Find Academic Supervisors

• Big Data
• Social Network Mining
• Stream Processing
• +12 more

Dr. Thanh Tam Nguyen is a Lecturer at the School of Information and Communication Technology, Griffith University, Gold Coast Campus. He holds a PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and is a Fellow of the Higher Education Academy (FHEA). His academic and research profile is centered on advancing Big Data and Smart technologies through efficient and trustworthy AI systems. His educational background includes a Doctor of Science and a Master of Science, both from EPFL. He is actively involved in high-impact research and has published over 65 papers in top-tier venues such as SIGMOD, VLDB, SIGIR, ICDE, IJCAI, VLDBJ, TKDE, and Pattern Recognition, with over 35 in CORE A* journals and conferences. His work has attracted more than 4,200 citations (h-index 35+). Dr. Nguyen's research focuses on Big Data Analytics, Social Network Mining, Stream Processing, Privacy-Preserving Machine Learning, Recommender Systems, Explainable AI, and Graph Neural Networks . He aims to bridge human insights with data models to ensure transparency and trust in data-driven decisions. His recent work explores misinformation management, machine unlearning, and federated learning, with applications in social networks, healthcare, and sustainable agriculture. The 15 most recent publications reflect a strong trend in privacy-preserving AI, explainability, federated learning, and graph-based modeling . Key themes include machine unlearning, adversarial robustness in recommender systems, heterogeneous graph representations, and trustworthy AI deployment. His work is frequently published in ACM and IEEE venues, indicating sustained excellence in computer science and AI research. Fellow of the Higher Education Academy (FHEA) Dr. Nguyen has secured over $1 million in research funding from government (DFAT, A4I, AKF), industry (CSIRO, Ubitech, Johnson & Johnson), and international bodies (NAFOSTED, ETRI, KARI). He serves as a guest editor for IEEE Journal of Biomedical and Health Informatics, area chair for ACL and EMNLP, and reviewer for top journals like TKDE, JVLDB, and CSUR. He supervises multiple PhD students and collaborates with leading international researchers such as Prof. Karl Aberer (EPFL), Prof. Björn Schuller (Imperial College), and Prof. Hongzhi Yin (UQ). He is a key contributor to the Responsible Big Data Lab at Griffith University and leads projects on misinformation management, AI safety, and sustainable agriculture through AI-powered traceability. His impact extends to policy and industry, particularly in cybersecurity and trustworthy AI adoption.

Roman KrämerView profile

Researcher

• Engineering
• Production engineering
• Automation engineering
• +9 more

Roman Krämer is a Research Associate at the Institute for Production Engineering and Systems (IPTS) at Leuphana University of Lüneburg, where he has been working since 2023 at the Professorship for Modeling and Simulation of Technical Systems and Processes. Previously, from 2020 to 2022, he served as a Research Associate at the Chair of Process Measurement Technology and Intelligent Systems within the same institute. Mr. Krämer holds a Master of Science in 'Management & Engineering' (2016-2019) and a Bachelor of Engineering in 'Engineering (Industrial)' (2012-2016), both from Leuphana University of Lüneburg. His professional background includes work as a Project and Sales Engineer (2019-2020), involvement in assembly planning at an automobile manufacturer during his master's studies (2018-2019), and software development for Machine Vision Systems (2017-2018). Roman Krämer's research spans multiple engineering disciplines with a focus on production systems and digital technologies. His primary expertise includes Production engineering, Automation engineering, and Electrical engineering, with specialized knowledge in Production management, Supply chain management, Lean production, and Factory planning. He is particularly active in the areas of Modeling & Simulation, Artificial intelligence, and Digital twin technologies, applying these to optimize production and logistics processes. His work bridges theoretical approaches with practical industrial applications, focusing on improving efficiency in manufacturing systems. Dr. Krämer's recent publication demonstrates the application of cutting-edge AI technologies to traditional engineering challenges. His work on using GPT-based Large Language Models for accelerating simulation model creation reflects his interdisciplinary approach, combining artificial intelligence with production engineering to develop innovative solutions for industry. Roman Krämer is actively involved in research projects including the TrICo Subproject focused on Community Sustainable Production and the diZi-FTS project developing a digital twin for virtual commissioning of automated guided vehicles. These projects reflect his commitment to applying digital technologies to address real-world production challenges. At Leuphana University, Mr. Krämer contributes to the research ecosystem of the Institute for Production Engineering and Systems, which houses multiple research groups working on manufacturing technology, product development, measurement technology, and production management. His work connects with various ongoing projects addressing challenges in Industry 4.0 and digital transformation of manufacturing processes.

