جست‌وجوی استاد و پژوهشگر

• Reinforcement Learning
• Computational Decision-Making
• Artificial Intelligence
• +۵ مورد دیگر

Carlo D'Eramo is Professor and Head of the Reinforcement Learning and Computational Decision-Making professorship at the Center for Artificial Intelligence and Data Science (CAIDAS) of University of Würzburg. He additionally serves as an independent group leader of hessian.AI, focusing on developing lightweight methods to obtain adaptive autonomous agents that can handle real-world complexity. His academic journey includes: B.Sc. in Computer Engineering from Politecnico di Milano (2011) M.Sc. in Computer Engineering from Politecnico di Milano (2015) Double degree in Computer Science from University of Illinois at Chicago (2015) Ph.D. in Information Technology from Politecnico di Milano (2019) Postdoctoral research at TU Darmstadt's Intelligent Autonomous Systems group (2019-2022) D'Eramo leads the LiteRL research group investigating how agents can efficiently acquire expert skills accounting for real-world complexity. His research spans multiple reinforcement learning domains including multi-task, curriculum, adversarial, options, and multi-agent RL. His work bridges theoretical advances with practical applications, particularly in robotics and decision-making systems. His recent publications (2023-2025) demonstrate significant contributions across exploration strategies, neural network architectures, multi-agent coordination, and physics-informed machine learning. His work frequently appears in top-tier venues including TMLR, RLJ, IEEE PAMI, ICML, and ICLR, with multiple papers receiving spotlight or oral presentation designations. Professional activities include: Senior area chair for RLC Area chair for AAAI, ACML, AISTATS, NeurIPS, and ICLR Reviewer for DFG and ERC proposals Creator of MushroomRL reinforcement learning framework D'Eramo actively contributes to academic community service while mentoring researchers in his group. His work on lightweight methods aims to make reinforcement learning more practical for real-world applications across various domains.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

Paul Millerمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuroscience
• Dynamical Systems
• +۵ مورد دیگر

Paul Miller is a Professor of Biology at Brandeis University and a member of the Volen National Center for Complex Systems. His research focuses on computational neuroscience, integrating dynamical systems theory with experimental data to understand neural mechanisms underlying decision-making, memory, and sensory processing. He holds a Ph.D. from the University of Bristol (UK) and a B.A. from Cambridge University. Before joining Brandeis in 2000, he was at Georgetown University and conducted postdoctoral work in theoretical physics at Oak Ridge National Laboratory. Education: B.A., Cambridge University (1991) Ph.D., University of Bristol (1994) Research Interests: Computational Neuroscience emphasizes modeling neural circuits for short-term/long-term memory, decision-making processes, and the dynamics of neural ensemble activity. His work bridges theoretical approaches (e.g., hidden Markov modeling, spiking neuron networks) with experimental data from cortical and hippocampal systems. He explores how quasistable attractor states enable robust computation and how homeostatic mechanisms stabilize neural activity. Notable Contributions: Developed models explaining how integral feedback control enables robust sequential decision-making, elucidated synaptic mechanisms for spatiotemporal discrimination, and analyzed the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in long-term memory stability. His textbook An Introductory Course in Computational Neuroscience (MIT Press) is widely used in graduate programs. Labs/Teams: Leads the Computational Neuroscience Lab at Brandeis, collaborating with experimentalists to validate theoretical models. Active in interdisciplinary projects involving neurophysiology, systems biology, and mathematical neuroscience.

Hugo Larochelleمشاهده پروفایل

دانشیار

• Neural Networks
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Hugo Larochelle is an Associate Professor at the Université de Montréal's Department of Computer Science and Operations Research (DIRO) within the Faculty of Arts and Sciences. His expertise spans Neural Networks, Deep Learning, and Machine Learning, with contributions to fields like Computer Vision and Natural Language Processing. A graduate of DIRO (Bacc 2004, PhD 2009), he held roles at the University of Sherbrooke (2011–2016) and co-founded Whetlab (acquired by Twitter in 2015). Currently at Google Brain, he balances academic and industrial research, leading projects like the UNIQUE initiative exploring neuroscience-AI intersections. He actively contributes to Quebec’s AI ecosystem through MILA and has received prestigious awards, including the 2019 Diplômé d'honneur. His work emphasizes ethical AI, reproducibility, and interdisciplinary applications. Education: Baccalauréat (Computer Science), Université de Montréal, 2004 Doctorat (Computer Science), Université de Montréal, 2009 Research Interests: Hugo’s work focuses on advancing deep learning techniques for real-world applications, including environmental monitoring (e.g., tree crown segmentation via drone imagery), AI ethics, and model unlearning. He explores intersections between neuroscience and AI, leveraging interdisciplinary approaches to solve complex problems. Recent efforts emphasize benchmark standardization (e.g., EEVEE/GATE) and improving model efficiency through architecture innovations like SoftMoE. Articles Trends: Recent publications highlight contributions to instance segmentation (SAM models), ethical AI (unlearning frameworks), and interdisciplinary applications (e.g., bird species modeling with remote sensing). His work bridges theoretical advancements with practical tools for industries and environmental science. Scientific Awards: 2019 Diplômé d'honneur (Université de Montréal). Advising & Grants: Supervised PhD students in topics like neural networks, code modeling, and program execution (e.g., Sara Hooker, David Bieber). Co-PI on the UNIQUE project (2019–2024, FRQNT-funded), exploring neuro-AI synergies. Recipient of CIFAR funding for ICRA research (2017–2022). Labs & Collaborations: Active contributor to MILA as an associate member. Leads interdisciplinary efforts in AI for environmental challenges and healthcare imaging optimization.

