جست‌وجوی استاد و پژوهشگر

Lydia E. Kavrakiمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Computational Biomedicine
• +۱۱ مورد دیگر

Lydia E. Kavraki is the Noah Harding Professor of Computer Science and Professor of Bioengineering, Electrical and Computer Engineering, and Mechanical Engineering at Rice University . As Director of the Ken Kennedy Institute , she leads initiatives in AI, data science, and computing across seven schools and 27 departments. B.A., University of Crete (1989) Ph.D., Stanford University (1994) Her research bridges robotics , AI , and computational biomedicine . In robotics, she pioneered sampling-based motion planning (OMPL library) and recently developed microsecond-level planning frameworks. In biomedicine, her work focuses on peptide-HLA interactions , personalized immunotherapy , and drug metabolite prediction , with tools like APE-Gen and DINC. Recent publication trends highlight her interdisciplinary work: 2022 PNAS Nexus study on HLA charge interactions , 2020 Frontiers in Immunology work on TCR cross-reactivity , and 2017 structural modeling of immune checkpoints. Themes include structure-based drug discovery , AI-driven motion planning , and ethical AI considerations . Scientific honors include: IEEE Frances E. Allen Medal (2023) ACM-AAAI Allen Newell Award (2020) ACM Athena Lecturer (2017) Three National Academy memberships (NAS/NAE/NAM) NSF CAREER, Sloan Fellowship, Whitaker Investigator Kavraki actively mentors students and postdocs, with over 40 alumni in academia and industry. Her Ken Kennedy Institute leadership spans AI in Health Conference organization and industry partnerships .

Marta Fairénمشاهده پروفایل

استادیار

• Virtual Reality
• Collaborative Virtual Environments
• Haptic Interfaces
• +۳ مورد دیگر

Marta Fairén is an Assistant Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the ViRVIG (Visualització, Realitat Virtual i Interacció Gràfica) Research Group and serves as a senior researcher at the Virtual Reality Center of Barcelona . Her work focuses on advanced VR systems and software development platforms. Department of Computer Science (CS), UPC ViRVIG Research Group Virtual Reality Center of Barcelona Research Interests Virtual Reality software and interfaces with emphasis on collaborative environments Haptic device integration for tactile feedback Visualization of large-scale models in immersive settings Distributed systems for graphics applications GPU-accelerated algorithms for collision detection Educational technology in computer graphics and programming Research Trends The 15 most recent articles demonstrate ongoing work in VR training applications (2024), medical education (2023-2020), haptic rendering techniques (2016-2007), and educational software frameworks (2020-2006). Key themes include GPU optimization, surface detail modeling, and adaptive teaching tools. Scientific Awards Advising & Grants No formal student advisees are listed in the provided content, and grant details are not explicitly mentioned. Labs & Teams Active member of the ViRVIG Research Group and senior researcher at the Virtual Reality Center of Barcelona , focusing on collaborative VR environments and distributed graphics systems.

Fabio Patriziمشاهده پروفایل

دانشیار

• Formal Methods
• Knowledge Representation
• Machine Learning
• +۷ مورد دیگر

Fabio Patrizi is an Associate Professor in Computer Science at the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome. He serves as Coordinator of the Bachelor in Information Engineering program at the Latina site. His academic career includes significant service roles such as Editorial Board Member for the prestigious Artificial Intelligence Journal (AIJ) since 2023, Area Chair for ICSOC 2023, and co-chair for the KR 2025 track on Knowledge Representation & Reasoning and Planning & Scheduling. Patrizi's educational background led to National Scientific Habilitation for Full Professorship for sector SC 09/H1 (now GSD 09/IINF-05). His research spans Formal Methods, Knowledge Representation, Machine Learning, Reasoning about Action, Planning in AI, Service-oriented Computing, and Business Processes. His work focuses on theoretical, methodological, and practical aspects including Behavior and Service composition, Planning programs, MAS Verification, Reasoning About Actions, Infinite-plan synthesis, Data abstraction techniques, and Reinforcement Learning with non-Markovian Rewards. His publication record includes over 70 scientific papers in top-level international journals and conferences. Recent research trends show a strong focus on temporal reasoning in reinforcement learning, planning frameworks, process mining, and verification techniques. His work bridges theoretical foundations with practical applications in AI planning and verification. ICDT Test of Time Award on Automatic verification of data-centric business processes (2009) RMIT Visiting Researcher's Award (2011) ICAPS 2024 Outstanding SPC Award Patrizi has supervised 2 PhD students and actively serves on program committees for major AI conferences including AAAI, IJCAI, ICAPS, AAMAS, KR, and ECAI. His research projects include MARLeN (Principal Investigator, 2023-2025), AIPlan4EU (Task Leader, 2021-2023), DRAPE (Principal Investigator, 2019-2022), WhiteMech (Participant, 2019-2024), TAILOR (Participant, 2020-2023), VerySynCopated (Principal Investigator, 2014-2016), ACSI (Participant, 2010-2013), and SM4All (Participant, 2008-2011). He is a member of the Data Management & Service-Oriented Computing and Artificial Intelligence & Knowledge Representation research groups at DIAG, contributing to collaborative efforts in advanced AI research and applications.

