جست‌وجوی استاد و پژوهشگر

Santu Karmakerمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Natural Language Processing
• +۲ مورد دیگر

Santu Karmaker is an Assistant Professor at the University of Central Florida in the Department of Computer Science within the College of Engineering. His research focuses on democratizing AI and data science through advancements in natural language processing, information retrieval, and machine learning. He earned a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign and a Master of Science in Computer Science from Bangladesh University of Engineering and Technology. Ph.D. in Computer Science – University of Illinois Urbana-Champaign M.S. in Computer Science – Bangladesh University of Engineering and Technology Karmaker's research explores semantic similarity alignment, conversational data science, infrastructure as code defects, and embedded NLP applications. His work addresses zero-shot learning, Bangla language processing, and prompt taxonomy for LLMs. He has secured over $1.4 million in grants from the NSF, AFOSR, ARO, and USDA. Recent publications span premier venues like EMNLP, ACL, TMLR, and ACM Transactions on Intelligent Systems and Technology. He actively contributes to academic service as an action editor for ACL Rolling Review and communication/tutorial chairs for conferences. Current affiliations include the Laboratory for Information and Decision Systems (MIT) postdoctoral collaboration and UCF's embedded systems research initiatives.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۶ مورد دیگر

Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.

Marten van Dijkمشاهده پروفایل

استاد

• Computer Security
• Cryptography
• Secure Processor Design
• +۷ مورد دیگر

Dr. Marten van Dijk is a Full Professor in the Computer Security department at Vrije Universiteit Amsterdam (VU) since 2022 and a Group Leader for Computer Security at CWI since 2020. He also holds a Gratis Full Research Professor position at the University of Connecticut's ECE Department since 2020. Previously, he served as Associate and Full Professor at the University of Connecticut and held research roles at MIT CSAIL, RSA Laboratories, and Philips Research. PhD in Mathematics (1997, Eindhoven University of Technology) M.S. in Mathematics (Cum Laude, 1993) M.S. in Computer Science (Cum Laude, 1991) His research focuses on foundational computer security problems using cryptographic principles, including secure processor design, oblivious computation, and privacy-preserving machine learning. Notable contributions span Physical Unclonable Functions (PUFs), Aegis secure processor architecture, and oblivious RAM protocols. 15+ publications in 2023-2025 address topics like PUF cryptanalysis, differential privacy in federated learning, and Byzantine fault tolerance Key journals: IEEE Transactions on Computers, Journal of Cryptology, ACM CCS Conference Award highlights include: IEEE Fellow (2022) for secure processor design and encrypted computation IEEE Technical Achievement Award (2023) Intel Test of Time Award (2022) ACM CCS Best Paper (2013) A. Richard Newton Technical Impact Award (2015) His technical leadership spans hardware security (blu-ray error correction codes), cryptographic protocol design, and machine learning privacy frameworks. Current projects focus on secure processors with hardware-enforced isolation and differential privacy optimization.

• Bayesian Optimization
• Reinforcement Learning
• Artificial Intelligence Ethics
• +۱۲ مورد دیگر

