جست‌وجوی استاد و پژوهشگر

Xavier Puigمشاهده پروفایل

پژوهشگر

• Embodied AI
• Artificial Intelligence
• Computer Science
• +۴ مورد دیگر

Xavier Puig is a Research Scientist at FAIR, focusing on Embodied AI and human-centered intelligent systems. Previously, he earned his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), advised by Professor Antonio Torralba. He also holds a double degree in Computer Science and Telecommunications from UPC's CFIS program. Education Ph.D., MIT (CSAIL), 2022 B.S./B.S., UPC (CFIS program), 2016 Research Interests span building agents that collaborate with humans through goal anticipation, task coordination, and realistic behavior modeling in simulation environments. His work integrates Embodied AI , Multi-agent Systems , and 3D Scene Understanding with applications in Human-Robot Collaboration and Interactive Decision-Making . Recent Trends in his publications emphasize Planning in Multi-agent Environments , Language-guided Robotics , Procedural Scene Generation , and Human Foundation Models for embodied tasks. Scientific Awards Best Paper Award, NeurIPS Cooperative AI Workshop (2020) Spotlight Presentation, ICLR (2021)

Roland Goeckeمشاهده پروفایل

استاد

• Affective Computing
• Computational Behaviour Analysis
• Social Signal Processing
• +۶ مورد دیگر

Professor Roland Goecke is a leading academic in affective computing and head of the School of Systems & Computing at UNSW Canberra. He holds a PhD in Computer Science from the Australian National University (2004) and has extensive experience in roles such as Professor at the University of Canberra and Senior Research Scientist at Seeing Machines. His research focuses on algorithms for human-computer interaction, mental health applications, and multimodal signal processing. Key research interests include affective computing, computational behavior analysis, and AI-driven solutions for health and well-being. He has secured funding from ARC, NHMRC, and US NSF, and serves on editorial boards of IEEE Transactions on Affective Computing and Pattern Recognition . His work spans mental health, video/image analysis, and assistive robotics. Prof. Goecke is an experienced research supervisor, guiding students in areas like human-robot interaction and computer vision. He is a senior member of IEEE and actively contributes to the academic community through conferences and mentorship. Current affiliations include UNSW’s Canberra campus, where he leads interdisciplinary research initiatives. His lab focuses on translating computational methods into real-world applications for health monitoring and human performance analysis.

• Machine Learning
• Robotics
• Human-Robot Interaction
• +۴ مورد دیگر

Joseph Camp is a Professor of Comparative Pathobiology and Secretary of Faculties at Purdue University. His work spans interdisciplinary areas including machine learning, robotics, and human-robot interaction. He leads the Global Engineering Program and holds affiliations across technical and administrative roles. His research focuses on reinforcement learning, multi-agent systems, and applying large language models to enhance decision-making and action anticipation in robotics and healthcare contexts. Key research interests include neuro-symbolic systems for short-context action prediction, robust reinforcement learning under noisy feedback, and geometric reasoning for zero-shot robotic grasping. His work also explores disentanglement in concept-residual models and transfer learning in multi-agent environments. He has published extensively on topics ranging from probabilistic imitation learning to bio-inspired robot locomotion design. While no specific awards or grants are listed, his contributions to human-robot interaction (HRI) and AI-driven medical devices reflect a strong focus on practical applications. His lab’s recent work integrates large language models with traditional robotics algorithms to improve adaptability in dynamic environments. No advising relationships or student names are explicitly mentioned in the provided texts.

Ken Goldbergمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Deep Reinforcement Learning
• +۵ مورد دیگر

