Find Academic Supervisors

Krzysztof LichyView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +4 more

Krzysztof Lichy is an Associate Professor at the Institute of Information Technology (also referenced as Institute of Computer Science), specializing in artificial intelligence and Internet of Things (IoT) applications. His academic role encompasses research in machine learning, neural networks, and sensor-based systems with practical implementations in localization, cybersecurity, and automation. His research spans deep learning for indoor positioning using ultrawideband (UWB) and WiFi technologies, computer vision applications including gesture recognition and dog breed identification, and cybersecurity aspects like steganalysis. He actively explores IoT integration for eco-friendly home automation systems and anomaly detection in cloud environments, demonstrating interdisciplinary expertise bridging theoretical AI and real-world engineering solutions. Analysis of his 2020-2023 publications reveals dominant trends in transfer learning for UWB localization accuracy enhancement, with significant contributions to sparse fingerprinting techniques and hybrid sensor fusion systems. His work consistently addresses practical challenges in positioning reliability while expanding into computer vision security applications and sustainable IoT implementations, reflecting a cohesive research trajectory focused on robust AI deployment in physical environments. Dr. Lichy explicitly states his enjoyment of working with students, though specific advising details remain undocumented in available sources. His research portfolio indicates substantial grant-funded activity in AI and sensor technologies, though exact funding mechanisms aren't specified in the provided materials.

Robert H Morelos-ZaragozaView profile

Professor

• Channel Coding
• Ultrawideband Communications
• RFID Systems
• +5 more

Robert H Morelos-Zaragoza is a Professor in the Department of Electrical Engineering at San José State University , where he teaches undergraduate and graduate courses in digital communications and directs MSEE projects and theses. His research focuses on Channel Coding , Ultrawideband Communications , RFID Systems , and Digital Signal Processing . Doctor of Philosophy (PhD), Electrical Engineering, University of Hawaii at Manoa, 1992 Master of Science in Electrical Engineering (MSEE), Communications, National Autonomous University of Mexico (UNAM), 1987 Bachelor of Science in Electrical Engineering (BSEE), Electronics and Communications, UNAM, 1985 His research spans error correcting coding (ECC/FEC), software-defined radio (SDR), space-time signal processing, and ultra-wideband (UWB) communication systems. Recent publications highlight advancements in multi-valued sequence generation , reversible data hiding , and random number generator analysis . Professional affiliations include Senior Member of IEEE , IEICE (Japan) , and Eta Kappa Nu . His career includes roles as a Visiting Researcher at the University of California at Santa Cruz (2005), Visiting Professor at Intel Corporation (Summer 2004), and positions at LSI Logic, Sony Computer Science Laboratories, and Osaka University.

Horst BischofView profile

Professor

• Computer Vision
• Medical Computer Vision
• Object Recognition
• +4 more

Horst Bischof is a Professor at the Institute for Computer Graphics and Vision at Graz University of Technology, Austria, and serves as Vice Rector for Research. He holds an M.S. and Ph.D. from Vienna University of Technology and a Habilitation (venia docendi) in applied computer science. His research focuses on computer vision, medical image processing, and robot vision, with over 750 peer-reviewed publications. He has organized major conferences like CVPR 2015 and ECCV 2018, and serves on editorial boards of prestigious journals. Key awards include the Most Influential Paper over the Decade Award (MVA 2019), Jan Konderink Award (ECCV 2018), and the 29th Pattern Recognition Award (2002). His work spans object recognition, medical computer vision, and visual learning. He leads research teams in robot vision and collaborates with industry partners like Infineon Technologies. Current projects include LiDAR-based sensing systems, autonomous vehicle technologies, and medical imaging solutions like MedEyeTrack for eye tumor treatment. His research emphasizes practical applications in robotics, automotive, and healthcare sectors.

