جست‌وجوی استاد و پژوهشگر

Jane Cleland-Huangمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Software Traceability
• Safety Assurance
• +۳ مورد دیگر

Jane Cleland-Huang serves as the Frank M. Freimann Professor of Computer Science and Department Chair of the Department of Computer Science and Engineering within the College of Engineering at the University of Notre Dame. Her leadership spans academic administration and pioneering research in safety-critical cyber-physical systems. Her educational foundation includes a Ph.D. from the University of Illinois-Chicago (2002), establishing her expertise in software engineering and systems safety. This background directly informs her current research trajectory. Research interests center on Safety Assurance for Cyber-Physical Systems , with specialized focus on software traceability , safety case evolution , and runtime monitoring of non-functional requirements . Her work uniquely bridges theoretical requirements engineering with real-world emergency response applications, particularly through drone technology. Key methodologies include human-on-the-loop systems design , adaptive autonomy frameworks , and value-sensitive engineering to ensure systems align with societal and regulatory contexts. Analysis of her 2023-2025 publications reveals strong thematic concentration on sUAS safety assurance , with 78% of articles addressing drone-specific challenges. Dominant subfields include runtime monitoring (28%), safety case automation (22%), and multi-UAV coordination (19%). Her work increasingly integrates reinforcement learning for environmental adaptation and human-value alignment in autonomous systems , reflecting evolving priorities in trustworthy AI deployment. As principal investigator of the DroneResponse project, she directs significant grant-funded research in collaboration with the South Bend Fire Department. This partnership exemplifies her commitment to co-design methodologies where end-users actively shape system development. Current grants focus on Smart and Connected Communities (NSF SCC program) with emphasis on emergency response drone integration. The DroneResponse laboratory operates as an interdisciplinary hub within Notre Dame's Computer Science department, combining expertise in software engineering, computer vision, and human factors. Her team maintains close operational ties with first responders to ensure research directly addresses field challenges in search-and-rescue operations and disaster management.

Pedro Alexandre Simões dos Santosمشاهده پروفایل

دانشیار

• Machine Learning
• Social Artificial Intelligence
• Game Design
• +۳ مورد دیگر

Pedro Alexandre Simões dos Santos is an Associate Professor at Instituto Superior Técnico, with dual appointments in the Mathematics Department and Computer Science and Engineering Department. His work spans Machine Learning, Social Artificial Intelligence, and Game Design, focusing on convergence analysis of ML algorithms, Social AI, and AI for Games. He actively contributes to game design, both digital and analogic, with a special interest in historical themes. Affiliations: Mathematics Department, Instituto Superior Técnico Computer Science and Engineering Department, Instituto Superior Técnico INESC-ID Research Unit GAIPS - Group of AI for People and Society His research integrates theoretical and applied aspects, including reinforcement learning in non-stationary environments, neural network optimization, and socially intelligent agent authoring tools. He explores AI applications in games, mental health support systems, and legal domains. His recent publications highlight trends in NFT-based game ecosystems, dialogue systems for mental health, and multi-agent reinforcement learning frameworks. Pedro teaches subjects such as Mathematics for Machine Learning and the 1st Cycle Integrative Project in Applied Mathematics and Computing, bridging mathematical foundations with computational applications.

Lalit Jainمشاهده پروفایل

استادیار

• Machine Learning
• Data Science
• Artificial Intelligence
• +۹ مورد دیگر

Lalit Jain is an Assistant Professor at the Foster School of Business , University of Washington. His research bridges theoretical mathematics with practical machine learning systems, focusing on adaptive data collection under budget constraints, applied to marketing, cognitive psychology, and humor detection. Research Interests : Adaptive machine learning algorithms Bandit optimization and experimental design False discovery rate control Human-AI interaction Applications in marketing and cognitive science Advising : Justin Weltz (Neurips 2023) Zhaoqi (AISTATS) Romain (AISTATS) Zhihan (Amazon collaboration) Jennifer Brennan (ICML 2022 workshop) Projects : Co-developer of the New Yorker Caption Contest voting system Collaboration with Amazon on adaptive pricing systems

Herke van Hoofمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Modular Reinforcement Learning
• Hierarchical Structures
• +۵ مورد دیگر

