Find Academic Supervisors

• Machine Learning
• Robotics
• Artificial Intelligence
• +5 more

DUPUIS Yohan is a Research Director at CESI, leading the Engineering and Numerical Tools research team. His work focuses on perception and mapping for cyber-physical systems, intelligent robotics, and transportation systems. He holds an HDR from the University of Rouen Normandy (2019) and a PhD in Electrical Engineering (2012). He coordinates major national projects including the France 2030 CAIRE project (AI sector), ASTRID Robotics SCOPES, and Battery School initiatives. Education includes an Engineering Diploma from ESIGELEC (2009), MSc in Electrical Engineering from Union Graduate College (USA), and a PhD focused on omnidirectional vision for biometrics. His research spans semantic mapping, autonomous systems, and sensor fusion with applications in smart cities, industrial automation, and maritime networks. Key research interests include: Multi-agent cooperative perception SLAM algorithms for low-texture environments Autonomous vehicle localization Human-robot interaction in industrial contexts LiDAR and radar sensor systems He advises 8 active PhD students working on topics like human-robot affordance modeling, object pose estimation, and mobility prediction in smart cities. Notable contributions include the SynWoodScape autonomous driving dataset and innovative digital twin methodologies for industrial workstations. Led projects include: France 2030 CAIRE project (AI sector coordination) ASTRID Robotics SCOPES (2022-2025) Battery School initiative (2022-2027) ConfluenceS Excellence program (2024-2032) His publications span over 50 peer-reviewed articles, with recent focus on multimodal perception systems and AI-driven urban mobility solutions.

Philippe XuView profile

Researcher

• Autonomous Systems
• Computer Vision
• Robotics
• +3 more

Philippe Xu is a Researcher at the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS) at ENSTA Paris. His work focuses on autonomous systems, sensor fusion, and robotics, with a strong emphasis on localization, perception, and decision-making for autonomous vehicles. He has contributed to projects involving LiDAR integration, HD map-based navigation, and machine learning for object detection and semantic segmentation. Key research areas include cooperative localization in vehicle platoons, map-aided annotation systems, and fusion of evidential classifiers for improved perception. His work often addresses challenges in safety-critical autonomous systems, such as integrity management and real-time data processing. Recent publications (2020–2024) highlight advancements in LiDAR-based obstacle detection, lane-level context analysis, and the application of Dempster-Shafer theory in neural networks.

Milan TubaView profile

Professor

• Nature-inspired optimization algorithms
• Image Processing
• Neural Networks
• +5 more

Milan Tuba is a Professor of Computer Science and Mathematics at Singidunum University in Belgrade, Serbia. He holds academic roles including former Vice Rector for International Relations at Singidunum University, Head of the Department for Mathematical Sciences at State University of Novi Pazar, and Dean of the Graduate School of Computer Science at John Naisbitt University. His expertise spans multiple institutions, including Vanderbilt University and New York University in the U.S., where he led NSF projects and founded the Microprocessor Lab and VLSI Lab. Education: He earned B.S., M.S., and Ph.D. degrees in Mathematics and Computer Science from the University of Belgrade and New York University. Research Interests: Tuba focuses on nature-inspired optimization algorithms applied to image processing, neural networks, and computer networks. His work integrates metaheuristics with machine learning, addressing challenges in healthcare, energy forecasting, and cloud computing. Recent projects include developing optimized algorithms for medical image analysis, wind energy prediction, and cloud resource scheduling. Scientific Contributions: With over 300 publications (h-index 53), he has authored/co-edited books on topics like target localization and hardware design. His awards include inclusion in Stanford University's top 2% global scientists list (2020–2024). Advising & Leadership: Tuba has mentored dozens of PhD/Master's students across universities in Serbia, Bosnia, and the U.S. He leads research groups and serves on editorial boards for journals and conferences. His labs focus on VLSI design, neural networks, and optimization. Labs/Teams: Founder of Microprocessor Lab and VLSI Lab at U.S. institutions, and currently directs AI-focused research at Singidunum University.

