Find Academic Supervisors

• Wireless Communications
• Artificial Intelligence
• Big Data
• +5 more

David Contreras Bárcena is an Adjunct Professor at the Higher Technical School of Engineering (ICAI) of Comillas Pontifical University , where he has been a faculty member since 1998. He currently serves as the Head of Studies for the Degree in Mathematical Engineering and Artificial Intelligence (iMAT) and previously directed the Department of Telematics and Computing for five years. Education: PhD in Computer Engineering, Comillas Pontifical University (2010) Computer Engineer, Comillas Pontifical University (1998) Research Focus: His expertise spans wireless communications , artificial intelligence , big data , blockchain , cloud computing , and IoT systems . He explores applications in healthcare, smart cities, and assistive technologies. Publications & Impact: His work addresses cutting-edge challenges in Bluetooth-based indoor positioning, sustainable bike-sharing systems, and AI-driven medical document retrieval. Recent articles emphasize sustainability and advanced AI applications. Awards & Recognition: Director of award-winning final degree projects (2023-24, 2022-23, 2017-18, 2010-11) Sexenio Research Accreditation (2010-2015) Advising & Grants: He has supervised PhD theses on probabilistic modeling of bike-sharing demand and semantic similarity in biomedical retrieval. His projects include EU Horizon 2020 initiatives like RAYUELA (AI for youth internet safety) and COVID-19 predictive modeling (GAUDETE). Labs & Teams: Active in the Intelligent Systems and Bioengineering areas, he collaborates on multidisciplinary projects integrating AI, IoT, and sustainability.

Nisheeth SrivastavaView profile

Associate Professor

• Cognitive Science
• Artificial Intelligence
• Computational Modeling
• +7 more

Nisheeth Srivastava is an Associate Professor in the Department of Computer Science & Engineering and Cognitive Science at the Indian Institute of Technology, Kanpur. His research bridges computational models of cognition with real-world applications in AI and local problem-solving. Cognitive Science : Mathematically modeling human behavior inattention, memory, and decision-making. Artificial Intelligence : Integrating cognitive insights into AI agent design for human-like behavior. Solving Local Problems : Applying technical knowledge to address challenges in resource-constrained environments. His recent work explores predictive modeling limitations , probability inference from sparse data , and decentralized multi-agent systems , with trends spanning decision theory, machine learning, and user behavior analysis. Lab affiliates include Visiting Research Scientist Muhammad Aurangzeb Ahmed, focusing on agent-based simulations of religious affiliation.

• Robotic Ultrasound
• Implicit Neural Representations
• Medical Image Segmentation
• +2 more

Dr. Mohammad Farid Azampour is a Researcher at the Chair of Computer Aided Medical Procedures within the Department of Informatics at the Technical University of Munich (TUM). His work bridges robotics, medical imaging, and deep learning, with affiliations extending to the German Heart Center Munich (DHM) and collaborations across TUM's NARVIS and IFL labs. Research Focus: Azampour specializes in physics-inspired AI for healthcare, including robotic ultrasound systems, neural representations for medical image reconstruction, and anatomy-aware deep learning. His projects emphasize real-time surgical guidance, weakly supervised learning for clinical data, and multi-modal registration (e.g., CT-US/MRI fusion). Teaching & Supervision: He coordinates courses such as Medical Augmented Reality , Advanced 3D Computer Vision , and Innovation Generation in Healthcare . Azampour actively mentors students through IDPs, theses, and practical projects in computer-aided medical procedures. Publications: His recent work (2020-2025) demonstrates consistent focus on enhancing medical imaging through neural networks (e.g., Ultra-NeRF for ultrasound, diffusion models for simulation) and improving surgical robotics via real-time navigation/registration. Key themes include reducing annotation dependency, leveraging physics-based models, and enabling intraoperative decision-making.

