Find Academic Supervisors

• Parallel and Distributed Computing
• High Performance Computing
• Edge-Cloud Computing
• +3 more

Overview Dr. Jean-François DOLLINGER is a Researcher-Lecturer at CESI LINEACT (Strasbourg campus), affiliated with the Engineering and Numerical Tools research team. His academic roles include teaching Computer Science courses (undergraduate/graduate) and supervising student projects in algorithmics, programming, databases, and networks. Education: PhD in Computer Science (2011-2015), University of Strasbourg - ICube Lab MSc in Computer Science (2009-2011), University of Strasbourg (Highest Honors) BSc in Computer Science (2008-2009), University of Strasbourg (Honors) Research Interests: Focuses on edge-cloud computing, high-performance distributed systems, combinatorial optimization in IoT networks, and smart city infrastructure. Specializes in optimizing federated learning, WSN deployment strategies, and hybrid CPU/GPU execution frameworks. Advising & Collaboration: Supervises PhD/Master’s students (e.g., A. BAAHMED on federated learning, K. BOUHOUCH on OpenStack edge deployment) Collaborated with Indonesian universities (Mercu Buana) on RPL protocol extensions Research Team: Leads the Engineering and Numerical Tools group, developing frameworks for edge-cloud infrastructures and BIM-based WSN deployments in smart buildings.

Akihiko ToriiView profile

Assistant Professor

• Computer Vision
• 3D Reconstruction
• Place Recognition
• +6 more

Akihiko Torii is an Assistant Professor (Research Associate) at the Okutomi & Tanaka Lab in Tokyo Institute of Technology. He holds a PhD from Chiba University and completed a postdoctoral fellowship at the Center for Machine Perception (CMP) at Czech Technical University in Prague. His research focuses on computer vision, with expertise in 3D reconstruction, place recognition, localization, and structure from motion. He has contributed to influential datasets like the 24/7 Tokyo, Pittsburgh (Pitts250k), SF Revisited, and InLoc datasets, advancing visual localization and feature matching techniques. **Education and Background:** PhD in Computer Science, Chiba University Postdoctoral Researcher, Czech Technical University (2014–2018) **Research Interests:** His work spans Computer Vision , including 3D Reconstruction , Place Recognition , Feature Matching , and Multi-View Stereo . He emphasizes practical applications such as indoor/outdoor localization under changing conditions. **Awards and Recognition:** 2017: CVPR Outstanding Reviewers Award 2016: IEEE Japan Chapter Young Author Award 2015: Tokyo Tech Young Investigator Award Multiple Best Paper Awards (2013–2014) **Key Projects:** Developed algorithms like Multi-View Inverse Rendering and NetVLAD , and contributed to benchmarking frameworks for visual localization accuracy. His work bridges theoretical advancements and real-world applications in robotics and autonomous systems.

Bernie TiddemanView profile

Professor

• Computer Vision
• Machine Learning
• Data Analysis
• +3 more

Bernie Tiddeman is a Professor in the Department of Computer Science at Aberystwyth University. His research focuses on computer vision, machine learning, and their applications in fisheries technology, anomaly detection, and data analysis. He has led projects funded by DEFRA, the European Maritime and Fisheries Fund, and the Welsh Government, addressing challenges in environmental monitoring, automated data capture, and heritage preservation. His work contributes to UN Sustainable Development Goals related to sustainable fisheries and responsible consumption. Key research interests include deep learning for object detection (e.g., crabs and lobsters), reinforcement learning in non-stationary environments, and neural architecture search. He collaborates with researchers like Dr. Seb Gregory Dal Toe and Muhammad Iftikhar on fisheries data automation and computer vision systems. Notable projects include the Automated Video-Based Capture of Crustacean Fisheries Data and the Improved Shellfish Data Collection Project. His recent publications (2023-2025) highlight advancements in anomaly detection, crustacean identification, and low-power hardware solutions. Tiddeman has supervised over a dozen PhD candidates and actively participates in educational initiatives like the Wales Collaborative for Learning Design.

