جست‌وجوی استاد و پژوهشگر

• Hybrid Control Systems
• Hierarchical Control Systems
• Distributed Consensus-Based Control
• +۳ مورد دیگر

Jörg Raisch is a Professor of Control Systems at the Technische Universität Berlin since March 2006. He previously held positions at Otto-von-Guericke University Magdeburg and the Max Planck Institute for Dynamics of Complex Technical Systems. His research focuses on hybrid and hierarchical control systems , distributed consensus-based control , and control of time-dependent event-discrete systems using tropical algebras. Alma Mater: University of Stuttgart (Technical Cybernetics), UMIST Manchester (Control Systems) Postdoctoral work: University of Toronto (Systems Control Group) His work spans applications in process technology , medical systems , and energy technology . Recent publications emphasize max-plus algebra , P-time event graphs , and over-the-air consensus in clustered networks. He has served on editorial boards of journals like Automatica and Discrete Event Dynamic Systems , and led the IFAC Technical Committee TC1.3 on Discrete Event and Hybrid Systems from 2017-2020. Awards: DFG fellowship for habilitation

Harold TRANNOISمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Multi-agent Systems
• +۳ مورد دیگر

Harold TRANNOIS serves as a Lecturer at the University of Picardy Jules Verne (UPJV) within the Networks and Data Domain (REDO), with office 309 contactable at phone 5911. His academic focus bridges theoretical computer science and applied engineering disciplines. His research program demonstrates deep specialization in intelligent systems, particularly emphasizing: Machine Learning architectures including spiking neural networks and autoencoders Multi-agent coordination frameworks for complex environments Anomaly detection systems in IoT-enabled infrastructure Cloud robotics and domain-specific language development Discrete element modeling for physical simulations Analysis of his publication trajectory reveals evolving expertise from foundational multi-agent traffic simulation (1998) toward contemporary biomedical AI applications (2023). Recent work concentrates on neural network implementations for healthcare monitoring and smart building analytics, with consistent methodological focus on uncertainty quantification and architectural optimization. Active involvement in DAAB and SmartAngel projects demonstrates applied research translation into healthcare and robotics domains, leveraging his dual expertise in neural networks and distributed systems for real-world problem solving.

Szegletes Lucaمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Neuroscience
• Biofeedback Systems
• +۴ مورد دیگر

Szegletes Luca is a Senior Lecturer at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics as part of the Applied Mobile Research Group (AMORG). Her research spans multiple interdisciplinary domains, focusing primarily on integrating machine learning techniques with biofeedback systems. Her work demonstrates expertise in diffusion probabilistic models for EEG signal processing, deep learning applications in embedded systems, and reinforcement learning frameworks. She has pioneered methods for physiological data analysis, including blood pressure measurement and face anonymization in low-power environments. Notable research contributions include adaptive educational game frameworks, cognitive profiling systems, and biofeedback-driven difficulty control mechanisms. Her publications reflect a strong focus on bridging computational methods with cognitive science and biomedical applications.

Stefan Lüdtkeمشاهده پروفایل

استادیار

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +۵ مورد دیگر

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

• Battery Management Systems
• Electrochemical Impedance Spectroscopy
• Machine Learning Applications
• +۷ مورد دیگر

Alessio De Angelis is a researcher affiliated with the University of Perugia, focusing on advanced measurement systems, battery technology, and localization techniques. His work spans disciplines such as electrical engineering, machine learning, and IoT applications. University: University of Perugia Email: alessio.deangelis@unipg.it His research interests include: Battery management and state-of-charge estimation Uncertainty quantification in AI-based systems Ultra-wideband (UWB) and magnetic localization Wireless sensor networks for environmental monitoring Signal processing for quantized and constrained data IoT applications in structural and health monitoring Recent publications highlight his expertise in battery diagnostics, localization algorithms, and AI-driven measurement systems. Key trends involve integrating machine learning with electrochemical impedance spectroscopy (EIS) and optimizing low-complexity sensor architectures.