Andreas G. AndreouView profile

Professor

• Computing Machinery
• Sensory Information Processing
• Theoretical Neuroscience
• +6 more

Andreas G. Andreou is a Professor at Johns Hopkins University with primary appointments in the Department of Electrical and Computer Engineering, and secondary appointments in the Department of Computer Science and the Whitaker Biomedical Engineering Institute. He co-founded the Johns Hopkins University Center for Language and Speech Processing (CLSP) and co-directs the Andreou Lab alongside Philippe Pouliquen. His research focuses on brain-inspired microsystems for sensory information processing, neuromorphic computing, and theoretical neuroscience. Education: Born in Nicosia, Cyprus; resides in Baltimore, Maryland. Research Interests: Computing Machinery, Sensory Information Processing, Theoretical Neuroscience, Pattern Analysis, Machine Intelligence, Microsystems Technologies, and Integrated Circuits. Awards: IEEE Fellow, 3rd Best Paper Award at IEEE BioCAS 2018, and recognition for the award-winning Stethovest wearable acoustic sensing array. His lab explores energy-efficient computing beyond Moore's Law, integrating neuroscience principles into microsystem design. Recent projects include quantum-inspired neuromorphic optimizers, AI-generated chips using ChatGPT4, and applications in cardiac acoustics and wearable technology. Collaborations span institutions like NSF, JHU-APL, and the Telluride Neuromorphic AI workshop. His work is supported by grants from DARPA, NSF, NIH, ONR, AFRL, and JHU-APL. He also holds honorary professorships at the University of Cyprus and Universidad Nacional del Sur.

Tobias GrosserView profile

Associate Professor

• Compilers
• Polyhedral Compilation
• Performance Programming
• +7 more

Tobias Grosser is an Associate Professor in Compiler Design at the Department of Computer Science and Technology, University of Cambridge. His research spans compilation, programming language design, and performance programming with applications to climate science, quantum computing, and hardware design. He leads an active research group focusing on making compilers more modular, predictable, and trustworthy while bridging the gap between developers and compilers. His research interests include: Compilers and polyhedral compilation Static and dynamic program analysis High-performance computing and loop optimization Domain-specific compilation for accelerators (GPU, FPGA) Formal verification for compiler correctness Machine learning applications to compiler design His recent publications demonstrate significant contributions to multi-level intermediate representations (MLIR), compiler verification using Lean, and performance modeling. His work on xDSL provides a Python-native compiler framework that enables rapid prototyping of compiler infrastructure. His research group has developed tools like FPL (Fast Presburger Library) for loop optimization in deep learning and scientific computing. Scientific awards include two Amazon Research Awards (2023-2024) for work in automated reasoning, a Google PhD Fellowship, and an Ambizione Fellowship at ETH Zurich. He has advised numerous PhD students and researchers who have gone on to positions at Google, ETH Zurich, NVIDIA, and Intel. He actively collaborates with industry through the Amazon Scholars program and hosts regular Compiler Social events in Cambridge. His research vision emphasizes connecting compiler technology with societal challenges like climate change while maintaining a strong commitment to open-source development and diversity in computer science.

• Machine Learning
• Deep Learning
• Data Standardization
• +6 more

Tengxiang Su serves as a Postdoctoral Research Associate at Cardiff Business School, Cardiff University. He holds a PhD in Computer Engineering from Cardiff University's School of Engineering and an MSc degree from University College London (UCL). As a professional member of BCS (The Chartered Institute for IT), he contributes to advancing AI applications in construction and real estate domains. His research spans multiple AI disciplines with emphasis on machine learning integration with Building Information Modeling (BIM) systems. Key interest areas include data standardization for property valuation, knowledge graph construction through natural language processing, digital twin synchronization using LiDAR technology, and LLM fine-tuning for domain-specific applications. His work consistently bridges theoretical AI advancements with practical construction industry challenges. Recent publications demonstrate a clear trajectory toward automated valuation models and AI uncertainty mitigation in the Architecture, Engineering and Construction (AEC) sector. His 2022-2024 works particularly focus on damage assessment through digital twins and autonomous knowledge mining, showing increasing sophistication in handling complex construction data. Currently engaged in a Knowledge Transfer Partnership project (2023-2025) titled 'Data-driven strategic decision making in charity marketing' between Cerebra and Cardiff University, this work was recognized by Welsh Government for the KTP 50 Years Event (March 20, 2025). The project represents his expanding application of AI methodologies beyond traditional construction domains into nonprofit sector analytics. His technical expertise manifests across multiple platforms including GitHub where he maintains repositories related to time-series prediction (PM2.5 pollution modeling using LSTM networks) and NLP sentiment analysis, demonstrating practical implementation skills alongside theoretical research.