Kenneth Marinoمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Kenneth Marino is a Research Scientist at DeepMind , set to join the University of Utah as an Assistant Professor at the Kahlert School of Computing in Fall 2025. He earned his PhD in Machine Learning from Carnegie Mellon University (funded by NDSEG and NSF GRFP fellowships) and completed his undergraduate studies in Computer Engineering with a minor in Computer Science at Georgia Tech . Dr. Marino's research focuses on the intersection of Computer Vision , Natural Language Processing , and Reinforcement Learning , with emphasis on: Multimodal agents operating on the web, in simulation, and in real-world environments Evaluating AI systems and creating high-impact datasets (e.g., A-OKVQA , OK-VQA ) Incorporating semantic knowledge into end-to-end learning frameworks Embodied reasoning through language-guided planners and neural reporters Language model distillation for agent supervision Human-AI collaboration to improve RL generalization His work has been published in top venues including ICML , NeurIPS , ECCV , CVPR , and ICLR , with recent trends emphasizing: Relational reasoning in LLMs Object-centric world modeling Continual learning for embodied agents Reporter neural networks for agent control Legal AI benchmarking (e.g., BriefMe ) Knowledge graph integration for visual classification Scientific recognition includes: NDSEG Fellowship NSF GRFP Fellowship He has served on the MLD PhD Admissions Committee and as an Area Chair for ECCV 2024 , with prior teaching experience at Columbia University (COMS 6998) and Carnegie Mellon University (16-824, 10-401).

Paulo Rauberمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Paulo Rauber is a Lecturer in Artificial Intelligence at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He holds a PhD from the joint program between University of Campinas (Brazil) and University of Groningen (Netherlands), and previously conducted postdoctoral research at the Swiss AI Lab (IDSIA) under Jürgen Schmidhuber. Education: BSc in Computer Science, Federal University of Santa Catarina (2011) PhD in Computer Science, University of Campinas & University of Groningen (2017) Research: Focuses on Bayesian reinforcement learning methods addressing exploration, planning, and generalization. His work spans scalable algorithms for non-stationary environments and deep reinforcement learning applications. Teaching: Teaches postgraduate courses such as Neural Networks and Deep Learning, Artificial Intelligence in Games, and Data Mining. Grants & Projects: Secured a Swiss National Science Foundation grant (NEUSYM, ~$700,000) in 2019, and leads projects like Colosseum and Hindsight Policy Gradients. Affiliations: Member of the Centre for Multimodal AI and actively supervises PhD students in reinforcement learning and Bayesian methods.

Tobias Glasmachersمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization Algorithms
• +۶ مورد دیگر

Prof. Dr. Tobias Glasmachers is a Full Professor at the Institut für Neuroinformatik , Ruhr-Universität Bochum, Germany, specializing in the Theory of Machine Learning . He leads the Optimization of Adaptive Systems group and holds appointments in both Computer Science and Interdisciplinary AI research. Key Research Areas : Optimization algorithms, evolutionary computation, reinforcement learning, supervised learning, and neural networks Technical Focus : Gradient-based methods, support vector machines, and adaptive coordinate descent Applications : Robotics, waste sorting facilities, 3D game environments (e.g., Doom/Minecraft), and human-centered AI design Notable Contributions : Development of LM-MA-ES evolution strategy, Hessian Estimation Evolution Strategy, and tachAId tool for ethical AI design. His work bridges theoretical analysis with practical implementations across diverse domains. Teaching : Offers courses in Informatik 1 - Programmieren, Machine Learning: Supervised Methods, and Evolutionary Algorithms. Supervises numerous Bachelor's and Master's theses on AI/ML applications.

