جست‌وجوی استاد و پژوهشگر

Enes Ayanمشاهده پروفایل

مدرس

• Artificial Intelligence
• Deep Learning
• Medical Image Analysis
• +۳ مورد دیگر

Enes Ayan serves as a Doctor Lecturer in the Department of Computer Engineering at Kirikkale University's Faculty of Engineering and Natural Sciences, where he has maintained continuous academic affiliation since his 2014 appointment as Research Assistant. His career trajectory includes completing both master's (2015) and doctoral degrees (2019) in Computer Engineering at the same institution following his 2013 bachelor's graduation from Süleyman Demirel University. Education Bachelor's: Computer Engineering, Süleyman Demirel University (Isparta), 2013 Master's: Computer Engineering, Kirikkale University, 2015 Doctorate: Computer Engineering, Kirikkale University, 2019 Research Focus Dr. Ayan's work centers on deep learning applications across three primary domains: medical imaging (specializing in dental diagnostics, radiology, and endoscopy), security systems (weapon detection and traffic monitoring), and agricultural technology (crop pest classification). His research bridges theoretical AI advancements with practical healthcare solutions, particularly through explainable AI frameworks for dental caries detection and pneumonia diagnosis. Publication Trends Analysis of his 15 most recent publications (2020-2025) reveals intensifying specialization in dental AI applications (60% of 2024-2025 output), with significant contributions to caries detection under prostheses and tooth numbering systems. Concurrently, his work maintains strong threads in security-focused computer vision (UAV-based traffic analysis, weapon detection) and agricultural AI, demonstrating methodological versatility through genetic algorithm optimizations and ensemble CNN architectures. Professional Context No information regarding student supervision, research grants, laboratory facilities, or scientific awards appears in the source materials. His academic progression from Research Assistant to Doctor Lecturer indicates standard career advancement within Kirikkale University's engineering faculty without notable interruptions.

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.

• Machine Learning
• Kernel Methods
• Model Selection
• +۱۱ مورد دیگر

Gavin Cawley is a Professor in the School of Computing Sciences at the University of East Anglia (UEA), with additional affiliations to the Data Science and AI group, the Centre for Ocean and Atmospheric Sciences, and the Statistics group. His research spans machine learning, bioinformatics, climate modeling, and environmental data analysis. His primary research interests include Machine Learning , Kernel Methods , Model Selection , Bayesian Regularization , Bioinformatics , and Climate Modeling . He has made significant contributions to understanding overfitting in model selection, sparse logistic regression for gene and cancer classification, and time series classification using ensemble methods. His work bridges theoretical machine learning with practical applications in biology, archaeology, and environmental science. The recent trend in his publications shows a strong interdisciplinary focus, combining machine learning with climate science (e.g., Arctic sea ice prediction) and molecular biology (e.g., protein domain movements). His work often involves developing and evaluating statistical models for complex real-world problems, emphasizing robustness, interpretability, and predictive accuracy. While no specific awards are listed in the provided text, his extensive publication record in top journals such as Journal of Machine Learning Research , Bioinformatics , and Neural Networks , along with high citation counts (e.g., over 1,800 citations for his 2010 paper on overfitting), indicates significant recognition in the academic community. He has also contributed to organizing major machine learning challenges, such as the ChaLearn AutoML and Active Learning challenges. He has supervised or collaborated with numerous researchers across disciplines, though specific student names are not listed. His work involves methodological development in model selection, kernel learning, and survival analysis, often applied to biological and environmental datasets. He has been involved in projects related to predictive uncertainty, ozone forecasting, and microbial growth modeling. While no specific lab or team name is mentioned, his affiliations with the Data Science and AI group and the Centre for Ocean and Atmospheric Sciences suggest active participation in interdisciplinary research teams focused on data-driven environmental and biological modeling.