• Robot Control
• Sensor-Based Target Tracking
• Artificial Intelligence
• +3 more

Professor Kim Jong-hyuk is affiliated with the Department of Defense Systems Engineering at Sejong University, where he conducts advanced research in robotics and defense systems. His work spans multiple disciplines including artificial intelligence, sensor-based tracking, and signal processing. Robot control Defense systems engineering Underwater localization Transformer-based path planning His recent publications (2024-2025) focus on underwater target tracking with heterogeneous sonar sensors, fault-tolerant control for marine vehicles, and adaptive path planning algorithms for aerial and underwater robots. These works demonstrate expertise in handling environmental disturbances, optimizing sensor networks, and developing robust control systems. Current research trends show increasing focus on 3D localization techniques using hybrid TOA-AOA measurements, deep learning applications for underwater object classification, and distributed robotics systems that maintain functionality under DoS attacks. His work also addresses practical challenges like voltage-based braking controls for electric vehicles and power-efficient underwater robot coordination.

Kostas DaniilidisView profile

Professor

• Computer Vision
• Robotics
• Geometric Deep Learning
• +4 more

Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania, where he has been a faculty member since 1998. A distinguished IEEE Fellow, he has held leadership roles such as Director of the GRASP Laboratory (2008–2013), Associate Dean for Graduate Education (2012–2016), and Faculty Director of Online Learning since 2016. His academic journey includes an undergraduate degree in Electrical Engineering from the National Technical University of Athens (1986) and a PhD in Computer Science from the University of Karlsruhe (1992). Education : National Technical University of Athens (BEng), University of Karlsruhe (PhD) Daniilidis is renowned for his contributions to geometric deep learning, data association, event-based cameras, and vision-based manipulation and navigation. His work spans visual odometry, omnidirectional vision, 3D pose estimation, and structure from motion, with a focus on integrating deep learning with geometric principles for real-time applications. His publications highlight advancements in event camera datasets, spherical CNNs for rotation-equivariant representations, and unsupervised motion learning. These works bridge robotics, computer vision, and neural networks, emphasizing dynamic scene analysis and robust perception systems. Scientific Awards : IEEE Fellow Best Conference Paper Award at ICRA 2017 Best Student Paper Finalist at Robotics Science and Systems 2018 Daniilidis has contributed to key academic roles, including Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (2003–2007) and co-chairing conferences like IEEE 3DPVT 2006 and ECCV 2010. He leads research initiatives in the GRASP Laboratory, focusing on cutting-edge robotics and perception technologies.

• Quantum Computing
• Geospatial Information Processing
• Text Mining

Siya BAO is an Assistant Professor at Waseda University's Faculty of Science and Engineering, focusing on quantum computing applications in combinatorial optimization and geospatial information processing. Currently affiliated with the Green Computing Systems Research Organization, her work bridges theoretical computer science with practical implementations in travel planning, indoor localization, and financial forecasting. Key research interests include: Quantum Computing Geospatial Information Processing Text Mining Based on 15 recent publications, her research trajectory demonstrates: Quantum annealing solutions for multi-day trip planning Machine learning applications in financial time series analysis Advancements in smartphone-based pedestrian dead reckoning Innovations in QUBO model formulation for constrained optimization Scientific recognition includes: DICOMO 2023 Outstanding Paper Award IEEE ICSC 2020 Best Student Opponent 2017 Telecommunications Advancement Foundation Travel Aid Research activities span: Principal investigator for JSPS-funded geospatial optimization projects (2021-2024, 2024-2027) Contributing author to Springer's 'Machine Learning for Indoor Localization and Navigation' (2023) Extensive conference presentation history including IPSJ-ONE 2024 invited talk on quantum computing applications

• Intelligent informatics
• Safety engineering
• Social systems engineering
• +8 more

