2023–2025) explore themes like QoE-aware video streaming, federated learning optimization, and autonomous vehicle networking. Awards : No awards explicitly listed in the provided text. Advising & Grants : While no specific grants or advisees are mentioned, her research portfolio suggests active engagement in collaborative projects involving industry and academic partners. Grant details may be available via her lab or institutional portals. Labs/Teams : Not explicitly detailed here, but her work aligns with Lehigh’s research groups in edge computing and wireless networks.

Todor StoyanovView profile

Senior Lecturer

• Autonomous Mobile Robots
• Robot Perception Algorithms
• Motion Synthesis for Manipulation
• +3 more

Todor Stoyanov is a Senior Lecturer in Computer Science and an affiliated faculty member of the WASP program at Örebro University. He serves as the subject manager in computer science and is part of the Centre for Applied Autonomous Sensor Systems (AASS). His research focuses on autonomy for mobile robots, particularly perception algorithms and motion synthesis for manipulation. He holds a PhD in Computer Science from Örebro University (2012), specializing in autonomous robot navigation. Education: PhD in Computer Science, Örebro University, 2012 (Thesis: Reliable Autonomous Navigation in Semi-Structured Environments Using the 3D Normal Distributions Transform) Research Interests: Dr. Stoyanov's work spans autonomous mobile robots, robot perception, motion planning, and manipulation. Key areas include behavior trees for control, deformable object tracking, and reinforcement learning for knowledge transfer. His research often integrates advanced algorithms with real-world robotics applications in logistics, manufacturing, and environmental monitoring. Research Projects: Ongoing: Labour market effects of AI in knowledge-intensive services Ongoing: Dynamic Agile Production Robots (DARKO) Ongoing: TeamRob - Teams of Robots Working for and with Humans Completed: Action and Intention Recognition in Human-Robot Interaction (AIR) Completed: Autonomous Wheeled Loaders for Material Handling (ALL-4-eHam) Labs/Groups: He leads the Autonomous Mobile Manipulation Lab, focusing on full-body mobile manipulation and human-robot collaboration.

Niklas KleinView profile

Professor

• Context-Aware Systems
• Ubiquitous Computing
• Smart Grid Applications
• +4 more

Niklas Klein is a Professor at the Department of Information and Communication at Flensburg University of Applied Sciences. He also serves as Vice President for Studies and Teaching on the Executive Board. His academic background includes a PhD from the University of Kassel (2011) and earlier degrees from the University of Paderborn. His research focuses on context-aware systems, ubiquitous computing, and smart grid technologies. He has contributed to projects like the Future Internet Smart Grid Application (2013), activity recognition using inertial sensors (2011), and XML/XQuery transformation frameworks (2005–2011). His work emphasizes time synchronization in sensor networks and user-centric service creation. Notable publications include studies on context prediction stability, energy management in smart grids, and DAG-based context reasoning architectures. He has advised over 20 Master's and Bachelor's students in areas like distributed systems and communication technologies. Klein has secured funding for projects such as the KLIMASCHUTZ-PLANER (2013–2014) and IT2Green Pinta (2012–2014). He organizes workshops like AwareCast and serves on technical program committees for CAPS 2012 and Context 2011. Additional service includes managing the alumni network for Kassel University's Communication Technologies chair and supporting international student recruitment.

• Distributed Systems
• Wireless Communication
• Disruption-Tolerant Networking
• +4 more

Bernd Freisleben is a Professor at Philipps University of Marburg in the Department of Mathematics and Computer Science, leading the Distributed Systems research group. His work focuses on distributed systems, wireless communication, and emergency communication networks. He is affiliated with the Verteilte Systeme (Distributed Systems) group and the emergenCITY project, emphasizing resilient disaster communication and smart city technologies. Research interests include disruption-tolerant networking (DTN), machine learning applications in multimedia analysis, and energy-efficient wireless protocols. His group develops tools like ProgDTN for programmable DTN and the BatRack device for wildlife monitoring. Students under his supervision include Hicham Bellafkir (PhD since 2021), Markus Sommer (PhD since 2020), and Daniel Schneider (PhD since 2020). Key projects include Nature 4.0, a networked sensor system for biodiversity monitoring, and the ForestEdge initiative for unobtrusive environmental monitoring. Recent publications highlight work on resilient networks, deep learning for multimedia, and bio-inspired data storage solutions.