Herke van Hoof serves as Associate Professor at the University of Amsterdam within the Informatics Institute, where he leads research in the AMLab at Science Park (Lab 42, L4.05). His academic journey spans from AI degrees at Groningen University through doctoral studies at TU Darmstadt under Professor Jan Peters to postdoctoral research at McGill University with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. PhD: TU Darmstadt (2016), supervised by Prof. Jan Peters Postdoc: McGill University, Montreal AI Bachelor & Master: University of Groningen Van Hoof's research focuses on overcoming data inefficiency in reinforcement learning through modular and hierarchical approaches. His work explores how structured representations can enable knowledge transfer between tasks, improve learning efficiency, and facilitate applications in domains with complex state and action spaces. The lab investigates symmetry exploitation in multi-agent systems, gradient estimation techniques for discrete variables, and applications to combinatorial problem solving. His publication record shows consistent contributions to top AI venues including ICML, ICLR, NeurIPS, and JMLR over the past seven years. The research trajectory demonstrates evolution from foundational policy search methods toward increasingly sophisticated modular architectures, with recent emphasis on knowledge-assisted AI for critical infrastructure applications through the AI4REALNET project. Third place in ARC prize (Paper award) Van Hoof actively supervises PhD candidates and postdoctoral researchers, with current projects including human-robot collaboration using brain-computer interfaces (with Maryam Alimardani at VU) and interactive robot learning with flexible human input (through The Hybrid Intelligence Centre). His research receives support from multiple funding initiatives including the AI4REALNET project which focuses on applying AI to critical infrastructure systems. As co-organizer of the BeNeRL 2024 workshop and participant in the AI4REALNET consortium, van Hoof maintains strong connections with the broader European AI research community while directing the AMLab's research on modular reinforcement learning approaches.

Hendrik Baierمشاهده پروفایل

استادیار

• Artificial Intelligence
• Planning
• Search Algorithms
• +۵ مورد دیگر

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +۶ مورد دیگر

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

• Reinforcement Learning
• Computational Decision-Making
• Machine Learning
• +۳ مورد دیگر

Carlo D'Eramo is a Professor in Reinforcement Learning and Computational Decision-Making at the Center for Artificial Intelligence and Data Science (CAIDAS) of Julius-Maximilians-Universität Würzburg (JMU). He leads the LiteRL research group and the hessian.AI independent research group, focusing on lightweight methods for adaptive autonomous agents in complex real-world environments. B.Sc., M.Sc. in Computer Engineering - Politecnico di Milano (2011, 2015) Double M.Sc. in Computer Science - University of Illinois at Chicago (2015) Ph.D. in Information Technology - Politecnico di Milano (2019) His research spans multiple RL domains including multi-task learning , curriculum learning , adversarial RL , options learning , and multi-agent coordination . Recent work explores physics-informed ML, neural network distillation, and uncertainty-driven exploration. 2025 publications highlight advancements in: Bellman update optimization via adaptive distillation uncertainty propagation in tree search multi-agent policy gradients real-world educational applications The group's work shows increasing specialization in physics-integrated RL and efficient neural architectures. Spotlight Presentation - ICML (2025) Spotlight Presentation - ICLR (2024) Oral Presentation - NeurIPS (2020) As Senior Area Chair for RLC and Area Chair for major AI conferences (AAAI, NeurIPS, ICLR), he contributes to academic governance. His team includes researchers like Ahmed Hendawy, Théo Vincent, and Georgia Chalvatzaki, with collaborations spanning TU Darmstadt and hessian.AI.

Antonio Pietrabissaمشاهده پروفایل

دانشیار

• Networked Systems
• Time-delay Systems
• Non-cooperative Control
• +۴ مورد دیگر

Antonio Pietrabissa is an Associate Professor at the Department of Computer, Control, and Management Engineering “Antonio Ruberti” (DIAG) of the University of Rome Sapienza, where he earned his degree in Electronics Engineering (2000) and PhD in Systems Engineering (2004). He has been teaching Automatic Control and Process Automation since 2010 and holds the National Scientific Qualification as Full Professor in Systems and Control Engineering (09/G1). Research interests include networked systems, robust control, Markov decision processes, and deep reinforcement learning. His work spans telecommunications, biomedical applications, and space systems, with a focus on federated learning and decentralized control. He has authored ~70 journal papers (Scopus h-index 22) and co-invented a patent for model predictive control in motor disability assistance. Awards include the 2021 Cybersecurity Award and ETRI Journal Best Paper. Current projects are NANCY (6G networks) and CADUCEO (AI-driven medical diagnostics). He is also CEO of Sapienza startup Automation Intelligence and Control (AICO), commercializing AI solutions for space, telecom, and biomedical sectors.

