Find Academic Supervisors

Richard M. VoylesView profile

Professor

• Robotics
• Wireless Control Networks
• Dexterous Hexrotor
• +11 more

Richard M. Voyles is the Daniel C. Lewis Professor of Engineering Technology at Purdue University's Polytechnic Institute with a courtesy appointment in the Department of Computer and Information Technology. He serves as Director of the Robotics Accelerator and Head of the Collaborative Robotics Lab. Prior to his full-time return to Purdue, he served as Assistant Director of Robotics and Cyber-Physical Systems at the White House Office of Science and Technology Policy and previously led the National Robotics Initiative at the National Science Foundation. Dr. Voyles holds a B.S. in Electrical Engineering from Purdue (1983), an M.S. in Manufacturing Systems Engineering from Stanford (1989), and a Ph.D. in Robotics from Carnegie Mellon (1997). His research spans multiple domains of robotics including rescue robotics, UAV development, gesture-based programming, cyber-animal systems, and self-adaptive robotic systems. He has received numerous honors including being named an IEEE Fellow and receiving Purdue's 'Seeds for Success' Award. His research output demonstrates significant contributions across robotics theory and application, with particular emphasis on novel robot designs like the MOTHERSHIP robot for urban search and rescue, the Dexterous Hexrotor for physical interaction with environments, and the development of the ReFrESH architecture for self-adaptive robotic systems. His work has been supported by NSF through multiple grants including the NSF Center for Robots and Sensors for the Human Well-Being (RoSe-HUB). Dr. Voyles has advised numerous graduate students who have gone on to academic and industry positions, including several who have become professors at other institutions. His research has resulted in significant publications across major robotics conferences and journals, with recent work focusing on nuclear decommissioning applications, neuromorphic computing for robotics, and advanced UAV capabilities. Daniel C. Lewis Professor IEEE Fellow Purdue 'Seeds for Success' Award As an educator, Dr. Voyles teaches courses in robotics, real-time computer vision, and cyber-animal systems. He has been instrumental in developing new MS and PhD degrees in Robotics Technology at Purdue, set to launch in Fall 2024. His lab, the Collaborative Robotics Lab, maintains strong industry partnerships through the RoSe-HUB center, working with numerous companies on applied robotics research.

Martin BilodeauView profile

Professor

• Technology and Health Technologies
• Motor System
• Neurosciences
• +7 more

Martin Bilodeau is a Full Professor in the Department of Rehabilitation Sciences at the Faculty of Health Sciences, University of Ottawa. His research focuses on physical factors influencing mobility during normal and pathological aging, with affiliations to the Life Research Institute (2019-present), uOttawa Brain and Mind Research Institute (2018-present), and Bruyère Research Institute (2017-present). Ph.D., Biomedical Sciences (Rehabilitation), Université de Montréal (1993) Bachelor of Science, Physiotherapy, Université de Montréal (1988) His work spans Technology and Health Technologies , Muscle Fatigue , Postural Control , and Virtual Reality Rehabilitation . Recent studies examine fear of movement in chronic conditions, remote concussion assessment protocols, and telerehabilitation efficacy for TBI patients. Articles highlight aging-related mobility barriers, sensor-based monitoring, and neuromuscular interactions. Key affiliations include: Life Research Institute (2019–present) uOttawa Brain and Mind Research Institute (2018–present) Bruyère Research Institute (2017–present) Current supervision includes Miriam Goubran (Ph.D. expected 2024) and Nathalie Todam Nguepnang.

Domagoj ŠeverdijaView profile

Assistant Professor

• Combinatorial Optimization
• Computational Linguistics
• Natural Language Processing
• +4 more

Domagoj Ševerdija is an Assistant Professor at the School of Applied Mathematics and Informatics at Josip Juraj Strossmayer University of Osijek, where he leads the Computer Science and Machine Learning Research Group. He holds a PhD in Electrical Engineering (2013) and a dual BS in Mathematics and Computer Science (2007) from the University of Osijek. His research spans computational linguistics, natural language processing, machine learning, and combinatorial optimization, with applications in bioinformatics, robotics, and energy systems. Recent work focuses on neural language models, domain adaptation techniques, and efficient algorithm design. Publications show strong interdisciplinary trends: computational linguistics (Croatian morphology, sentence embeddings), bioinformatics (cell typing, RNA splicing), and algorithm optimization (terrain guarding, matrix operations). Recent papers emphasize knowledge distillation, domain adaptation, and compressed representations. Awards: Best paper award in AIS - Artificial Intelligence Systems track (MIPRO 2023) Research funding includes: Computer-Assisted Corpus Linguistics (UNIOS, 2019-2020) Croatian Identity Network Framework (Adris Foundation, 2020-2021) Croatian Language in Global Cloud (Adris Foundation, 2019-2020) Leads the Computer Science and Machine Learning Research Group, coordinating projects in NLP, computational geometry, and AI applications.