Irina RishView profile

Professor

• Machine Learning
• Deep Learning
• Neuroscience
• +9 more

Irina Rish is a Full Professor at the University of Montreal's Department of Computer Science and Operations Research and a core member of Mila – Quebec AI Institute. She holds the Canada Excellence Research Chair in Autonomous Artificial Intelligence and a CIFAR AI Chair. Her research focuses on machine learning, neural data analysis, and neuroscience-inspired AI. She leads the Autonomous AI Lab and has contributed to projects like the Covi contact-tracing app during the pandemic. Education: MSc and PhD in AI from University of California, Irvine; MSc in Applied Mathematics from Moscow Gubkin Institute. Awards include multiple IBM accolades and prestigious editorial roles (e.g., IEEE TPAMI Associate Editor). Research Interests: Continual Learning, Deep Learning, Sparse Modeling, Reinforcement Learning, Neuroimaging Analysis Affiliations: MILA, CIRCA (Brain and Learning Research Center), Canada Excellence Research Chair Publications span over 120 papers, 64 patents, and books on sparse modeling. Collaborates with industry leaders like IBM, Microsoft, and Samsung. Grants: Leads U.S. Department of Energy’s INCITE project on scalable foundation models. Advises over 30 students in AI and neuroscience-related fields. Labs/Teams: Directs the Autonomous AI Lab at Mila, co-founded Nolano.ai as CSO.

• Computer Vision
• Image Processing
• Autonomous Systems
• +6 more

Antoine Manzanera is a Professor HDR at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS) and the Systèmes autonomes et Robotique (SAR) department. His research focuses on Computer Vision, Image Processing, and Autonomous Systems, with notable contributions to medical imaging applications and robotics. He holds a Habilitation Degree from Université Pierre et Marie Curie (2012) and has taught at ENSTA, Université Paris Sorbonne, and international institutions. BSc Mathematics, Université Claude Bernard Lyon 1 (1991) MSc Theoretical Computer Science, Université Claude Bernard Lyon 1 (1993) PhD Signal and Image Processing, Télécom ParisTech (2000) Research interests span Computer Vision fundamentals, including Video Analysis, Scene Reconstruction, and Autonomous Visual Learning. His work bridges theory and practice, addressing challenges in medical diagnostics (e.g., colonoscopy analysis, Parkinson's biomarkers) and robotics (e.g., drone navigation, autonomous driving datasets). Recent articles highlight advancements in multimodal data fusion, deep learning optimization, and medical imaging tools. Collaborations include projects on synthetic databases for colon depth estimation and Riemannian geometry for oculomotor analysis. His contributions to open-source frameworks like Video++ and educational initiatives (e.g., DroMOOC on drones) further his impact.

Chen Chang-WenView profile

Professor

• Multimedia Signal Processing
• Multimedia Systems
• Image/Video Processing
• +1 more

Chen Chang-Wen is a Chair Professor of Visual Computing at The Hong Kong Polytechnic University (2021–present). Previously, he was Empire Innovation Professor at the University at Buffalo (2008–2021) and held leadership roles including Dean of the School of Science and Engineering at CUHK Shenzhen (2017–2020). His research focuses on multimedia systems, signal processing, and communication. He is a Fellow of IEEE and SPIE, and has received numerous awards including the Alexander von Humboldt Research Award (2010) and the SUNY Chancellor's Award (2016). Chen has authored over 420 publications and holds three US patents. His work emphasizes interdisciplinary applications in visual computing, including contributions to autonomous systems, edge networks, and video quality assessment. He currently serves as Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems and leads initiatives in global university rankings and accreditation for engineering programs.

• Business Process Management
• Process Mining
• Artificial Intelligence
• +3 more

Henrik Leopold is a Professor affiliated with Kühne Logistics University and Hasso Plattner Institute. His research focuses on Business Process Management (BPM), Process Mining, and the application of AI technologies like Large Language Models (LLMs) in process automation. He has co-authored over 138 publications, including impactful works on process mining in supply chains, robotic process automation effects, and explainable anomaly detection in event logs. Key research interests include: Process Mining & Business Process Optimization AI-driven Process Automation Process Model Matching & Compliance Checking Natural Language Processing in BPM Process Analytics & Data-Driven Decision-Making Recent work emphasizes interdisciplinary approaches combining process mining with LLMs for tasks like event log extraction and prompt engineering. He actively contributes to BPM conferences such as BPM, CAiSE, and HICSS, often serving as program committee member. His research addresses both technical challenges (e.g., anomaly detection) and organizational aspects (e.g., process automation adoption barriers). Notable contributions include development of Sketch2Process for BPMN sketch recognition and foundational work on process model validation. Current activities explore future directions in BPM through collaborative ranking of research challenges and benchmarking LLM capabilities for process tasks.