Hamed Rajabi JorshariView profile

Senior Lecturer

• Biomechanics
• Biomimetics
• Bioinspired Design
• +12 more

Dr. Hamed Rajabi Jorshari is a Senior Lecturer in Mechanical Engineering and Design at London South Bank University (LSBU), where he serves as the Director of the Mechanical Intelligence (MI) Research Group. He holds dual doctorates in Engineering and Natural Sciences and has published over 80 papers in top journals such as PNAS , Science Robotics , and Advanced Science . His interdisciplinary research bridges biology and engineering to develop bioinspired solutions in robotics, structural design, and materials. Research Interests: Biomechanics of insect flight, wings, and cuticle Bioinspired robotics and adaptive structures Lightweight and fracture-resistant designs Mechanical Intelligence (MI) for engineering innovation Structural reinforcement and deployable systems His recent publications reveal a strong focus on bioinspired tensegrity, compliant joints, insect wing morphometry, and adaptive robotics—demonstrating a consistent trend in translating biological mechanisms into functional engineering systems. His tools like WingGram and WingMesh are advancing computational modeling in biomechanics. Scientific Awards: KiNSIS Best PhD Dissertation Award (2020) Kiel Life Science Postdoctoral Award (2019) ISBE Distinguished Student Award (2017) IFIA Gold Medal for bio-inspired robotic joint Royal Academy of Engineering Global Talent Endorsement (2021) Dr. Rajabi has successfully supervised over 60 postgraduate students, including 15 PhD candidates, and secured multiple research grants as principal investigator. He is actively involved in science communication through the YouTube channel Science4Everyone . Laboratories and Teams: He leads the Mechanical Intelligence (MI) Lab at LSBU, which focuses on bioinspired design, soft robotics, and structural innovation. The lab includes PhD students, postdocs, and visiting researchers from institutions worldwide, and is equipped with advanced 3D printers, CNC laser cutters, mechanical testing machines, and high-speed cameras.

Niclas VödischView profile

Researcher

• Continual Learning for Robotics
• Machine Perception
• Simultaneous Localization and Mapping (SLAM)
• +3 more

Niclas Vödisch is a Ph.D. student and researcher at the Autonomous Intelligent Systems Lab in the Department of Computer Science at the University of Freiburg. Supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada, he is actively contributing to the field of robotics and AI as a member of the ELLIS Society. His research focuses on enhancing machine perception and SLAM systems using deep learning methods, with primary applications in mobile robotics and autonomous driving. Education: Ph.D. Student at University of Freiburg (June 2021 - June 2025, thesis submitted, defense pending) Visiting Ph.D. Student at University of Zurich (June 2024 - December 2024) M.Sc. in Computational Science and Engineering at ETH Zurich (September 2018 - May 2021) Visiting Undergraduate Student at Carnegie Mellon University (August 2016 - May 2017) B.Sc. in Computational Engineering Science at RWTH Aachen University (September 2014 - June 2018) Vödisch's research interests center around Continual Learning for Robotics, Machine Perception, and Simultaneous Localization and Mapping (SLAM). His work addresses the critical challenge of reducing dependency on extensive annotated training data in robotic perception systems. Through innovative approaches leveraging foundation models, he has developed methods that achieve high performance with minimal supervision, making robotic systems more adaptable to real-world environments. His research spans theoretical advances in deep learning and their practical implementation in autonomous systems. An analysis of his publication record reveals a clear progression from foundational SLAM techniques to sophisticated continual learning frameworks and foundation model applications. A unifying theme across his work is data efficiency in robotic perception, with increasing emphasis on collaborative multi-agent systems and cross-modal information integration for robust performance in challenging conditions. His recent publications demonstrate growing influence in the robotics community, evidenced by the IROS 2024 Best Paper Award for his BEVCar work. Scientific Awards: IROS 2024 Best Paper Award on Cognitive Robotics (BEVCar) IROS 2024 Best Student Paper Award Finalist (BEVCar) Dean's List at Carnegie Mellon University (fall 2016) DAAD full scholarship for CMU studies (2016-2017) Niclas has mentored numerous Master's students on projects spanning LiDAR panoptic segmentation, collaborative scene graph generation, and autonomous driving systems. His teaching portfolio includes co-organizing seminars on Robot Learning and Learning with Limited Supervision, as well as leading the FreiCAR practical autonomous driving course across multiple semesters. His research has received substantial funding from the German Research Foundation (DFG) Emmy Noether Program, NVIDIA academic grants, and Qualcomm Technologies Inc., reflecting the significance and potential impact of his work. As a key contributor to the Autonomous Intelligent Systems group at Freiburg, Vödisch collaborates closely with the Robot Learning group and has extended his research network through his visiting position at the University of Zurich's Robotics and Perception Group. His interdisciplinary approach bridges computer vision, robotics, and machine learning, positioning him at the forefront of research in AI-powered autonomous systems with practical real-world applications.

