جست‌وجوی استاد و پژوهشگر

Janusz A. Starzykمشاهده پروفایل

استاد

• Machine Learning
• Neural Networks
• Robotics
• +۷ مورد دیگر

Janusz A. Starzyk is a Professor of Electrical Engineering and Computer Science at Ohio University's Russ College of Engineering and Technology, and holds a concurrent professorship at the University of Information Technology and Management in Rzeszów, Poland. He earned his M.Sc. and Ph.D. in Electrical Engineering from Warsaw University of Technology and a habilitation from Silesian University of Technology. His research focuses on embodied intelligence, machine learning, neural networks, and VLSI design, with notable contributions to self-organizing systems and reinforcement learning. Starzyk has supervised 47 M.Sc. and 16 Ph.D. students, and his work spans over 190 peer-reviewed publications. He leads the Embodied Intelligence Lab at Ohio University and collaborates with institutions worldwide, including Nanyang Technological University and the Institute for Artificial Intelligence in Switzerland. His awards include the Best Research Paper Award from Ohio University and nominations for IEEE Fellow. He has secured $3.8 million in research grants, with projects in GPS signal processing, radar target recognition, and machine learning applications. His patents include innovations in object identification systems and self-organizing learning hardware. Starzyk's expertise bridges academia and industry, with roles as a consultant for companies like Magnolia Broadband and Sarnoff Research.

• Cybersecurity
• Deep Learning
• Blockchain
• +۷ مورد دیگر

Yuansong (John) Qiao is a Research Fellow at the Software Research Institute (SRI), Technological University of the Shannon: Midlands Midwest. His work spans cybersecurity, blockchain, and reinforcement learning, contributing to technologies like decentralized edge environments, intrusion detection systems, and immersive 360° video analytics. He supervises PhD students and actively explores applications in robotics, digital twins, and IoT. Recent publications highlight his focus on multi-view deep learning for anomaly detection , blockchain integration with AI , and ROS-based reinforcement learning . These works address challenges in cyber threat identification, decentralized finance, and smart manufacturing. His methodological innovations include knowledge graphs for security analytics and spatial audio analysis for VR environments. Qiao’s research aligns with UN Sustainable Development Goals, particularly in Quality Education (through supervised student projects) and Industry Innovation (via tools like Containerchain and UniROS). His collaborations span cybersecurity frameworks, decentralized robotics, and privacy-preserving systems for smart cities.

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Marco Bagatella is a doctoral researcher at the Institute of Machine Learning (ETH Zürich), focusing on advanced machine learning and artificial intelligence research. His work spans reinforcement learning, behavioral cloning, and causal inference, often addressing challenges in generalization, exploration, and policy adaptation. Contact: marco.bagatella@inf.ethz.ch Specializes in Reinforcement Learning (including offline and multi-task settings) Expertise in causal modeling and counterfactual data augmentation Investigates graph neural networks for biological systems Active contributor to AI/ML publications (15+ recent works) His research explores problem space transformations, optimal transport for zero-shot imitation learning, and temporal logic-based exploration. Current projects focus on improving policy robustness and adaptability across diverse domains. Scientific awards: No formal honors listed yet. Collaborates with ETH Zurich's machine learning teams on cutting-edge algorithm development and theoretical analysis.

• Network Science
• Applied Dynamical Systems
• Mathematical Biology
• +۴ مورد دیگر

Joel Nishimura is an Associate Professor at Arizona State University's School of Mathematical and Natural Sciences. His research spans several interconnected fields including network science, dynamical systems, and mathematical biology. He focuses on how simple rules can create complex behaviors and structures, with applications in biological systems, network algorithms, and engineered protocols. Dr. Nishimura received his Ph.D. from Cornell University in 2013. His dissertation explored decentralized coordination and how actors with limited information can coordinate their timing. His research interests include Network Science, Applied Dynamical Systems, Mathematical Biology, Mathematical Modeling, and Game Theory. A unifying feature of his work is examining how simple rules can create complex behaviors and structures. In biological settings, these simple models capture key qualitative features of observed behaviors, suggesting generalizable principles. In network settings, simple rules form the basis for predictive algorithms, while in engineered settings, they serve as backbones for robust decentralized protocols. Analysis of Dr. Nishimura's recent publications reveals a strong focus on network science and its applications across multiple domains. His work spans theoretical foundations of graph theory and network models, applications to social and biological systems, and connections to decision-making processes and game theory. A notable trend is his consistent exploration of how simple rules or interactions can produce complex emergent phenomena, whether in network structures, biological behaviors, or decision-making processes. Dr. Nishimura has supervised several undergraduate and graduate students, as indicated by the asterisks and other symbols in his publication record. His teaching includes courses such as Applied Linear Algebra (MAT 343), Individualized Instruction (MAT 499), Honors Directed Study (MAT 492), and Honors Thesis (MAT 493). His research has been supported by various grants, though specific details are not provided in the available information. His GitHub repository for the "Double Edge Swap MCMC Graph Sampler" suggests involvement in computational tools development for network science research.