Eduardo César Garrido Merchán is an Assistant Professor in the Intelligent Systems area at the Technological Research Institute of the Comillas Pontifical University. He is affiliated with both the Faculty of Economics and Business Administration, where he teaches Machine Learning, Statistics, and Quantitative Models, and with the Higher Technical School of Engineering - ICAI. His research bridges artificial intelligence, ethics, and societal impact. Education: PhD cum laude in Computer Science and Telecommunications Engineering, Autonomous University of Madrid Master's in Artificial Intelligence, Polytechnic University of Madrid (José Cuena Prize) Bachelor's in Computer Engineering (Honors), Pontifical University of Comillas His research interests lie at the intersection of Bayesian Optimization , Deep Reinforcement Learning , and AI Ethics . He investigates methods to make AI systems more inclusive, fair, and socially responsible, particularly in applications related to ESG finance, social inclusion, and human-AI interaction. He critically examines philosophical aspects of AI, such as machine consciousness and the limits of computational models of mind. His recent publications demonstrate a strong trend in applying advanced machine learning techniques—especially Bayesian optimization and deep reinforcement learning—to complex, real-world problems in finance and ethics. He integrates explainability and multi-objective optimization to address environmental, social, and governance challenges, while also publishing critical perspectives on the philosophical foundations of AI. Scientific Awards: José Cuena Prize for Master’s in Artificial Intelligence He supervises researchers interested in AI, ethics, and society, guiding them toward developing AI agents and policies that promote justice and equality. Though no grants are explicitly mentioned, his active publication record in high-impact journals suggests ongoing research support. He has participated in thesis tribunals and delivered invited lectures, including at the CIUTI Conference 2025 on AI in language mediation. He is actively involved in training and dissemination, having taught courses on Gaussian Processes and Bayesian Optimization and contributed opinion pieces on AI and humanism. His work reflects a commitment to both technical rigor and ethical responsibility in AI development.

Alberto Sorrentinoمشاهده پروفایل

دانشیار

• Numerical Analysis
• Inverse Problems
• Medical Imaging
• +۳ مورد دیگر

Alberto Sorrentino serves as an Associate Professor in the Department of Mathematics at the University of Genoa, actively teaching Mathematical Methods for Bioengineering, Numerical Calculation, and Reverse Problems courses for Mathematics and Bioengineering degree programs through the 2025-2026 academic year. His research specializes in Numerical Analysis and Inverse Problems , developing computational frameworks for Medical Imaging and Neuroscience applications. Key contributions include probabilistic source localization tools for M/EEG, validation methodologies for electrical source imaging, and Bayesian approaches to neuroimaging inverse problems under extreme conditions like space environments. Recent publications (2023-2024) reveal a consistent focus on uncertainty quantification in brain imaging, advanced EEG analysis pipelines for space medicine, and optimization of regularization techniques for MEG connectivity. His work bridges computational mathematics with biomedical challenges through interdisciplinary collaboration. Information regarding academic advising, research grants, laboratory facilities, or team structures is not documented in the available materials.

Juseong Leeمشاهده پروفایل

استادیار

• Predictive Maintenance
• Industrial Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Juseong Lee is an Assistant Professor at the Eindhoven University of Technology (TU/e), affiliated with the Department of Industrial Engineering and Innovation Sciences. His research focuses on integrating data-driven models and artificial intelligence into industrial processes, with a specific emphasis on predictive maintenance approaches for engineering systems. PhD in Aerospace Engineering from Delft University of Technology (2022) MSc and BSc in Aerospace Engineering from Korea Advanced Institute of Science and Technology (2018, 2016) His work employs methodologies such as digital twins, multi-objective optimization, and deep learning to enable real-time decision-making in business contexts. He aims to improve the reliability, efficiency, and sustainability of Industry 4.0 systems through his research. Recent publications highlight his expertise in predictive maintenance, with applications to aircraft systems, landing gear brakes, and turbofan engines. Key techniques include Gaussian process learning, adaptive sampling, deep reinforcement learning, and stochastic Petri nets. These studies often involve collaborations with researchers like Mihaela Mitici. Lee's research is supported by the European Union's Horizon 2020 program (Grant No. 769288). He teaches courses on quality and reliability engineering, maintenance optimization, and service logistics.