Eleazar Lealمشاهده پروفایل

دانشیار

• Spatial Databases
• Stream Databases
• Database Algorithms
• +۳ مورد دیگر

Eleazar Leal serves as an Associate Professor in the Swenson College of Science and Engineering at the University of Minnesota Duluth, where he advances research and education in database systems and high-performance computing. His academic work bridges theoretical computer science with practical data-intensive applications. His research program focuses on optimizing database technologies for modern hardware architectures, with particular emphasis on: Spatial database systems for geographic information processing Real-time analytics in stream database environments Parallel algorithm design for multicore processors GPU acceleration techniques for database operations Scalable data mining methodologies Hardware-aware query optimization These interconnected research areas address critical challenges in big data processing, enabling efficient handling of complex spatial and temporal datasets through innovative hardware-software co-design approaches. Dr. Leal maintains active collaborations with industry partners to translate theoretical advances into practical database solutions, with ongoing work exploring the integration of machine learning techniques into next-generation database engines. Professional correspondence should be directed to eleal@d.umn.edu or via telephone at +1 218 726 8452, with appointments scheduled exclusively through email consultation.

• Artificial Intelligence
• Computer Science
• Genomics
• +۱۱ مورد دیگر

Nikolaos Alachiotis is an Associate Professor at the Digital Society Institute, specializing in Artificial Intelligence, Field-Programmable Gate Arrays (FPGA), and Genomics. His work bridges Hardware Acceleration with Bioinformatics , focusing on scalable solutions for genomic analysis and evolutionary biology. Research Themes: AI Hardware, FPGA Optimization, Population Genetics Key Collaborations: Delft University of Technology, Zenodo, Frontiers in High-Performance Computing Research Focus Alachiotis develops deep learning and FPGA-based tools for selective sweep detection and phylogenetic inference , enabling faster genomic analyses. His work spans convolutional neural networks , spiking networks , and adaptive computing for bioinformatics and radio astronomy. Recent articles highlight trends in AMD Versal SoC applications, deep learning for natural selection , and scalable genomic pipelines . Scientific Awards Outstanding Student Paper Award (2023) Supervised Work His supervised projects include Accelerating Selective Sweep Detection and Effective Data Preprocessing Techniques , often in collaboration with institutions like Zenodo and Frontiers journals.

Richard J. Boucherieمشاهده پروفایل

استاد

• Operations Research
• Probability
• Healthcare Logistics
• +۵ مورد دیگر

Richard J. Boucherie is a Full Professor at the University of Twente, affiliated with the Digital Society Institute, TechMed Centre, and the Department of Mathematics of Operations Research. His work integrates operations research, stochastic modeling, and queueing theory to optimize complex systems in healthcare, transportation, and logistics. Research Interests: Prof. Boucherie's expertise spans: Healthcare Logistics : Bed census forecasting, patient flow optimization, and clinical scheduling Stochastic Systems : Queueing networks, Markov decision processes, and distributionally robust optimization Applied AI : Reinforcement learning, data mining, and predictive analytics for service systems Publication Trends: His recent articles (2023-2025) demonstrate a strong focus on pandemic-responsive healthcare optimization, including dynamic bed allocation, laboratory workflow design, and fairness in resource distribution during crises. Methodologically, they advance stochastic programming, queueing theory, and Bayesian inference for clinical applications. Awards & Honors: Canadian OR Society Practice Prize (2012) ORTEC Excellence in Advanced Planning Award (2012) Academic Leadership: He has supervised 37 research projects and contributes to institutional initiatives through the TechMed Centre (health innovation) and Digital Society Institute (data-driven solutions).

