جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Machine Learning
• Biomedical Informatics
• +۸ مورد دیگر

Feifan Liu is an Associate Professor at UMass Chan Medical School with appointments in Population and Quantitative Health Sciences, Radiology, and Health Informatics and Implementation Science. Holding a PhD in Computer Science from the Chinese Academy of Sciences and postdoctoral training in Machine Learning at the University of Texas at Dallas, he founded the AI for Health (iAI4Health) Lab , focusing on AI-driven healthcare analytics. Education: BS & MS in Electrical Engineering - Shandong University PhD in Computer Science - Chinese Academy of Sciences Postdoc in Machine Learning - University of Texas at Dallas His research bridges Natural Language Processing with Biomedical Informatics , concentrating on suicide risk prediction, HIV prevention, cancer informatics, and cardiometabolic disease management using Deep Learning and Interpretable AI . He develops computational frameworks for adverse drug reaction detection, radiology report analysis, and health equity assessment. Recent work focuses on Health Equity Algorithms and Multi-site Validation through the OHDSI network. His 15 most recent publications (2019-2025) span topics like Medical Visual QA Systems , Gene Mutation Extraction , Citation Network Analysis , and Pharmacovigilance , with keywords indicating cross-disciplinary work in AI, Data Science, and Clinical Research. Scientific Awards: NIH AIM-AHEAD Leadership Fellow (2022) International Challenge Winner (Medical QA 2018, Gene Relation Extraction 2019) As PI/MPI on multiple NIH grants (R01, R21, R34), he leads projects on suicide risk algorithms, PrEP care prediction, and diagnostic uncertainty measurement. The iAI4Health Lab under his direction explores AI applications for clinical decision support and population health analytics.

Giorgio Guglieriمشاهده پروفایل

استاد

• Aerospace Engineering
• Flight Mechanics
• Flight Dynamics
• +۵ مورد دیگر

Giorgio Guglieri is a Full Professor of Flight Mechanics at Politecnico di Torino, where he serves as Head of the Department of Mechanical and Aerospace Engineering (DIMEAS). He is a member of the Academic Senate, the Executive Committee of Specialising Master's Programmes, and the steering board of the PhD Program in Aerospace Engineering. His work is centered at the intersection of flight mechanics, autonomous systems, and human-machine interfaces. Professor Guglieri's research spans multiple key areas in aerospace engineering. His primary interests include flight dynamics and control of both fixed-wing and rotary aircraft, flight simulation technologies, unmanned aerial systems for urban applications, and human factors in aviation. His work has significant applications in urban air mobility, precision agriculture using drones, and enhancing aviation safety through physiological monitoring of pilot workload. Analysis of his recent publications reveals strong trends toward integrating artificial intelligence with traditional aerospace engineering. His research group has produced significant work on UAV navigation in GNSS-denied environments, reinforcement learning for path planning, physiological monitoring for pilot stress assessment, and digital twin technologies for predictive maintenance. The research shows increasing focus on practical urban applications of drone technology, particularly in logistics, precision agriculture, and urban air mobility solutions. Professor Guglieri actively supervises numerous PhD students working on cutting-edge aerospace projects including drone navigation systems, pilot workload monitoring, urban air logistics, and advanced flight control systems. He leads multiple research projects funded by EU programs (including Horizon 2020), national research councils, and industry partners such as Leonardo, ENAC, and various agricultural technology companies. He heads the Flight Mechanics Research Team at Politecnico di Torino, which operates the STREAM Robotics Laboratory and collaborates extensively with international partners including ZHAW, University of Maryland, and multiple European research institutions. The team has developed patented technologies including an anti-sloshing tank for agricultural spraying drones and fixed-wing micro UAVs.