• Intelligent control
• Machine learning
• Cyber-physical systems
• +۵ مورد دیگر

Hao Xu serves as an Associate Professor at the University of Nevada, Reno, holding the Ralph E. and Rose A. Hoeper Professorship. His research laboratory operates from SEM Building, Room 337D, with contact via haoxu@unr.edu. His research spans critical domains in intelligent transportation systems: Intelligent control and machine learning for cyber-physical systems Networked control systems and unmanned aircraft applications Power control, smart grid integration, and wireless sensor networks Recent publications (2023-2025) demonstrate concentrated expertise in roadside LiDAR applications, developing algorithms for vehicle/pedestrian detection, trajectory prediction, and safety analysis under challenging conditions including snow and heavy traffic. His work integrates deep learning with optimization techniques to enhance data processing robustness, particularly for vulnerable road user protection and near-miss event quantification. While no specific scientific awards beyond his named professorship were documented, his research directly addresses critical transportation safety challenges through innovative sensor applications and data analytics. Information regarding student advising, research grants, and laboratory infrastructure details was not provided in available materials, though his publication output indicates active collaboration with transportation agencies on smart infrastructure development.

• Artificial Intelligence
• Reinforcement Learning
• Machine Learning
• +۶ مورد دیگر

Richard S. Sutton is a pioneering researcher and Professor of Computing Science at the University of Alberta, where he serves as Chief Scientific Advisor for the Alberta Machine Intelligence Institute (Amii). He is also a Canada CIFAR AI Chair, Senior Fellow at CIFAR, and Research Scientist at Keen Technologies. Sutton is widely recognized as one of the founders of reinforcement learning, a field in which he continues to lead globally. His work has profoundly shaped modern AI research and applications. Education: B.A., Psychology, Stanford University (1978) M.S., Computer Science, University of Massachusetts (1980) Ph.D., Computer Science, University of Massachusetts (1984) Sutton's research focuses on identifying computational principles underlying intelligence and goal-directed behavior. He emphasizes learning from experience and extending reinforcement learning to create empirically grounded approaches to knowledge representation based on prediction. His work bridges artificial and natural intelligence, exploring how systems can predict and influence the world through learning, perception, action, and cognition. Sutton's research has produced foundational contributions including temporal-difference learning theory, actor-critic algorithms, the Dyna architecture, and Horde architecture. His recent publications reveal a continued focus on reinforcement learning fundamentals while expanding into broader AI applications. The articles show strong emphasis on theoretical foundations, convergence analysis, and practical implementations of reinforcement learning algorithms. There's also evidence of applying these techniques to real-world problems like pandemic forecasting, suggesting growing interest in practical societal applications of his theoretical work. Scientific Awards: 2024 Turing Award (with Andrew Barto) Fellow of the Royal Society (2021) Fellow of the Royal Society of Canada (2016) CAIAC Lifetime Achievement Award (2018) Outstanding Achievement in Research Award, UMass Amherst (2013) Sutton has mentored approximately 60 early-career researchers, including notable figures like David Silver (lead researcher behind AlphaGo), Doina Precup (DeepMind Montreal), Adam White (Amii Fellow), and Cam Linke (CEO of Amii). His Reinforcement Learning & Artificial Intelligence Lab at the University of Alberta has become a global hub for RL research. Sutton is currently collaborating with John Carmack through Keen Technologies to accelerate AGI development, documented in part through 'The Alberta Plan' which outlines his vision for creating long-lived computational agents. Sutton founded the Reinforcement Learning & Artificial Intelligence (RLAI) Lab at the University of Alberta and co-authored the seminal textbook 'Reinforcement Learning: An Introduction' with Andrew Barto. His work has been cited over 130,000 times and featured in major publications including Science, The Economist, New York Times, and Wall Street Journal. He is also known for his 'Tea Time Talks' tradition at Amii, fostering new ideas among researchers and students.

• Theoretical Computer Science
• Algorithms
• Edge Estimation
• +۵ مورد دیگر

Holger Dell is a Lecturer in Theoretical Computer Science and Algorithms at the IT University of Copenhagen. His research focuses on computational complexity, graph theory, and algorithmic efficiency. Active in polynomial-time algorithms and oracle-based methods Contributions to edge estimation in hypergraphs and fairness in node embeddings Key collaborations with BARC (Basic Algorithms Research Copenhagen) project Research trends show expertise in causal modeling, finite field polynomial solving, and graph embedding techniques. Recent work emphasizes algorithmic fairness and abstract causal relationships. Participated in a major project funded by the Villum Foundation (2017-2024) as a collaborator.

