جست‌وجوی استاد و پژوهشگر

• Distributed Computing
• Fault-Tolerant Systems
• Self-Stabilization
• +۷ مورد دیگر

Sébastien Tixeuil is a Professor at Sorbonne Université, affiliated with LIP6 (CNRS 7606), a leading computer science laboratory in Paris, France. He is a Senior Member of the Institut Universitaire de France (IUF), a prestigious recognition of excellence in research and teaching. His work is centered in the Department of Computer Science within the Faculty of Science and Engineering. His research focuses on the theoretical and practical foundations of distributed systems, with emphasis on fault tolerance, self-stabilization, mobile robotics, and dynamic networks. He investigates how systems can recover from faults autonomously and operate correctly under uncertainty, adversarial conditions, and changing environments. His recent work extends into IoT, blockchain, and formal verification of distributed protocols. The most recent publications highlight trends in distributed decision-making under partial information, autonomous classification of IoT devices, resource-efficient stabilization algorithms, solving combinatorial games like Quixo, and indulgent rendezvous protocols for fault-prone robots. These works reflect a strong integration of formal methods, algorithm design, and real-world applicability in networked and robotic systems. He has been honored as both a Senior Member and previously an Honorary Member of the Institut Universitaire de France, among the highest academic distinctions in France. Current Research Projects: GRIFIN (ANR) TURFU-NET (ANR) CyberNemo (EU) NEMO (EU) Students Supervised: Sébastien Bouchard – Proof assistant for probabilistic robots Alice Di Carlo – Blockchains for Healthcare Organizations Anthony Honorat – Networks of Fat Robots Giovanni Farina – Byzantine Tolerance in Dynamic Networks Adam Heriban – Realistic Robot Networks Nesrine Ammar – Internet of Things He leads or contributes to major collaborative research initiatives funded by the French National Research Agency (ANR) and the European Union. His advising spans PhD and Master’s students, focusing on cutting-edge topics in distributed computing and networked systems. His lab, embedded within LIP6 and the former NPA team (Networks and Systems), fosters interdisciplinary research in autonomic and resilient systems.

• Artificial Intelligence
• Robotics
• Motion Planning
• +۸ مورد دیگر

Maxim Likhachev is a Professor at the Robotics Institute and National Robotics Engineering Center , both within the School of Computer Science at Carnegie Mellon University (CMU). His research focuses on Artificial Intelligence and Robotics , particularly in real-time graph search algorithms , planning under uncertainty , and high-dimensional mobile manipulation . He co-founded TravelWits.com and RobotWits (acquired by Waymo), and his lab develops the Search-based Planning Library (SBPL), integrated into ROS. Research Themes : Graph search-based planning for deterministic and probabilistic domains Decomposition of hard planning problems into sequential graph searches Incremental algorithms for dynamic environments Experience-driven heuristic optimization Scientific Awards : Gold Edison Award (2013) for industrial mobile manipulator project DARPA Urban Challenge Winner (2007) for autonomous SUV navigation Students : Supervises current PhD students including Itamar Mishani , Muhammad Suhail Saleem , and Rishi Veerapaneni , alongside Masters students like Gabriel Olin . Past advisees include Dhruv Saxena (now at Uber), Michael Phillips (now at Uber), and Shivam Vats (now at Waymo). Lab & Projects : Leads the Search-based Planning Laboratory at CMU, working on systems like K-MAX helicopter , PR2 robots , and industrial mobile manipulators for tasks including autonomous paint stripping and aerial navigation.