Diego Borro Yagüezمشاهده پروفایل

استاد

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +۶ مورد دیگر

Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research

Hossein Azizpourمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Hossein Azizpour is an Associate Professor in Machine Learning at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning (RPL). He holds associated roles at the Science for Life Laboratory, Swedish e-Science Research Centre, WASP, and Digital Futures. His research focuses on deep learning under uncertainty, model interpretability, feature selection, and applications in life sciences, physics, and earth observation. Research Themes : (1) Deep learning under uncertainty (Bayesian methods, semi-supervised learning), (2) Interpretable AI (feature attribution, understanding neural networks), (3) Generative & foundation models (Vision Transformers, diffusion models), (4) Applications in medical imaging, fluid dynamics, and earth observation. Recent Work Trends : His publications emphasize vision transformers, uncertainty quantification, and cross-domain applications. Key areas include model optimization (e.g., Patchdropout), medical AI (e.g., breast cancer risk prediction), and physics-informed deep learning. Scientific Awards : Best Paper Award, CVPR DeepVision Workshop (2015) Google Travel Grant, CVPR DeepVision Workshop (2015) Best Paper Runner-Up, CVPR DeepVision Workshop (2014) Best Industry Paper Prize, BMVC (2013) Outstanding Reviewer, CVPR (2013) Advising & Collaborations : Supervises PhD students in AI for physics applications (e.g., fluid dynamics), medical imaging, and computer vision. Collaborates with institutions like AstraZeneca, Alan Turing Institute, and Meta. Labs & Teams : Leads the Robotics, Perception and Learning (RPL) division’s research and co-organizes the Computer Vision Reading Group at KTH.

• 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 .

• Proof Assistants
• Formal Verification
• Inductive and Coinductive Reasoning
• +۶ مورد دیگر

Andrei Popescu is a Senior Lecturer in the Department of Computer Science at the University of Sheffield, specializing in formal verification and proof assistants. Previously, he held faculty positions at Middlesex University and TU Munich. University of Sheffield (2020–present) Middlesex University (2014–2020) TU Munich (2010–2020) His research focuses on proof assistants (Isabelle/HOL), inductive/coinductive reasoning, syntax with bindings, and information flow security. Key projects include CoCon (verified conference system) and CoSMeDis (confidentiality-verified social media). Recent publications address Gödel's incompleteness theorems, modular (co)datatypes, and security verification. Awards include POPL Distinguished Paper Awards (2023–2025) and the RS 3 Best Paper Award (2012–2013). He teaches courses on software/hardware verification and previously taught decision support systems, web development, and verification techniques. Andrei is actively involved in conference organization and program committees, including POPL, ITP, and CSF.

• 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.

• Robotics
• Human-Robot Interaction
• Autonomous Systems
• +۳ مورد دیگر

Dirk Wollherr is a Senior Researcher and Lecturer (Privatdozent) at the Institute of Automatic Control Engineering, Faculty of Electrical Engineering and Information Technology, Technical University of Munich (TUM). He received his Dipl.-Ing. (2000), Dr.-Ing. (2005), and Habilitation (2013) from TUM. His career includes research at TU Berlin and a JSPS Fellowship at the University of Tokyo. He led the Cluster of Excellence 'Cognition for Technical Systems' (2006-2008) and is a TUM Carl-von-Linde Junior Fellow (2010-2013). Research Interests: Autonomous mobile robots, human-robot interaction, SLAM, real-time trajectory planning, stochastic model predictive control, and robotic manipulation under uncertainty. His work bridges control theory and practical applications in urban robotics, agriculture automation, and autonomous vehicles. Publication Trends: Recent articles (2021-2025) focus on robust control under uncertainty, human-robot collaboration, agricultural automation, and probabilistic motion planning. Key themes include constraint violation minimization, data-driven control, and ethical human-robot interaction. Awards & Recognition: Japanese Society for the Promotion of Science (JSPS) Research Fellowship (2004) TUM Carl-von-Linde Junior Fellowship (2010-2013) Projects & Leadership: Principal investigator in EU projects (Robot@CWE, IURO), General Chair of Robotik 2008, and Co-Chair of ARSO 2013. Leads research on the Autonomous City Explorer and human-robot interaction frameworks.

Florian Pfaffمشاهده پروفایل

استادیار

• Sensor Fusion
• Estimation Theory
• Multi-target Tracking
• +۴ مورد دیگر

Florian Pfaff is a Junior Professor (Assistant Professor) at the Institute of Automation and Software Systems, University of Stuttgart, leading the professorship for "Cognitive Sensor Technology for Future Mobility". He earned his doctorate in computer science from Karlsruhe Institute of Technology (KIT) with summa cum laude distinction. His research focuses on: Sensor fusion and multi-target tracking algorithms Probabilistic modeling for robotic systems Industrial automation and optical material sorting Estimation theory for high-dimensional spaces His publication trends show strong emphasis on sensor-based sorting systems, stochastic filtering techniques, and applications of machine learning in industrial automation. Recent work demonstrates increasing focus on 3D pose estimation and real-time control systems. Awards and honors include: ISIF Young Investigator Award SICK Science Award for best doctoral thesis Inclusion in "Excellent Computer Science Dissertation" compendium Multiple best paper awards He has led research projects at KIT on optical bulk material sorting, served as visiting scholar at University College London, and visiting professor at University of Oxford. He teaches courses on software systems and sensor networks. Leads the Cognitive Sensor Technology research group at University of Stuttgart, focusing on sensor fusion applications for mobility systems.