• Distributed Algorithms
• Metric Graph Theory
• Theoretical Computer Science
• +3 more

Jérémie Chalopin is a Directeur de Recherche (DR CNRS) at LIS (Laboratoire d'Informatique et Systèmes) in Marseille, where he is a member of the DALGO research team. His position at CNRS is affiliated with Aix-Marseille University, one of France's largest multidisciplinary universities. As a senior researcher with habilitation to supervise research (awarded in 2020), he plays a significant role in the French theoretical computer science community. Chalopin's research primarily focuses on distributed algorithms and metric graph theory , with substantial contributions to understanding the theoretical foundations of distributed computing systems and the geometric properties of graphs. His work bridges theoretical computer science with practical applications in mobile agent systems, programmable matter, and network algorithms. He has developed fundamental results in areas including leader election, graph exploration, metric embeddings, and sample compression schemes for graph structures. Analysis of his recent publications reveals a strong trend toward interdisciplinary work combining distributed computing with geometric graph theory. His research on balls in graphs has applications in machine learning, while his work on programmable matter addresses cutting-edge challenges in distributed robotics. The consistent high-quality output in top theoretical venues demonstrates sustained research productivity across multiple subfields. As a habilitated researcher at CNRS, Chalopin supervises PhD students and participates in research projects, though specific details about current students or grants aren't provided in the source material. His long-standing collaboration with Victor Chepoi (appearing in numerous publications) represents a significant research partnership in metric graph theory. Chalopin is based at LIS in the TPR2 building (office 05.28, 5th floor) where he works as part of the DALGO team, which focuses on algorithms and theoretical computer science. His research environment provides strong connections to both the French and international theoretical computer science communities through collaborations with researchers across Europe and beyond.

• Signal processing
• Audio processing
• Spatial audio
• +7 more

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

Anirudha MajumdarView profile

Associate Professor

• Control
• Robotics
• Dynamical Systems
• +7 more

Anirudha Majumdar is an Associate Professor in the Mechanical and Aerospace Engineering (MAE) department at Princeton University and Associated Faculty in Computer Science. He also holds a part-time visiting research scientist position at Google DeepMind in Princeton, focusing on robotics and AI integration. His research centers on enabling robots to generalize safely and reliably in human-centered environments, combining nonlinear control theory, optimization, motion planning, and machine learning. Key applications include autonomous UAV flight, robotic manipulation, and legged locomotion. He has published extensively on topics like uncertainty quantification, safe reinforcement learning, and formal guarantees for robotic systems. Educated with a Ph.D. in Electrical Engineering and Computer Science from MIT (2016) and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania (2011). Postdoctoral scholar at Stanford University's Autonomous Systems Lab (2016-2017). His work emphasizes algorithmic innovation and theoretical rigor. Recent publications highlight advancements in robust control, vision-language models for robotics, and frameworks for safe AI deployment. Scientific Awards Sloan Fellowship ONR Young Investigator Program (YIP) award NSF CAREER award Google Faculty Research Award (twice) Amazon Research Award (twice) He leads the Intelligent Robot Motion Lab and has received multiple accolades for teaching and research excellence at Princeton.

• Robotics
• Machine Learning
• Tactile Sensing
• +4 more

Yasemin Bekiroglu is an Associate Professor at Chalmers University of Technology in the Control Engineering department. Her research focuses on data-efficient learning from multisensory data for robotics applications. PhD from Royal Institute of Technology (KTH) in 2012 Her work spans Robotics , including Grasp Planning , Adaptive Control , and Gaussian Processes . Recent publications emphasize Tactile Sensing and Multisensory Integration . Key trends in her research include Motion Planning , Safe Trajectory Generation , and Implicit Surface Representation for robotic systems. She has served as a reviewer and Associate Editor for leading robotics conferences and journals. Best Paper Award at RAHA 2016 Best Manipulation Paper Award at ICRA 2013 IROS CoTeSys Cognitive Robotics Best Paper Finalist in 2013 Yasemin contributes to projects like Dexterous Robot Assistant , advancing everyday object manipulation technologies.

