Nicholas Bambos is the R. Weiland Professor in the School of Engineering at Stanford University, holding a joint appointment in the Department of Electrical Engineering and the Department of Management Science & Engineering. He served as the Fortinet Founders Department Chair of the Management Science & Engineering Department from 2016 to 2020. His academic career spans over three decades, with previous positions as an assistant professor (1989-1995) and tenured associate professor (1995-1996) at UCLA before joining Stanford in 1996. Prof. Bambos's primary research interests focus on the architecture and high-performance engineering of computer systems and networks, along with data analytics emphasizing medical and health-care applications. His work spans multiple domains including networking and the Internet, cloud computing, multimedia streaming, computer security, and digital health. Methodologically, his contributions extend to network control, online task scheduling, routing and distributed processing, and machine learning and artificial intelligence. His research has resulted in over 300 peer-reviewed publications that demonstrate a strong interdisciplinary approach, bridging theoretical computer science with practical healthcare applications. The trajectory of Prof. Bambos's recent publications reveals a strategic expansion from traditional networking and systems research into healthcare analytics, particularly opioid use prediction and digital health monitoring. His work increasingly integrates machine learning techniques with domain-specific medical knowledge, showing a clear evolution toward solving complex societal challenges through technological innovation. Many publications demonstrate collaborative work across engineering, medical, and data science disciplines, reflecting the growing importance of interdisciplinary research in addressing modern healthcare challenges. His significant scientific achievements have been recognized through numerous prestigious awards: R. Weiland Professorship in Engineering (2016-present) Eugene L. Grant Teaching Award (2014) IBM Faculty Award (2002) Cisco Systems Faculty Scholar (1999-2003) National Young Investigator Award from NSF (1992-1997) Prof. Bambos has graduated over 40 doctoral students who have gone on to leadership positions in academia, Silicon Valley industries, technology startups, finance, and venture capital. His research has been supported by significant funding, including a $30 million Stanford Networking Research Center which he directed from 1999 to 2005. Beyond traditional academic roles, he has served on various editorial boards, scientific committees, and as a consultant and co-founder of technology startups, demonstrating his commitment to translating academic research into real-world impact. He leads the Computer Systems Performance Engineering Lab (Perf-Lab) at Stanford, which comprises doctoral students and industry visitors engaged in various research projects. His lab serves as an interdisciplinary hub connecting theoretical computer science with practical applications in healthcare, energy, and networking domains. The lab's collaborative environment fosters innovation across traditional academic boundaries, reflecting Prof. Bambos's broader research philosophy of addressing complex problems through integrated, multi-disciplinary approaches.
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نمایش ۷۲۱-۷۳۵ از ۲٬۸۵۶ استاد راهنما
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Fredrik Jönssonمشاهده پروفایل
مدرس ارشد
- Information Theory
- Machine Learning
- Cyber Security
- +۳ مورد دیگر
Fredrik Jönsson is a Senior Lecturer at the Department of Computer Science , Kristianstad University , within the Faculty of Natural Science . His work bridges information theory , machine learning , and cryptography , focusing on applications like cryptanalysis using machine learning and privacy-preserving machine learning . He earned a PhD in Information Theory (2002) and an MSc in Computer Science and Engineering (1997), both from Lund University . His research spans coding theory , stream cipher cryptanalysis , and machine learning for cybersecurity . Research Trends : Recent publications highlight quasi-cyclic codes , finite field GF(17) applications , and CPLEX-based code optimization . Older work includes fast correlation attacks , convolutional codes , and linear code analysis , reflecting his foundational contributions to cryptographic security . Collaborative Projects : Active in projects like Inventering av förutsättningar inför en eventuell ansökan om tillstånd att utfärda examen på forskarnivå , aiming to enhance doctoral degree offerings at Kristianstad University. Teaching : Delivers courses in computer security and supervises bachelor theses in cybersecurity, with a broader background in computer science education .