Ken Goldberg is a Professor of Industrial Engineering and Operations Research at UC Berkeley, with secondary appointments in EECS, Art Practice, and UCSF's Radiation Oncology. He joined UC Berkeley in 1995 and holds a PhD from Carnegie Mellon University (1990) and dual degrees from the University of Pennsylvania (1984). His research spans robotics, automation, and art, focusing on artificial intelligence, medical robotics, and human-robot interaction. He leads the AUTOLab and is a co-founder of the Berkeley Artificial Intelligence Research (BAIR) Lab. Goldberg's art installations, including collaborations with Sundance films, explore the intersection of technology and humanity. He has pioneered cloud robotics, surgical robotics, and collaborative filtering systems. Notable awards include the IEEE Fellow (2005), Joseph Engelberger Robotics Award (2000), and NSF Presidential Faculty Fellow (1995). His work integrates technical innovation with societal impact, emphasizing ethical AI and automation. Educations: PhD in Computer Science, Carnegie Mellon University (1990) BSEE in Electrical Engineering, University of Pennsylvania (1984) BSE in Economics, UPenn Wharton (1984) Research Interests: Goldberg's work bridges robotics, AI, and art. Key areas include cloud robotics (FogROS2), surgical robotics (Cal-MR), AI ethics, and interactive systems. His research in dexterous manipulation and grasp planning (Dex-Net) has advanced robotic capabilities in manufacturing and healthcare. Publications: Over 300 papers, including influential works on grasp planning, robotic learning, and collaborative filtering. Recent focus on AI in robotics, surgical assistance, and ethical automation. Awards: IEEE Fellow (2005) Joseph Engelberger Robotics Award (2000) NSF Presidential Faculty Fellow (1995) Advising and Grants: Supervised numerous students in robotics, automation, and AI. Active in funding initiatives like the National Robotics Initiative and NSF grants. Labs/Teams: Leads AUTOLab and co-founded the Berkeley Center for New Media (BCNM). Collaborates with industry partners like Ambi Robotics and Jacobi Robotics.

• Machine Learning
• Computer Vision
• Robotics
• +۵ مورد دیگر

Qiang Qiu is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He actively contributes to the Vertically Integrated Projects (VIP) program, fostering interdisciplinary collaboration between faculty, students, and projects. His research spans machine learning, computer vision, robotics, and generative AI, with particular emphasis on diffusion models, transformer fine-tuning, and graph neural networks. Key themes include improving model generalizability through orthogonal low-rank embeddings, developing efficient parameter-tuning strategies, and advancing visuotactile manipulation in robotics. Recent publications reveal trends in attention control for text-to-image alignment, posterior sampling for diverse image generation, and federated learning security. His work addresses gradient conflicts in machine unlearning and explores multimodal sensing for robotic manipulation.

Stefan Borgwardtمشاهده پروفایل

پژوهشگر

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +۸ مورد دیگر

Stefan Borgwardt is a Researcher and Teaching Associate at the Chair of Automata Theory, Faculty of Computer Science, Technische Universität Dresden, working under Prof. Franz Baader. He leads key research projects such as Practical Planning with Ontologies (PPO) and contributes to the Center for Perspicuous Computing (CPEC), funded by DFG and SNF. His work bridges theoretical logic and practical AI systems. Research Interests: Knowledge Representation and Reasoning Description Logics and Ontology-Mediated Query Answering Explainable AI and Proof Visualization (Evee/ Evonne systems) Temporal, Fuzzy, and Probabilistic Logics Automated Planning with Ontologies Logic-Guided Natural Language Generation His recent publications (2021–2025) show a strong trend toward explainability in logical reasoning, with a focus on modular, interactive, and user-friendly proof systems. He investigates reasoning complexity in expressive description logics and develops practical tools for ontology explanation and planning. His work often combines symbolic AI with real-world applications such as sensor data interpretation and autonomous systems. Scientific Awards: Harold Boley Distinguished Paper Award (RuleML+RR'23) Best Paper Award (RuleML+RR'22, JELIA'19, AI'15) Quality Champion for ECAI'23 reviewing Commerzbank Award for PhD thesis (2014) Distinguished Student Paper Awards (DL'14, DL'13) N. J. Lehmann Award (2011) Stefan Borgwardt actively advises and collaborates with students and early-career researchers on projects involving explanation, planning, and reasoning. He has secured competitive funding through DFG and SNF grants and plays a leadership role in the KR community as PC co-chair, senior PC member, and steering committee member for major conferences like DL, KR, and JELIA. He is also a reviewer for top-tier journals including Artificial Intelligence and Journal of the ACM . Labs and Teams: He is a core member of the Chair of Automata Theory at TU Dresden and contributes to the Center for Perspicuous Computing (CPEC), where he works on projects A3 (Description Logic Explications) and E2 (Safe Handover in Mixed-Initiative Control) in collaboration with Vera Demberg and Antonio Krüger.