KÜBRA UYARمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Medical Image Analysis
• +۲ مورد دیگر

KÜBRA UYAR is a Lecturer at Alanya Alaaddin Keykubat University's Rafet Kayış Faculty of Engineering, Department of Computer Engineering. She previously worked as a Research Assistant at Selçuk University's Faculty of Technology (2022-2023) and continues her academic contributions in image processing and artificial intelligence. PhD in Computer Engineering (2022), Selçuk University MSc in Computer Engineering (2017), Selçuk University BSc in Computer Engineering (2014), Melikşah University Double Major in Mathematics (2014), Melikşah University Her research focuses on Artificial Intelligence , Computer Vision , and Medical Image Analysis , with a strong emphasis on Machine Learning and Image Processing . Current work includes developing explainable AI models for retinopathy diagnosis and optimizing CNN architectures for biomedical applications. Recent publications highlight trends in Deep Learning for agriculture (chestnut classification), Medical Imaging (retinopathy, leukocyte detection), and Optimization (CNN hyperparameters, B-spline algorithms). Her work bridges Computer Science with Healthcare and Industrial Applications . Key projects include the Data-Intensive and Computer Vision Research Laboratory Infrastructure Project (2020-2021) and the Selçuk University Weather Monitoring System (2018-2020), both funded by higher education institutions.

Tomás Fernández Penaمشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Parallel and Distributed Systems
• Quantum Computing
• +۵ مورد دیگر

Tomás Fernández Pena is a Full Professor at the University of Santiago de Compostela (USC) and Senior Researcher at the Research Center in Intelligent Technologies (CiTIUS) . With a career spanning over three decades, he has held academic positions since 1990 and contributed extensively to High Performance Computing (HPC), Big Data, and emerging quantum computing fields. Ph.D. in Physics from USC (1994) Senior Member of IEEE Associate Editor for IEEE Transactions on Computers and IEEE Access Research Contributions : His work focuses on parallel systems architecture, cloud computing middleware, and quantum simulation optimization. He has pioneered methods for NUMA systems, LiDAR data processing, and Big Data applications in bioinformatics/cheminformatics. His recent articles show increasing emphasis on quantum computing frameworks and distributed quantum processing. Scientific Recognition : Holds four Spanish Ministry of Education six-year research excellence periods (sexenios de investigación) and has served as Principal Investigator in 3 public projects and co-investigator in 31 EU/Xunta de Galicia funded initiatives. Supervised 7 Ph.D. theses and published 43+ international journal papers. International Collaborations : Maintains academic connections through funded research stays at Loughborough University, University of Tennessee, and University of Illinois Urbana-Champaign. Active in IEEE and participates in global conferences like Euro-Par and CHEP.

Bracha Lauferمشاهده پروفایل

مدرس

• Acoustic Source Localization
• Speech Signal Processing
• Gaussian Processes
• +۳ مورد دیگر

Dr. Bracha Laufer is a senior lecturer at the School of Electrical Engineering , part of the Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University. Her research focuses on acoustic source localization, speech signal processing, and machine learning techniques for audio engineering. Her recent work explores conformal prediction and manifold-based approaches for robust source localization, deep learning architectures for sound source separation, and simplex geometry in multichannel signal analysis. These publications highlight interdisciplinary applications of machine learning and statistical methods in acoustics. Dr. Laufer's research integrates Bayesian inference , probabilistic graphical models , and uncertainty quantification to address challenges in adverse acoustic environments. She has contributed to advancements in multi-microphone speaker localization and speech inpainting .