Kurt Helmesمشاهده پروفایل

استاد

• Operations Research
• Stochastic Processes
• Control Theory
• +۳ مورد دیگر

Prof. Dr. Kurt Helmes is a Professor of Operations Research at the Humboldt University of Berlin , Faculty of Economics. He holds the SAP Endowed Professorship for Organizational Economics – Future of Work and has been affiliated with the Economic Society at HU e.V. His office is located in Room 325, and he can be contacted via helmes@wiwi.hu-berlin.de . Education: Dipl.-Math. (Mathematics), Dr. rer. nat. (Doctorate in Natural Sciences, 1976), Habilitation (1982) Prof. Helmes specializes in stochastic models of operations research and adaptive stochastic control theory . His work bridges mathematical rigor with applications in finance, economics, and dynamic systems. Key research themes include exit time problems, Lévy processes, minimax estimation, and linear programming techniques for stochastic optimization. His publications span journals like Annals of Statistics , IEEE Transactions on Automatic Control , and Mathematics of Operations Research , reflecting interdisciplinary contributions. Articles focus on stochastic calculus, control theory, and probabilistic optimization, often addressing uncertainty in dynamic environments. Prof. Helmes collaborated with researchers such as N. Christopeit, R. Rishel, V. Beneš, and R. Stockbridge, contributing to fields like Markov processes, robust estimation, and stochastic control.

• Artificial Intelligence
• Computer Vision
• Domain Adaptation
• +۶ مورد دیگر

Tatiana Tommasi is an Associate Professor at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin, actively contributing to advanced AI research and education. She serves as Deputy Coordinator of the National Doctoral College in Artificial Intelligence and Scientific Advisor for the European AI, Data, and Robotics Association (ADRA) and ELLIS Network. Her affiliations include the SmartData@PoliTO Laboratory and VANDAL Visual and Multimodal Applied Learning Laboratory. Her research focuses on algorithm fairness, computer vision, domain adaptation, autonomous driving, and robot learning. Recent work includes improving neural network reliability through fault-aware design, 3D semantic novelty detection, and visual relationship reasoning for robotic grasp planning. She has contributed to European research projects like ELSA (Secure and Safe AI) and commercial initiatives in space technologies and autonomous vehicle uncertainty quantification. Selected 2024-2025 publications demonstrate expertise in foundation models, fairness metrics, uncertainty estimation, and geometric deep learning. Her team leads commercial research projects on industrial robotics trajectory generation and satellite data analysis. She teaches core courses in Computer Engineering, Automotive Engineering, and Data Science, mentoring PhD students in Artificial Intelligence and Computer Systems Engineering. Research keywords: Artificial Intelligence, Machine Learning, Computer Vision, Domain Adaptation, Robot Learning, Transfer Learning, Fairness in AI, Uncertainty Quantification, Autonomous Driving, 3D Point Cloud Analysis, Vision Transformers, Industrial Robotics.

Steve Southwardمشاهده پروفایل

دانشیار

• Automotive Engineering
• Vehicle Dynamics
• Control Systems
• +۵ مورد دیگر

Steve Southward is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech, part of the College of Engineering. His roles include membership on the Advisory Board, Leadership committees, and Emeritus Faculty. He holds the John Jones Faculty Fellowship and is associated with chaired professorships in the department. Education: Ph.D., M.S., and B.S. in Mechanical Engineering from Michigan State University (1990, 1986, 1983). Research focuses on Ground Vehicle Chassis/Suspension Performance , Dynamic Systems Modeling , and Real-Time Adaptive Control . Projects include autonomous vehicle navigation, active suspension systems, and vibration cancellation. His work bridges theoretical control algorithms with practical applications in automotive and aerospace systems. Recent publications (2024–2011) emphasize autonomous vehicle control, traction systems, and adaptive signal processing. Key trends include integrating machine learning with classical control theory for improved system reliability. Scientific Award: John Jones Faculty Fellowship Advising and grants: While no students are listed, his research has been supported by grants related to vehicle dynamics and control systems. He collaborates on lab initiatives involving advanced suspension testing and acoustic monitoring. Labs/Teams: Engages with automotive and aerospace labs at Virginia Tech, focusing on testing rigs and sensor integration for real-world applications.