- Algorithms and Computing Theory
- Computer Software and Software Engineering
- Cloud Computing
- +۶ مورد دیگر
Prof. Dr. Tolga Ovatman is a faculty member at the Department of Computer Engineering, Istanbul Technical University , where he has been serving as Head of Department since 2024. His academic career spans roles from Research Assistant (2004-2012) to Associate Professor (2019-2023) and full Professor (2023-present). He previously held administrative roles such as Vice Dean (2018-2022) and Deputy Head of Department (2016-2018). PhD in Computer Engineering (2005-2011) MS in Computer Engineering (2003-2005) BSc from Hacettepe University (1999-2003) His research focuses on model checking , replicated state machines , cloud computing , and object-oriented software . Recent work addresses computation offloading in 6G networks , collaborative text editing data structures , and energy-efficient environmental monitoring systems . Key projects led include Design of a Multiplexed State Machine Storage System for Edge Computing (2022-2024) and Microservice Compatible Symphony Infrastructure Research (2020). He has supervised numerous theses on topics ranging from collaborative text editing to AI applications in watershed management . Publications span IEEE Transactions , Springer , and conferences like CSCE and CLOSER .
- Data Mining
- Data Science
- Artificial Intelligence
- +۳ مورد دیگر
Mariam Adedoyin-Olowe is a Lecturer and Programme Leader for the MSc Artificial Intelligence at Birmingham City University . Her research focuses on Data Mining, Data Science, and Artificial Intelligence, particularly in analyzing social media data for event detection and decision-making support. Doctorate in Computing Science from Robert Gordon University First degree from University of Portsmouth Research Interests center on applying data mining techniques to Twitter data for tracking real-world events, sentiment analysis, and customer segmentation. Her work bridges computational methods with practical applications in politics, sports, and healthcare domains. Key Publications include studies on: Transformer-based transfer learning for cross-lingual news events AI-driven mental fatigue detection systems Temporal clustering methods for social media event resolution Deep learning approaches for sound classification Rule dynamics frameworks for political event detection Academic Affiliations include membership in BCU's Data Mining Group and Data Analytics & Artificial Intelligence Research Group, as well as former involvement with RGU's Data Science and Information Retrieval groups. She regularly reviews for international conferences and journals.
- Greenland
- Climate
- Climate change
- +۵ مورد دیگر
Sune Olander Rasmussen is an Associate Professor at the Niels Bohr Institute , University of Copenhagen , specializing in Ice, Climate and Geophysics . With a background in geophysics, mathematics, and complex systems dynamics, his work focuses on integrating large ice core datasets and developing tools for climate data analysis. Research Interests : Greenland ice core studies, climate change, Dansgaard-Oeschger events, volcanic eruption impacts on climate, firn seismic properties, and probabilistic age modeling. Scientific Contributions : Key developer of the GICC05 time scale, with significant publications in Climate of the Past , Nature Communications , and PNAS . Awards : Carlsberg Foundation Distinguished Associate Professor. Leadership Roles : Chair of the NEEM dating consortium (since 2008) and the INTIMATE research network (since 2011).
Dr. Suren Chilingaryan is a Data Processing and Software Performance Expert at the Institute for Process Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT), where he has been working since 2007. His expertise lies in developing high-performance computing solutions for scientific experiments, with a focus on data acquisition systems, parallel computing architectures, and optimization of image processing software for scientific applications. His educational background includes: PhD in Computer Science (2006) from the Institute for Informatics and Automation Problems of the National Academy of Science of the Republic of Armenia Diploma in Mechanics and Mathematics (2001) from Moscow State University, with thesis on neural networks Dr. Chilingaryan's research spans high-performance computing, parallel hardware architectures, and scientific data management. He specializes in developing efficient algorithms for data acquisition and control systems, particularly for particle physics experiments. His current activities include high-speed data acquisition and control, management of large data, and optimization of image processing software for parallel computing architectures. He has pioneered techniques for GPU-accelerated data processing that enable real-time analysis of massive datasets from experiments like KATRIN (neutrino mass measurement), PANDA, and ASEC (Aragats Space Environmental Center). His recent publications demonstrate a strong focus on neutrino physics, cosmic ray phenomena, and high-speed data acquisition systems. The integration of FPGA and GPU technologies in his research has enabled breakthroughs in real-time data processing for scientific experiments, particularly in synchrotron imaging and particle detection applications. His work bridges computer science and experimental physics, with particular emphasis on developing scalable infrastructure for managing and analyzing large scientific datasets. Dr. Chilingaryan leads development of key software infrastructure including: UFO (Ultra fast X-Ray imaging platform) for smart experiments using GPU computing ADEI (Advanced Data Extraction Infrastructure) for time-series data management High-throughput DAQ Platform for FPGA-based scientific experiments His technical leadership supports major international collaborations including KATRIN, PANDA, and ASEC, providing critical data management solutions that enable new types of scientific investigations across particle physics, astrophysics, and atmospheric science disciplines.