• Computational Thinking
• Neural Networks
• Machine Learning
• +۶ مورد دیگر

Anthony V. Robins is a Professor in the Department of Computer Science at the University of Otago, New Zealand. His work spans neural networks , computational cognition , computer science education , and machine learning . With over three decades of publication history, Robins has contributed to understanding catastrophic forgetting , prototype analysis , and reinforcement learning in artificial neural networks. His recent research (2024-2025) focuses on Hopfield networks and Boltzmann neural networks , particularly their applications in state classification , supra-classical logic , and sequential learning . Collaborations with researchers like Hayden McAlister and Lech Szymanski highlight his work in memory systems and Hebbian learning . Robins has also made significant contributions to high school computing education in New Zealand, analyzing curriculum implementation , student perspectives , and teacher challenges through publications in venues like ACM Trans. Comput. Educ. and ACE. His work on self-regulated learning and educational frameworks addresses systemic issues in computing education.

• Machine Learning
• Automated Machine Learning (AutoML)
• Hyperparameter Optimization
• +۳ مورد دیگر

Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.

• Building Energy Systems
• Machine Learning Applications
• Model Predictive Control
• +۳ مورد دیگر

Martin Rätz is a researcher and team leader at the Institute for Energy Efficient Buildings and Indoor Climate of RWTH Aachen University , where he leads the Building Automation research team since January 2025. His work focuses on integrating advanced technologies like machine learning, IoT, and cloud computing into building automation systems to enhance energy efficiency and operational transparency. M.Sc. in Mechanical Engineering (Energy Engineering specialization), RWTH Aachen University Erasmus studies at Instituto Superior Técnico in Lisbon His research interests include: Model predictive control for building systems Energy-efficient control algorithms Machine learning applications in energy systems IoT integration for smart buildings Cloud computing in building automation Demand-controlled ventilation systems His recent publications demonstrate expertise in: Machine learning model validation Fault detection in district heating networks Energy weather data generation tools AI-driven building system optimization Dynamic ventilation strategies for healthcare settings Scientific honors include: Springorum Denkmünze for academic excellence (2018) Dean's List recognition (2018, 2019) As team leader, he directs projects such as: Monitoring 120 buildings in the OM4ABDO initiative Developing the BUDO standard for data point naming Advancing cloud-based building automation frameworks

Bruna G. Palmمشاهده پروفایل

مدرس

• Statistical Modeling
• Signal Processing
• Remote Sensing
• +۴ مورد دیگر

Bruna G. Palm is a Lecturer at the Department of Mathematics and Natural Sciences, Blekinge Institute of Technology (BTH), Sweden. She holds a BA (2014) and PhD (2020) in Statistics from Brazilian institutions and has held visiting and research fellow positions at BTH and the Aeronautics Institute of Technology (ITA). Education: BA in Statistics (2014), PhD in Statistics (2020) Current Role: Lecturer at BTH Collaborations: Saab AB, Sweden Her research bridges statistical modeling with remote sensing and signal processing , focusing on SAR image analysis , change detection , and machine learning applications. She has also contributed to environmental data forecasting and educational pedagogy in STEM. Key trends include Bayesian inference , autoregressive models , and Rayleigh distribution techniques for SAR imagery. Her work on resin composite masking extends into dentistry , highlighting interdisciplinary reach. She has no listed scientific awards but maintains active profiles on LinkedIn , Google Scholar , and ORCID .

Carlos Cardonhaمشاهده پروفایل

استادیار

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +۶ مورد دیگر

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

• Numerical Analysis
• Machine Learning
• Optimization
• +۳ مورد دیگر

Luca ZANNI is a Full Professor at the Department of Physical, Computer and Mathematical Sciences, Università di Modena e Reggio Emilia. His academic career spans numerical analysis, machine learning, and optimization methods, with a focus on gradient projection algorithms, stochastic gradient techniques, and their applications in imaging and deep learning. His research includes developing adaptive steplength rules for gradient methods, variance control in stochastic optimization, and GPU-accelerated reconstruction techniques for medical imaging. He teaches courses such as Numerical Calculation with Python, Fundamentals of Machine Learning, and Mathematical Methods for Machine Learning, covering topics like numerical optimization, support vector machines, and deep learning. He has contributed to methodologies for hyperparameter-free training, active set identification in constrained optimization, and edge-preserving image reconstruction. His work bridges theoretical advancements in optimization with practical implementations in large-scale machine learning and medical imaging applications.