Hamid Eghbal-zadehمشاهده پروفایل

پژوهشگر

• Adversarial Machine Learning
• Representation Learning
• Generative Models
• +۴ مورد دیگر

Hamid Eghbal-zadeh is a Postdoctoral Researcher at the Institute of Computational Perception at Johannes Kepler University Linz (JKU). Previously, he worked as a Research Scientist at Meta and held roles at the Institute for Machine Learning at JKU. His research focuses on adversarial machine learning, representation learning, and robust deep learning models. He has made significant contributions to acoustic scene analysis, generative models, and reinforcement learning. Education : PhD in Machine Learning from JKU (2019), defended with distinction. BSc/MSc details not explicitly stated in text. Research Highlights : Developed Mixture Density GANs to address mode collapse in GANs Pioneered receptive-field regularization techniques for audio classification Contributed to top-performing systems in DCASE challenges (2016-2019) Active in reinforcement learning and domain adaptation research Awards/Competitions : Multiple top placements in international challenges (DCASE, MediaEval), outstanding reviewer recognitions (ICML 2022, ICLR 2022). Advising : Supervised 6+ students in topics like acoustic scene classification, generative retrieval, and sound event detection.

• Control Systems
• Data-Driven Control
• Optimization
• +۳ مورد دیگر

Jeremy Coulson is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin–Madison. His research focuses on control systems theory, data-driven methodologies, optimization, and their applications in robotics and energy systems. He completed his PhD at ETH Zurich (2022), MASc and BASc degrees from Queen’s University (2017, 2015). Education PhD, Automatic Control Laboratory, ETH Zurich (2022) MASc in Mathematics & Engineering, Queen’s University (2017) BASc in Mechanical Engineering & Applied Mathematics, Queen’s University (2015) Research Interests Dr. Coulson explores intersections between control theory, optimization, and machine learning. Key areas include data-driven control strategies for complex systems, uncertainty quantification in predictive models, and adaptive control for robotics and energy infrastructure. His work emphasizes foundational questions in system modeling and decision-making under uncertainty. Notable Awards 2022 IEEE Best Young Author Award 2022 ETH Medal (Outstanding Doctoral Thesis) 2019 European Control Conference Best Student Paper Teaching & Advising He teaches courses in optimization, linear systems, and advanced control (e.g., E C E 717, E C E 821). He advises students in master’s and doctoral research, focusing on hands-on projects in robotics and energy systems. No grants explicitly mentioned in provided texts.

Igor Gilitschenskiمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Igor Gilitschenski is an Assistant Professor in Computer Science at the University of Toronto, leading the Toronto Intelligent Systems Lab (TISL). He focuses on developing probabilistic and learning-based techniques for robotic perception and decision-making, aiming to enable robust interactive autonomy. Prior to this, he held roles at MIT CSAIL, ETH Zurich's Autonomous Systems Lab, and the Karlsruhe Institute of Technology (KIT). His research interests include autonomous systems, computer vision, and deep learning applied to robotics. He collaborates with institutions like the Vector Institute and is a Vector Research Scholar. His work spans robot learning, simulation-driven policy training, and safety-critical systems. Notable projects include pseudo-simulation for autonomous driving, vision-language-action models, and neural radiance fields for 3D scene representation. He actively recruits PhD students to work at the intersection of computer vision, deep learning, and robotics, emphasizing controllable simulation engines and safe learning frameworks. Recent research trends in his articles highlight advancements in generative models, reinforcement learning, and perception systems for dynamic environments. He emphasizes collaboration across disciplines, including event-based vision and language-guided reasoning for robotic tasks.

Jos Elfringمشاهده پروفایل

استادیار

• World Modeling Techniques
• Artificial Intelligence
• Robotics
• +۶ مورد دیگر

Jos Elfring is an Assistant Professor in the Robotics section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). His research focuses on world modeling techniques, AI, automated driving, and sensor fusion. He holds an MSc and PhD from TU/e, with his PhD thesis on Semantic World Modeling for Autonomous Robots (2014). He worked in industry roles at TNO, TomTom, and others from 2014-2024, balancing part-time academia until moving to a full-time faculty role in 2024. Education: MSc and PhD from TU/e, internship at University of Canterbury (2007). Research interests include motion prediction, localization, and state estimation. Notable achievements: Best Paper Award at RoboCup 2013 and contributions to RoboEarth, a seminal robotics knowledge-sharing project. Courses taught include 'Data Fusion & Semantic Interpretation.' His work spans automated driving systems, probabilistic modeling, and collaborative robotics. Key collaborations include EU projects and industry partnerships. Labs/teams: Member of Tech United RoboCup@Home team. Current focus: semantic world models for autonomous systems.