Cynthia MatuszekView profile

Assistant Professor

• Robotics
• Machine Learning
• Human-Robot Interaction
• +5 more

Cynthia Matuszek is an Assistant Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She directs UMBC’s Interactive Robotics and Language lab, focusing on enabling robots to learn from human interaction using natural language. Her research spans robotics, machine learning, human-robot interaction (HRI), and natural language processing (NLP). Notably, she was recognized in IEEE's 2018 '10 to Watch in AI' list. Dr. Matuszek holds a Ph.D. and M.S. in Computer Science & Engineering from the University of Washington (2014, 2010) and a B.S. in Computer Science from the University of Texas at Austin (2000). Her work bridges theoretical and applied domains, addressing challenges in grounded language learning, multimodal data fusion, and ethical AI deployment. Her recent publications emphasize cybersecurity (e.g., malware detection using compact features), neurosymbolic reasoning, and the integration of large language models into robotics. The lab’s work often explores VR-based human-robot collaboration scenarios, leveraging photogrammetry and simulation for reproducible studies. Key thematic trends in her articles include: Advancing grounded language learning through cross-modal alignment and interactive labeling Developing ethical AI systems capable of moral reasoning and command rejection Pioneering domain-specific cybersecurity solutions using tensor factorization and vector stores Expanding multilingual and multimodal capabilities for robots in unstructured environments Her research also addresses societal impacts, including studies on gender stereotypes in AI datasets and privacy implications of pervasive robotics. The lab’s work often intersects with NSF-funded initiatives, such as the CAREER award for inclusive robotic learning environments.

Jin SunView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Deep Neural Networks
• +7 more

Jin Sun is an Assistant Professor in the School of Computing and a faculty fellow at the Institute for Artificial Intelligence at the University of Georgia. He holds a Ph.D. in Computer Science from the University of Maryland, College Park (2018). His research focuses on computer vision and deep learning , particularly in understanding human behavior, 3D geometry, and applying AI to healthcare, agriculture, and societal challenges. He is a Lilly Teaching Fellow and actively collaborates across disciplines. Education: Ph.D. Computer Science, University of Maryland (2018) Affiliations: School of Computing, Institute for AI, Franklin College of Arts & Sciences His research interests include machine learning , generative models , object detection , and context modeling . Recent work spans precision agriculture (e.g., cotton boll analysis), healthcare AI (e.g., bed occupancy detection), and multimodal geospatial applications. Grants & Funding: NIH R21 Grant (PI, 2024) USDOT Grant (Co-PI, 2025) UGA Presidential Interdisciplinary Seed Grant (Co-PI, 2024) Google Inc. Sponsorship (2022–2024) Key Contributions: Developed frameworks for 3D scene reconstruction, AR-driven data collection systems, and benchmark datasets for zooplankton recognition and enzymatic function prediction. His work bridges AI with real-world applications in environmental science and healthcare.

Andrei AtanovView profile

Researcher

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +3 more

Andrei Atanov is a Researcher at the Laboratoire d'intelligence et d'apprentissage visuels (VILAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on advanced topics in artificial intelligence, computer vision, and machine learning, with particular emphasis on multimodal learning, vision-language models, and robust generalization. He is affiliated with the Department of Computer Science and contributes to projects exploring innovative solutions for vision tasks using computationally designed sensors and diffusion models. His work often bridges theoretical advancements with practical applications in robotics and generative systems. Key research areas include developing large vision-language models, optimizing vision algorithms for low-sensor environments, and enhancing model robustness through diversification strategies. His publications span topics from 3D data augmentation to uncertainty estimation in deep learning, reflecting a broad yet technically deep expertise in AI fundamentals and applications.

• Computer Vision
• Robotics
• Mixed Reality
• +3 more

Zuria Bauer is a Lecturer in the Department of Computer Science at ETH Zürich. Her research focuses on advancing computer vision, robotics, and mixed reality technologies with applications in human-robot interaction, scene understanding, and assistive technologies. Key areas include monocular depth estimation, 3D scene reconstruction, and neural rendering techniques. Her work integrates theoretical advancements with practical implementations, such as enhancing robotic perception systems for real-world environments and developing intuitive mixed reality interfaces. Bauer has contributed to datasets like UASOL and frameworks like MaRINeR, which address challenges in novel view synthesis and object manipulation. Research trends in her articles emphasize cross-disciplinary approaches, combining deep learning with robotics to solve problems in healthcare accessibility (e.g., COMBAHO system) and environmental perception for autonomous systems. She explores both hardware-software co-design (e.g., low-cost wearable sensors) and algorithmic innovations (e.g., NeRF-based augmentation). While no formal awards are listed, her publications reflect sustained contributions to advancing perception technologies across dynamic scenes, robotic interaction, and assistive applications.