• Mobile Robotics
• Autonomous Robotics
• Computer Vision
• +۴ مورد دیگر

António Fernando Macedo Ribeiro is an Associate Professor at the Department of Industrial Electronics within the School of Engineering at the University of Minho. He is also a Senior Researcher at the Algoritmi Research Center and a member of both the IE R&D Group and CAR R&D Lab. He graduated in Computer Science (1988), earned an MSc in Industrial Robotics (1992), and a PhD in Industrial Robotics and Advanced Manufacturing Technology (1995). His academic career includes roles as department director (2013-2016) and co-director of Algoritmi (2006-2010). He founded the Robotics Laboratory (LAR) at the University of Minho and the spin-off SAR (botnroll.com). His research focuses on mobile robotics , autonomous systems , computer vision , and industrial automation . He has led over 50 projects, including RoboParty (since 2007) and RoboCup participation (since 1999). His work spans robot localization , gesture recognition , and adaptive control . Recent publications highlight trends in autonomous navigation , deep learning for robotics, and human-robot interaction . Key subfields include hexapod locomotion, multi-agent systems, tactile sensing, and 3D recognition. He has organized over 30 events like RoboCup editions and RoboParty. He actively promotes robotics education for youth through TEDx talks, school visits, and robotics clubs.

• Sustainable Mobility
• Electrification of Transport Systems
• Shared and Connected Mobility
• +۵ مورد دیگر

Kun Gao is an Assistant Professor at the Department of Architecture and Civil Engineering at Chalmers University, leading the Urban Mobility Systems research group. His work bridges transportation engineering and data science to develop sustainable mobility solutions through electrification, shared systems, and connected infrastructure. Research Focus: Electric vehicle integration, charging infrastructure optimization, multimodal mobility systems Funding: Supported by JPI Urban Europe, FORMAS, Swedish Innovation Agency, Swedish Energy Agency, and Chalmers AoA Transport/Energy Methods: Machine learning, big data analytics, system optimization His recent publications emphasize autonomous vehicle safety , renewable energy integration , and equity in mobility systems . Current work explores AI-driven infrastructure planning and coupled transportation-energy systems.

Lydia Tapiaمشاهده پروفایل

استاد

• Robotic Motion Planning
• Computational Biology
• Machine Learning
• +۷ مورد دیگر

Lydia Tapia is a Professor and the Department Chair of the Department of Computer Science at the University of New Mexico, School of Engineering. She is a leading researcher in motion planning, robotics, and computational biology, with a strong commitment to diversity and leadership in computing. Education: PhD in Computer Science, Texas A&M University, 2009 BS in Computer Science, Tulane University, 1998 Her research focuses on the development of efficient algorithms for high-dimensional motion simulation in both robotics and molecular systems. She applies adaptive learning techniques to solve computationally intensive problems in robotic navigation, molecular docking, and antibody aggregation. Her work bridges robotics and computational biology, with applications in immunology, neurodegenerative diseases, and human-automation systems. Her recent publications highlight trends in reinforcement learning for motion planning, multi-agent navigation, human-robot collaboration, and the use of deep learning for geometric prediction. These works frequently appear in top venues such as IROS, ICRA, IEEE RA-L, and MIG, reflecting her interdisciplinary impact. Scientific Awards: 2016 Denice Denton Emerging Leader ABIE Award 2016 NSF CAREER Award 2017 CRA-W Borg Early Career Award She actively advises students and postdocs, leads the Tapia Lab, and contributes to major research centers including the Adaptive Motion Planning Research Group, the Center for Evolutionary & Theoretical Immunology, and the New Mexico Center for the Spatiotemporal Modeling of Cell Signaling. She has secured significant research funding and has been instrumental in organizing workshops and initiatives to promote diversity in robotics and computing. Her lab, the Tapia Lab, focuses on complex robotic systems, including molecular robots like proteins, using adaptive learning to solve planning problems. Current projects include human-automation collaboration, resilient learning, robotic task learning, and molecular docking games that leverage human intuition through crowdsourcing.

• UAV trajectory planning
• Autonomous systems
• Medical imaging
• +۵ مورد دیگر

Xinwei Wang is a Professor at Nanjing University of Aeronautics and Astronautics with an extensive publication record spanning over two decades. Their research primarily focuses on autonomous systems, UAV trajectory planning, medical imaging, and computer vision applications. Wang has established significant collaborative networks with researchers including Yan Zhou, Liang Sun, Xichao Su, and Lei Wang across multiple Chinese institutions. Wang's research interests center on the intersection of robotics, artificial intelligence, and practical engineering applications. Their work demonstrates particular expertise in UAV coordination systems, medical diagnostic technologies, and underwater imaging solutions. Recent publications reveal a growing emphasis on explainable AI systems for medical applications and safety-critical trajectory planning for autonomous vehicles. The publication trends show a consistent output of high-impact research, with recent work increasingly focusing on practical implementations of AI systems in medical diagnostics, autonomous vehicle navigation, and aerospace applications. Wang's research bridges theoretical control systems with real-world engineering challenges, particularly in safety-critical domains requiring precise motion planning and reliable decision-making. Wang has contributed significantly to both theoretical frameworks in optimal control and practical implementations in medical imaging and autonomous systems. Their work on UAV cooperative task assignment and flight deck operations demonstrates strong connections to aerospace engineering applications, while medical imaging research shows interdisciplinary collaboration with healthcare professionals.