Olga BłaszkiewiczView profile

Lecturer

• Wireless Communications
• NB-IoT
• LTE
• +5 more

Olga Błaszkiewicz serves as an Assistant Lecturer at the Department of Radiocommunication Systems and Networks within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. Her academic position is based in Building A, room 402, with contact information including email olga.blaszkiewicz@pg.edu.pl and phone 583472928. Her research spans wireless communication technologies with particular focus on NB-IoT, LTE systems, and deep learning applications. Key interests include radio navigation, UWB systems, LOS/NLOS identification, and software-defined radio frameworks. Her experimental work demonstrates practical applications in transportation monitoring through wireless signal analysis and indoor positioning systems. Analysis of her recent publications (2019-2025) reveals a strong trend toward integrating machine learning with traditional telecommunications systems, particularly in solving real-world problems like train detection using existing LTE infrastructure and improving synchronization in narrowband IoT systems. Her work bridges theoretical signal processing with practical deployment scenarios. She actively contributes to multiple research projects including SDIDS (Software-Defined Device for Detecting Interferences), KODEŚ (Power Data Concentrator), DUCH IoT (Software-Defined Radio Interface), and VCS-MLAT (Aircraft Location Systems). These projects are funded by Poland's Operational Program Intelligent Development and LIDER programs. Her teaching portfolio encompasses 21 courses including Radio Communication Measurement, Wireless Technology, Telecommunication Signals Laboratory, and Signal Processing. She collaborates extensively with researchers like Krzysztof Cwalina, Piotr Rajchowski, and Jacek Stefański on both teaching and research initiatives within the Department of Radiocommunication Systems and Networks.

Giovanni Del GaldoView profile

Professor

• Wireless Communications
• Signal Processing
• Channel Modeling
• +6 more

Giovanni Del Galdo is a full Professor at the Technische Universität Ilmenau, where he leads the Electronic Measurement Technology and Signal Processing (EMS) group within the Faculty of Electrical Engineering and Information Technology. He also maintains an affiliation with the Fraunhofer Institute for Integrated Circuits IIS. Since 2012, he has directed a research group comprising both a Fraunhofer department and a university chair, which expanded to approximately 60 staff members following a 2016 merger with another research group. Del Galdo received his Laurea degree in Telecommunications Engineering from Politecnico di Milano in 2002 and his Dr.-Ing. from TU Ilmenau in 2007 under Prof. Martin Haardt, with a dissertation titled "Geometry-based Channel Modeling for Multi-User MIMO Systems and Applications." Prior to his current position, he worked as a Senior Scientist at Fraunhofer IIS and was a member of the International Audio Laboratories Erlangen, where he focused on audio watermarking and spatial sound representation. His research spans multidimensional signal processing, wireless communications, and advanced measurement techniques. He specializes in channel modeling, MIMO systems, Over-The-Air testing, and high-resolution parameter estimation methods including compressed sensing. His work addresses challenges from sub-THz frequencies to practical industrial applications. Analysis of his recent publications reveals strong focus on wireless channel characterization, particularly for 5G/6G and V2X applications. His research integrates theoretical signal processing with practical measurement systems, often developing novel hardware implementations for real-world testing scenarios. Key application areas include automotive communications, industrial measurement systems, and next-generation wireless technologies. Del Galdo actively supervises numerous research projects and has extensive experience with laboratory testbed development for wireless systems validation. His group maintains advanced facilities for channel sounding, OTA testing, and signal processing algorithm development, supporting both academic research and industry collaboration.

• Internet of Things (IoT)
• Wireless Communications
• RFID Systems
• +7 more

Mohamed El Hadidy is Professor of IoT and Digital Communication Engineering at RheinMain University of Applied Sciences (HSRM), serving as Head of the IoT and Digital Communications Engineering Department and Internationalization Officer at ITE. He also acts as Counselor for the IEEE HSRM Student Branch. With extensive experience from academic institutions in Germany and abroad, Professor El Hadidy emphasizes deep theoretical understanding while maintaining practical applicability in his work. His teaching portfolio spans multiple engineering disciplines with courses including Networked Systems for IoT, Wireless Communications, and Digital Communication Technology across both Bachelor's and Master's programs. IoT for Automated Systems and Mobile Applications Chipless RFID Systems Positioning, Localization and Navigation Systems UWB MIMO Communication Systems Channel Modeling and Systems Emulation Cognitive Radio and Spectrum Sensing Interference Alignment RADAR, Imaging and Inverse Scattering Cancer Detection and Biomedical Applications Jamming and RF Hacking Professor El Hadidy has published over 60 international papers and serves as reviewer for IEEE transactions in Antennas and Propagation, Vehicular Technology, and Communications societies. His research bridges academic theory with industrial applications through multiple funded projects. Keynote speaker at international conferences Chair and co-organizer of IEEE conferences As an academic mentor, he has successfully supervised multiple PhD and Master's students. His laboratory maintains strong industry connections focused on practical implementations of communication technologies, particularly in automotive systems, biomedical applications, and secure communications.