Hamed Al-Raweshidyمشاهده پروفایل

استاد

• Wireless Networks
• Communications Engineering
• 6G Technology
• +۷ مورد دیگر

Professor Hamed Al-Raweshidy is a distinguished Professor of Communications Engineering at Brunel University London, where he serves as Director of the Wireless Networks and Communications Centre (WNCC) and Director of Postgraduate Studies in Electronic and Computer Engineering. With over three decades of academic and industry experience, he has established himself as a leading researcher in wireless communications and networking technologies. His educational background includes a BEng and MSc from the University of Technology in Baghdad (1977 and 1980), a Post Graduate Diploma from Glasgow University (1987), and a PhD from Strathclyde University (1991). Prior to his current position, he worked with prestigious organizations including Space and Astronomy Research Centre in Iraq, PerkinElmer in the USA, Carl Zeiss in Germany, British Telecom in the UK, and several renowned universities. Professor Al-Raweshidy's research focuses on next-generation wireless technologies, particularly in the areas of 6G networks, quantum communications, artificial intelligence applications in networking, radio over fibre technologies, Internet of Things (IoT), edge computing, and cybersecurity. He has published over 370 papers in international journals and conferences, demonstrating his prolific contribution to the field. His recent work shows a strong emphasis on integrating quantum technologies and AI with wireless communications, particularly for healthcare applications, secure network operations, and efficient resource management. The trend in his publications indicates a strategic shift toward practical implementations of theoretical concepts in real-world scenarios, with particular attention to energy efficiency and security in next-generation networks. Member of NETworld 2020 Network, advising the European Commission on research directions Principal investigator for EPSRC and European projects including the MAGNET EU project (2004-2008) Editor of the first book on Radio over Fibre Technologies for Mobile Communications Networks Guest editor for the International Journal of Wireless Personal Communications As an academic leader, Professor Al-Raweshidy serves on numerous editorial boards, conference committees, and review panels including the EPSRC peer Review College and panels for the EU Commission, Hong Kong, and Cyprus. He is frequently invited to deliver lectures at prestigious institutions worldwide and serves as an external examiner for several top universities. His leadership extends to industry collaboration, where he acts as a consultant for major telecommunications companies including Vodafone, Ericsson, Nokia, Siemens, and others, bridging the gap between academic research and commercial applications.

Shizhe Chenمشاهده پروفایل

پژوهشگر

• Vision-and-Language
• Embodied AI
• 3D Computer Vision
• +۲ مورد دیگر

Shizhe Chen is a researcher at the WILLOW team in INRIA Paris , focusing on vision-and-language and embodied AI. Previously, he completed postdoctoral research at WILLOW with Ivan Laptev and Cordelia Schmid. PhD in Computer Science from Renmin University of China (2020) BSc in Computer Science from Renmin University of China (2015) His research bridges vision-and-language with embodied AI , emphasizing 3D scene understanding , robotic manipulation , and vision-language navigation . Recent projects include the GRAIL workshop (CVPR'25) on generalizable robotic manipulation and NextBestPath (ICLR'25) for efficient mapping. Key publication trends show expertise in 3D reconstruction (HORT, Online 3D Scene Reconstruction), robotic navigation (ViViDex, Object Goal Navigation), and vision-language systems (DUET, Think Global Act Local). Collaborations span INRIA, Microsoft Research Asia, and Carnegie Mellon University. 2022 REVERIE Challenge (2nd place) 2021 REVERIE & SOON Challenges (winner) 2019-2024 multiple TRECVID, CVPR, and ACM Multimedia awards 2020 Jingdong Scholarship & Beijing Outstanding Graduate Award 2016-2017 National Scholarships As co-supervisor, he guides PhD students in robotics and computer vision projects at INRIA Paris, collaborating with teams at Microsoft, Carnegie Mellon, and Louis Vuitton. His DUAL-SCALE GRAPH TRANSFORMER (CVPR'22 Oral) framework significantly advanced vision-language navigation benchmarks.

Torsten Lilgeمشاهده پروفایل

پژوهشگر

• Nonlinear Systems
• Discrete-Time Control Systems
• Observer Design
• +۳ مورد دیگر

Dr.-Ing. Torsten Lilge is a Senior Researcher/Lecturer at the Institute of Automatic Control Engineering , part of Leibniz University Hannover 's Faculty of Electrical Engineering and Computer Science. He has been a core member of the university since 1991 and held senior roles including Deputy Faculty Information Officer and Mentor at the Mechatronics Center Hannover. Research focus on nonlinear systems and discrete-time control Specialized in observer design for systems with unknown inputs and data fusion applications Key contributor to human-robot interaction and mechatronic stabilization solutions Publications show expertise in: 2025 : Global reactive obstacle avoidance using circular fields 2024 : Robust predictive control for energy systems 2023 : Real-time sampling techniques and educational robotics applications 2021-2019 : Multi-agent trajectory planning and antagonistic impedance control His work bridges theoretical control systems with practical implementations in robotics, with a particular emphasis on nonlinear observer design and dynamic modeling for real-world applications.