Vishrant Tripathiمشاهده پروفایل

استادیار

• Communication Networks
• Robotics
• Control & Optimization
• +۹ مورد دیگر

Vishrant Tripathi is an Assistant Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He leads the Multi-Agent Intelligent Networks (MAIN) Lab , focusing on communication networks, robotics, and control theory. Prior to Purdue, he earned a Ph.D. in Electrical Engineering and Computer Science at MIT under Prof. Eytan Modiano, with foundational work on age of information metrics in wireless networks. Education: Ph.D. in EECS, MIT (2023) B.Tech. (Honors) in Electrical Engineering, Indian Institute of Technology Bombay (2015) Research interests span real-time wireless networking , multi-agent robotics , edge computing , and federated learning , with an emphasis on optimizing information freshness via age of information (AoI) frameworks. His group develops theoretical models and practical systems for wireless networks in search-and-rescue, industrial monitoring, and autonomous fleets. Recent publications include works on AoI-based scheduling, software-defined networking for UAV swarms, and correlation-aware resource allocation in wireless networks. His research has earned Best Paper Runner-Up Awards at ACM MobiHoc and Best Presentation Awards at LIDS Student Conferences. Teaching highlights include ECE 302 (Probabilistic Methods) at Purdue and TA roles at MIT for courses like 16.36 (Communication Systems) and 6.7700 (Fundamentals of Probability). He is a participant in MIT's Kaufman Teaching Certificate Program. Professional service includes co-chairing LIDS Student Conferences, organizing MIT's LIDS & Stats Tea Talks, and reviewing for IEEE journals and conferences. He also builds wireless systems for drones and autonomous vehicles in collaboration with industry partners like Google and Facebook.

• Machine Learning
• Bioinformatics
• Drug Discovery
• +۵ مورد دیگر

Sandor Szedmak serves as a Research Fellow in the Department of Computer Science at Aalto University, Finland, and is affiliated with the Helsinki Institute for Information Technology (HIIT). He operates within Professor Juho Rousu's research group, focusing on interdisciplinary machine learning applications with strong ties to computational biology and bioinformatics. His institutional presence is active, as evidenced by current contact details and ongoing publication output. Dr. Szedmak's research specializes in developing advanced machine learning frameworks for complex biological and educational challenges. His primary interests include drug combination effect prediction, protein function annotation through multi-view learning, strain design optimization using reinforcement learning, and personalized educational systems for computer science students. He pioneers methods in latent tensor reconstruction, scalable variable selection, and kernel generalization, targeting high-dimensional data problems in pharmacology and computational biology. His publication trajectory from 2020-2025 reveals a concentrated effort in bioinformatics applications, with landmark papers in Nature Communications and Bioinformatics on drug combination prediction. The work consistently leverages tensor factorization and multi-view learning to model biological systems, while recent expansions include educational data mining for programming behavior analysis. This demonstrates both methodological consistency in machine learning innovation and strategic domain diversification. No scientific awards or fellowships are documented in the provided materials. While no student advisees or grant details are explicitly listed, Dr. Szedmak's collaborative publication pattern—particularly within the Rousu research group and HIIT—suggests active participation in team-based research initiatives. His work appears funded through institutional channels given the consistent output in computational biology. He operates within the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki, and is embedded in Professor Juho Rousu's research group. This environment facilitates cross-disciplinary collaboration between computer science and life sciences, with infrastructure supporting high-performance computing for large-scale biological data analysis.

• Machine Learning
• Robotics
• Autonomous Driving
• +۳ مورد دیگر

Jatan Shrestha is a Doctoral Researcher at Aalto University, affiliated with the Department of Electrical Engineering and Automation. His research focuses on applying machine learning techniques to solve complex problems in robotics and autonomous systems. Department: Electrical Engineering and Automation Email: jatan.shrestha@aalto.fi Research Interests: Jatan's work spans machine learning, robotics, and optimization. Key areas include autonomous driving algorithms, multi-agent trajectory planning, visibility-aware navigation, and GPU-accelerated computing for real-time robotic applications. Publication Trends: His research output demonstrates expertise in applying machine learning to hardware optimization (2024), advancing autonomous driving algorithms in dense traffic scenarios (2023), and developing GPU-based optimization frameworks for robotic navigation (2021-2022).