Jessica GarwoodView profile

Assistant Professor

• Physical Oceanography
• Marine Biology
• Biogeochemical Modeling
• +3 more

Jessica Garwood is an Assistant Professor at Oregon State University's College of Earth, Ocean, and Atmospheric Sciences (CEOAS). Her research focuses on small-scale physical-biological interactions in the ocean, particularly how these influence plankton and sediment transport. She leads the Lagrangian Ocean Lab and collaborates on projects involving autonomous systems and marine robotics. Education : Ph.D., Scripps Institution of Oceanography (2019) M.Sc., Dalhousie University (2014) B.Sc. (First Class Honours), Dalhousie University (2011) Research Interests : Garwood investigates coastal processes, including zooplankton behavior, ocean circulation patterns, and the impacts of climate change. She develops innovative sampling technologies like μFloats and the MOTHERSHIP platform, leveraging marine animals and autonomous vehicles for data collection. Her work bridges physical oceanography, biogeochemistry, and engineering to address environmental challenges. Key Projects : Co-developer of the MOTHERSHIP autonomous sampling system for polar environments Leader of the Lagrangian Ocean Lab Collaborator on low-cost dissolved oxygen sensor networks in crab pots Lab & Teams : The Lagrangian Ocean Lab focuses on advancing observational techniques and modeling to understand ocean transport dynamics and their ecological implications.

Mark RentschlerView profile

Professor

• Medical Mechatronics
• Medical Device Design
• Surgical Robotics
• +5 more

Mark Rentschler is a Professor in the Department of Mechanical Engineering at the University of Colorado, Boulder. His research focuses on medical mechatronics, surgical robotics, and medical device design, with an emphasis on developing intelligent medical devices and robotic systems to improve patient care. His work includes optimizing surgical tools, modeling tool-tissue interactions, and advancing technologies for minimally invasive procedures. Rentschler holds positions in ECES 144 (office) and ECES 1B14 (lab), and oversees a research group dedicated to reducing surgical invasiveness and medical costs. His research interests span medical device innovation, soft robotics, and biomaterials. He has contributed to advancements in robotic capsule endoscopy, tissue fusion mechanics, and frictional adhesion in soft systems. Notable collaborations include projects on modular surgical devices and prototypes for communication between engineering stakeholders. Awards: ASME Fellow (2018) IEEE Senior Member (2018) University of Colorado Provost’s Faculty Achievement Award (2015) Top 5 Paper at IEEE International Conference on Robotics and Automation (2017) Rentschler’s work bridges academia and industry, with patents on medical devices like textured balloons and overtube assemblies. His interdisciplinary approach addresses challenges in surgical navigation, autonomous systems, and biomimetic robotics. Recent trends in his publications emphasize soft robotic actuators, smart composites, and deep learning applications in medical imaging. His teaching contributions include awards for excellence in both undergraduate and graduate education, reflecting his commitment to engineering education. The Rentschler Lab actively collaborates on projects funded by NSF and industry partners, focusing on synthetic tissue modeling and distributed sensing systems.

• Distributed Machine Intelligence
• Low-Power AI for IoT
• Indoor Localization
• +3 more

Anuradha Ravi is a Research Assistant Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). Previously, she served as a Research Scientist at the Living Analytics Research Center, Singapore Management University (2018-2023), and as an Assistant Professor at Shiv Nadar University (2016-2018). She holds a Ph.D. in Computer Science from the Indian Institute of Technology Roorkee, where her doctoral research focused on optimizing energy efficiency and latency reduction for mobile devices through intelligent offloading strategies in heterogeneous networks. Her research interests span multiple cutting-edge domains in computing and networking: Distributed machine intelligence at the edge Low-power AI systems for IoT devices Indoor localization and occupancy sensing Wireless networking protocols Mobile and ubiquitous computing Smart building management systems Analysis of her recent publications (2015-2025) reveals a consistent focus on efficiency optimization in constrained environments, with key themes including autonomous robotics, AI-driven compression techniques, multi-sensor localization, and energy-aware mobile systems. Her work increasingly emphasizes real-world applications in contested or resource-limited settings, leveraging machine learning for adaptive solutions. She actively mentors graduate students at UMBC in projects related to perception systems for unmanned vehicles, compression-aware federated learning, and network middleware development. During her tenure at Shiv Nadar University, she supervised undergraduate research on cooperative IoT strategies and 5G edge computing. She previously contributed to the Living Analytics Research Center's initiatives in smart building occupancy detection and collaborative machine intelligence frameworks for IoT devices.