Mohit Bansalمشاهده پروفایل

استاد

• Natural Language Processing
• Multimodal Machine Learning
• Grounded and Embodied Semantics
• +۴ مورد دیگر

Mohit Bansal is the John R. & Louise S. Parker Distinguished Professor and Director of Graduate Admissions in the Computer Science Department at the University of North Carolina Chapel Hill. He leads the MURGe-Lab (UNC-AI Group) and serves as Lead (Core AI) for the ENGAGE NSF-AI Institute. Previously, he was a Research Assistant Professor at TTI-Chicago. Dr. Bansal earned his Ph.D. from UC Berkeley in 2013 under Dan Klein and his B.Tech. from IIT Kanpur in 2008. His research spans Natural Language Processing and Multimodal Machine Learning , with specific expertise in multimodal generative models, grounded and embodied semantics (language with vision/speech for robotics), faithful language generation, reasoning and planning agents, and interpretable deep learning. He employs techniques from structured prediction, reinforcement learning, and model editing to address challenges in compositional generalization and robustness. His recent work focuses on multimodal understanding, vision-language navigation, model merging, and evaluating/factuality in generative models. Trends show increasing emphasis on trustworthy AI, with projects addressing hallucination reduction, cultural bias diagnosis, and safe generation. His publications span top venues including ACL, CVPR, NeurIPS, and ICML, with significant contributions to multimodal foundation models and parameter-efficient learning. AAAI Fellow (2025) Presidential Early Career Award for Scientists and Engineers (PECASE) (2025) IIT Kanpur Young Alumnus Award (2023) DARPA Director's Fellowship (2019) NSF CAREER Award (2019) Microsoft Investigator Fellowship (2019) Outstanding Paper Awards at ACL, CVPR, EACL, COLING, and CoNLL Dr. Bansal has advised numerous PhD students who now hold positions at top institutions including UT Austin, NTU Singapore, JHU, Meta, and Adobe. His lab secures substantial funding from NSF, DARPA, NIH, and ONR, including the $20M NSF-AI Institute on Engaged Learning where he serves as Core AI Lead. Current projects include DARPA's Environment-driven Conceptual Learning (ECOLE) and ONR's Science of Artificial Intelligence program. The MURGe-Lab (Multimodal Understanding, Reasoning, and Generation) develops foundational models for multimodal tasks, with recent work on VideoTree for long video reasoning, SELMA for skill-specific text-to-image experts, and LASeR for adaptive reward model selection. The lab collaborates extensively with industry partners including Google, Meta, and Microsoft.

• Robotics
• Mechanical Engineering
• Mechanism Design
• +۳ مورد دیگر

Dr. Mathias Hüsing is an Adjunct Professor at the Institute of Mechanism Technology, Machine Dynamics and Robotics within the Faculty of Mechanical Engineering at RWTH Aachen University. His research focuses on robotics, compliant mechanism design, and energy-efficient mechanical systems, with applications in surgical manipulation, additive manufacturing, and human-robot interaction. Research Interests: Robotics (specifically compliant grippers, parallel kinematics, and hybrid robots) Energy-efficient mechanism design (cam systems, dynamic balancing) Smart manufacturing (digital tool chains, multidirectional additive manufacturing) Human-robot collaboration (inclusive workplaces, task adaptation) Sensor integration (calibration methods, non-contact safety systems) Algorithm development (motion planning, optimization techniques) Trends in Publications show a focus on surgical robotics, human-robot collaboration, and additive manufacturing. His work combines mechanism theory with practical implementation, emphasizing industrial applications and energy efficiency. Institute Affiliation includes leadership roles at the Institute of Mechanism Technology, Machine Dynamics and Robotics. He actively develops educational tools like the Software Mechanism Developer and integrates robotics into Industry 4.0 frameworks.

