Find Academic Supervisors

• Millimeter-Wave Communication
• Integrated Circuits
• RFIC Design
• +7 more

Rod Kim is an Assistant Professor at the Department of Electrical and Computer Engineering, Stevens Institute of Technology, within the Charles V. Schaefer, Jr. School of Engineering and Science. His research focuses on millimeter-wave communication systems, integrated circuits, and wireless interconnects for applications in space exploration, medical sensing, and harsh environments. He holds a PhD in Electrical Engineering from the University of California, Los Angeles (2017). Kim’s research interests include antenna design, CMOS technology, and sensor systems for lunar and Mars missions, as well as biomedical applications like melanoma detection using millimeter-wave reflectometry. His work emphasizes energy-efficient designs and robust communication under extreme conditions. His recent articles highlight advancements in ceramic fiber interconnects for high-temperature environments, UAV-based snowpack sensing systems, and digital proximity transceivers for planetary missions. These contributions bridge theoretical innovation with practical implementation in space instrumentation and medical diagnostics. Kim has served on several institutional and professional committees, including the IEEE Transactions on Microwave Theory and Techniques as an Associate Editor. His expertise spans roles as an RFIC designer, postdoctoral researcher at JPL, and consultant in medical technology. He leads projects in millimeter-wave spectrometry and wireless power transfer, collaborating on 3D-printed antennas and biodegradable sensor materials. His lab explores cutting-edge solutions for next-generation communication and sensing systems.

Saad BiazView profile

Professor

• Unmanned Aerial Systems (UAVs)
• Computer Vision
• Autonomous Navigation
• +3 more

Saad Biaz is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. He specializes in autonomous systems, UAV navigation, collision avoidance algorithms, and computer vision. His research emphasizes developing robust solutions for unmanned aerial vehicles in GPS-denied environments and integrating advanced sensor technologies. Dr. Biaz holds a Ph.D. in Computer Science from Texas A&M University and a Ph.D. in Electrical Engineering (Nouveau doctorat) from Université Henri Poincaré, along with advanced degrees in Electrical Engineering from the same institution. His work has been recognized with awards such as the 2021 Undergraduate Research Faculty Mentoring Award from the Computing Research Association, highlighting his dedication to student mentorship and research excellence. His publications span topics including LiDAR-based SLAM, deep learning for collision avoidance, and UAV path planning using point clouds. He contributes to interdisciplinary initiatives like the Center for Artificial Intelligence and Cybersecurity Engineering at Auburn.

Timothy C. HavensView profile

Professor

• Machine Learning
• Signal Processing
• Sensor Fusion
• +7 more

Timothy C. Havens is the William and Gloria Jackson Professor in Electrical and Computer Engineering and Computer Science at Michigan Tech University. He serves as Director of the Great Lakes Research Center and Institute of Computing and Cybersystems, overseeing multidisciplinary research in autonomous systems, environmental monitoring, and AI. He also directs the PRIME Lab, focusing on pattern recognition and machine learning. Havens holds a Ph.D. in Electrical and Computer Engineering from the University of Missouri (2010), advised by Jim Keller. His postdoctoral training included an NSF/CRA Computing Innovation Fellowship at Michigan State University under Anil Jain. He has taught extensively at Michigan Tech, including courses like Machine Learning (EE/CS5841), Data Mining (CS4821), and Signals and Systems (ECE3830). His research spans sensor fusion, underwater imaging, autonomous robotics, and explainable AI. Notable projects include reconstructing Great Lakes temperature data via deep learning, developing robust ROS-based autonomous systems, and advancing fuzzy integral techniques for interpretable machine learning. He emphasizes practical applications in environmental science, transportation safety, and defense technology. Havens' work has produced over 150 refereed publications and numerous patents. He prioritizes student training in LaTeX, programming, and publication practices, requiring all advisees to master these tools. His lab collaborates with industry and government agencies on projects involving sensor networks, underwater robotics, and AI security.