Rama ChellappaView profile

Professor

• Computer Vision
• Pattern Recognition
• Signal and Image Processing
• +5 more

Prof. Rama Chellappa is a College Park Professor at the University of Maryland, affiliated with the Electrical and Computer Engineering Department and the UMIACS Institute. He holds dual appointments in the A.J. Clark School of Engineering and Computer Science. His academic career includes roles as Chair of ECE (2011–2018) and previously at the University of Southern California. Education: B.E. (Honors) from University of Madras (1975), M.E. from Indian Institute of Science (1977), M.S.E.E. and Ph.D. from Purdue University (1978, 1981). He has held prestigious titles like Minta Martin Professorship and Distinguished Faculty Research Fellow. Research focuses on computer vision, biometrics (face/gait analysis), secure AI systems, and medical imaging applications. Key interests include 3D modeling from video, hyper-spectral processing, and AI-driven healthcare solutions like aging and tumor evolution modeling. Awards: NAI Fellow (2020), IEEE Jack S. Kilby Medal (2020), K.S. Fu Prize (2012), multiple IEEE/OSA fellowships, and teaching awards (e.g., Poole and Kent Senior Faculty Teaching Award). Service contributions include Editor-in-Chief of IEEE TPAMI, leadership roles in IEEE Signal Processing Society, and conference organization. Holds four patents and co-authored influential books on neural networks and image processing. Labs/Teams: Active in UMD’s Computer Vision and Pattern Recognition groups, collaborating on projects like AI+AgeTech collaboratories and multi-modal biometric systems.

Emre KoyuncuView profile

Associate Professor

• Unmanned Aerial Vehicle Engineering
• Air Traffic Management
• Path Planning
• +3 more

Emre Koyuncu is an Associate Professor in the Department of Aeronautical Engineering at Istanbul Technical University. His research focuses on unmanned aerial vehicle (UAV) engineering, air traffic management, and control systems. He is actively involved in EU-funded projects such as AIRMOB and AI4HyDrop, addressing challenges in drone operations, trajectory planning, and airspace safety. His work integrates reinforcement learning, particle filters, and control theory to enhance UAV autonomy and flight safety. Key projects include development of GNSS-denied navigation systems, stealth-maneuver strategies for aircraft, and resilient air traffic management frameworks. Research Interests: UAV navigation, trajectory optimization, control barrier functions, and stochastic control systems. Projects: Leading EU initiatives on drone safety (AIRMOB), AI-based navigation (AI4HyDrop), and resilient air traffic networks (START project). Awards: Received the Boeing Faculty Encouragement Grant (2015). His publications span topics like optimal recovery diffusion control, cooperative terrain-based navigation, and collision avoidance algorithms. He collaborates internationally on aerospace systems, emphasizing practical applications in aviation safety and autonomy.