Paul Boniolمشاهده پروفایل
پژوهشگر
Paul Boniol is a researcher at Inria, affiliated with the VALDA project-team—a collaboration between Inria Paris, École Normale Supérieure, and CNRS. His work focuses on time series analytics, anomaly detection, and machine learning applications. Ph.D. in Computer Science and Applied Mathematics (University of Paris, EDF R&D) Visiting Ph.D. at University of Chicago Education: Grenoble INP ENSIMAG Engineering School Research interests span: Unsupervised anomaly detection in large time series Time series management systems Machine learning for predictive maintenance Graph-based time series analysis Explainable AI for temporal data Recent publications emphasize advancements in: Weakly supervised anomaly localization Graph embedding techniques Model selection frameworks Interactive visualization tools Smart meter data analysis Scientific recognition: Paul Caseau Thesis Prize 2022 Lambdamu Congress Research-Industry Prize 2022 BDA & INFORSID Ph.D. Prizes 2022
A.A.A. Qahtanمشاهده پروفایل
استادیار
Dr. A.A.A. Qahtan is an Assistant Professor in the Data Intensive Systems research group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His academic appointment focuses on advancing research and education in data-intensive computing with particular expertise in data stream mining, data cleaning, and explainability of machine learning techniques. Dr. Qahtan completed his PhD studies at KAUST (King Abdullah University of Science and Technology) under the supervision of Xiangliang Zhang and Soujin Wang. Prior to joining Utrecht University, he worked as a postdoc at QCRI (Qatar Computing Research Institute) where he developed pattern functional dependencies (PFDs) for data cleaning. His research spans several critical areas in data science: Data Stream Mining and Real-time Processing Data Cleaning and Quality Assessment Pattern Recognition and Functional Dependencies Outlier and Anomaly Detection Concept Drift Detection in Streaming Data Fairness in Machine Learning Systems Missing Data Imputation Techniques Dr. Qahtan's publication record demonstrates consistent contributions to top-tier venues including PVLDB, KDD, ICDE, and SIGMOD. His recent work shows a progression from foundational data cleaning techniques to advanced applications in categorical data analysis, fairness in AI, and cryptocurrency market analysis. His research bridges theoretical foundations with practical applications across multiple domains. Dr. Qahtan actively contributes to academic education at Utrecht University, teaching courses including Data Analytics, Data Science and Society, Data Wrangling and Data Analysis, and Databases across multiple academic years from 2019 to 2024.
Sampath Kannan is the Henry Salvatori Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. His research spans Algorithmic Fairness Combinatorial Algorithms Program Reliability Streaming Computation Computational Biology . His work focuses on theoretical computer science and its applications, particularly in ensuring correctness through program analysis, developing algorithms for massive data sets, and addressing bioinformatics challenges like evolutionary tree reconstruction. Recent publications highlight interdisciplinary efforts in oncology (2025) , game theory (2024) , and fairness in machine learning (2023) . Notable awards include Fellow of the AAAS (2019) Test of Time Award, Runtime Verification (2019) Penn India Research and Engagement Fund (2018) Fellow of the ACM (2013) ACM SIGACT Distinguished Service Award (2012) . He has also received advising recognitions like the Outstanding Faculty Advising Award (2005) and Ford Foundation 'Best Advisor' (2004). He has advised PhD students Eshwar Ram Arunachaleswaran Yiqiao Bao Anubhav Baweja and taught courses including CIS 677 - Advanced Topics in Algorithms & Complexity and GCB 537 - Advanced Computational Biology .