Jeroen Van SchependomView profile

Researcher

• Multiple Sclerosis
• Cognitive Dysfunction
• Neurodegenerative Diseases
• +7 more

Jeroen Van Schependom is a researcher at Vrije Universiteit Brussel’s Neurology department, specializing in artificial intelligence-assisted modeling in clinical sciences. He leads projects such as FWOTM1254 (2024–2028) and FWOTM1215 (2024–2027) focused on multiple sclerosis (MS) and Alzheimer’s disease. His work aims to develop novel biomarkers and improve understanding of disrupted cortical networks in neurodegenerative conditions. His research integrates neuroimaging , computational neuroscience , and machine learning to analyze brain network dynamics, particularly information processing speed deficits and cognitive rehabilitation in MS. Key methodologies include connectome analysis , transcranial stimulation , and vision-based signal processing . Notable scientific awards include the Marie-Curie Seal of Excellence (2019) and the Research Prize of the Belgian Neurological Society (2018) . His recent publications explore smartphone-based cognitive screening, vaccination efficacy in MS patients, and AI-driven neurophysiological modeling. Projects : 28 ongoing, including 3D imaging techniques for tissue analysis and transcranial current stimulation for cerebral blood flow improvement. Public Engagement : Delivered talks on AI in healthcare (2023) and barriers to open science (2024).

Fabio MorbidiView profile

Associate Professor

• Robotic Vision
• Multi-robot Systems
• Event-Based Perception
• +3 more

Fabio Morbidi is an Associate Professor (Maître de Conférences HDR) at the Université de Picardie Jules Verne (UPJV), France, where he heads the Robotic Perception group at the MIS laboratory since 2022. His office is located at 33 Rue Saint-Leu, Amiens. He holds a Ph.D. in Robotics and Automation from the University of Siena (2009) and conducted postdoctoral research at Northwestern University, University of Texas at Arlington, and Inria Grenoble. His research focuses on multi-robot systems, event-based vision, autonomous vehicles, and assistive robotics. Research interests span robotic vision, distributed control, and sensor fusion for applications in exploration, wheelchair assistance, and drone technology. His work emphasizes real-world implementations, such as the SpheriCol driving assistant for power wheelchairs and event-based perception for autonomous vehicles. Awards include ICRA'25 Best Paper Award Finalist and contributing to an IEEE RAM issue that received the APEX 2024 Awards of Excellence. He actively advises PhD students and coordinates ANR projects (e.g., CERBERE, DEVIN) focused on robotic perception. He serves as Associate Editor for IEEE Transactions on Robotics and IEEE Robotics and Automation Letters.

Luke ReisnerView profile

Adjunct Professor

• Surgical Robotics
• Raman Spectroscopy
• Medical Visualization
• +3 more

Dr. Luke Reisner is an Adjunct Professor in the Department of Electrical and Computer Engineering at Wayne State University's College of Engineering. His research focuses on surgical robotics, Raman spectroscopy, and medical visualization, with applications in autonomous camera control for medical, military, and space domains. Education: Ph.D. in Computer Engineering (Wayne State University, 2012), M.S.E. in Computer Engineering (University of Michigan–Dearborn, 2005), B.S.E. in Computer and Electrical Engineering (University of Michigan–Dearborn, 2003) Research Interests: Surgical robotics: Developing autonomous camera systems for the da Vinci Surgical System, focusing on task analysis, remote presence interfaces, and recording/playback platforms for surgical training. Key innovations include entropy-based bleeding detection algorithms and sensor-integrated robotic systems. Raman Spectroscopy: Applications in medical diagnostics for ulcerative colitis and pediatric brain tumors, combined with biosensor integration for optimized port placement and real-time analysis in vivo. Medical Visualization: Advancing surgical imaging techniques, including bleeding assessment visualization and sensor-integrated virtual reality for surgical training. Patents: U.S. Patent 9,439,556 (2016): "Intelligent Autonomous Camera Control for Robotics with Medical, Military, and Space Applications" U.S. Application 14/913,793 (2016): "Camera Control System and Method" Labs & Teams: Collaborated with surgical robotics teams to implement autonomous camera control and biosensor-integrated systems. His work bridges engineering principles with clinical applications, emphasizing real-time data processing and human-in-the-loop robotic systems.