• Reinforcement Learning
• Internet of Things (IoT)
• Cognitive Robotics
• +۴ مورد دیگر

Iñaki Vazquez Gomez is a Lecturer and Researcher at the University of Deusto , affiliated with the College of Engineering and Department of Computing, Electronics and Communication Technologies . He holds a PhD in Computer Science and Artificial Intelligence (University of Deusto and Lancaster University) and served as Director of Deusto Institute of Technology (2020-2023). His research focuses on Reinforcement Learning , Internet of Things , and Cognitive Robotics , with applications in human-robot interaction and ambient intelligence. Member of European Internet of Things Council Founder of technology startups Editorial board member of International Journal of Ambient Computing and Intelligence His 15 most recent publications (2005-2024) span themes of Autonomous Systems , Context-Aware Computing , and Smart Environments , with subtopics including semantic device communication, adaptive positioning algorithms, and collaborative robotics architectures. 2024: Adaptive Robot Behavior via Reinforcement Learning 2021: Gesture Recognition for Resource-Constrained Devices 2015: Context-Aware Service Matching in AAL Scientific honors include: X Premio UD-Banco Santander de Investigación (2015) Sexenio de Investigación (2018-2025) Profesor Doctor de Universidad Privada (2009-2025) He supervised PhD students like Asier Gonzalez Santocildes and Ignacio Fidalgo Astorquia, while leading grants such as AI-Driven Cognitive Robotics (2021-2024) and Quantum Technologies (2021-2022). His work includes developing middleware for smart spaces and founding the Sentient Things project for self-adapting environmental systems.

Andriy Miranskyyمشاهده پروفایل

دانشیار

• Applied Machine Learning
• Big Data
• Blockchain
• +۴ مورد دیگر

Dr. Andriy Miranskyy is an Associate Professor in the Department of Computer Science at Toronto Metropolitan University. His research focuses on applied machine learning, quantum computing, cloud computing, and software engineering, with notable contributions to anomaly detection in cloud systems and quantum software engineering. He leads the AMiR Lab, exploring risk mitigation and software development challenges in emerging technologies. Education: Ph.D. in Computer Science from The University of Western Ontario (2011). Research Interests: He investigates quantum software engineering methodologies, cloud-native systems governance, and big data applications. His work bridges theoretical advancements with industrial-scale implementations, such as IBM Db2 quantum safety case studies and cloud monitoring tools like CloudHeatMap. He emphasizes practical solutions for flakiness detection in quantum programs and sustainable software development practices. Awards: Recognitions include the Rogers Cybersecure Catalyst Fellowship (2023-2024), IBM CAS Best Project (2021), and a Guinness World Record for pioneering a 3Pb data warehouse (IBM DB2 team). Teaching: Teaches courses like CPS 840 (Quantum Computing), CPS 847 (Software Tools for Startups), and CPS 731 (Software Engineering). Labs/Teams: Directs the AMiR Lab, focusing on risk-aware software engineering in quantum and cloud domains.

Martial Hebertمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Autonomous Systems
• +۶ مورد دیگر

Martial Hebert is the Dean and University Professor of Robotics at Carnegie Mellon University's School of Computer Science (SCS), leading since August 2019. His career spans decades at CMU's Robotics Institute (RI), where he served as Director (2014-2019) and Professor (1999-present). Broad research interests in computer vision, perception for autonomous systems, and 3D environment modeling. Current PhD advisees include Zhipeng Bao, with numerous past PhD and Master's students listed. Editor-in-Chief of the International Journal of Computer Vision and member of IEEE Robotics and Automation Society. His research focuses on computer vision and robotics , emphasizing 3D data interpretation, object recognition, and machine learning applications. Recent articles highlight advancements in 3D vision , diffusion models , and disaster response robotics , reflecting a trajectory from foundational algorithms to applied autonomous systems. Notably, he pioneered the first master's program in computer vision in the U.S. Hebert's leadership in academic and research spheres includes directing the RI and securing an operating budget peak during his tenure. His work bridges perception, intelligence, and autonomous systems, with applications in disaster scenarios , LiDAR point cloud detection , and video forecasting .