Bassel ArafehView profile

Senior Lecturer

• Wireless Sensor Networks
• Mobile Cloud Computing
• Ad-hoc Networks
• +3 more

Bassel Arafeh is a Senior Lecturer in the Department of Computer Science at the New Jersey Institute of Technology (NJIT), located at the Guttenberg Information Technologies Center. His academic career spans over four decades, with expertise in computer science and electrical engineering. He holds a Ph.D. in Computer Science from Texas A&M University (1986), an M.E. in Electrical Engineering from Texas A&M (1980), and a B.S. in Electrical Engineering from Cairo University (1974). Dr. Arafeh's research focuses on wireless sensor networks, mobile cloud computing, and ad-hoc networks. He has extensively studied routing protocols, energy efficiency, and fault-tolerant systems. Key areas include underwater wireless sensor networks (UWSN), grid-based routing, and mobile cloud task offloading. His work emphasizes practical solutions for network reliability, path optimization, and resource management in dynamic environments. His research also extends to scheduling algorithms for parallel computing and network-on-chip (NoC) architectures. Notable contributions include protocols like GARP (Grid-Based Adaptive Routing) and EMGGR (Energy-Efficient Multipath Geographic Routing), designed to enhance underwater communication efficiency. He has explored broadcasting algorithms in MANETs and techniques for reducing latency and improving throughput in distributed systems. Arafeh's broader contributions include load-balancing strategies for grid computing and fault-tolerant communication schemes in interconnection networks. His work bridges theoretical computer science with practical applications, addressing challenges in both hardware and software systems.

Oscar De SilvaView profile

Assistant Professor

• Robotics
• Control Systems
• Sensor Design
• +6 more

Dr. Oscar De Silva is an Assistant Professor in the Department of Mechanical Engineering at Memorial University of Newfoundland. He holds a B.Sc. from the University of Moratuwa, Sri Lanka, and a PhD from Memorial University. His expertise spans instrumentation, controls, mechatronics, and robotics, with a focus on sensor design, state estimation, and navigation systems. Dr. De Silva's research interests include state estimation, control systems, nonlinear dynamics, navigation systems, sensor design, localization, mapping, and intelligent prosthetics. He has contributed to projects such as ice detection systems for marine safety and computer vision for robotics applications. Before his current role, he worked as a research fellow at the American Bureau of Shipping-Harsh Environment Technology Centre and taught at Memorial University as a sessional instructor. His work emphasizes practical applications in autonomous systems, industrial inspection, and environmental monitoring. Notable achievements include the IMechE UK Award for Outstanding Achievement and a Gold Medal in Mechanical Engineering. His research trends focus on integrating AI, LiDAR, and radar technologies for navigation and safety systems in robotics and marine environments. Awards: IMechE UK Award for Outstanding Achievement Gold Medal in Mechanical Engineering (University of Moratuwa) Fellow of Graduate Studies (Memorial University) Advising and Grants: While no specific student names are listed, his role as an Assistant Professor involves academic supervision. His grants and collaborations likely align with his research in robotics and sensor systems, though specific details are not provided here. Labs: He is associated with the Intelligent Systems Lab at Memorial University and previously contributed to the ABS HETC. His work often involves multidisciplinary teams focused on real-world engineering challenges.

Chris JoslinView profile

Professor

• Computer Graphics
• Medical Imaging
• Virtual Reality
• +5 more

Chris Joslin is a Professor at the School of Computer Science, Carleton University. His office is located in 4302 Canal Building, and he can be reached at Chris.Joslin@carleton.ca. He specializes in interdisciplinary research areas including computer graphics, medical imaging, virtual reality, computer vision, and human-computer interaction. His work bridges theoretical advancements with practical applications in animation, 3D modeling, and medical visualization. Research interests include developing novel techniques for 3D editing, medical image processing, and immersive virtual environments. Notable contributions include advancements in 3D Gaussian splatting, AI-driven MRI analysis, and robust sensor fusion for autonomous systems. His publications span from foundational studies on motion retargeting to applied work in procedural audio generation for soft-body simulations. Recent trends in his articles emphasize integration of deep learning with traditional computer vision tasks, optimization of medical imaging workflows, and enhancing accessibility in virtual reality systems. Despite prolific output, no scientific awards are explicitly mentioned in the provided texts. Advising and grant details remain unspecified, though his involvement in collaborative projects like VPARK and ISIS suggests engagement with interdisciplinary teams. His work is anchored at Carleton’s Herzberg Laboratories, a hub for advanced computational research.