Christoph RehekampffView profile

Researcher

• Additive Manufacturing
• Medical Robotics
• 3D Printing
• +4 more

Dr.-Ing. Christoph Rehekampff is a Senior Researcher at the Chair of Microtechnology and Medical Device Technology (Technical University of Munich), where he has been a team member since May 2018. His work bridges Additive Manufacturing , Medical Robotics , and Precision Engineering . Research Focus: His research spans 3D Printing (metal/polymer material jetting, FDM), Medical Device Design (dental zirconia coloring, sensor encapsulation), and Robotics (mobile platforms, origami-inspired arms). He explores automated design , closed-loop control , and support structure optimization . Publication Trends: His recent work (2023) includes inline topology measurement , UWB-based mobile robots , and workspace-specific arm design . Earlier studies (2021-2019) focused on material jetting , dental implant coloration , and sensor integration . Scientific Awards: MACHINES Advanced Manufacturing Best Student Paper Award (2nd price, 2021) IEEE ROBIO 2023 Best Paper Award Finalist Teaching: He lectures on Messtechnik und medizinische Assistenzsysteme (MMA) and Mikrotechnische Sensoren und Aktoren (MSA) .

• Geomatics
• GNSS positioning
• Indoor positioning
• +4 more

Paolo Dabove is an Associate Professor at the Department of Environmental, Land and Infrastructure Engineering (DIATI) at Politecnico di Torino, where he has served since November 2021. He holds multiple significant roles including Functions aggregated to the Vice-Rector for Education regarding activities related to first and second level teaching and the training offer, and is a Component of the SISCON Interdepartmental Center - Safety of Infrastructures and Constructions. His academic work spans teaching, research, and editorial responsibilities across multiple domains of geospatial technology. Professor Dabove's research interests focus on Geomatics, with particular expertise in GNSS positioning, indoor positioning, and photogrammetry. His work centers on developing algorithms for quality control of GNSS positioning in real-time using both geodetic and low-cost instruments. He has made significant contributions to monitoring techniques with Geomatics instruments, low-cost INS and GNSS systems for mobile mapping, and innovative methodologies for landslides monitoring using networks of GNSS mass-market receivers. His research extends to smartphone-based positioning solutions and atmospheric monitoring through GNSS signal exploitation. His publication record demonstrates a consistent focus on advancing geospatial technologies, with particular emphasis on making sophisticated positioning systems more accessible through low-cost solutions. Recent work shows increasing integration of machine learning techniques with traditional geospatial methods, and expanding applications into diverse fields including transportation systems, museum visitor studies, underground structure monitoring, and climate-related research. His publications reveal strong international collaboration patterns and growing interest in open-source approaches to geospatial technology. Premio giovani autori SIFET (SIFET Young Authors Award) conferred by SIFET: Società Italiana di Fotogrammetria E Topografia, Italy (2015) Fellow - Geographic Free and Open Source Software, Italia (2020-2021) Fellow - Associazione Georisorse e Ambiente (GEAM), Italia (2020-2022) Professor Dabove actively supervises doctoral students, with Milad Bagheri currently working under his guidance in the Civil and Environmental Engineering program. He leads multiple significant research projects including META-MUSEUM (2024-2027), PAIN AND GAIN (2023-2025), and CNMS – Spoke 7 (2022-2025), demonstrating substantial grant acquisition capabilities. His research portfolio spans competitive EU-funded projects, national PRIN grants, and commercial contracts with organizations including ESA. As Editor-in-Chief of Geoengineering Environment And Mining (GEAM) journal and active participation in professional organizations including the International Association of Geodesy (IAG) Working Group and ISPRS Working group V/3 (which he chairs), Professor Dabove maintains strong connections to the international geospatial research community. His leadership of the GFOSS.it association since 2020 highlights his commitment to open-source geospatial technologies.

Michael I. MigaView profile

Professor

• Image-guided surgery and intervention
• Computational modeling for therapeutic applications
• Soft-tissue mechanics
• +9 more