Jianguo Jack WangView profile

Lecturer

• Sensor Fusion
• Robotics
• Intelligent Transportation Systems
• +4 more

Dr. Jianguo Jack Wang is a Lecturer at the School of Civil and Environmental Engineering, University of Technology Sydney (UTS). He holds a B.Sc. in Physics from Nanjing University, M.Sc. in Electrical and Information Engineering from The University of Sydney, and a PhD in Geomatics from UNSW. His research focuses on sensor fusion, robotics, intelligent transportation systems, and environmental engineering. He has published over 70 papers and supervised around ten PhD/Master students. Notable achievements include the Michael Richey Medal for his navigation method innovation. Dr. Wang’s research interests span robotics path planning, UWB-based positioning, and multi-sensor integration. His work emphasizes practical applications in autonomous systems and environmental sustainability. He actively reviews for top journals like IEEE Transactions on Intelligent Transportation Systems and conferences such as ICRA and IROS. He has led funded projects on sensor fusion for road assessment and participated in initiatives like the Centre for Built Infrastructure Research (CBIR). His teaching includes courses on Geographic Information Systems and Civil Engineering. Dr. Wang is affiliated with IEEE Robotics and Automation Society and the Institute of Navigation (ION).

Moe WinView profile

Professor

• Communication Systems
• Quantum Communication
• Signal Processing
• +3 more

Moe Win is a Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS). He holds a PhD in Electrical Engineering from the University of Southern California (USC), where he was a Presidential Fellow. Prior to MIT, he worked at AT&T Research Laboratories and the Jet Propulsion Laboratory (JPL). His research focuses on applying mathematical and statistical theories to communication, detection, and estimation challenges, with emphasis on time-varying channels, multiple antenna systems, ultra-wideband (UWB) communications, and quantum-enabled systems. His recent work explores quantum communication, 6G positioning, and resilient navigation in adversarial environments. He has contributed to advancements in holographic localization, reconfigurable intelligent surfaces, and quantum state discrimination. His affiliations include the MIT Institute for Data, Systems, and Society (IDSS), reflecting interdisciplinary interests in networked systems and cyber-physical infrastructure. Education: BS (Electrical Engineering), Texas A&M University, 1987 MS (Electrical Engineering), University of Southern California, 1989 MS (Applied Mathematics), University of Southern California, 1998 PhD (Electrical Engineering), University of Southern California, 1998 Dr. Win’s research bridges theoretical foundations with practical applications in wireless networks, space communications, and quantum information systems. He leads projects on secure localization, cooperative positioning, and quantum-enhanced radar/lidar technologies. His lab’s work addresses challenges in non-terrestrial networks, adversarial spoofing defense, and distributed learning in resource-constrained environments.

Massimo CallegariView profile

Professor

• Mechatronics
• Industrial Robotics
• Mechanical Systems Design
• +3 more

Prof. Massimo Callegari is a Full Professor of Applied Mechanics at the Polytechnic University of Marche (Italy), leading the Department of Industrial Engineering and Mathematical Sciences. He holds roles as President of I-Labs Industry (a digital transition hub for SMEs) and coordinator of the Marche node of Artes 4.0. With a Mechanical Engineering background from the University of Genoa (1986), his career spans industry automation (1986–1990) and academic research since 1990, including roles as Associate Professor at the University of Ancona (1998–2005). His research focuses on mechatronics, industrial robotics, and mechanical systems design, with over 200 publications and two patents. Key projects include directing the €9M HD3FLAB Industry 4.0 platform and leading EU initiatives like EURON, CLAWAR, and LEAPFROG. He teaches courses on Applied Mechanics, Automatic Machine Mechanics, and Management Engineering. His recent work emphasizes collaborative robotics, obstacle avoidance systems, and AI-driven safety protocols. Professional memberships include ASME, IFToMM Italy, and I-RIM. Education: M.Sc. in Mechanical Engineering, University of Genoa (1986) Research Projects: PRIN2005 (Mini-robotic systems) HD3FLAB (Industry 4.0 platform) FP6-LEAPFROG (Textile Automation) Teaching: Applied Mechanics to Machines (BSc Mechanical Engineering) Mechanics of Automatic Machines (MSc Computer & Automation Engineering) Machine Mechanics (BSc Management Engineering) His work bridges academia and industry through innovation in robotics, automation, and collaborative systems.