• Computational Robotics
• Artificial Intelligence
• Path Planning
• +3 more

Valentin Hartmann is affiliated with the Computational Robotics department at ETH Zürich. His research focuses on advanced robotics, motion planning, and multi-agent systems. He develops algorithms for optimal path planning, task allocation, and autonomous decision-making in complex robotic environments. His work addresses challenges in multi-robot coordination, manipulation, and scalable solutions for construction and industrial automation. Key contributions include benchmark datasets for multi-agent planning and novel methods for efficient motion and task scheduling. Recent research highlights include advancements in kinodynamic motion planning (e.g., iDb-A*, iDb-RRT), optimization of multi-arm systems, and robotic construction applications. His projects emphasize reproducibility through dedicated planning tools (Planner Developer Tools) and leverage material integration for collective building tasks.

• Statistical Process Control
• Probability Theory
• Quality Control
• +3 more

Laila El Ghandour is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. Her research focuses on statistical process control, probability theory, and quality control methodologies. She contributes to UN Sustainable Development Goals through innovative applications of control charts and statistical modeling. Her work includes analysis of SPRT charts, gamma processes, and Weibull distributions. She has received two Certificate of Best Paper Awards in 2023 and 2024 for collaborative research on control chart performance and parameter estimation. Her recent publications explore advanced control systems, statistical monitoring techniques, and optimization of quality assurance processes. Collaborations include international conferences on robotics, signal processing, and computational science.

• Empirical Software Engineering
• Software Quality
• Requirement Engineering
• +3 more

Deepti Mishra is a Professor at the Department of Computer Technology and Informatics at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. She leads the Intelligent Systems and Analytics Research Group and the Educational Technology Laboratory. Previously, she held positions as Associate Professor at Monash University (Malaysia) and Assistant Professor at Atilim University (Turkey). Her research focuses on Empirical Software Engineering, Human-Robot Interaction, and Sustainability. Current projects include collaborative research-based education for wind farms and marine plastic pollution solutions, funded by The Research Council of Norway and DIKU. Her research interests span Human-Computer Interaction, Software Quality, and Requirement Engineering. Recent work emphasizes social robotics applications in healthcare, stress mitigation, and educational environments. She actively contributes to the Excited Centre for Excellent IT Education and the NTNU Active Aging Team. Over 50 peer-reviewed publications demonstrate her expertise in AI-driven robotics, edge computing, and software ecosystem decision-making. Publications highlight innovations in gesture-controlled robotics frameworks, multi-pose action recognition systems, and ROS-based human-like robot actions. Key methodologies include systematic reviews, multi-criteria decision analysis, and deep learning architectures. Ongoing projects aim to bridge technology and societal challenges through interdisciplinary collaboration.

Cameron CoombeView profile

Researcher

• Artificial Intelligence
• Robotics
• Environmental Science
• +4 more

Cameron Coombe is a PhD Researcher at the Queensland University of Technology (QUT) Centre for Robotics. His research focuses on solving the Abstraction and Reasoning Corpus (ARC) to advance AI systems capable of human-like data efficiency and error correction, with potential applications in environmental monitoring. Education: Cameron holds a dual Bachelor of Mechatronics Engineering and Environmental Science from QUT (2021). His capstone projects included designing a vision system for a tree-climbing robot and evaluating aerial photogrammetry software for engineering and scientific applications. Research Interests: His work bridges AI, robotics, and environmental science, emphasizing machine learning for complex problem-solving and soft robotics innovations. He explores how AI systems can be optimized to address real-world challenges such as environmental data analysis. Professional Experience: Before his PhD, Cameron worked at CSIRO’s Soft Robotics team, enhancing vibrational jamming grippers for industrial applications. His research integrates interdisciplinary approaches to advance robotics and AI. Labs/Teams: He is affiliated with the QUT Centre for Robotics, contributing to projects that merge theoretical AI advancements with practical robotics solutions.