Dr. Michael I. Miga is the Chair of the Department of Biomedical Engineering, Harvie Branscomb Professor, and Professor of Biomedical Engineering, Radiology and Radiological Sciences, Neurological Surgery, Otolaryngology, and Computer Science at Vanderbilt University. He serves as Director of the Vanderbilt Institute for Surgery and Engineering (VISE) and leads the Biomedical Modeling Laboratory. Dr. Miga joined Vanderbilt University in Spring 2001 and has established himself as a leader in computational approaches to surgical medicine. Dr. Miga's educational background includes: Ph.D. in Biomedical Engineering from Dartmouth College M.S. in Mechanical Engineering from the University of Rhode Island B.S. in Mechanical Engineering from the University of Rhode Island Dr. Miga's research focuses on bridging the gap between diagnostic imaging advancements and procedural medicine through innovative computational approaches. His work centers on complex biophysical models, methods of automation and procedural field surveillance, data-driven procedures, therapy forecasting, and disease information integration associated with digital twins. He pioneers platform technologies designed for the dual purpose of treatment and discovery in surgical and interventional settings, with particular emphasis on medical robotics, computer vision, and AI applications. Dr. Miga's recent publications reveal a consistent focus on image-guided surgery across multiple organ systems including brain, liver, breast, and kidney. His work addresses critical challenges in soft tissue deformation, intraoperative imaging, and computational modeling to enhance surgical precision. A significant portion of his research involves developing and validating model-based approaches for deformation correction in image-guided procedures, demonstrating strong translational potential across various surgical specialties. Dr. Miga's significant professional recognitions include: Induction into the American Institute for Medical and Biological Engineering College of Fellows (top 2% of medical and biological engineers) in 2014 Charter membership on the Biomedical Imaging Technology (BMIT-B) Study Section of the NIH Center for Scientific Review (2010-2014) Charter membership on the Bioengineering, Technology, and Surgical Sciences (BTSS) Study Section since 2017 As Principal Investigator on multiple NIH grants focused on image-guided brain, liver, kidney, and breast surgery, Dr. Miga maintains a robust research program. He serves as PI and Director of the NIH T32 training program 'Training Program for Innovative Engineering Research in Surgery and Intervention,' which emphasizes the creation of translational technologies for treatment and discovery. His laboratory provides a unique immersive educational environment that emphasizes clinical immersion as a primary focus for engineering researchers in surgery and intervention. Dr. Miga co-founded and directs the Vanderbilt Institute for Surgery and Engineering (VISE), an interdisciplinary, trans-institutional entity designed to bring engineers and physicians together to impact healthcare. His Biomedical Modeling Laboratory focuses on three primary research thrusts: enhancing soft-tissue image guided surgery in multiple organs, developing model-driven interventional guidance for neoadjuvant chemotherapy, and creating model-enhanced ablative therapy delivery systems. His work represents a paradigm shift toward integrated, modular technologies that optimize therapy delivery through procedure-specific and patient-specific computational approaches.

• Manufacturing Engineering
• Robotics
• Industry 4.0
• +4 more

Dr. Pedro Ferreira is a Senior Lecturer in Manufacturing Systems at Loughborough University. He holds a MEng (equiv) and PhD from the University of Nottingham and is a Fellow of the Higher Education Academy (FHEA). His career spans academic and industry roles, including a Research Associate at the University of Nottingham, Senior Research Fellow, Technical Manager at Maersk Line IT, and Lecturer at Loughborough since 2015. Education: MEng (equiv) in Electrical, Electronic, and Computer Science Engineering, New University of Lisbon, Portugal (2006) PhD in Manufacturing Engineering and Operations Management, University of Nottingham, UK (2011) Research Interests: Dr. Ferreira's work focuses on manufacturing systems , Industry 4.0 , robotics , and sustainability . He explores human-robot collaboration , tactile sensing , and data-driven approaches for optimizing production systems. His recent studies address challenges like battery lifecycle management, cost-effective sensor solutions, and aligning education with UN sustainability goals. Article Trends: His publications emphasize robotic automation , sensor innovation , and adaptive manufacturing systems . Key themes include tactile sensor applications for hardness classification, Industry 4.0 complexity indices, and symbiotic human-machine learning frameworks. Grants & Advising: While no specific grants or students are listed, his research portfolio indicates involvement in projects related to production system optimization, wearable sensors, and educational frameworks for sustainability. Labs/Teams: Engages with cross-disciplinary teams at Loughborough, focusing on advanced manufacturing and Industry 4.0 integration.