Chao ShenView profile

Assistant Professor

• Robotics Control
• Autonomous Underwater Vehicles
• Model Predictive Control (MPC)
• +5 more

Dr. Chao Shen is an Assistant Professor at the Department of Systems and Computer Engineering, Carleton University, within the Faculty of Engineering and Design. His research focuses on control theory, machine learning, and optimization applied to robotics systems, autonomous underwater vehicles (AUVs), and intelligent control of mechatronic systems. He leads the Robotics Control and Optimization Laboratory and has supervised multiple capstone projects, including a winning team in the 2022 departmental competition. Dr. Shen holds a PhD from the University of Victoria. He has been appointed as a Technical Editor for IEEE/ASME Transactions on Mechatronics (2023) and Associate Editor for IEEE Canadian Journal of Electrical and Computer Engineering (2022). He also served as Technical Program Chair for the 2022 IEEE Electrical Power and Energy Conference. His research interests include perception and navigation for robotics, cooperative control of multi-agent systems, and distributed model predictive control (MPC) for large-scale systems. Notable contributions include a recently published Springer book on 'Advanced Model Predictive Control for Autonomous Marine Vehicles' (2023) and award-winning work funded by NSERC Discovery Grants. Dr. Shen actively mentors graduate and undergraduate students in topics like robust MPC for visual servoing, UAV coordination, and energy-optimal control strategies. His lab offers funded PhD and MASc positions focusing on control theory applications in robotics and autonomous systems.

Andrew OsborneView profile

Assistant Professor

• Nuclear Engineering
• High Performance Computing
• Reactor Design
• +6 more

Andrew Osborne is an Assistant Professor in the Department of Mechanical Engineering at the Colorado School of Mines. He holds a Ph.D. in Nuclear Physics from the University of Glasgow (2006) and has a unique interdisciplinary background, including a postdoctoral research period at the University of Texas at Austin (2009-2014) and a Research Assistant Professor appointment at the Colorado School of Mines before joining the faculty in 2018. Education: Ph.D. in Nuclear Physics (2006), University of Glasgow, Scotland. Osborne's research focuses on applying high performance computing and reactor theory to model coupled multiphysics phenomena in nuclear reactor systems. His work spans advanced reactor design, hybrid energy systems , numerical methods , and software engineering for scientific applications. Recent publications highlight his innovative integration of machine learning techniques with Monte Carlo simulations for nuclear reactor analysis, exploration of space nuclear power systems , and contributions to renewable energy integration and radiation detection technologies. Osborne's scholarly output demonstrates expertise in computational modeling of fast reactors , neutron transport , and fission wave dynamics . His interdisciplinary approach bridges nuclear engineering, data science, and energy systems analysis, with applications ranging from Earth-based sustainability to Martian colonization energy solutions. He actively contributes to nuclear safety and security through work on radiation source localization in urban environments and nuclear nonproliferation technologies. His software development background informs research on computational efficiency and robust simulation methods . Contact information: osbornea@mines.edu , Office: Brown Hall W370A, Phone: 303-384-2003

Fan YeView profile

Professor

• Mobile and embedded sensing systems
• AI/ML algorithms
• Data-centric wireless communication
• +3 more

Fan Ye is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, serving as Graduate Program Director. His research focuses on mobile/embedded sensing systems, AI/ML for Computational Screening and Surveillance (CSS), data-centric wireless communication, edge computing, and IoT. He leads projects in health monitoring infrastructure (e.g., Proteus) and vehicular peer model training (e.g., Roadtrain), emphasizing scalability and manageability. His work bridges hardware-software integration for real-world applications like non-contact vital signs monitoring and aging-in-place sensor tech. Research trends reflect a strong emphasis on practical systems: 2024 publications highlight RF-based health monitoring innovations, edge computing optimizations, and user-centric sensor development. Earlier work includes contributions to decentralized machine learning, self-calibrating indoor tracking, and UWB-depth sensor fusion. Collaborations span academia and industry, addressing challenges in privacy, energy efficiency, and real-time data processing. Ye’s lab develops scalable solutions for pervasive edge environments, addressing issues like multicast rate control, optimal producer selection (OPSEL), and blockchain resource allocation. His teaching contributions include collaborative approaches to aging technology education. Current projects aim to engage older adults in shaping low-cost sensor systems and improving post-COVID health monitoring through unobtrusive home sensors. Notable infrastructure includes the Proteus health monitoring platform and SCALING for plug-and-play indoor tracking. His systems emphasize robustness and adaptability, leveraging acoustic, radar, and vision modalities for multi-factor authentication and indoor mapping.