Kyle E. FitchView profile

Assistant Professor

• Arctic Cloud Microphysics
• Riming Processes
• Space Weather
• +3 more

Kyle E. Fitch is an Assistant Professor of Atmospheric Science at the Air Force Institute of Technology (AFIT) within the Department of Engineering Physics. He serves as the Atmospheric Science M.S. Curriculum Chair, directing coursework for 10 students and teaching 3-4 courses annually, including programming and physics refresher modules for new students. PhD in Atmospheric Sciences, University of Utah (2020) MS in Applied Physics, AFIT (2016) BS in Atmospheric Sciences, University of Washington (2006) His research focuses on Arctic cloud microphysics , including riming processes, graupel formation in thin clouds, and aerosol impacts on precipitation. He integrates space weather studies (e.g., ionospheric electron density, solar flares) and machine learning for predictive modeling. His work addresses Department of Defense weather priorities through funded research. Recent publications highlight collaborations on attention-based neural networks for lightning prediction, GNSS radio occultation for ionospheric modeling, and WRF-LES simulations of Arctic clouds. He presented at the 105th AMS Annual Meeting and QuIESCENT Arctic Workshop . Graduate School Team of the Year (2024) Dean’s Distinguished Teaching Professor (2022/2023) Advanced Technical Intelligence Award (2016) Distinguished Graduate, AFIT (2016) As a Weather and Environmental Sciences Officer in the Air Force, he has deployed to Afghanistan and worked with the National Air and Space Intelligence Center. His technical expertise includes VIIRS cloud base height validation , Multi-Angle Snowflake Camera analysis, and atmospheric model development .

Dinesh RajanView profile

Professor

• Wireless Networking
• Information Theory
• 3D Imaging
• +3 more

Dinesh Rajan is the Cecil H. Green Professor in the Department of Electrical and Computer Engineering at the Lyle School of Engineering , Southern Methodist University (SMU) . He currently serves as the Associate Dean for Faculty Affairs and Curriculum Innovation and previously chaired the ECE department for over 10 years. Education: Ph.D. in Electrical and Computer Engineering, Rice University M.S. in Electrical and Computer Engineering, Rice University B.Tech in Electrical Engineering, Indian Institute of Technology, Madras Research Interests: Dinesh Rajan's research spans wireless networking , information theory , and 3D computational imaging . His work includes probabilistic modeling of motion blur in depth maps, location-specific analysis for vehicular systems, mobile beamforming, and smartphone-based structural health monitoring. His recent work integrates advanced signal processing with real-world applications in transportation and infrastructure. Scientific Awards: NSF CAREER Award Fulbright Specialist Golden Mustang Teaching Award (SMU Internal) Senior Ford Research Award (SMU Internal) Advising & Curriculum Leadership: As Associate Dean, Rajan leads initiatives in curriculum innovation and faculty development. His leadership in the ECE department for over a decade reflects his commitment to academic excellence and student success. His research group has contributed to cutting-edge projects in wireless systems and imaging technologies.

Stanley ChanView profile

Professor

• Image Restoration
• Computational Photography
• Photon-Limited Imaging
• +5 more

Stanley Chan is the Elmore Family Professor of Electrical and Computer Engineering at Purdue University, located in West Lafayette. He holds a B.Eng. from the University of Hong Kong (2007), an M.A. in Mathematics from UC San Diego (2009), and a Ph.D. in Electrical Engineering from UC San Diego (2011), followed by a postdoc at Harvard (2012-2014). His research focuses on computational imaging, photon-limited imaging, and statistical signal processing, with particular expertise in turbulence mitigation, generative models, and LiDAR systems. In 2024, he was promoted to full professor and became Chair of the CNSIP area in ECE. His research lab, the Intelligent Imaging Lab (i2Lab), explores innovative sensors and algorithms for imaging under challenging conditions. Key projects include generative photography, turbulence mitigation via transformers, and high-dynamic-range imaging. Chan’s work has been recognized with awards such as the IEEE Signal Processing Society Best Paper Award (2023) and the Outstanding Engineering Teacher distinction (2021). Chan advises numerous graduate students, many of whom have successfully defended their theses, and teaches courses on probability, machine learning, and imaging theory. His lab is funded by agencies like DARPA, NSF, and Samsung, with projects addressing topics like adversarial robustness and single-photon sensor reconstruction.