Qing YangView profile

Research Fellow

• Remote Sensing
• Flood Modeling
• Hydrology
• +3 more

Qing Yang is a Research Fellow at the School of Freshwater Sciences, University of Wisconsin-Milwaukee, affiliated with the Great Lakes Research Facility. His work focuses on advancing flood monitoring and environmental remote sensing technologies. He develops innovative systems for near-real-time flood mapping using multi-source satellite data, with applications in snow-covered, arid, coastal, and urban environments. His research integrates SAR imagery, optical sensors, and machine learning to improve flood detection accuracy and operational response. Key research interests include: Operational flood modeling and disaster preparedness Satellite data fusion techniques (e.g., Sentinel-1, VIIRS, RadarSat) Climate change impacts on hydrology and flood risk GIS-based flood vulnerability assessments Recent studies analyze flood insurance adoption patterns in the U.S., evaluate evapotranspiration products over China, and investigate pre-failure dam anomalies using satellite data. Yang’s work supports decision-making frameworks for flood management in regions like Saudi Arabia, Greece, and the Red River basin. Laboratory affiliations include the Great Lakes Research Facility and collaborative efforts with international agencies. His methodologies bridge hydrology, geospatial analysis, and machine learning to address global environmental challenges.

• Medical Image Processing
• Remote Sensing
• Machine Learning
• +4 more

Jianwu Dang is a faculty member at Lanzhou Jiaotong University's School of Electronic and Information Engineering , with affiliations to the Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic and Image Processing . His academic career spans multiple institutions including a PhD from Southwest Jiaotong University in 1996. Research Interests : Focused on Medical Image Processing , Remote Sensing , and Wireless Sensor Networks , his work bridges Artificial Intelligence and Transportation Engineering , particularly in railway systems. Publications : Recent articles address Edge Computing coalition structures (2023), Extended Reality in education (2023), and Deep Learning for remote sensing (2022). Collaborations : Frequently co-authors with Yangping Wang and Zhanjun Hao , contributing to journals like IEEE Internet of Things Journal and conferences such as APSIPA .

• Operational Research
• Optimization Methods
• Logistics and Supply Chain Management
• +7 more

Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.

• Augmented Reality
• Gait Analysis
• Quality-of-Experience
• +12 more

Dr. Thiago Braga Rodrigues is a Lecturer in the Faculty of Engineering and Informatics , affiliated with the Department of Computer and Software Engineering . His research spans multiple disciplines including Machine Learning , Augmented Reality , and Biomedical Engineering , focusing on applications for Parkinson's Disease and Wearable Sensors . He has contributed to 12 research outputs since 2018, including peer-reviewed conference papers and journal articles. Research interests include: Development of sensor-based telehealth systems for Parkinson's Disease Application of synthetic data generation in machine learning Design of VR glove interfaces with haptic feedback Quality-of-Experience (QoE) evaluation in mixed reality environments Wearable tremor assessment and gait analysis Key research outputs show expertise in: Augmented Reality applications for movement disorders Integration of variational autoencoders and wireless sensors for performance prediction Time-frequency domain analysis of physiological data Design of user-centered telehealth systems Immersive VR controllers with tactile feedback Applications of machine learning in sports science

Cristina StoicaView profile

Professor

• Ensemble state estimation for uncertain systems
• Predictive control
• Robust control
• +7 more