• Robot Learning under Uncertainty
• Reward Inference from Human Input
• AI Safety
• +۵ مورد دیگر

Daniel S. Brown is an Assistant Professor at the University of Utah’s Robotics Center and School of Computing. His research focuses on robot learning under uncertainty, reward inference from human input, and AI safety. He completed his PhD in Computer Science at UT Austin (2020) under Prof. Scott Niekum and was a postdoc at UC Berkeley working with Anca Dragan and Ken Goldberg. He leads the Aligned, Robust, and Interactive Autonomy (ARIA) Lab, which explores human-robot collaboration, safe AI systems, and emergent swarm behaviors. His work emphasizes practical methods for ensuring robustness and safety in autonomous systems. Education: PhD in CS (UT Austin, 2020) Research interests include: Bayesian methods for reward learning, interactive imitation learning, and scalable human-swarm interaction. His NIH-funded projects integrate AI with assistive technologies like powered neck exoskeletons. Key publications focus on Bayesian inverse reinforcement learning, demonstration sufficiency, and robust policy optimization. His NIH Trailblazer Award (2023) supports adaptive control systems for medical exoskeletons. Awards: RSS Pioneer (2021), NeurIPS Workshop Best Paper (2020) Teaching includes courses like 'Human-AI Alignment' and collaborative work on interdisciplinary projects combining robotics with game design and healthcare.

Kai Liuمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Data Mining
• +۴ مورد دیگر

Kai Liu is an Assistant Professor in the Computer Science Division at Clemson University . He holds a Ph.D. in Computer Science from the Colorado School of Mines (adviser: Dr. Hua Wang). His research focuses on the intersection of Machine Learning and Optimization , particularly in designing robust, efficient, and scalable algorithms for data mining applications. Key areas include non-negative matrix factorization, tensor completion, and healthcare analytics such as Alzheimer's disease analysis. Teaching: Liu has taught advanced courses including Optimization for Machine Learning , Advanced Machine Learning , and Applied Data Science at Clemson University. He also contributed to undergraduate courses like Discrete Structures . Research Contributions: His work emphasizes theoretical guarantees for algorithms, with applications in bioinformatics (e.g., HIV-1 drug resistance prediction), computer vision, and healthcare. Notable methods include spherical principal component analysis and accelerated tensor completion techniques. Professional Service: Liu serves on program committees for top conferences including ICML, NeurIPS, AAAI, and CVPR. He is a recognized expert in first-order optimization methods and high-dimensional statistics. Personal Interests: Beyond academia, Liu engages in traditional Chinese culture (calligraphy, painting, poetry), enjoys hiking, and is an avid reader of biographies. He is also a marathon runner and long-time supporter of Real Madrid.

Mei Siمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Virtual Reality
• Serious Games
• +۷ مورد دیگر

Mei Si is an Associate Professor in the Department of Cognitive Science at Rensselaer Polytechnic Institute (RPI) and serves as the Graduate Program Director for Critical Game Design. She specializes in artificial intelligence applications for virtual and mixed realities, including interactive narratives, embodied conversational agents, and serious games. Her work includes the award-winning Tactical Language Training System for military language/culture education, and VR-based therapies for children with ADHD. Si’s research bridges computer science, cognitive science, and education, focusing on immersive environments for learning and behavioral change. Education: No formal education details explicitly stated in text. Research Interests: Dr. Si’s research emphasizes interactive narrative systems, AI-driven conversational agents, and VR/AR applications in education and health. She explores how user interaction enhances learning efficacy, particularly through serious games and virtual interventions. Recent projects include mindfulness therapy apps, drug discovery games (CureQuest), and emotion-aware AI systems. She investigates human-AI interaction, including theory of mind modeling and ethical alignment frameworks. Publications: Over 50 peer-reviewed articles since 2011. Recent trends include quantum reinforcement learning, LLM applications in education, VR gaming therapies, and cepstrum-based audio modeling. Her work spans interdisciplinary domains like health informatics, robotics, and cybersecurity. Awards: Military-funded Tactical Language Training System recognized as award-winning, though specific awards not named. Her contributions to game-based medical education and interactive storytelling have garnered field-wide acclaim. Grants & Advising: Lead developer of U.S. military-funded language training systems. Collaborates on NIH-funded HIV prevention games (e.g., SOLVE project). Supervises projects in quantum-ML, mindfulness apps, and cross-cultural education tools. Active in conference organizing (e.g., AIIDE, ICIDS). Labs/Teams: Leads RPI teams developing immersive learning platforms and health-tech solutions. Co-develops CureQuest and SpeakEasy language training tools. Engages in multidisciplinary collaborations spanning engineering, psychology, and public health.