Reiko HishiYama is a Professor at WASEDA University's Faculty of Science and Engineering, School of Creative Science and Engineering. With a Doctor of Informatics from Kyoto University (2005), she has established herself as a leading researcher in intelligent informatics, safety engineering, social systems engineering, and educational technology. Her educational background includes: Doctor of Informatics from Kyoto University (2005) HishiYama's research spans multiple interdisciplinary fields with a strong focus on the intersection of technology and society. Her primary research interests include artificial intelligence, social informatics, business informatics, communication design, computer vision, mobile computing, and IoT (Internet of Things). She particularly focuses on how these technologies can be applied to community management, tourism development, and cultural preservation through innovative approaches such as augmented reality and network analysis. Analysis of her recent publications reveals a consistent focus on applying network motif analysis to diverse domains including tourism, luxury goods markets, and community management. Her work increasingly integrates augmented reality technologies with traditional cultural experiences and demonstrates a strong commitment to practical applications that address real-world community challenges, particularly in low-digitalized environments. Her scientific achievements have been recognized with numerous prestigious awards: The 12th FOST Award (2019) The 6th WASEDA e-Teaching Award (2018) The 7th Career Development Essay Contest for Young Professionals and Women Engineers (2015) The 4th FOST Award (2011) Pacific Rim International Workshop on Multi‐Agents PRIMA Runner Up Paper Prize (2004) The 1st KK-MAS Competition Grand Prize (2001) As an educator and researcher, Professor HishiYama has been instrumental in developing innovative teaching methods, particularly in AI ethics education using the case method. Her professional engagements extend beyond academia through various committee memberships including SoftBank Corp. External Director, HAYAO NAKAYAMA Foundation for Science & Technology and Culture Planning Committee, and ASAHI INTELLIGENCE SERVICE CO., LTD. External Director. She maintains active roles in numerous professional societies including IEICE, IEEE, and ACM. Her research laboratory (HishiYama Lab) focuses on developing social support systems through the designable evaluation network framework, with applications in community management, tourism development, and multilingual communication. The lab has produced numerous publications and practical applications that bridge theoretical research with real-world implementation.

• Cloud Computing
• Distributed Systems
• Network Functions Virtualization (NFV)
• +7 more

Prof. Dr. Thomas Michael Bohnert is a faculty member at ZHAW School of Engineering, specializing in distributed systems and cloud computing. He has led multiple projects including: Bringing FIWARE to the NEXT step (completed), Enterprise Cloud Robotics Platform (completed), Apache Cloudstack for NFV (completed), Solidna cloud storage (completed), T-NOVA NFV services (completed), and Mobile Cloud Network (completed). Research Focus: Cloud robotics Network Functions Virtualization (NFV) Software-Defined Networking (SDN) Mobile cloud networking Resilient cloud systems Energy-efficient cloud platforms Publications Trends: His work spans 2012-2024 with consistent focus on cloud-native application design, NFV orchestration, SDN implementation, and mobile cloud integration. Key themes include self-managing systems, distributed computing frameworks, and infrastructure optimization. Collaborations: Frequently collaborates with Andrew Edmonds, Giovanni Toffetti Carughi, Piyush Harsh, and Sandro Brunner across EU projects and industry partnerships.

• Robotics
• Artificial Intelligence
• Machine Learning
• +6 more

Sebastian Thrun is a Professor at Stanford University, USA, with a prolific research career spanning robotics, artificial intelligence, and machine learning. His work has significantly impacted autonomous vehicle technology, computer vision, and medical AI applications. Thrun's research interests focus on robotics, particularly simultaneous localization and mapping (SLAM), autonomous driving systems, and probabilistic state estimation techniques. His work extends to deep learning applications in medical imaging, notably achieving dermatologist-level skin cancer classification. He has pioneered approaches in meta-learning, vector quantization, and efficient clustering algorithms using multi-armed bandits. His recent publications demonstrate a strong trend toward improving efficiency in machine learning algorithms, particularly in areas like decision trees, vector quantization, and nearest neighbor search. Thrun has also made significant contributions to medical AI, applying deep learning to skin cancer detection and molecular property prediction. Max Planck Research Award (2011) Thrun has advised numerous PhD students who have become prominent researchers in robotics and AI, including David Stavens, Anna Petrovskaya, and Jesse Levinson. His research has been supported by significant grants, particularly for autonomous vehicle development, including Stanford's entry in the DARPA Urban Challenge (Junior). His work bridges theoretical advances with practical applications across multiple domains. Thrun has led research teams focused on autonomous driving systems, 3D reconstruction, and medical AI applications. His work on the DARPA Urban Challenge demonstrated advanced capabilities in urban autonomous navigation, while his more recent research explores the intersection of deep learning and efficiency optimization in machine learning algorithms.