Weizi LiView profile

Assistant Professor

• Autonomous Systems
• Urban Mobility
• Multi-Agent Simulation
• +3 more

Weizi Li is an Assistant Professor of Computer Science in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He is also a member of the Institute for Future Mobility and directs the Fluidic City Lab. His research focuses on leveraging multi-agent simulation and reinforcement learning to design cyber-physical infrastructure for urban environments, particularly in autonomous systems and urban mobility. Li holds a PhD in Computer Science from the University of North Carolina at Chapel Hill and completed a postdoctoral fellowship at MIT. Education: PhD in Computer Science from UNC Chapel Hill (advisor: Ming C. Lin), with prior affiliations at Disney Research, Lenovo Research, and others. Research Interests: Li explores topics such as mixed traffic control, autonomous vehicle coordination, energy-efficient path planning for eVTOLs, and privacy-preserving traffic management. His work combines machine learning, robotics, and transportation engineering to address urban challenges. Recent projects are supported by the NSF, U.S. Department of Transportation, and NVIDIA. Lab and Teams: The Fluidic City Lab develops solutions for fluidic urban systems, with a focus on adaptive traffic control and smart infrastructure. Collaborations span academia and industry.

• Game Research
• Serious Games
• Extended Reality (VR/AR/MR)
• +7 more

Elif Surer is an Associate Professor and Associate Director at Middle East Technical University's Graduate School of Informatics, Department of Multimedia Informatics. She leads the Entertainment Computing and Interactive Systems Laboratory (ECISLab) and actively contributes to METU's entrepreneurship initiatives through GIMER (Entrepreneurship Research Center). Her academic leadership extends to international projects including EU Horizon Europe initiatives and collaborations with institutions like Stanford University's University Innovation Fellows program. Dr. Surer's research focuses on the intersection of game technologies, extended reality, and practical applications across diverse domains. Her work spans serious games for CBRNe (Chemical, Biological, Radiological, Nuclear, and Explosive) training, virtual reality applications for mining safety, archaeological visualization, and educational technology. She has developed frameworks for heterotopias as discursive playgrounds, modular serious game development for virtual laboratories, and advanced techniques in persona building for game design. Her interdisciplinary approach bridges computer science with fields like archaeology, healthcare, and occupational safety, creating innovative solutions that address real-world challenges through immersive technologies. Her publication record demonstrates consistent contributions to top journals including IEEE Transactions on Games, Virtual Reality, and JMIR Serious Games. Recent work shows increasing focus on biological network visualization in XR environments, advanced testing frameworks for deep learning systems, and sophisticated persona modeling techniques that go beyond traditional approaches. Her research trajectory reflects growing integration of AI with immersive technologies, particularly in specialized training applications and scientific visualization. METU Performance Awards (2020, 2021) Best Poster award at METU Graduate School of Informatics Open Research Day TÜBİTAK Above-Threshold-Awards Featured in Researchers Active in Technical Games Research list Dr. Surer actively supervises numerous graduate students across multiple projects, with recent completions including theses on clickbait detection, VR for mining safety, and adaptive serious games for children with learning difficulties. She leads significant research funding including EU Horizon Europe projects like eNOTICE-2 (focused on CBRNe training), the MR4MS project for mine safety, and the Quantum Flagship project QuTE4E. Her laboratory environment fosters collaboration between computer science, architecture, and other disciplines, creating opportunities for students to engage in cutting-edge research with practical applications. As director of ECISLab, she maintains strong connections with industry partners including METU Teknokent, and actively participates in entrepreneurship initiatives through METU GIMER. Her work with the University Innovation Fellows program demonstrates commitment to developing entrepreneurial skills among students, while her research on virtual reality applications continues to expand into new domains including healthcare, archaeology, and emergency response training.

• Robotic Grasping
• Mobile Manipulation
• Motion Planning
• +3 more

Martin Rudorfer is a Lecturer in Applied AI & Robotics at Aston University's School of Engineering and Applied Science. He leads research in robotic manipulation and develops algorithms for real-world robotics applications. Education: PhD in Industrial Automation, TU Berlin (Dr.-Ing.) Senior Fellow of AdvanceHE/HEA (2024) Research Focus: His research spans robotic grasping, motion planning in confined spaces, mobile manipulation, and simulation-based robotics. Current projects include RM4D for efficient robot reachability analysis, joint grasp-motion planning frameworks, and point cloud registration optimization. Rudorfer emphasizes reproducibility in robotics research and addresses ethical considerations in AI. Teaching: Leads modules on Robotics and Autonomous Systems, Mathematics for AI, and Game Development.