• Computer Vision
• Computer Graphics
• Computational Photography
• +۶ مورد دیگر

Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA

Gerard Gohمشاهده پروفایل

عضو هیئت علمی

• Interventional Radiology
• Vascular Interventions
• Embolization
• +۶ مورد دیگر

Gerard Goh is an Adjunct Clinical Professor in the Department of Surgery at Monash University, within the School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences. He serves as Head of Interventional Radiology and Consultant Diagnostic & Interventional Radiologist at Alfred Health, Melbourne. He completed his medical and radiology training in Adelaide and has held academic positions internationally, including as Clinical Lecturer at the Royal College of Surgeons Ireland and Honorary Senior Lecturer at St George’s University of London. Areas of research interest include: Vascular and complex vascular interventions Embolization techniques Trauma radiology Clinical and first-in-human trials Robotic interventional radiology Medical education in radiology His recent publications reflect a strong focus on advancing interventional techniques, particularly in peripheral arterial disease, uterine artery embolization, and the integration of AI in medical writing. He has led multiple clinical trials involving novel endovascular devices such as drug-eluting stents and balloons. His editorial leadership includes roles as Regional Editor for Cardiovascular and Interventional Radiology , Senior Editor for Interventional Radiology , and Associate Editor for Journal of Medical Imaging and Radiation Oncology . Scientific awards and honors: FRANZCR – Fellow of the Royal Australian and New Zealand College of Radiologists EBIR-ES – European Board of Interventional Radiology - Experienced Specialist FSIR – Fellow of the Society of Interventional Radiology FCIRSE – Fellow of the Cardiovascular and Interventional Radiological Society of Europe GAICD – Graduate of the Australian Institute of Company Directors He has been actively involved in advising and mentoring through research collaborations, particularly in medical education projects such as assessing interventional radiology teaching in Australian medical schools. He has secured significant research grants as Principal Investigator on multiple device trials and feasibility studies. His professional service includes leadership roles as Dean and Board Member of the Faculty of Clinical Radiology at RANZCR, past President of the Interventional Radiology Society of Australasia, and Executive Committee Member of APSCVIR. He is also a council member of CIRSE and examiner for EBIR. He is an honorary research fellow at the National Trauma Research Institute, reflecting his contributions to trauma care and research.

Homayoun Najjaranمشاهده پروفایل

استاد

• Autonomous Vehicles and Robots
• Control Systems
• Artificial Intelligence
• +۷ مورد دیگر

Homayoun Najjaran is a Professor of Mechanical Engineering and Electrical and Computer Engineering at the University of Victoria (UVic), cross-listed between departments. He founded the Advanced Control and Intelligent Systems (ACIS) laboratory, focusing on AI, robotics, and control systems. Dr. Najjaran earned his Ph.D. from the University of Toronto in 2002 and holds a Professional Engineer (P.Eng.) license in British Columbia. As a Fellow of the Canadian Society for Mechanical Engineering (CSME), he leads research in autonomous systems, industrial automation, and smart manufacturing. His work integrates machine learning, computer vision, and MEMS technologies to enhance system reliability and safety. Research interests span control systems, robotics, AI-driven manufacturing, and wearable technologies. Notable projects include vacuum bagging leakage prediction in composite manufacturing and sim-to-real domain adaptation for robotics. He collaborates with industry partners in aerospace, healthcare, and automation, emphasizing practical applications. Dr. Najjaran also serves as President of Advanced Engineering Solutions Inc., providing technical consulting services. Awards & Recognition Fellow of the Canadian Society for Mechanical Engineering (CSME) Professional Engineer (P.Eng.) in British Columbia Lab & Collaborations The ACIS Lab develops AI-driven solutions for industrial automation, human-robot collaboration, and digital twins. Partnerships include aerospace, automotive, and healthcare sectors.