Yingjie ChenView profile

Professor

• Virtual Reality
• Human-Computer Interaction
• AI in Computer Graphics
• +3 more

Yingjie Chen is a Professor at Purdue University's Purdue Polytechnic Institute, Department of Computer Graphics Technology. He holds a Ph.D. in Human-Computer Interaction from Simon Fraser University (2011), a Master of Science in Information Technology (2007), and a Bachelor of Engineering from Tsinghua University (1992). His research focuses on interdisciplinary domains including Virtual Reality, AI in Computer Graphics, Human-Computer Interaction, and Information Visualization. He has led multiple grants, including projects on VR training systems for Cryo-EM and precision medicine using Graph AI. Education: Ph.D., Human-Computer Interaction, Simon Fraser University (2011) M.Sc., Information Technology, Simon Fraser University (2007) B.Eng., Thermal Engineering, Tsinghua University (1992) Dr. Chen’s research emphasizes practical implementation and evaluation, blending theory with real-world applications. His work spans VR training systems, autonomous driving perception, and clinical decision support systems. Recent grants include funding for VR-based Cryo-EM training and AI in precision medicine. Research Interests: Virtual Reality (VR) System Design AI-Driven Computer Graphics Visual Analytics for Healthcare Human Factors in Autonomous Systems Grants & Awards: "Virtual Reality Augmented Hands-on Cryo-EM Training" (2020–2023) "Graph AI for Precision Medicine" (2021–2023) VAST Challenge Awards (2017, 2019) His lab, VA.Tech , develops innovative visual systems for interdisciplinary challenges. Collaborations span healthcare, transportation, and environmental science.

Miad FaezipourView profile

Associate Professor

• Healthcare Technology
• Digital/Biomedical Embedded Systems
• Artificial Intelligence
• +4 more

Dr. Miad Faezipour is an Associate Professor at Purdue University's School of Engineering Technology, affiliated with the Regenstrief Center for Healthcare Engineering (RCHE) and Applied AI Research Center (AARC). She leads the D-BEST lab, focusing on digital/biomedical embedded systems. Her academic journey includes a B.Sc. from the University of Tehran and M.Sc./Ph.D. from the University of Texas at Dallas. Prior to Purdue, she served at the University of Bridgeport for a decade. Education: B.Sc. Electrical Engineering, University of Tehran, Iran (2002) M.Sc. Electrical Engineering, University of Texas at Dallas (2006) Ph.D. Electrical Engineering, University of Texas at Dallas (2010) Postdoctoral Research, University of Texas at Dallas (2010–2011) Research Interests: Dr. Faezipour's work spans healthcare technology, AI-driven bio-data augmentation, biomedical embedded systems, and medical signal/image processing. She pioneers innovations in AI-based diagnostics, including respiratory health analysis, surgical tool classification, and burn severity assessment. Her labs integrate hardware-software co-design principles with clinical applications. Key Contributions: Developed frameworks for real-time EEG monitoring and sleep stage detection. Advanced AI models for medical image segmentation and anomaly detection. Designed smart sensing systems for healthcare monitoring in space exploration and respite care. Labs/Teams: Director of the Digital/Biomedical Embedded Systems & Technology (D-BEST) Laboratory at Purdue, fostering interdisciplinary research in healthcare engineering and AI.