• Digital Games
• Emerging Mixed Reality Technologies
• Human-Computer Interaction
• +6 more

Professor Marcus Carter at The University of Sydney (Faculty of Arts and Social Sciences) is a leading researcher in Human-Computer Interaction , currently holding an Australian Research Council Future Fellowship for his work on 'The Monetisation of Children in the Digital Games Industry'. With a BA Hons and PhD in History and Philosophy of Science from Melbourne, he directs the Sydney Games and Play Lab . Focuses on digital games, mixed reality, and social impacts of technology Expert in animal-computer interaction through projects like 'Kinecting with Orangutans' Recognized as Australia's top HCI researcher in 2021 by The Australian His research explores VR/AR ethics , player experience analysis , and immersive technology governance . Publications span game monetization, metaverse politics, and conservation applications of XR. Current projects examine disability inclusion in VR and ethics of immersive data collection. Key awards include: ARC Future Fellowship FT220100076 ($911,616) Best Paper at ACM CHI 2017 and ACM ToCHI 2017 2020 & 2021 Researcher Rising Star by The Australian Supervises HDR students in topics ranging from blockchain gaming to social VR, and contributes to industry reports like the 2024 SXSW Games analysis. Leads innovative research bridging game studies with social responsibility frameworks.

Sven LončarićView profile

Professor

• Computer Vision
• Image Processing
• Medical Imaging
• +5 more

Academician Professor Sven Lončarić serves as a Full Professor in the Department of Electronic Systems and Information Processing at the Faculty of Electrical Engineering and Computing, University of Zagreb. With his office located in room D-119 and contactable via phone at 6129-891, he maintains an active presence in both teaching and research activities within the institution. His work bridges theoretical computer science with practical applications across multiple domains. Professor Lončarić's research spans computer vision, image processing, and deep learning with significant contributions to medical imaging, particularly in retinal analysis using optical coherence tomography. His work in color constancy and illumination estimation has advanced techniques for handling multi-illuminant scenes, while his research in ultrasound image analysis has practical applications in non-destructive testing. He also explores computer security aspects of machine learning models, particularly in backdoor detection and removal. His recent publications demonstrate a strong trend toward applying deep learning techniques to solve complex problems in medical imaging, retail automation, and industrial applications. The concentration of work in retinal imaging and color constancy shows his commitment to advancing computer vision fundamentals while simultaneously addressing practical challenges in healthcare and industry. His research increasingly incorporates anomaly detection methods and robust security practices for machine learning systems. Professor Lončarić actively contributes to the academic community through organizing events like the Croatian Computer Vision Workshop and the First Croatian Symposium on Artificial Intelligence, providing platforms for knowledge exchange in his fields of expertise. His work on astronomical data processing demonstrates the versatility of his methodological approaches across diverse scientific domains. Within the Department of Electronic Systems and Information Processing, he participates in the Center of Excellence for Computer Vision, contributing to the institution's research profile in artificial intelligence and computer vision. His work on medical imaging applications connects the faculty with healthcare institutions, facilitating interdisciplinary collaborations that translate technical advances into medical practice.