Zeeshan RanaView profile

Research Fellow

• Autonomous Systems
• Computational Fluid Dynamics
• Computing, Simulation & Modelling
• +4 more

Dr. Zeeshan Rana MSc, PhD, FHEA, MIET, MPEC is a Research Fellow at Cranfield University with expertise spanning Computational Fluid Dynamics , Autonomous Systems , and Artificial Intelligence Applications . His research focuses on high-speed aerodynamics , drone tracking systems , and renewable energy fluid dynamics . Education : MSc in Mechanical Engineering PhD in Computational Fluid Dynamics & Aerodynamics (Cranfield University) His work bridges hypersonic flow analysis with practical applications in Formula One aerodynamics , planetary rover navigation , and textile wastewater treatment . Recent research integrates machine learning with CFD simulations to solve complex engineering problems. Scientific trends in his publications (2025–2013) reveal consistent focus on: Computational Aerodynamics (ILES, WENO schemes, SU2 validation) Autonomous Systems (drone tracking, visual SLAM, sign language modeling) Renewable Energy (VAWT turbines, microbial fuel cells) Scientific Contributions: Fellow of the Higher Education Academy (FHEA) Member of the Institution of Engineering and Technology (MIET) Member of the Professional Engineering Community (MPEC) Current affiliations include the Digital Aviation Research and Technology Centre (DARTeC) , contributing to reinforcement learning-based surveillance and multi-sensor fusion systems for drone tracking.

Vijay KumarView profile

Professor

• Robotics
• Autonomous Systems
• Machine Learning
• +3 more

Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering and holds faculty appointments in the Departments of Mechanical Engineering and Applied Mechanics , Computer and Information Science , and Electrical and Systems Engineering at the University of Pennsylvania . Bachelor of Technology, Indian Institute of Technology Kanpur PhD, The Ohio State University (1987) Research Interests: Vijay Kumar's work spans robotics , autonomous systems , and machine learning , focusing on trajectories, control, and collaboration. Key subfields include quadrotor dynamics , semantic SLAM , multi-robot coordination , event-based vision , and LLM integration for robotic systems. His research addresses challenges in autonomous flight , resilient control , and agricultural robotics . Recent Publication Trends: Over the past two years, Kumar's research has emphasized language-specified missions , event camera applications , neural radiance fields , and time-optimal path planning for quadrotors and multi-robot teams. Notable themes include safety guardrails for LLM-powered robots , 4D metric-semantic mapping , and adaptive replanning in dynamic environments . Scientific Awards: Fellow, AAAS (2021) Fellow, ASME and IEEE ASME Mechanisms and Robotics Award (2012) IEEE Distinguished Service Award (2012) Engelberger Robotics Award (2014) IEEE Pioneer Award (2018) Elected to National Academy of Engineering (2013), American Philosophical Society (2018), and American Academy of Arts and Sciences (2022) Advising and Leadership: Kumar has mentored numerous PhD students who have won Best Student Paper Awards at major robotics conferences. He founded the General Robotics, Automation, Sensing and Perception Lab (GRASP) at Penn, which has spun off multiple robotics startups like Exyn Technologies . He has served on editorial boards of leading journals and held advisory roles in national science policy.

Hudyjaya Siswoyo JoView profile

Associate Professor

• Robotics
• Mechatronics
• Industrial Automation
• +5 more

Associate Professor Hudyjaya Siswoyo Jo is a faculty member at the Faculty of Engineering, Computing and Science at Swinburne University of Technology Sarawak Campus. He holds a BEng (Hons) and PhD from Swinburne University of Technology and is a Chartered Engineer (Engineering Council UK) with professional memberships in the Institution of Mechanical Engineers and the Institute of Electrical and Electronic Engineers. His research expertise spans multiple domains of robotics and automation, with particular focus on: Mechatronics System Design Industrial & Building Automation Industrial Internet of Things Human Machine Interaction Agriculture Mechanisation Professor Siswoyo Jo has successfully completed numerous manufacturing and automation projects focusing on Industry 4.0 implementation for Small-Medium Enterprises in Sarawak. He has served as a technical consultant for significant projects including the Borneo Cultures Museum, the second largest museum in Southeast Asia. His research has resulted in multiple patents, including "Vehicle for Collecting and Transporting System and Method Thereof" (Australia, 2020) and "Autonomous Harvester" (Malaysia, 2021). His scientific achievements include: Gold Award and IET Best Automation and Control Concept Special Award (2020) Swinburne Vice-Chancellor Community Engagement Award (2019) Swinburne Vice-Chancellor Industry Engagement Award (2018) Best Paper Award at the International Conference on Robotics and Mechanical Engineering (2016) Professor Siswoyo Jo has secured multiple research grants including SDEC TRG Grants as both Principle Investigator and Co-Investigator for projects related to UAV-based parking enforcement, AGV-based IoT solutions, and smart waste collection systems. He actively supervises postgraduate students and welcomes research higher degree candidates in his areas of expertise. His laboratory work focuses on practical applications of robotics and automation technologies in industrial and community settings, with strong industry partnerships that provide students with real-world project experience.