Marcello Restelliمشاهده پروفایل

استاد

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Marcello Restelli is a Professor at the Department of Electronics, Information, and Bioengineering (DEIB) of Politecnico di Milano, affiliated with the School of Industrial and Information Engineering. His research focuses on Reinforcement Learning (RL), Machine Learning, Robotics, and Control Systems. He explores challenges in real-world RL applications, multi-agent systems, and optimization under uncertainty. Key contributions include developing scalable algorithms for sparse-reward environments, POMDPs, and safe robotics. He leads projects like the EU-funded I3LUNG initiative, integrating AI for personalized lung cancer care. His work bridges theory and practice, addressing topics such as exploration strategies, regret minimization, and causal feature selection. He has contributed to frameworks like Gym4ReaL for benchmarking real-world RL systems and Storehouse for warehouse optimization. Recent efforts emphasize energy systems, financial decision-making, and explainability in extreme event prediction. His publications highlight advancements in inverse RL, online learning, and transfer learning. Collaborations span academia and industry, reflecting his impact on both theoretical and applied AI domains.

• Robotics
• Resilient Robotic Autonomy
• Robot Control
• +۵ مورد دیگر

Prof. Konstantinos Alexis is a Professor in the Department of Engineering Cybernetics at the Norwegian University of Science and Technology (NTNU). His research focuses on resilient robotic autonomy, emphasizing robot control, path planning, and Simultaneous Localization and Mapping (SLAM). He leads major international grants from agencies like DARPA, NSF, and the Norwegian Research Council, advancing autonomous systems for extreme environments such as subterranean exploration and search-and-rescue operations. His work integrates advanced control strategies with field robotics, demonstrated through projects like the DARPA Subterranean Challenge. Key contributions include robust model predictive control for UAVs, contact-based inspection planning, and graph-based exploration path planning. Alexis has organized workshops on aerial robotics at top conferences like ICRA and ICUAS, and his team develops open-source tools for robotics autonomy. Award-winning publications include the IET 2014 Premium Award and multiple best-paper recognitions. His research extends to solar-powered UAVs, thermal-inertial localization, and multi-modal sensor fusion for degraded environments. The Autonomous Robots Lab at NTNU is central to his research activities.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Lennard Hilgendorf is a Researcher at the Department of Computer Science , University of Copenhagen . His work focuses on Machine Learning with applications in quantum computing, medical imaging, natural language processing, and environmental sustainability. Research Trends: His recent publications highlight interdisciplinary work at the intersection of quantum mechanics and machine learning, efficient AI architectures for environmental sustainability, explainable models for medical diagnostics, and multimodal approaches to ecological monitoring. Key keywords include Machine Learning , Quantum Computing , Medical AI , and Environmental Science . Labs & Collaborations: Affiliated with the SCIENCE AI Centre and the TreeSense Centre , which specialize in foundational machine learning research and remote sensing for global tree resources, respectively.

Alexander G. Ororbia IIمشاهده پروفایل

استادیار

• Predictive Processing
• Active Inference
• Spiking Neural Networks
• +۷ مورد دیگر