• Accessibility Research
• Sign Language Recognition
• Human-Computer Interaction
• +4 more

Danielle Bragg is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, specializing in accessibility research with a focus on technologies for deaf and hard-of-hearing communities. Her work bridges human-computer interaction, computer vision, and inclusive design to create practical solutions for sign language recognition, translation, and communication accessibility. Her research interests center on making technology accessible through innovative approaches to sign language processing, assistive communication systems, and inclusive design methodologies. She investigates how to build robust sign language recognition systems that work in real-world settings, addresses bias in AI models for sign language understanding, and develops community-centered approaches to dataset collection. Her work particularly emphasizes involving the Deaf community in the research and development process to ensure technologies meet actual needs. Dr. Bragg's publication record shows a consistent trajectory from her PhD work at the University of Washington (2018) through to current research in 2025, with increasing leadership in the field of accessibility technology. Her recent work explores cutting-edge applications of AI for sign language processing, gaze-based interfaces, and systematic analysis of how large language models are transforming human-computer interaction research. Her collaborative approach is evident in her extensive co-authorship network, working with both technical researchers and community stakeholders to advance the field of accessibility technology through interdisciplinary partnerships.

• Biomedical Engineering
• Medical Imaging
• Optical Engineering
• +5 more

Dr. Viktor Gruev is a distinguished academic holding multiple professorships at the University of Illinois: Professor of Electrical and Computer Engineering, Bioengineering, and Biomedical and Translational Sciences. He also serves as the Program Leader at the Beckman Institute for Advanced Science and Technology and the Cancer Center at Illinois. His research focuses on bioinspired imaging systems, with applications in medical diagnostics, underwater geolocation, and polarization-based technologies. Gruev’s work integrates optical engineering, nanotechnology, and biomedical innovation. Key areas of expertise include multispectral imaging sensors, polarization sensors for tumor detection, and polarization-based underwater navigation systems. He has pioneered devices such as the 9-band camera for image-guided surgery and bioinspired polarization event cameras, leveraging machine learning and bio-inspired design principles. His research has led to advancements in fluorescence-guided surgical tools, nanoparticle contrast agents, and polarization division multiplexing for optical communication. Gruev’s interdisciplinary projects bridge engineering and medicine, with collaborations spanning cancer imaging, marine robotics, and material science. He holds leadership roles in university research initiatives and is affiliated with the Office of the Vice Chancellor for Research and Innovation.

Dimitrios KanoulasView profile

Professor

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Dimitrios Kanoulas is a Professor in Robotics and AI at the Department of Computer Science, University College London (UCL). He holds the UKRI Future Leaders Fellowship and leads research in perception and learning for robotics, focusing on articulated robots navigating and manipulating in uncertain environments. His work emphasizes theoretical advancements in sensing, real-time mapping, and modeling of surface contact areas for locomotion on uneven terrain, leveraging SLAM systems and manipulation techniques. Research interests include robotics perception, SLAM, path planning, and reinforcement learning. His recent work explores adversarial attacks in path planning, high-fidelity simulators (e.g., Unreal Robotics Lab), and robust localization methods like AIR-HLoc and LiteVLoc. He also investigates humanoid robot stability, teleoperation systems, and autonomous mobile manipulators. Key contributions span legged locomotion, manipulation under uncertainty, and safety-critical control systems. His articles reflect trends in combining neural networks with traditional robotics frameworks, emphasizing real-world deployability and robustness. Beyond technical work, he emphasizes human-robot interaction through teleoperation interfaces and wearable technologies. Awards: UKRI Future Leaders Fellow Labs/Teams: RPL Lab at UCL Grants/Advising: No specific grants listed, but his research is supported by UKRI funding. Advising details not explicitly provided in text.

Rob WorleyView profile

Research Fellow

• Robotics
• Autonomous Systems
• Sensor Networks
• +3 more

Dr. Rob Worley is a Research Fellow at the University of Sheffield’s School of Electrical and Electronic Engineering since 2022, following his PhD studies initiated in 2018. His research focuses on robot navigation and localization within buried pipe networks, with a particular emphasis on acoustic and visual sensing technologies. He leads the Pipebot Patrol project, advancing autonomous inspection systems for water and sewer infrastructure. His work integrates state estimation, artificial intelligence, and robotics to address challenges in feature-sparse underground environments. Worley’s research interests span robot localization algorithms, SLAM (Simultaneous Localization and Mapping), and sensor fusion, with applications to cylindrical pipe networks. His projects leverage cylindrical regularity and hybrid metric-topological models to enhance navigation accuracy. Notable contributions include acoustic echo localization using microphone arrays and visual SLAM techniques for sewer networks. He has published extensively on topics like error detection in particle filters and condition monitoring in underground pipes. Advising and grants: While no specific advisees are listed, his projects likely involve collaborative research teams. He has contributed to the Pipebots initiative since its inception, focusing on localization and sensing advancements. Labs/Teams: Active member of the University of Sheffield’s robotics research group, contributing to the development of autonomous inspection technologies for infrastructure maintenance.