Jürgen Scheibleمشاهده پروفایل

استاد

• Electronic Design Automation
• Analog Integrated Circuit Design
• Automation of Circuit and Layout Design
• +۵ مورد دیگر

Jürgen Scheible is a Professor at the Faculty of Technology, Reutlingen University, specializing in Electronic Design Automation (EDA). He leads the Electronics & Drives Lab and the Reutlingen Energy Center (REZ), focusing on analog integrated circuit design automation, machine learning integration, and power electronics methodologies. His career spans academic and industrial roles, including 18 years at Robert Bosch GmbH before joining Reutlingen University in 2010. 1987: Dipl.-Ing. in Electrical Engineering, Karlsruhe Institute of Technology (KIT) 1991: Dr.-Ing. in Electrical Engineering, KIT His research bridges procedural automation with artificial intelligence in analog IC design, addressing challenges in performance optimization, constraint handling, and thermal analysis of bond wires. Key projects include SWARM (self-organizing layout automation) and Bond Calculator (thermoelectrical simulation tool). Recent publications emphasize differentiable surrogate models, reinforcement learning applications, and knowledge-based methodologies in EDA. He received the Südwestmetall Award for Scientific Talent in 1992 and has authored 2+ textbooks on analog layout fundamentals. 2024: Keynote on analog/digital design divergence 2023: Co-authored foundational EDA textbook 2022: Reinforcement learning integration Labs and teams under his leadership conduct cutting-edge research in MEMS sensor parasitics, hybrid layout techniques, and full-custom IC design. His work establishes systematic frameworks for next-generation EDA tools.

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

F. Russell is a Professor in the Department of Political Science at the University of California, Berkeley. His research bridges artificial intelligence, political theory, and public policy, focusing on ethical alignment, safety mechanisms, and governance frameworks for advanced AI systems. Institution: University of California, Berkeley College: College of Letters & Science Department: Political Science Russell's work addresses critical challenges in AI safety, including reward learning, deception mitigation, and policy design for autonomous systems. His recent publications explore theoretical foundations of AI alignment, observational uncertainty, and scalable solutions for assistance games. Selected trends from his publications include: Advancing AI safety through formal frameworks Understanding reinforcement learning dynamics Developing robust human-AI interaction models Addressing ethical and regulatory challenges Exploring cognitive modeling in AI systems Improving interpretability of language models

Ken M. L. Yiuمشاهده پروفایل

استاد

• Databases
• Query Processing
• Spatiotemporal Data
• +۷ مورد دیگر

Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.

Lisa Meedenمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Developmental Robotics
• +۳ مورد دیگر

Lisa Meeden is the Neil R. Grabois Professor of Natural Sciences and Engineering at Swarthmore College, where she teaches in the Computer Science Department and participates in the Cognitive Science Program . She holds a Ph.D. in Computer Science with a minor in Cognitive Science from Indiana University and a B.A. in Mathematics from Grinnell College. Her research focuses on artificial intelligence , robotics , and developmental systems , particularly exploring how robots can develop intelligence through self-organization and intrinsic motivation . She has designed educational tools like the Artificial Intelligence Toolkit (aitk) with Doug Blank and James Marshall, which provides accessible AI learning resources for browser-based experimentation. Her recent publications with students like Levin Ho and Morgan McErlean examine AI ethics , robot exploration mechanisms , and machine learning pedagogy . The fifteen most recent articles cover topics from evolutionary robotics to cognitive modeling , with specific subfields including neural network architectures , behavioral adaptation , and educational program design . She has received grants including the NSF CCLI EMD for robot laboratory development and the National Humanities Center grant (2022-2024) for Responsible Artificial Intelligence Curriculum Design . Her lab work with students involves adaptive robotics and self-organizing systems , including experiments with NEAT , GNG , and IAC frameworks for robot learning.

• Machine Learning
• Artificial Intelligence
• Quantum Computing
• +۴ مورد دیگر

Andreas Bjerregaard Jeppesen is a Research Fellow at the Department of Computer Science, University of Copenhagen, specializing in Machine Learning. His work bridges theoretical and applied research across diverse domains including quantum computing, biomedical informatics, environmental monitoring, and AI ethics. The Machine Learning section at DIKU explores foundational algorithms and their applications in Medical data analysis Remote sensing Biological modeling Sustainable AI Information retrieval . Andreas contributes to interdisciplinary projects like the SCIENCE AI Centre, leveraging the department's compute cluster for intensive simulations. His recent publications highlight trends in Quantum-inspired neural architectures Generative models for protein sequences Energy-aware AI development Neurological applications of ML Climate-conscious computing . Collaborations span computational biology, quantum chemistry, and federated learning for precision medicine.

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +۳ مورد دیگر

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

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.