• Machine Learning
• Artificial Intelligence
• Decision Making under Uncertainty
• +7 more

Lagoudakis Michael is a Professor at the School of Electronic & Computer Engineering of the Technical University of Crete since 2005. His academic journey includes a PhD in Computer Science from Duke University (2003), a Master's from the University of Louisiana, Lafayette (1998), and a Diploma in Computer Engineering and Informatics from the University of Patras (1995). Education: PhD in Computer Science, Duke University, 2003 MSc in Computer Science, University of Louisiana, Lafayette, 1998 Diploma in Computer Engineering and Informatics, University of Patras, 1995 Research interests span Machine Learning, Reinforcement Learning, Decision Making under Uncertainty, Multi-Agent Systems, Robotics (particularly robotic team coordination), Complex Systems, and DNA Computing. His work has produced significant publications in top venues like Robotics: Science and Systems , IEEE IROS , Journal of Machine Learning Research , and NIPS . He has contributed to projects such as EURECA-PRO (2020–present), DialogRL (2016–2020), and LSPI (2000–2003). Scientific awards include the Outstanding Dissertation Award from Duke University's Department of Computer Science and the Outstanding Teaching Assistant Award received twice. He is a member of AAAI, IEEE, and ACM.

Maren BennewitzView profile

Professor

• Robot Navigation
• Active Perception
• Intelligent Manipulation
• +4 more

Maren Bennewitz is a Professor at the University of Bonn specializing in humanoid robots. She serves as Vice Rector for Digitalization and leads research at the Lamarr Institute for Machine Learning and Artificial Intelligence . As a principal investigator in national and European projects, she contributes to the executive board of the Cluster of Excellence PhenoRob and co-founded the Center for Robotics at Bonn. Key affiliations: University of Bonn, Lamarr Institute, PhenoRob Cluster, Center for Robotics Her research focuses on robot navigation , active perception , intelligent manipulation , and personalized human-robot interaction . Recent work explores privacy-preserving navigation, agricultural robotics, and neuromorphic obstacle avoidance. Publications highlight applications in crop monitoring, multi-agent safety, and VR-based explainability systems. Notable trends in her research include agricultural robotics (AID4Crops, PhenoRob), multi-objective reinforcement learning , and human-swarm interaction . The Lab at University of Bonn develops solutions for dynamic environments, balancing technical innovation with ethical considerations in robotics.

Ruohan GaoView profile

Assistant Professor

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

Ruohan Gao is an Assistant Professor in the Department of Computer Science at University of Maryland, College Park, leading the UMD Multisensory Machine Intelligence Lab. He holds affiliations with UMIACS, MRC, and AIM institutes. His research focuses on Computer vision and machine learning Multisensory machine intelligence (sight, sound, touch) Robotic manipulation through sensory fusion Audio-visual spatialization Sim2Real transfer methods His recent publications (2025-2022) span ICCV (5 papers) CVPR (6 papers) CoRL (4 papers) ECCV (2 papers) BVMC (1 paper) covering topics like differentiable acoustic rendering, audio-visual navigation, and embodied AI systems. Scientific recognition includes Michael H. Granof Award (UT Austin Top Dissertation) BMVC Best Paper Runner-Up CVPR Highlight Paper CVPR Best Paper Finalist He actively mentors students and collaborates with institutions including Stanford University, UT Austin, and Northwestern University (future affiliation).