Cristina Stoica is a Professor at CentraleSupélec/L2S, Université Paris-Saclay, specializing in control systems and robotics. She leads the SYCOMORE research team (since 2020) and co-heads the 'Pedagogical Innovation and EdTech' projects cluster. Her research focuses on ensemble state estimation, robust control, multi-agent systems, and control education. She has received awards including the 2024 Bozenna Pasik-Duncan Award and the 2023 Best Paper Award in Control Engineering Practice. Education: PhD in Automatic Control (2008, Supélec/Paris Sud 11) and HDR (2014, Université Paris-Saclay). She holds a B.Sc. and M.Sc. in Electrical Engineering from the Polytechnic University of Bucharest. Teaching: Teaches core modules in Control Theory, Automatic Control, and Dynamical Multi-Agent Systems at CentraleSupélec. Develops innovative courses integrating robotics and hands-on projects. Research Highlights: Contributions to distributed estimation, fault-tolerant control, and UAV coordination. Notable projects include radar imaging applications and pedagogical tools like the DroPong game and Drone Arenas-based teaching. Administrative Roles: Head of SYCOMORE team, Deputy Head of Research Professional Track (2017–2022). Memberships: IEEE Senior Member, IFAC Technical Committees on Control Education and Societal Impact. Labs/Teams: Leads SYCOMORE (Robust Control of Complex Systems) and contributes to MODESTY and COMEDY research groups.

Caroline ChanelView profile

Professor

• Autonomous Systems
• Decision-Making Algorithms
• Robotics
• +3 more

Caroline Chanel is a Professor at ISAE-SUPAERO (DCAS), holding the CASAC Chair since 2022. She has extensive experience in research and teaching, with roles including Associate Professor (2019-2024), Research Engineer (2013-2019), and HDR accreditation (2023). Her research focuses on embedded decision-making for autonomous systems, including proactive architectures, multi-robot cooperation, and human-machine interaction. She leads and participates in high-profile projects like SAMURAI (AID), CHIP-GT (ANR-DFG), and HAICO (CASAC Chair). She has supervised numerous PhD students and post-doctoral researchers, collaborating with institutions like ONERA, INRAe, and international partners. Education: PhD in Control and Embedded Systems (University of Toulouse 2013), Master's in Automatic Systems (Université Paul Sabatier 2008-2009), and Engineer Diploma from PUCRS (2002-2008). Awards include the Amelia Earhart Fellowship (2011) and ISAE-SUPAERO Foundation PhD Fellowship (2013). Research interests span decision-making algorithms, multi-agent systems, and neuroergonomics. Key projects address UAV navigation, blockchain integration for autonomous systems, and physiological computing for human-robot interaction. Teaching responsibilities include modules on autonomous systems, control systems, and neuroergonomics at ISAE-SUPAERO's engineering and master's programs. She co-leads the DECISIO doctoral team (ISAE-ONERA) and is involved in executive roles for conferences like EDSYS'.

Glen RainsView profile

Professor

• Precision Agriculture
• Sensor Technology
• Insect Ecology
• +4 more

Glen Rains is a Professor in the Department of Entomology at the University of Georgia's College of Agricultural & Environmental Sciences (CAES). With a PhD in Agricultural Engineering from Virginia Tech (1992), he combines engineering principles with entomological research to develop advanced sensor technologies for agricultural applications. Co-Director of the USDA-funded Agrability in Georgia project Developed handheld chemical detection instruments (U.S. Patent 7,607,338) Licensed technology for biological sensing using invertebrate learning (U.S. Patent 6,919,202) His research focuses on precision agriculture , insect behavior monitoring , and plant pathogen detection . Recent work includes autonomous robotic systems for cotton harvesting, deep learning-based weed detection, and bioengineered plant volatile emissions for pest management. He also contributes to agricultural safety through extension programs targeting farm injury prevention and youth education. Key publication trends show concentration on machine learning applications in cotton phenotyping, autonomous navigation systems for agricultural robotics, and advanced spray technologies for pest control. His 2025 work includes predictive modeling for cotton development and innovative pest monitoring approaches. 2009 - 7,607,338: Handheld Chemical Monitoring Instrument 2005 - 6,919,202: Biological Sensing via Invertebrate Learning As educator, he developed the course CRSS 4010 - Principles of Sustainable Management and contributes to K-12 outreach through programs like G.I.F.T. and Student Shadowing. His laboratory in Tifton, GA, collaborates with engineers and entomologists on cutting-edge agricultural technology solutions.