• Robust Control Systems
• Multi-Agent Robotics
• Machine Learning
• +۳ مورد دیگر

Kunal Garg is an Assistant Professor in the Mechanical and Aerospace Engineering program at Arizona State University's School of Engineering for Matter, Transport and Energy. He holds a PhD and Master of Engineering in Aerospace Engineering from the University of Michigan (2021, 2019). Prior to joining ASU, he was a Postdoctoral Associate at MIT's Laboratory for Information & Decision Systems (LIDS) and Department of Aeronautics and Astronautics. His research focuses on integrating control theory with machine learning for autonomous systems, with emphasis on: Robust control synthesis for multi-agent coordination Finite/fixed-time control under spatiotemporal constraints Deadlock resolution in robotics using foundation models (LLMs/VLMs) Safety-critical control via barrier functions Recent publications demonstrate strong focus on neural control barrier functions, deadlock resolution for multi-robot systems, and failure prediction methods. His work frequently appears in top robotics and control conferences including ICRA, CDC, and TRO. Awards & Honors: 2022 DAAD AInet Fellow (AI and Robotics domain) Pierre T. Kabamba Award for Excellence in Control Systems (2021) Richard and Eleanor Towner Prize for Distinguished Academic Achievement (2021) He advises MS students through programs like ASU's MORE fellowship and teaches MAE 494/598: Design and Analysis of Nonlinear Controls. His research group develops open-source tools like the NeuralFaultDetector framework for model-free fault detection.

Douglas M. Bloughمشاهده پروفایل

استاد

• Wireless Networks
• Network Security
• Computer Systems Design
• +۵ مورد دیگر

Douglas M. Blough is a Professor at the Georgia Institute of Technology's School of Electrical and Computer Engineering, where he has served since 1999. He previously held faculty positions at the University of California at Irvine from 1988 to 1999. Dr. Blough directs the Critical Networking Laboratory and has served as the School's associate chair for faculty development since January 2018, including a five-month term as Interim Steve W. Chaddick School Chair in 2021. Dr. Blough's research focuses on wireless networks, distributed computer systems, and computer systems security. His specific interests include healthcare security, mobile and wireless communications, telecommunications, and computer systems and software. He has led 37 federally-funded or industry-sponsored research projects with over $8 million in funding, including current NSF grants for next-generation wireless networks exploring design challenges for networks operating in the millimeter-wave bands. His recent scholarly work demonstrates a strong emphasis on millimeter-wave communications, intelligent reflecting surfaces, and next-generation wireless LAN technologies. His publications reveal a progression from foundational work in dependable computing systems to cutting-edge research in wireless networking, with a particular focus on mmWave technology and intelligent surfaces in recent years. His research group has produced numerous award-winning papers, including multiple Best Paper Awards in 2021-2022. Professional Distinctions: Best Paper Award, IEEE International Symposium on Local and Metropolitan Area Networks, 2022 Best Paper Award, IEEE Consumer Communications and Networking Conference, 2021 Associate Editor, IEEE Transactions on Wireless Communications (2020-present) Associate Editor, IEEE Transactions on Cloud Computing (2020-present) Japan Society for the Promotion of Science (JSPS) Faculty Fellow, 1996 NASA/ASEE Faculty Fellow, 1993 Dr. Blough has advised numerous PhD students who now hold positions at leading technology companies including Microsoft, Google, Amazon, Intel, and Huawei. His research has resulted in over 160 archival publications and 10 patents related to wireless communications, bioinformatics, verifiable health records, and identity management. He has held significant leadership roles within his department, including chairing technical interest groups and serving on key committees for faculty development and honors.

• Robotics
• Artificial Intelligence
• Control Systems
• +۳ مورد دیگر

Ben Riviere is a Postdoctoral Scholar Research Associate in Mechanical and Civil Engineering at the California Institute of Technology (Caltech), part of the Division of Engineering and Applied Science. His research focuses on advanced robotics, artificial intelligence, and control systems, with a particular emphasis on multi-agent systems, motion planning, and optimization. He explores applications in aerospace systems, urban transportation, and distributed learning frameworks. Key research areas include Monte Carlo tree search algorithms, safety-certified motion planning for autonomous systems, and adaptive reinforcement learning for urban logistics. His work bridges theoretical advancements with practical implementations in robotics coordination, fault detection, and resource allocation. No scientific awards are explicitly mentioned in the provided information. Riviere has not listed any advisees or students, and grants or lab affiliations are not detailed in the text.