Christos Kaklamanisمشاهده پروفایل

استاد

• Algorithm design and analysis
• Computational complexity
• Communication networks
• +۷ مورد دیگر

Christos Kaklamanis is a Full Professor and Chair of the Department of Computer Engineering and Informatics at the University of Patras, Greece, with significant leadership roles including President of Computer Technology Institute & Press "Diophantus" (CTI) from 2016-2021. His academic career includes serving as Vice-Chair of the Department (2009-2011 and 2003-2005) and Director of the Division of Applications and Foundations of Computer Science (1997-2003). Dr. Kaklamanis earned his S.B. in Computer Science and Engineering from MIT (1986), followed by S.M. (1989) and Ph.D. (1992) from Harvard University. He completed postdoctoral work at DIMACS (Center for Discrete Mathematics and Theoretical Computer Science) and worked as a research consultant for NEC Research Institute, Princeton. His research spans theoretical and applied computer science with expertise in algorithm design, computational complexity, communication networks, parallel and distributed computing, and algorithmic game theory. He has made significant contributions to network algorithms, particularly in optical networks and wireless communication, with recent expansion into computational social choice and educational technology applications. His work bridges theoretical foundations with practical implementations, especially in developing educational tools that make complex algorithms accessible. Analysis of his publication history reveals an evolution from foundational algorithm research toward practical applications of theoretical concepts, particularly in educational technology and community-oriented computing solutions. His recent work focuses on creating web and mobile applications that address real-world challenges in campus management, cultural tourism, and community engagement while maintaining strong theoretical underpinnings. Elected member of EATCS council (2009-2021) Active in ACM, IEEE, SIAM, and Technical Chamber of Greece Program Committee Chair for WAOA (Workshop on Approximation and Online Algorithms) Conference co-chair for ICALP (International Colloquium on Automata, Languages and Programming) As an educator, Dr. Kaklamanis has taught core courses including Theory of Computation, Parallel Algorithms, Communication Algorithms, and Cryptography. He has led major research initiatives including EU-FET projects CRESCCO and AEOLUS (as coordinator), EU-ICT Project EULER, and the "DIGITAL SCHOOL" project focused on national educational platforms. His leadership extends to directing research laboratories in Combinatorial Algorithms, Distributed Systems and Telematics, and Pattern Recognition. His current work continues to bridge theoretical computer science with practical applications, particularly in educational technology, with numerous recent publications focused on developing interactive learning tools and platforms that leverage algorithmic principles for educational and community benefit.

• Human Activity Understanding
• Computer Vision
• Sensor Fusion
• +۶ مورد دیگر

Rahul Chaudhari is a Senior Researcher at the Chair of Media Technology, Technical University of Munich (TUM), where he has been working since August 2019. He is affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI) and contributes to research in human-computer interaction, particularly in human activity understanding. Dr. Chaudhari earned his doctoral degree (Summa cum Laude) in Communications and Signal Processing from TUM in 2015, following a Master's degree in Communications Engineering from TUM in 2009 and a Bachelor's degree in Electronics and Telecommunications from the University of Pune, India. His academic journey reflects a strong foundation in communications engineering and signal processing that has evolved into interdisciplinary research spanning haptics, computer vision, and artificial intelligence. His research focuses on Human Activity Understanding using Computer Vision, Sensor Fusion, and AI techniques. His current work centers on understanding Human-Object Interactions using camera data (RGB and Depth) recorded in indoor environments, with potential integration of wearable sensors or environmental sensors. His work has significant applications in improving human well-being, comfort, and convenience through intelligent environments and ambient assisted living systems. Dr. Chaudhari's publication record shows a clear evolution from foundational work in haptic communications to cutting-edge research in human-object interaction and 3D pose estimation. His recent publications demonstrate expertise across multiple domains including computer vision for activity recognition, synthetic data generation for training models, and neurosymbolic approaches to human-AI collaboration. This interdisciplinary approach reflects the increasingly connected nature of modern AI research. Best student paper award for Yujun Wang (2025) LMT Spin-off Wins Third Place at euRobotics Technology Transfer Award 2025 LMT featured in Süddeutsche Zeitung (2025) Diego Fernandez Prado selected as finalist for IEEE CASE Best Paper Award (2024) Dr. Chaudhari actively supervises student research, including Master's theses on advanced topics such as 'Simulation and Optimization for 6G Network Planning using Digital Twins.' His supervision work involves guiding students through complex technical challenges in simulation software evaluation, digital twin implementation, and network optimization. He also serves on thesis committees and provides mentorship to students working on related research topics. His research group operates within the broader context of TUM's initiatives in robotics and machine intelligence, contributing to projects like the Centre for Tactile Internet with Human-in-the-Loop (CeTI) and 5G Testbed Bayern. The group maintains a strong focus on both theoretical foundations and practical implementations, with research that bridges the gap between academic innovation and real-world applications in intelligent environments.