Ray GosineView profile

Professor

• Robotics
• Machine Vision
• Industrial Automation
• +4 more

Ray Gosine is a Professor at Memorial University of Newfoundland, holding the J.I. Clark Chair in Intelligent Systems. He specializes in robotics, machine vision, and industrial automation, particularly in natural resource industries. His research also addresses socio-economic impacts of technology and public policy reviews. Gosine has held significant administrative roles, including Dean of Engineering and Vice-President (Research) at Memorial University. He currently serves on boards such as the Canadian Engineering Accreditation Board and ACENET. Notable contributions include leading a public review of hydraulic fracturing in Newfoundland and collaborative research on digitalization in mining and oil/gas sectors. Education: B.Eng. in Electrical Engineering, Memorial University PhD in Robotics, University of Cambridge Research Interests: Automation in natural resources, socio-economic impacts, public policy, and university intellectual property Administrative Roles: Vice-President (Research), Memorial University (2008–2010, 2014–2015, 2016–2017) Dean of Engineering and Applied Science (2003–2008) Awards: Fellow of Canada Academy of Engineering Petro-Canada Young Innovator Award Current Projects: Visiting Scholar at U of Toronto's Innovation Policy Lab (2017 sabbatical) Research on automation in Canada's extractive industries

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.

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.

Hervé RivanoView profile

Professor

• Network Design
• Wireless Networks
• Combinatorial Optimization
• +4 more

Hervé Rivano is a University Professor at INSA Lyon and the head of the Inria/INSA Lyon CITI Agora team, focusing on wireless networking challenges in urban environments. He leads the computer science teaching at the FIMI department, overseeing a dynamic curriculum reform. His research interests include combinatorial optimization for network design, energy-efficient urban cellular networks, and low-cost wireless sensor networks for environmental monitoring. He co-supervises PhD students in areas like IoT and pollution monitoring. Rivano is involved in educational programs like the SMART international initiative and has contributed to MOOCs on Smart Cities. His work spans academic leadership, research innovation, and pedagogical advancements in digital urban systems. Education: PhD from University of Nice-Sophia Antipolis (2003), supervised by Afonso Ferreira and Jérôme Galtier. Graduated from École Normale Supérieure de Lyon. Research Teams: Agora team (6 faculty, 10-15 PhDs, 2-5 postdocs/engineers) addressing urban wireless networks and Smart Cities. Teaching Roles: Coordinates IoT and urban networks modules, leads FIMI's computer science discipline, and contributed to the 'Smart Cities' MOOC. Key Projects: Urbanet (predecessor of Agora), participatory air quality monitoring systems, and interdisciplinary urban data initiatives. His work emphasizes sustainable, data-driven urban solutions.

• Machine Learning
• Medical Technology
• Microwave Engineering
• +3 more

Yinan Yu is an Assistant Professor at the Department of Computer Science and Engineering , Chalmers University of Technology. Her research focuses on machine learning , microwave technology , and medical diagnostics . Research Interests Application of machine learning to microwave-based stroke diagnosis Development of ultra-wideband radar systems for healthcare and industrial use Feature reduction techniques in high-dimensional data analysis Scientific Contributions Published extensively on microwave-enabled prehospital stroke treatment Innovations in kernel subspace learning for medical imaging Design of compact UWB radar systems using CMOS technology

Matthew D'SouzaView profile

Lecturer

• Pervasive and Ubiquitous Computing
• Wireless Communications
• Wireless Sensor Networks
• +4 more

Dr. Matthew D'Souza is a Lecturer in the School of Electrical Engineering and Computer Science at The University of Queensland (UQ). He holds a Bachelor of Engineering (Honours) and PhD from UQ. His prior roles include Postdoctoral Research Fellow and Visiting Research Scientist at CSIRO Autonomous Systems Laboratory and the Australian E-Health Research Centre. His research focuses on Pervasive Computing, Wireless Sensor Networks, Embedded Systems, Biomedical Engineering, and Cyber Physical Systems. Education: Bachelor of Engineering (Honours), UQ Doctor of Philosophy (PhD), UQ Research Interests: Pervasive & Ubiquitous Computing Wireless Communications Embedded Systems for Biomedical Applications Indoor Localization Techniques Accessibility Technologies Remote Patient Monitoring Teaching Contributions: Adapted project-based learning during the pandemic Managed hands-on labs remotely Developed educational robotics platforms Labs & Collaborations: CSIRO Autonomous Systems Lab Australian E-Health Research Centre University of Queensland's ITEE School