Jian WanView profile

Lecturer

• Robotics and Autonomous Systems
• Artificial Intelligence and Machine Learning
• Control Systems
• +3 more

Dr. Jian Wan is a Lecturer in Mechatronics and Robotics at Aston University's School of Engineering and Innovation within the College of Engineering and Physical Sciences. Previously, he held a Lecturer position in Control Systems Engineering at the University of Plymouth from 2015–2022. He earned a PhD in Information Technologies (summa cum laude) from the University of Girona, Spain, and an MSc from Northwestern Polytechnical University. His research focuses on Constrained Optimization, Estimation and Control ; Artificial Intelligence and Machine Learning ; and Robotics and Autonomous Systems . He leads projects like using reinforcement learning for autonomous water quality monitoring and path planning for unmanned surface vehicles (USVs). His work integrates sensor networks, control algorithms, and machine learning for applications in marine robotics and semiconductor manufacturing. Dr. Wan has secured significant funding, including £90,722 from EPSRC for hybrid set-theoretic approaches in autonomous systems and £26,280 from GCRF for coastal marine monitoring in Malaysia. He collaborates internationally with institutions like Dalian Maritime University, ENSTA Bretagne (France), and Universiti Sains Malaysia. He advises PhD projects on autonomous sailing and teaches advanced courses such as Machine Learning (ME4MAL) and Autonomous System Design (ME4ASD). His teaching emphasizes practical applications of robotics and control systems. Key achievements include developing the WaveCat autonomous sailboat for marine observation and pioneering algorithms for USV platooning and obstacle avoidance. His lab focuses on smart manufacturing, sustainable development, and smart autonomous systems. Professional contributions include editing a special issue on Smart Manufacturing for the journal Sustainability and holding SFHEA membership.

Demet Çilden GülerView profile

Assistant Professor

• Satellite Attitude Estimation
• Kalman Filtering
• Nanosatellite Systems
• +3 more

Demet Çilden Güler serves as an Assistant Professor in the Department of Astronautical Engineering at Istanbul Technical University, where she pioneers research in satellite attitude determination systems with emphasis on Kalman filtering innovations for nanosatellite applications. Her work addresses critical challenges in spacecraft navigation under uncertain conditions including process noise variations and sensor faults. Her research portfolio prominently features Satellite Attitude Estimation and Adaptive Filtering Algorithms , with specialized focus on magnetometer calibration techniques and fault-tolerant navigation architectures. Current investigations explore singular value decomposition integration with unscented Kalman filters to enhance attitude estimation robustness in resource-constrained nanosatellite platforms operating under eclipse conditions and magnetic disturbances. Recent publications demonstrate a clear trajectory toward resilient spacecraft control systems, with 2024-2025 works concentrating on probabilistic gain adjustment for measurement fault mitigation, relative navigation for satellite formations using multi-EKF architectures, and attitude-independent sensor calibration methodologies applicable across diverse orbital environments. Her distinguished contributions have been recognized through multiple prestigious awards: Amelia Earhart Fellowship Award (2018) Best Student Paper Award (2015) En Başarılı Doktora Tez Ödülü (Most Successful Doctoral Thesis Award, 2022) ICMAE 2018 Best Presentation Award Serhat Özyar Young Scientist of the Year Honorary Award (2022) TÜBA-TEKNOFEST Doctoral Science Award (2022) As Principal Investigator for six nationally funded projects including 3D celestial body modeling and passive spacecraft relative attitude estimation, she directs research extending through 2027. Current initiatives address nanosatellite attitude determination during eclipse periods, rotational motion uncertainty effects, and albedo-based navigation solutions for space situational awareness.

• Machine learning
• Computer vision
• Signal processing
• +5 more

Jenni Raitoharju is an Assistant Professor at the University of Jyväskylä's Faculty of Information Technology. Her research focuses on developing novel machine learning algorithms with applications in environmental studies, autonomous systems, and computer vision. Key areas include deep learning, uncertainty estimation, one-class classification, and statistical machine learning. She leads projects such as the 2024–2026 Ministry of Environment-funded study on aquatic biodiversity monitoring and a 2023 Finnish National Agency for Education project on morphotaxonomic classification. Her work bridges theoretical advancements with practical implementations, as seen in publications like Linear-Time One-Class Classification with Repeated Element-Wise Folding (2024) and AquaMonitor: A multimodal multi-view image sequence dataset (2025). She actively contributes to maritime computer vision initiatives, including organizing the 2023 MaCVi Workshop. Jenni's research also involves ecological instrumentation, such as the Riverine Organism Drift Imager (RODI) for aquatic organism studies. Her projects emphasize interdisciplinary collaboration, combining machine learning with environmental science and engineering. While no awards or grants are explicitly listed, her 17+ publications since 2011 reflect sustained academic productivity. She coordinates lab efforts focusing on AI-driven ecological monitoring and autonomous systems.