Alexander G. Ororbia II is an Assistant Professor in the Department of Computer Science at Rochester Institute of Technology (RIT), where he directs the Neural Adaptive Computing (NAC) Laboratory. He is also an affiliate professor in the RIT Department of Psychology, an affiliate faculty member of RIT's Center for Applied Neuroscience, and affiliate faculty in RIT's Cognitive Science program. Ororbia received his Ph.D. in Information Science & Technology from Penn State University and a B.S.E. in Computer Science & Engineering from Bucknell University. His research focuses on developing computational models of neural information processing and synaptic plasticity, with significant contributions to predictive coding, active inference, spiking neural networks, neural-based cognitive modeling, and neurorobotic control. As Director of the NAC Laboratory, he leads research on biomimetic intelligence and brain-inspired computing, specifically developing neurocognitively plausible approaches to adaptation and memory formation in artificial neural systems. His publication trends show a strong focus on brain-inspired computing, with particular emphasis on predictive coding frameworks, active inference models, and spiking neural networks. Recent work has advanced the fields of lifelong machine learning, biomimetic intelligence, and backpropagation alternatives, with applications in robotics, computer vision, and cognitive modeling. His research bridges machine learning, computational neuroscience, and cognitive science to create more robust and adaptive artificial intelligence systems. IGERT Fellow NACME Sloan Scholar Jordan-Rednor Scholarship Bunton-Waller Scholarship Ororbia advises numerous Ph.D. and M.Sc. students working on projects related to spiking neural networks, predictive coding, active inference, and lifelong machine learning. His lab has secured research funding that supports multiple graduate students and postdoctoral researchers. The NAC Laboratory collaborates with institutions including Penn State University and has produced research published in top-tier venues including Nature Communications, Science Advances, NeurIPS, and ICRA. The NAC Laboratory, under Ororbia's direction, focuses on neurocognitively-inspired lifelong machine learning, developing novel learning algorithms and memory architectures for artificial neural systems. The lab explores both non-spiking and spiking neural networks, with applications in robotics, computer vision, and cognitive modeling. Current research directions include predictive processing implementations of cognitive architectures, mortal computation frameworks, and biologically plausible alternatives to backpropagation.

• Machine Learning
• Data Mining
• Graph Neural Networks
• +۵ مورد دیگر

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

• Deep Learning
• Reinforcement Learning
• Robotics
• +۷ مورد دیگر

Glen Berseth is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute. He holds the prestigious Canada CIFAR AI Chair position and co-directs the Robotics and Embodied AI Lab (REAL). Previously, he was a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR) working with Sergey Levine. Dr. Berseth's research focuses on solving sequential decision-making problems for real-world autonomous learning systems, with particular emphasis on human-robot collaboration, reinforcement learning, and various advanced learning paradigms including continual, meta, multi-agent, and hierarchical learning. His work bridges theoretical machine learning with practical robotics applications, aiming to create more capable and adaptable robotic systems. He has published extensively in top venues for robotics, machine learning, and computer animation. His recent publications demonstrate a strong focus on exploration strategies, representation learning, and efficient reinforcement learning algorithms. These works address critical challenges in scaling RL to real-world applications, improving generalization across different robot morphologies, and enabling autonomous learning without extensive human supervision. His research has practical implications for robotics, power grid control, and other complex systems requiring adaptive decision-making. Canada CIFAR AI Chair Dr. Berseth mentors a large group of graduate students across multiple institutions, supervising PhD and Master's candidates working on diverse aspects of robot learning and reinforcement learning. His lab receives significant research funding through his CIFAR AI Chair position and likely other grants supporting his robotics and AI research. The Robotics and Embodied AI Lab (REAL) he co-directs serves as a hub for interdisciplinary research at the intersection of machine learning and physical systems, fostering collaboration between computer scientists, roboticists, and domain experts.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Pan Xu is a tenure-track Assistant Professor at Duke University with joint appointments in the Department of Biostatistics & Bioinformatics , Department of Computer Science , and Department of Electrical & Computer Engineering . He earned his Ph.D. in Computer Science from the University of California, Los Angeles (2021) and completed postdoctoral training at the California Institute of Technology (2021-2022) . Research Focus: Machine Learning, Reinforcement Learning, Optimization, and High-Dimensional Statistics with applications in Bioinformatics and Healthcare. Key Contributions: Developing algorithms for robust sequential decision-making under uncertainty, improving Thompson Sampling efficiency, and advancing multi-agent reinforcement learning. Scientific Recognition: NSF award for exploration in decision-making (2023) Whitehead Scholar award (2023) PIMCO Postdoctoral Fellowship (2022) Best Paper Award at ACM FAccT (2023) Teaching: Offers graduate courses in machine learning and decision-making frameworks. Service: Serves as Area Chair for ICML, NeurIPS, and AAAI; Action Editor for TMLR.

• Artificial Intelligence
• Machine Learning
• Game Theory
• +۳ مورد دیگر

Marko Tot is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on artificial intelligence, machine learning, and game theory, with notable work in game AI, Monte Carlo methods, and human-computer interaction. He explores topics such as trajectory optimization in 3D games, sparse reward environments, and player behavior prediction through camera analysis. His recent publications emphasize innovative approaches to reinforcement learning and exploration strategies, particularly in challenging environments with limited reward signals. While no scientific awards are listed, his contributions highlight advancements in game development and algorithmic efficiency. Advising and grant details are not provided in available texts.