Stephen XiaView profile

Assistant Professor

• Embedded AI
• Mobile and Embedded Systems
• IoT and Wearables
• +7 more

Stephen Xia is an Assistant Professor in the Department of Electrical and Computer Engineering and by courtesy in Computer Science at Northwestern University. He directs the Intelligent Mobile and Embedded Computing (IMEC) Lab. His research focuses on embedded AI, mobile systems, IoT, and digital health, aiming to create smarter, safer environments through resource-efficient technologies. Xia holds a Ph.D. from Columbia University and a B.S. from Rice University. Education: Ph.D., Electrical Engineering, Columbia University B.S., Electrical Engineering, Rice University Research Interests: Embedded AI, mobile systems, IoT, wearables, edge computing, smart cities, cyber-physical systems, energy-efficient buildings, and digital health. His work spans applications like urban safety, healthcare monitoring, and energy optimization in commercial buildings. Labs/Teams: Director of the IMEC Lab, focusing on embedded intelligence, health-aware systems, and smarter environments. Grants/Projects: Includes projects on drone-based smart homes, low-cost fever screening, construction worker safety wearables, and energy footprint tracking in commercial buildings. Notable platforms include LegoSENSE and Anemoi.

• Computer Vision
• Image Processing
• Pattern Recognition
• +5 more

Prof Qiang Wu is a Professor and Deputy Head of School (Research) at the School of Electrical and Data Engineering, University of Technology Sydney. He is a core member of the Global Big Data Technologies Centre. His research focuses on computer vision, image processing, pattern recognition, and machine learning, with applications in video surveillance, biometrics, and human-computer interaction. He holds a B.Eng. and M.Eng. from Harbin Institute of Technology and a Ph.D. from UTS. Prof Wu has led industry collaborations with organizations like Meat & Livestock Australia, Canon, and Westpac Bank. He has authored over 300 publications, including works on pedestrian detection, virtual clothing fitting, and gait recognition patented in the U.S. He serves as an Associate Editor for IEEE Transactions on Multimedia and is a Senior IEEE Member. His research has been funded by grants from bodies like the Australian Egg Corporation and the Australian Research Council. He advises on PhD and Master’s students in computer vision and related areas.

Xin ChenView profile

Adjunct Assistant Professor

• Computer Vision
• Machine Learning
• Location-based Services (LBS)
• +11 more

Xin Chen holds the title of Adjunct Assistant Professor at Northwestern University and Adjunct Professor and PhD Advisor at Illinois Institute of Technology. He leads the Central United States team in Amazon’s Machine Learning Solutions Lab and previously served as Director of Engineering at HERE Technologies, pioneering automated map creation using computer vision and machine learning. He earned a Ph.D. in Computer Science and Engineering from the University of Notre Dame in 2006. Research Interests: Xin Chen’s work spans Computer Vision, Machine Learning, and Geospatial Technologies. He focuses on applications such as automated map creation, LIDAR data analysis, biometric systems, and AI-driven solutions for transportation and navigation. His research integrates advanced image processing techniques with real-world challenges like ADAS (Advanced Driver Assistance Systems) and geospatial visualization. Key Achievements: Recipient of the 2010 and 2011 IMPACT Awards at NAVTEQ, multiple HERE Hack Week awards, and the 2015 HERE Berkeley Hackathon top prize. He has served on NSF panels evaluating multi-million-dollar AI projects and holds over 50 U.S. patents in geospatial data analysis and computer vision. Teaching & Advising: Teaches courses like “Geospatial Vision and Visualization” and “Biometrics” at Northwestern and IIT. Advises PhD students, including Xi Zhang at IIT. His academic leadership includes program chair roles at CVPR and ACM SIGSPATIAL conferences. Labs & Collaborations: Collaborates with institutions like Carnegie Mellon, Columbia, and Notre Dame on geospatial and computer vision projects. His work bridges academia and industry, emphasizing practical AI solutions for enterprise clients through Amazon and HERE technologies.