Vincent Heuvelineمشاهده پروفایل

استاد

• Uncertainty Quantification
• Data Mining
• High-Performance Computing
• +۵ مورد دیگر

Prof. Dr. Vincent Heuveline is a distinguished academic and researcher currently serving as a Professor at Heidelberg University. He leads the "Engineering Mathematics and Computing Lab" (EMCL) at the Interdisciplinary Center for Scientific Computing and is also the Group Leader of the "Data Mining and Uncertainty Quantification" (DMQ) research group at HITS gGmbH. Additionally, he serves as the Director of the Computing Center at Heidelberg University, demonstrating his leadership in both academic research and institutional IT infrastructure. Vincent Heuveline was born in 1968 and pursued his education in Mathematics, Physics, and Computer Science at the Universities of Caen (France) and Würzburg (Germany). He earned his PhD in Computer Science in 1997 from the Université de Rennes and completed his habilitation in Mathematics at the University of Heidelberg in 2002. His academic career includes a professorship at Karlsruhe University (KIT) from 2004 until he moved to Heidelberg University in May 2013. Prof. Heuveline's research spans several cutting-edge areas in computational science and data analysis. His primary research interests include Uncertainty Quantification, Data Mining, High-Performance Computing, Hardware-aware Computing, Software Design for Scientific Computing, Optimization and Optimal Control for Large Systems, Cloud Computing, and IT Infrastructure. His work bridges theoretical computer science with practical applications in medical diagnostics, fluid dynamics, and metabolic modeling. An analysis of his recent publications reveals a strong interdisciplinary focus, particularly at the intersection of artificial intelligence, medical diagnostics, and computational science. He has made significant contributions to newborn screening technologies using machine learning, cardiac MRI analysis with AI enhancement, metabolic modeling for infants, and high-performance computing frameworks. His research demonstrates a consistent pattern of applying advanced computational methods to solve complex problems in healthcare and scientific domains. Prof. Heuveline leads two major research initiatives: the Engineering Mathematics and Computing Lab (EMCL) at the Interdisciplinary Center for Scientific Computing and the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. These labs bring together multidisciplinary teams to tackle challenges in scientific computing, uncertainty quantification, and data-driven research. His leadership extends to the institutional level as Director of Heidelberg University's Computing Center, where he oversees critical IT infrastructure supporting academic research.

• Data Mining
• Big Data
• Fuzzy Systems
• +۹ مورد دیگر

Carlos Jesús Fernández Basso is a Post-Doc Researcher at the Universidad de Granada in the Department of Computer Science. His work focuses on the intersection of Big Data, Data Mining, and Fuzzy Systems, with applications in energy efficiency, social network analysis, and medical diagnostics. His research interests include: Energy-efficient building management using IoT and Big Data Distributed frequent itemset mining and association rule discovery Fuzzy logic for data interpretability in complex systems Text mining and pattern detection in social media Recent publications highlight his contributions to Spark-based distributed algorithms, predictive control systems, and visualization of fuzzy association rules. He collaborates with researchers across disciplines, including applications in healthcare and cybersecurity.

Saday Sadayappanمشاهده پروفایل

استاد

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +۳ مورد دیگر

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.

• Infrastructure construction
• Optimization of soil stabilization processes
• Environmental aspects of construction investments
• +۳ مورد دیگر

Piotr Srokosz is a professor at the University of Warmia and Mazury in Olsztyn, affiliated with the Department of Civil Engineering and Geoengineering. His scientific discipline focuses on Civil Engineering and Transport, with a specialization in geotechnical and environmental engineering applications. Research involves soil mechanics and stabilization Develops advanced measurement systems for civil infrastructure Applies computational intelligence to engineering challenges Recent publications demonstrate expertise in integrating MEMS sensors, neural networks, and hybrid modeling approaches for soil behavior prediction and structural monitoring. His scientific work spans lunar soil simulant studies, eco-friendly stabilization techniques with chitosan, and optimization of geotechnical testing apparatuses. Key research trends include: Computational modeling of soil dynamics Application of AI/ML in civil engineering Development of contactless measurement systems Environmental impact assessment of construction The department maintains modern geotechnical infrastructure including modified resonant column devices and IoT-enabled sensor networks for advanced soil testing and structural safety monitoring.

Leonidas Akritidisمشاهده پروفایل

مدرس

• Natural Language Processing
• Deep Machine Learning
• Data Mining
• +۵ مورد دیگر

Leonidas Akritidis serves as a Lecturer at the Department of Science and Technology within the School of Science and Technology at International Hellenic University. He holds a PhD and Diploma in Electrical and Computer Engineering from University of Thessaly and Aristotle University of Thessaloniki respectively. His educational background includes: PhD in Electrical and Computer Engineering, University of Thessaly (2007-2013) Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1997-2003) Akritidis specializes in Natural Language Processing, Deep Machine Learning, and Data Mining with particular expertise in short text clustering, dimensionality reduction, and rank aggregation techniques. His research addresses challenges in sparse data environments and high-dimensional feature spaces, developing novel algorithms for text representation and processing. He has contributed significantly to parallel and distributed computing approaches for big data analytics. His publication trends reveal a consistent focus on NLP and machine learning applications, with recent work expanding into generative models for tabular data, medical image analysis, and advanced fault detection systems. The research demonstrates progression from foundational text processing techniques to more complex applications in healthcare, e-commerce, and software engineering. Akritidis has taught multiple courses including Big Data and Cloud Computing, Machine Learning Principles and Concepts, Mobile Application Development, and Web Programming. His research projects include CyberPi (intelligent cyber threat detection), NANOTRIM (transistor sizing optimization), and iMuSe (virtual museum platform). He collaborates extensively with researchers like Panayiotis Bozanis, Miltiadis Alamaniotis, and Athanasios Fevgas, primarily within the Greek academic community.