Steven Beitesمشاهده پروفایل

استاد

• Robotics in Construction
• Sustainable Housing
• Bio-based Materials
• +۵ مورد دیگر

Steven Beites is a tenured Professor at Laurentian University's McEwen School of Architecture, where he directs the Institute for Northern Housing Innovation and serves as Graduate Coordinator. He holds a Master of Architecture from the University of Toronto, a Master of Science in Digital and Material Technologies from the University of Michigan, and is a Ph.D. candidate at the European Graduate School in Switzerland. Previously, he taught at Toronto Metropolitan University and the University of Waterloo, with visiting appointments at Harvard’s Graduate School of Design. His research integrates robotics, material science, and bio-based materials to transform affordable housing in Northern Ontario, focusing on decarbonization, automated construction, and sustainable design. Key themes include: Robotic fabrication for in-situ construction Low-carbon bio-material assemblies Cable-driven parallel robot (CDPR) systems Community-driven design-build frameworks Publications emphasize robotic construction (68% of recent works), material innovation (21%), and pedagogical/community design (11%), with consistent focus on practical applications for housing affordability. Significant awards include: Voyageur Innovation Challenge 2025 (1st Place) Kathryne Kril-Atkins Innovation Fellowship National Award of Excellence from Canadian Society of Landscape Architects Azure Magazine’s Social Good AZ Award Over 15 national/international competition wins including Postmark Hotel (2024) and Venice Biennale exhibitions He secured $1.75M+ in grants (SSHRC, NFRF) establishing the Institute for Northern Housing Innovation, and advises the Ontario government on modular housing policy through the Council of Ontario Universities.

• AI
• Robotics
• Sequential Decision Making
• +۵ مورد دیگر

Siddharth Srivastava is an Associate Professor at the School of Computing and Augmented Intelligence , Arizona State University , where he directs the Autonomous Agents and Intelligent Robotics (AAIR) Lab. His research focuses on principled AI systems for generalizable planning , sequential decision-making under uncertainty , and user-driven AI assessment . He previously served as Staff Scientist at United Technologies Research Center and postdoctoral researcher at UC Berkeley . Education Ph.D. in Computer Science, University of Massachusetts-Amherst M.S. in Mathematics, Indian Institute of Technology (IIT) Kanpur His work spans AI planning , robotics , and neuro-symbolic methods , emphasizing safe, reliable AI systems that can learn abstractions , transfer knowledge , and self-assess capabilities . Recent publications focus on user-aligned explanations , integrated task-motion planning , and stochastic reinforcement learning . Scientific recognition includes: NSF CAREER award Top 5% Faculty Award (Fulton Schools of Engineering) Best Paper at ICAPS Outstanding Dissertation (UMass Amherst) Best Final Year Thesis (IIT Kanpur) Chambliss Medal (AAS) He has supervised multiple Ph.D. students including Pulkit Verma , Naman Shah , and Rushang Karia , and developed the JEDAI system for teachable robot planning. His research has been featured in BBC , Motherboard , and IEEE Spectrum .

Jiaoyang Liمشاهده پروفایل

استادیار

• Artificial Intelligence
• Multi-Agent Path Finding (MAPF)
• Robotics Coordination
• +۷ مورد دیگر

Jiaoyang Li is an Assistant Professor at Carnegie Mellon University's Robotics Institute. She earned her Ph.D. in Computer Science from the University of Southern California in 2022, advised by Sven Koenig, and her B.Eng. from Tsinghua University's Department of Automation. Research Interests: Multi-Agent Path Finding (MAPF), large-scale robot coordination, automated warehousing, multi-arm assembly systems, autonomous vehicle traffic management, and heuristic search algorithms. Key Research Areas: Foundations of MAPF, environment optimization for multi-agent systems, robust execution frameworks, learning-guided planning, and multi-arm coordination. Scientific Contributions: She has published extensively in ICAPS, IJCAI, AAAI, and SoCS conferences. Her work includes award-winning solutions for the NeurIPS 2020 Flatland Challenge, which involved coordinating thousands of agents in railway systems. Notable Awards: ICAPS-24 Best Student Paper, IFAAMAS Victor Lesser Distinguished Dissertation Award, USC Stevens Best Dissertation Award, and multiple competition wins. Leadership and Outreach: She has organized symposium tracks and workshops at ICAPS and AAMAS. Her research has practical applications in Amazon Robotics, airport taxiway planning, and real-time multi-robot execution systems.