Alessandro Di NuovoView profile

Professor

• Artificial Intelligence
• Robotics
• Machine Intelligence
• +7 more

Professor Alessandro Di Nuovo is Professor of Machine Intelligence at Sheffield Hallam University, where he is based in the Department of Computing within the School of Computing and Digital Technologies. He leads the AI, Robotics and Digital research theme at the Advanced Wellbeing Research Institute and founded the Smart Interactive Technologies (SIT) Research Laboratory, which conducts internationally renowned research in interdisciplinary applications of machine intelligence, particularly in healthcare and well-being. His research focuses on two primary streams: Neuro-developmental Robotics , which explores how machines can learn by simulating child-like brains embodied in humanoid robots, and Socially Assistive Robotics , which aims to support healthcare and social workers in caring for children, older adults, and disabled individuals. His work bridges cognitive science, robotics, and healthcare applications, with particular emphasis on neuromorphic engineering, cognitive architectures, and real-world implementation in clinical settings. His recent publications (2023-2025) demonstrate a strong focus on neuromorphic computing, human-robot interaction for elderly care and autism therapy, and ethical considerations in robotics. These works reveal a consistent pattern of interdisciplinary collaboration and methodological innovation, particularly in translating theoretical advances in developmental robotics into practical healthcare applications. The research shows increasing sophistication in integrating multiple sensing modalities and creating more natural, adaptive human-robot interactions. Senior Member of the IEEE (2014) As a research leader, Prof. Di Nuovo has secured over £2.7 million in funding and established a worldwide network of collaborators. He serves as Topic Editor-in-Chief of the International Journal of Advanced Robotic Systems and as Associate Editor for several prestigious journals. His leadership extends to major international projects including the Horizon Europe PRIMI project (€7.3 million, 2023-2027) and the H2020 PERSEO network (€4 million, 2021-2024), which train the next generation of interdisciplinary researchers. The Smart Interactive Technologies (SIT) Research Laboratory under Prof. Di Nuovo's leadership provides cutting-edge facilities for advanced machine learning and human-robot interaction experiments. The lab supports collaborative research projects funded by public bodies (European Union, UKRI), private companies (IBM, NVIDIA), and charities (Sheffield Children Hospital Trust), positioning it at the forefront of socially impactful robotics research with direct clinical validation pathways.

Nikita AraslanovView profile

Researcher

• Computer Vision
• 3D Reconstruction
• Semantic Segmentation
• +3 more

Dr. Nikita Araslanov is a Postdoctoral Researcher at the Technical University of Munich (TUM) in the School of Computation, Information and Technology, Department of Informatics 9 (Computer Vision Group). He also serves as a visiting faculty member at Google. His research focuses on semantic and 3D visual inference from video data, aiming to bridge perception and understanding in complex visual scenes. Dr. Araslanov earned his PhD in Computer Science from TU Darmstadt in the Visual Inference Lab, graduating with highest distinction. He holds a Master's degree in Computer Science from the University of Bonn, where he graduated with distinction in 2016. His research spans multiple areas of computer vision, with a particular emphasis on 3D reconstruction, semantic segmentation, and deep learning approaches for visual understanding. His work often combines theoretical insights with practical applications, addressing challenges in dynamic scene understanding, vision-language correspondence, and unsupervised learning paradigms. He has made significant contributions to bundle adjustment for dynamic scenes, hierarchical semantic segmentation using hyperbolic geometry, and novel approaches to unsupervised panoptic segmentation. Dr. Araslanov's research has been recognized with several prestigious awards, including being selected as a Best Paper Candidate at ICCV 2025 for his work on dynamic scene reconstruction, and having his Scene-Centric Unsupervised Panoptic Segmentation paper designated as a Highlight Paper at CVPR 2025 (top 3% of submissions). He has also received multiple oral presentation awards at major computer vision conferences including GCPR 2024, CVPR 2024, and ICLR 2024. Actively involved in the academic community, Dr. Araslanov serves as an Area Chair for CVPR 2025. He is committed to mentoring the next generation of researchers and regularly supervises master's theses, guided research projects, and research assistant positions (HiWi). His teaching includes courses on Deep Learning for Spatial AI (Summer Semester 2025) and Computer Vision 3: Segmentation, Detection and Tracking (Winter Semester 2024/25). As a member of the Computer Vision Group led by Prof. Dr. Daniel Cremers at TUM, Dr. Araslanov collaborates with a diverse team of researchers working on cutting-edge computer vision problems. The group maintains strong connections with industry partners and contributes significantly to the advancement of computer vision research through publications at top-tier conferences and journals.