Bruno Sinopoliمشاهده پروفایل

استاد

• Cyber-Physical Systems (CPS)
• Networked Control Systems
• Smart Infrastructures
• +۷ مورد دیگر

Bruno Sinopoli is a Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University, with courtesy appointments in the Robotics Institute and Mechanical Engineering Department. He holds affiliations with ICES, CyLab, and the Pittsburgh Digital Studio (PDL), and co-directs the Smart Infrastructure Institute (SII). His research focuses on cyber-physical systems, networked control systems, and resilient infrastructure, addressing challenges in smart grids, wireless sensor networks, and cybersecurity. Education: Ph.D. (2005), M.S. (2003), and Laurea (1998) in Electrical Engineering from UC Berkeley, Università di Padova, and University of California, Berkeley, respectively. Notable awards include the George Tallman Ladd Research Award (2010), NSF Career Award (2010), and Eliahu Jury Award (2006). Research Interests include networked embedded control systems, cybersecurity for critical infrastructure, and smart city technologies. He has led projects funded by NSF, NIST, and industry, such as a $3.2M NIST grant for first responder communication systems and NSF CRISP grants for infrastructure resilience. Key contributions include frameworks for secure control systems, resilient sensor networks, and adaptive video streaming algorithms. His work bridges theoretical control systems with practical applications in energy, transportation, and urban infrastructure. Students supervised include doctoral candidates like Dragana Bajovic (A.G. Milnes Award winner) and Yilin Mo, as well as master’s students in localization and control systems. Active collaborations span robotics, mechanical engineering, and interdisciplinary initiatives in smart infrastructure.

Kislaya Raviمشاهده پروفایل

پژوهشگر

• Multifidelity Uncertainty Quantification
• Gaussian Process
• Sparse Grids Methods
• +۲ مورد دیگر

Kislaya Ravi is a Doctoral Candidate at the Technical University of Munich (TUM), affiliated with the Chair of Scientific Computing within the TUM School of Computation, Information and Technology. She holds an M.Sc. in Computational Science and Engineering from TUM and degrees in Mechanical Engineering from the Indian Institute of Technology (BHU). Her research focuses on multifidelity uncertainty quantification, Gaussian processes, sparse grids methods, machine learning, and stochastic optimization. Education: M.Sc., Computational Science and Engineering, Technical University of Munich (202X) M.Tech., Machine Design, Indian Institute of Technology (BHU) (20XX) B.Tech., Mechanical Engineering, Indian Institute of Technology (BHU) (20XX) Research Interests: Kislaya’s work bridges computational methods and stochastic systems, emphasizing efficient uncertainty quantification techniques in physics and engineering. She explores multifidelity approaches to enhance computational efficiency in complex simulations and data-driven modeling. Teaching and Advising: Core instructor for Scientific Computing II and Algorithms for Uncertainty Quantification . Supervised multiple Master’s and Bachelor’s theses on topics like black-box optimization, surrogate modeling, and plasma instability analysis. Publications highlight contributions to multi-fidelity Gaussian processes, No-U-Turn sampling, and kinetic modeling. Her work appears in journals like Machine Learning: Science and Technology and conferences such as SIAM CSE and MCQMC.

Gavin Taylorمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۵ مورد دیگر

Gavin Taylor is a Professor of Computer Science at the United States Naval Academy (USNA), where he chairs the development of the new Data Science major. His research focuses on Machine Learning, including neural network vulnerabilities, reinforcement learning for IoT devices, and adversarial machine learning. He is currently on sabbatical as a Visiting Professor at the Norwegian OpenAI Lab (NTNU), collaborating with institutions like the University of Maryland and NTNU. Professor Taylor has been recognized with the 2022 Class of 1951 Civilian Faculty Teaching Excellence Award, emphasizing his commitment to education. He teaches courses such as SD312: Machine Learning and has developed curricula across Data Science, Cyber Operations, and Operations Research. His sabbatical work in Norway involves advancing AI research through interdisciplinary collaborations. His research trends emphasize practical AI applications—such as autonomous IoT systems—and ethical concerns like adversarial attacks and data poisoning. He actively seeks partnerships to engage students in real-world data science projects through the new major, scheduled to graduate its first class in 2025.