Joel Fischerمشاهده پروفایل

استاد

• Human-Computer Interaction
• Internet of Things (IoT)
• Disaster Response Systems
• +۳ مورد دیگر

Joel Fischer is a Professor of Human-Computer Interaction at the School of Computer Science, University of Nottingham, UK . He leads research at the Mixed Reality Lab focusing on IoT applications, disaster response systems, and human-agent/robot collaboration. His work bridges technical innovation with ethnographic insights into real-world practices. Current Affiliation: Professor, School of Computer Science, University of Nottingham Research Themes: IoT in domestic and industrial settings, disaster response coordination, ethnomethodology, collocated interaction, agent-based planning His recent projects include RoboClean (allergen-aware factory systems), The CharioT Project (energy management tools), and foundational work on human-agent collectives through the ORCHID project . Fischer’s publications span conferences like CHI, CSCW, AAMAS, and CTS, with a focus on translating field observations into system design principles. Notable achievements include a Best Paper Award at AAMAS 2015 and securing an EPSRC research grant in 2015. He collaborates with organizations such as Rescue Global and Hampshire Fire & Rescue , integrating user needs into AI-driven solutions. His work has been featured in media outlets like New Scientist and SciDevNet .

• Nonlinear Control
• Robust Control
• Optimal Control
• +۴ مورد دیگر

Marion Leibold is a Private Lecturer at the Faculty of Electrical Engineering , Technical University of Munich (TUM) . She holds a Habilitation (2020) and a Dr.-Ing. in Control Engineering (2007) from TUM, preceded by a Diploma in Techno-Mathematics (2002) from TU Munich. Research interests include Nonlinear and Robust Control , Model Predictive Control (MPC) , Safety in Regulation , and applications to Bipedal Robots and Autonomous Driving . Her work focuses on stochastic MPC , trajectory planning , and uncertainty handling in dynamic environments. Recent publications address greenhouse climate control , autonomous driving safety , data-driven MPC , and dual-arm robotic manipulation . She collaborates frequently with researchers like Dirk Wollherr and Martin Buss , with affiliations to the Chair of Control Engineering at TUM.

Farzeen Munirمشاهده پروفایل

پژوهشگر

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Farzeen Munir is a Postdoctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Mobile Robotics research group. Their work bridges robotics, computer vision, and biomedical signal processing. University: Aalto University Department: Department of Electrical Engineering and Automation Role: Researcher Email: farzeen.munir@aalto.fi Research Interests: Focus on robotics, autonomous driving, and machine learning. Key areas include: Pedestrian intent and trajectory prediction Multimodal sensor fusion (LiDAR, radar, thermal imaging) Deep learning for vehicle control and perception Domain adaptation in vision transformers Biomedical applications of evolutionary-neural systems Publication Trends: Recent work emphasizes context-aware models for autonomous driving, thermal imaging in low-light environments, and hybrid neural architectures combining attention mechanisms with domain adaptation. Collaboration spans AI, robotics, and biomedical engineering. Research Groups: Active in Aalto University's Mobile Robotics group, focusing on perception-action loops and real-world system validation.

Marcel Kösterمشاهده پروفایل

پژوهشگر

• GPGPU
• Particle Simulations
• Compilers
• +۱ مورد دیگر

Dr. Marcel Köster is a researcher affiliated with the Ubiquitous Media Technology Lab at the German Research Center for Artificial Intelligence (DFKI) and the Saarland Informatics Campus. His work focuses on GPGPU computing, particle simulations, compilers, and optimization techniques. Email: Marcel.Koester@dfki.de Phone: +49 681 85775 7750 Location: Gebäude D3 1, Room 0.13, Saarbrücken Research Interests Dr. Köster's research integrates GPU computing with simulation algorithms and compiler optimization. He contributes to advancements in parallel processing, domain-specific languages, and scientific visualization through both theoretical exploration and practical implementations. His publications highlight innovative applications of GPU acceleration to heuristic optimization, state generation, and particle simulations. These works demonstrate expertise in thread compaction, shared memory utilization, and warp scheduling. Teaching Experience Dr. Köster has taught multiple courses at HBK Saar, including: Artificial Intelligence (Summer 2019) Grundlagen der Medieninformatik (Winter 2016/17) Physical Simulations on Media Facades (Winter 2015/16) Core Lecture: Compiler Construction (Winter 2013/2014)

Andrea Censiمشاهده پروفایل

مدرس

• Autonomous Driving
• Robotics
• Control Systems
• +۳ مورد دیگر

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.