Michelangelo CeciView profile

Professor

• Data Mining
• Machine Learning
• Relational Data Mining
• +5 more

Michelangelo Ceci is a full professor at the Department of Computer Science, University of Bari, Italy. His academic career spans over two decades with significant contributions to data mining and machine learning research. He has established himself as a leading figure in the European data mining community through his extensive publication record and leadership roles in major conferences. Dr. Ceci's primary research interests focus on data mining and machine learning, with particular emphasis on multi-relational data mining, text mining, spatial data mining, spatio-temporal data mining, and semi-supervised/transductive learning. His work bridges theoretical foundations with practical applications across diverse domains including social network analysis, financial technology, healthcare informatics, and environmental monitoring. His research demonstrates a consistent trajectory toward increasingly complex data structures and more sophisticated modeling techniques. The most recent publications reveal several key trends: a growing focus on explainable AI systems, increased application of graph-based methods for spatio-temporal data, expansion into biomedical applications particularly in microbiome analysis, and development of robust methods for anomaly detection in cryptocurrency and social networks. His work increasingly integrates multiple data sources and perspectives through multi-view learning approaches. Dr. Ceci has served on the program committees of major international conferences including IEEE ICDM, IJCAI, ECMLPKDD, SIAM SDM, ECAI, ISMIS, PAKDD, DEXA, and ACM SAC. He has held editorial positions with journals such as IJSNM, IJDATS, and Journal on Advances in Intelligent Systems, and has been actively involved in organizing conference tracks and workshops. His research has been supported through significant projects including serving as national coordinator of FP7612944 MAESTRA and coordinator of a research unit in the PONREC project Vi-POC. He has participated in numerous national (PRIN-COFIN 2001, 2009) and international research projects (IST-1999-20882: COLLATE). Dr. Ceci has also contributed to the academic community through mentoring PhD students and early-career researchers, though specific names of advisees are not documented in the available information.

Hsinchun ChenView profile

Professor

• Artificial Intelligence
• Cybersecurity
• Cyber Threat Intelligence
• +6 more

Hsinchun Chen is a Regents' Professor of Management Information Systems at the University of Arizona's Eller College of Management, holding the Thomas R. Brown Chair in Management and Technology. As Director of the Artificial Intelligence Laboratory since 1989 and Director of the AZSecure Cybersecurity Program, he leads pioneering research in AI-driven security and health analytics. With over 34 years of academic leadership, he has graduated 36 Ph.D. students and authored 600+ publications. His educational background includes: BS from National Chiao-Tung University (Taiwan) MBA from SUNY Buffalo MS and Ph.D. from New York University Dr. Chen's research revolutionizes artificial intelligence applications across cybersecurity and health informatics. He developed the COPLINK/i2 system (acquired by IBM for $500M) used by 5,000+ law enforcement agencies globally, and created AZSecure for cyber threat intelligence and SilverLink for mobile health analytics. His work bridges data/text/web mining with practical solutions in security informatics, health analytics, and business intelligence, emphasizing real-world impact through systems deployed at scale. Recent publications (2020-2022) demonstrate his leadership in AI-driven threat intelligence, with breakthroughs in Dark Web analysis, cross-lingual cybersecurity, and health monitoring using wearable sensors. Key trends include adversarial deep learning for vulnerability detection, multimodal stock prediction, and transfer learning for activity recognition, consistently integrating technical innovation with domain-specific applications. His accolades include: Fellow of ACM, IEEE, AAAS, and AIS IEEE Big Data Security Pioneer Award (2022) INFORMS Design Science Award (2008, 2023) AIS Impact Award (2020) UA Extraordinary Faculty Award (2022) Dr. Chen has secured $60M+ in research funding from NSF, NIH, DOD, and other agencies, directing 100+ grants including the NSF Smart and Connected Health program. His 36 Ph.D. graduates include three ICIS Dissertation Award winners, with placements at top Research I institutions. The AZSecure CyberCorps program he leads achieves 100% placement at agencies like NSA and FBI, with teams ranking #4 nationally in cyber competitions. His Artificial Intelligence Laboratory operates as a nexus for interdisciplinary innovation, developing systems like COPLINK (5,000+ law enforcement deployments) and AZSecure (NSA/DHS-certified). Current initiatives include AI4BI for semiconductor analytics under TSMC and expansion of SilverLink for chronic disease management, maintaining partnerships with federal agencies and global institutions.

Andrea CensiView profile

Lecturer

• Autonomous Driving
• Robotics
• Control Systems
• +3 more

Dr. Andrea Censi is a Lecturer at the Department of Mechanical and Process Engineering, ETH Zurich. His research focuses on robotics, autonomous driving, and control systems with interdisciplinary applications in game theory and urban mobility. Key projects include karma economies for resource allocation and co-design frameworks for embodied intelligence. Research interests span: Autonomous motion planning and decision making Trustworthy AI for socio-technical systems Karma-based congestion management Co-design of robotic control systems Event-based vision and neuromorphic sensing Formal methods for autonomous vehicle safety Selected research trends from publications include: karma economies for fair resource allocation (2024-2025), co-design of perception and decision-making systems (2025), dynamic population games (2024), and safety margin analysis for autonomous vehicles (2023). He teaches 151-0323-00L Hands-on Self-Driving Cars with Duckietown , an accessible education platform for autonomy research.