Shiqi Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۳ مورد دیگر

Shiqi Zhang is an Associate Professor in the School of Computing at Binghamton University, part of the Thomas J. Watson College of Engineering and Applied Science. His research focuses on the intersection of artificial intelligence and robotics, particularly in developing algorithms for service robots in human-inhabited environments. He leads the Autonomous Intelligent Robotics (AIR) research group and coordinates AI tracks in the Computer Science program. Education: PhD in Computer Science, Texas Tech University (2013) MS and BS in Computer Science, Harbin Institute of Technology (China, 2008 and 2006) Visiting Student at Tsinghua University (2007-2008) Intern at Microsoft Research Asia (2012) Research Interests: Artificial Intelligence Robotics Human-Robot Interaction Machine Learning Task and Motion Planning Recent Research Trends: Zhang's work emphasizes integrating vision-language models, commonsense reasoning, and large language models (LLMs) for robust task planning and human-robot collaboration. Recent projects include safety-aware robotics, AR-enhanced collaboration, and ethical AI in healthcare. Awards: Outstanding Associate Editor for IEEE Robotics and Automation Letters (2024) Professional Contributions: Organized workshops at ICRA 2024, RSS 2024, and AAMAS 2022. Served on committees for KR-2023 and CoRL 2023. Co-developed courses like 'Artificial Intelligence and Robotics' in the Lyceum Program. Labs/Teams: Directs the AIR Group, focusing on embodied AI, mobile robotics, and human-robot systems. Collaborates with industry and academic partners on real-world applications.

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +۴ مورد دیگر

Eyal Amir is an Associate Professor at the University of Illinois at Urbana-Champaign, specializing in Artificial Intelligence with a focus on probabilistic reasoning, machine learning, and multi-agent systems. His work integrates formal logic, game theory, and cognitive science to address challenges in social dynamics, robotics, and educational technology. Research interests include deception detection in social games, bullying analysis through computer games, and scalable inference methods for complex systems. His contributions span theoretical advancements in relational Kalman filtering, lifted inference, and probabilistic modeling, alongside applied projects in environmental monitoring and behavioral interventions. Notable contributions include developing game-based methods for studying bullying and cyberbullying, real-time anomaly detection systems, and foundational work on logical filtering and knowledge representation. While no formal awards are listed here, his extensive publication record reflects impactful contributions to AI and interdisciplinary research. Eyal's advising and grant activities are not explicitly detailed in the provided text, but his research often involves collaborations across computer science, psychology, and engineering domains. No lab or team affiliations are specified in the current data.

Tobias Schubertمشاهده پروفایل

پژوهشگر

• Multi-Robot Localization
• Optical Motion Capture
• Machine Learning
• +۵ مورد دیگر

Tobias Schubert is a Researcher at the Autonomous Intelligent Systems group at Albert-Ludwigs-University Freiburg's Faculty of Engineering. He holds a PhD in Mathematics from the University of Regensburg (2013) and has extensive postdoctoral experience in robotics and computer science. His research focuses on multi-robot localization, optical motion capture, and human-motion analysis with applications in healthcare robotics. Notable projects include the MAKE IT ReaL motion tracking system, the Data Glove project for stroke patient rehabilitation, and the NaRKo compliant service robots for hospital logistics. Education: Diploma in Mathematics (2010), PhD in Mathematics (2013) Key Projects: Motion capture for humans/animals, Parkinson's disease monitoring, compliant robotics His work has been recognized with nominations for Best Student Paper (IROS 2016) and Best Application Paper (IROS 2016). He contributes to both theoretical robotics and applied healthcare solutions through interdisciplinary collaborations.