Dragos Truscanمشاهده پروفایل

استاد

• Software Testing
• Model-Based Testing
• AI in Test Automation
• +۷ مورد دیگر

Prof. Dragos Truscan is a Professor of Computer Science at the Faculty of Natural Sciences and Engineering within Åbo Akademi University , focusing on Information Technology and Mission Critical Software Systems . Research interests include validation and verification , automated test generation using model-based technology and AI , test left-shifting , and runtime monitoring of industrial software systems Active in Security Requirements Engineering , DevOps , Deep Learning , and Software Component analysis Recent work explores: 2025 - Reinforcement learning for security testing and visual fault localization 2024 - Explainability in network attack detection and hyperparameter optimization 2023-2024 - Model-based requirements engineering in large collaborative projects Scientific Recognition Best Paper Award (2024) Best Paper Award (2013) Academic Activities Organizing member of IEEE International Conference on Software Services Engineering (2025) Hosted scholars like Victoria Zsók and Dr. Dumitru Iulian Nastac

• Developmental Biology
• Cell Biology
• Image Analysis
• +۷ مورد دیگر

Karsten Siller is an Associate Professor at the University of Virginia's School of Data Science. He previously served as an Assistant Professor in the Department of Biology (College of Arts & Sciences) and held the role of Associate Director for Research Computing User Services. His work bridges data science with biology, focusing on scalable, reproducible data workflows and computational methods. Ph.D., Biology , University of Tübingen, Germany Dipl. Biol., Biology , University of Tübingen, Germany His research interests include: Building reproducible and resilient, scalable data analysis workflows for diverse computing environments. Developing user-friendly, extensible interfaces for modular data pipelines with integrated visualization. Advancing open-source software and computational tools for biology and image analysis. Key trends in his publications show expertise in: Fluorescence Lifetime Imaging (FLIM) for cellular metabolism studies. High-performance computing (HPC) for data pipeline management. Mechanistic insights into cell division and neurodegenerative processes via imaging and computational modeling.

Maciej Hukمشاهده پروفایل

استادیار

• Contextual Systems
• Data Mining
• Artificial Neural Networks
• +۹ مورد دیگر

Maciej Huk is an Assistant Professor at Wroclaw University of Science and Technology, working in the Department of Applied Informatics within the Faculty of Information and Communication Technology. He has been affiliated with the university since 2007, previously working in the Department of Artificial Intelligence and Department of Security of Computer Systems before moving to his current department in 2016. His academic credentials include Ph.D. and D.Sc. degrees in Computer Science from Wroclaw University of Technology (2007, magna cum laude) and an M.Sc. Eng. degree in Computer Science (magna cum laude). Dr. Huk's research spans multiple areas of artificial intelligence and computer science, with particular emphasis on contextual systems, artificial neural networks, and machine learning. His work explores contextual neural networks, selective attention mechanisms, genetic algorithms, and ensembles of classifiers. He has developed several research software systems including H2O Snowflake for distributed training of contextual neural networks, CxNNS, GACS for context-sensitive text mining, and DYDO for educational applications. His research bridges theoretical computer science with practical applications in IoT, embedded systems, and robotics. His publication record shows consistent productivity with research spanning from 2006 to the present (2025), demonstrating evolving focus from foundational neural network research to more applied contextual systems. The publication trends indicate a strong specialization in contextual neural networks, with recent work expanding into biomedical applications (CRISPR-Cas9 analysis), educational technology (attendance systems), and energy optimization. His work consistently addresses the challenge of context representation and utilization in machine learning systems. Dr. Huk serves as an Academic Editor for PLOS ONE Journal and Evolving Systems Journal, and has reviewed over 130 papers since 2016 for journals registered in ISI Web of Science. He has been actively involved in the academic community as a program and organization committee member for numerous conferences including ACIIDS (2016-2021), ICCCI (2016-2020), and others. He has chaired special sessions on Intelligent and Contextual Systems at IEEE conferences. His professional activities extend beyond pure academia, including serving as Software Architect/Data Analyses Architect at Gigaset Communications since 2008, and organizing clinical research including the Phase 3 randomized multicenter clinical study NCT04952519 on amantadine treatment for COVID-19 patients. He is certified to organize clinical trials with human subjects through NIH NIAID Good Clinical Practices 2.0.