Irfan Essaمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Irfan Essa is a Professor at the School of Interactive Computing , Georgia Institute of Technology, and serves as Senior Associate Dean in the College of Computing. He leads the Interdisciplinary Research Center for Machine Learning at Georgia Tech (ML@GT) and holds a Senior Staff Research Scientist position at Google Inc. His academic journey includes a Ph.D. from MIT (1994) and prior research faculty roles at MIT Media Lab (1988-1996). Research Focus : Computer Vision, Machine Learning, Robotics, Computational Journalism, and Human-Computer Interaction. Leadership : Co-founded ML@GT, Adjunct Faculty at Carnegie Mellon's Robotics Institute. Essa’s research explores Generative AI , Computational Video , Wearable Computing , and Vision-Language Models . His work impacts areas like Autonomous Systems , Video Analysis , and Social Computing . Recent projects include end-to-end trainable frameworks for Image Retargeting (HALO) and Video Generation (VideoPoet), emphasizing content-structure preservation and zero-shot capabilities. His 15 most recent publications (2023-2025) span CVPR , NeurIPS , ICML , and ECCV , focusing on Generative Media , Diffusion Models , and Human Activity Recognition . Notable contributions include Text-Free Diffusion , Wearable-Based Activity Recognition , and Layered Transformation Architectures . Awards : NSF CAREER, IEEE Fellow, Best Paper Awards at ICML 2024 and ICLR 2023. Students : Advisees include Varun Agrawal, Aneeq Zia, Unaiza Ahsan, and Daniel Castro Chin. Labs/Teams : Affiliated with ML@GT, Georgia Tech's Center for Experimental Research in Computer Systems (CERCS), and Google Research.

• Electronics
• Automations
• Electric Drives
• +۳ مورد دیگر

Cătălin-Iulian CHIVU serves as an Associate Professor at the Department of Engineering and Industrial Management within the Faculty of Technological Engineering and Industrial Management at Transilvania University of Brașov, Romania. His office is located in Building V, Room VI07, with contact details including phone/fax +40268.477113 and email catalin.c@unitbv.ro. His research spans electronics, automations, and electric drives, with significant contributions to industrial automation systems, conveyor/AGV software design, and electric drive technologies. He also investigates interdisciplinary intersections including AI ethics in large language models and virtual platform applications in educational resource management. This dual focus bridges traditional engineering domains with contemporary societal and pedagogical considerations. Recent publications (2021-2023) reveal a multidisciplinary trajectory spanning electrical engineering, industrial automation, educational technology, and AI ethics. His work demonstrates technical depth in drive systems and automation while addressing emerging societal implications of AI, reflecting an evolving research profile that integrates engineering fundamentals with human-centered applications. No scientific awards were documented in the available source material. Information regarding student supervision, research grants, laboratory affiliations, or collaborative teams was not provided in the source documentation. His academic activities appear centered on teaching and publication within his core engineering disciplines with recent expansion into technology ethics.

Julie Shahمشاهده پروفایل

استاد

• Robotics
• Human-Robot Interaction
• Autonomous Systems
• +۴ مورد دیگر

Julie Shah is the H.N. Slater Professor at the Department of Aeronautics and Astronautics and leads the Interactive Robotics Group at MIT’s Schwarzman College of Computing . She focuses on creating robots that work effectively alongside humans in dynamic environments, combining expertise in artificial intelligence, human factors, and systems engineering. BS, SM: Aeronautics and Astronautics (MIT) PhD: Autonomous Systems (MIT) Her research explores human-robot collaboration , emergent communication , and trust calibration in automated systems, with applications in manufacturing, surgery, and space exploration. Recent publications highlight advancements in language-guided abstraction , human-in-the-loop policy adaptation , and ethics-aware AI . Julie Shah has received prestigious accolades including the NSF CAREER award and recognition in the MIT Technology Review TR35 . Her lab’s work on industrial human-robot collaboration was named one of the 10 Breakthrough Technologies of 2013 by Tech Review. She previously worked at Boeing Research and Technology , where she developed innovative methods for human-robot teamwork in safety-critical domains. Her lab at MIT continues to push boundaries in robot locomotion , situational awareness , and sociotechnical systems .

George D Konidarisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۲ مورد دیگر

George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.