Vishnu Boddetiمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Biometrics
• +۷ مورد دیگر

Vishnu Boddeti is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), where he directs the Human Analysis Lab. His research focuses on trustworthy machine learning systems at the intersection of computer vision, biometrics, and machine learning. He obtained his Ph.D. from Carnegie Mellon University in 2012, followed by a postdoctoral fellowship at CMU's Robotics Institute. Education : Ph.D., Carnegie Mellon University (2012) B.Tech, Indian Institute of Technology Madras (2007) Research Interests : Privacy-preserving AI Fairness in machine learning Homomorphic encryption for biometrics Structural health monitoring via deep learning Causal representation learning Generative models and diffusion models Awards : IEEE Biometrics Council Best Student Paper Award (2023) IEEE-CCF Best Paper Award (2024) Best Paper Awards at ACCV, BTAS, and GECCO Facebook Research Grant (2021) Labs/Teams : Director of the Human Analysis Lab, focusing on applications in biometric security, trustworthy ML systems, and interdisciplinary AI solutions.

• Aerospace Engineering
• Control Systems
• Deep Learning
• +۵ مورد دیگر

Dr Will Jacobs is a Research Fellow at the University of Sheffield's School of Electrical and Electronic Engineering. His work focuses on advanced control systems, nonlinear system identification, and deep learning applications in aerospace and energy systems. He is affiliated with the Amy Johnson Building in Sheffield, S1 3JD, and can be contacted at w.jacobs@sheffield.ac.uk or +44 114 222 5671. Research interests include distributed digital twins for fleet management, fault detection in civil nuclear plants, and interpretable machine learning for complex systems. His recent work emphasizes resource-constrained system monitoring, economic control strategies for aerospace fleets, and frequency-domain analysis for nonlinear systems. Jacobs' interdisciplinary approach bridges electrical engineering, control theory, and applied mathematics. Publications span 2014–2024, with a focus on gas turbine health monitoring, Bayesian system identification, and convolutional neural networks for in-flight anomaly detection. His research contributes to both theoretical advancements and practical industrial applications in energy systems and aerospace engineering. No scientific awards are explicitly listed in the provided information. His advising record is not publicly detailed here, though his involvement in doctoral training and research collaborations is implied through project participation.

Anqi Wuمشاهده پروفایل

استادیار

• Machine Learning
• Computational Neuroscience
• Probabilistic Modeling
• +۳ مورد دیگر

Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE) within the College of Computing at Georgia Institute of Technology. She previously held a postdoctoral fellowship at Columbia University’s Zuckerman Mind Brain Behavior Institute and earned her Ph.D. in Computational and Quantitative Neuroscience from Princeton University, with a graduate certificate in Statistics and Machine Learning. Her research focuses on developing probabilistic modeling approaches and scalable inference algorithms for neural and behavioral analysis, with applications in robotics and real-world problems. Education: Ph.D., Computational and Quantitative Neuroscience, Princeton University (2014–2019) Graduate Certificate in Statistics and Machine Learning, Princeton University Postdoctoral Fellow, Center for Theoretical Neuroscience, Columbia University (2019–2022) Research Interests: Probabilistic Modeling for Neural Latent Discovery: Developing disentangled generative models to extract interpretable neural representations. Behavior Analysis: Extracting multi-layered information from animal/human behavior via hierarchical models and reinforcement learning. Efficient Inference: Advancing Bayesian neural networks, Gaussian processes, and scalable algorithms. Her work bridges machine learning, neuroscience, and robotics, with applications in brain decoding, motor control, and reward learning. Publications Trends: Recent work emphasizes reinforcement learning (e.g., EmoBipedNav for emotion-aware robotics), generative models (diffusion models for neural dynamics), and Bayesian methods (Gaussian processes for multi-region brain analysis). Applications span robotics, neuroscience, and spatio-temporal data modeling. Affiliations: Director, BRAINML Lab (BRAIN Intelligence and Machine Learning) Affiliated with Machine Learning Center, Neuro @ GT, and Bioengineering at Georgia Tech