• Machine Learning
• Parallel and Distributed Computing
• Graph Theory
• +۶ مورد دیگر

Giuseppe Agapito is an Associate Professor and senior research scientist at Magna Graecia University of Catanzaro, working within the Department of Law, Economics and Sociology. His academic profile spans both teaching responsibilities and active research in computational fields. His research focuses on computational intelligence techniques applied to complex real-world problems, particularly in machine learning, parallel and distributed computing, and graph theory. His work demonstrates strong interdisciplinary applications, especially in biology and omics sciences where he develops methods to leverage the vast amounts of data being produced. Agapito has published over 100 papers in international journals and conference proceedings. His recent publications show a strong focus on graph-based machine learning approaches, sentiment analysis techniques, and bioinformatics applications. His research trajectory indicates a progression from bioinformatics applications toward more general machine learning frameworks with applications across multiple domains. As an active member of the academic community, he serves as a reviewer for scientific journals and participates as chair and program committee member for national and international conferences. His editorial work for PeerJ Computer Science demonstrates his standing in the academic community. His teaching responsibilities include courses on Computer Science and Data Analysis within the Department of Law, Economics and Sociology, suggesting an interdisciplinary approach that applies computational methods to social sciences domains.

• Data Analysis
• Recommendation System
• Responsible AI
• +۶ مورد دیگر

Dr. Angela Huo is an Associate Professor at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She holds multiple leadership roles including Deputy Director of Transnational Education for the Faculty of Engineering and IT, Responsible Academic Officer for Teaching and Learning of the School of Computer Science, Course Director for the Bachelor of Computing Science (Honours) program, and site coordinator for the South Pacific Programming Contests region of the International Collegiate Programming Contest (ICPC). Dr. Huo's research spans several key areas in computer science, with a primary focus on Data Analysis, Recommendation Systems, and Responsible AI. She specializes in utilizing AI and data analysis techniques to improve the efficiency and security of modern software systems. Her recent research interests include recommendation systems, anomaly detection, and privacy-preserving through data mining and deep learning. She has made significant contributions across multiple domains including multimodal learning, federated learning, causal inference, and human behavior recognition. Dr. Huo's publication record demonstrates a strong trajectory in cutting-edge AI research, with recent work focusing on conditional diffusion models for trajectory prediction, hybrid recommendation systems, causal learning to mitigate echo chambers, and adaptive waste detection. Her research shows a consistent pattern of addressing real-world challenges through innovative AI approaches, often combining multiple techniques to achieve superior results. Dr. Huo has secured research funding for projects including "Churn prediction scoping study" and "Army sovereign Software-Defined Battlefield Network." She has been actively involved in teaching and curriculum development, having created Industrial sub-majors for Google, AWS and iOS, Information Security sub-major, Data Analytics for Cybersecurity Microcredentials, and Cybersecurity and Privacy major since 2019. She has taught programming and cybersecurity management courses at the undergraduate level and served as HDR Coordinator of the School of Computer Science during 2021-2022.

Myra Spiliopoulouمشاهده پروفایل

استاد

• Data Mining
• Knowledge Discovery
• Machine Learning
• +۴ مورد دیگر

Myra Spiliopoulou is a Professor of Business Information Systems at the Faculty of Computer Science, Otto-von-Guericke-University Magdeburg since February 2003, leading the Knowledge Management and Data Mining (KMD) working group. Her research bridges theoretical data mining with practical healthcare and business applications. Education: 1982-1986: Mathematics, University of Athens, Greece 1988-1994: PhD Student & Research Assistant, Department of Informatics, University of Athens 1992: PhD in Parallel Query Optimization, University of Athens 2000: Habilitation in Business Information Systems, Humboldt-University Berlin Research Focus: Prof. Spiliopoulou specializes in temporal pattern mining , ethical AI , and healthcare data analytics , with notable contributions to epidemic surveillance systems. Her work integrates machine learning with domain-specific knowledge for real-world decision support. She has delivered authoritative tutorials at premier conferences including KDD (2015, 2018, 2019), IEEE ICDM (2017), ECML/PKDD (2014, 2016), and AIME (2022, 2023), establishing her as a leading educator in data science methodologies. Contact: myra@iti.cs.uni-magdeburg.de

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +۸ مورد دیگر

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.