Benjamin Pierce is the Henry Salvatori Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research spans programming languages, formal verification, and sustainable computing, with a focus on practical applications in software reliability and security. He leads initiatives like Carbon Connect (NSF Expedition in Sustainable Computing) and serves on climate-focused committees such as Penn's Faculty Senate Select Committee on the Climate Emergency. Research Interests Pierce's work integrates theoretical and applied computer science, emphasizing: Programming Languages : Type systems, language-based security, and compiler verification Formal Methods : Computer-assisted verification, proof automation, and property-based testing Sustainability : Reducing computing's environmental impact through algorithmic efficiency and policy Recent Publications His 2023-2025 publications demonstrate a strong focus on enhancing software testing (e.g., Tyche for property-based testing), advancing formal verification tools (e.g., Coq deautomation), and pioneering sustainable computing frameworks. Climate-related research is a growing theme. Awards and Honors 2024: Distinguished Paper Award (ICSE) 2020: Best Paper Award (POPL) 2015: Most Influential Paper Award (ACM SIGPLAN) 2013: LICS Test of Time Award 2012: ACM Fellow Advising and Grants He advises 8 PhD students on topics ranging from type systems to verified compilation. Notable projects include: NSF-funded Carbon Connect expedition SHF grant for usable property-based testing Development of verification tools (VERSE, Unison) Professional Activities Serves on editorial boards for Journal of Functional Programming and Logical Methods in Computer Science , and organizes major conferences (PLDI, POPL, OOPSLA). Advocates for low-carbon virtual conferences.
Andrew J. Parkesمشاهده پروفایل
استادیار
- Optimization
- Operational Research
- Heuristics
- +۳ مورد دیگر
Andrew J. Parkes is an Assistant Professor (Lecturer) in Operational Research and Computer Science at the University of Nottingham , affiliated with the School of Computer Science and the COL research group (formerly ASAP). He has been a member of the LANCS Initiative's executive committee and co-organized multiple streams at international conferences like OR61, EURO 2019, and OR60. His research spans optimization techniques, heuristic methods, and applications in timetabling and financial modeling.
Thomas Eiter is a Full Professor at the Institute of Logic and Computation, Technical University of Vienna (TU Wien), where he serves as Head of Research Unit. He is a Full Member of the Division of Mathematics and Natural Sciences since 2022 and holds leadership roles within the university. His research focuses on knowledge representation and reasoning, computational logic, algorithms and complexity in AI, declarative problem solving, nonmonotonic logic programming and databases, and reasoning about actions and change. His work bridges theoretical foundations with practical applications in artificial intelligence, particularly in logic programming and knowledge-based systems. He has made significant contributions to Answer Set Programming (ASP), developing frameworks like DLV and HEX programs that enable sophisticated reasoning capabilities. His recent publications demonstrate a strong focus on stream reasoning (LARS framework), knowledge forgetting, modular reasoning systems, and the integration of logic programming with ontologies. His research shows consistent contributions to both theoretical foundations and practical implementations of AI systems over several decades. ACM Fellow (2020) Fellow of the European Association for AI (2006) Distinguished Paper Award of the 17th International Joint Conference on Artificial Intelligence (IJCAI, 2001) Prominent Paper Award of the Artificial Intelligence Journal (2013) Test of Time Award (10 years) of the International Conference on Logic Programming (2013) Eiter has led and participated in numerous research projects, both internationally funded (such as LogiCS@TUWien, Humane AI, AI4EU) and nationally funded (including projects like BILAI, TAIGER, and several FWF-funded initiatives). His research unit has received substantial support from European Commission programs (H2020) and Austrian funding agencies (FWF, FFG, WWTF). He is actively involved in the academic community as a member of the Austrian Academy of Sciences (ÖAW), Academia Europaea, and has served on the Executive Council of AAAI. His research unit maintains strong connections with international collaborators and has developed influential systems like the DLV answer set programming system.
Benjamin C. Pierce is the Henry Salvatori Professor of Computer and Information Science in the School of Engineering and Applied Science at the University of Pennsylvania. As a Fellow of the ACM, he has made significant contributions to programming language theory and formal methods. His academic leadership includes previous editorial roles as co-Editor in Chief of the Journal of Functional Programming and Managing Editor for Logical Methods in Computer Science. His research spans multiple interconnected domains in programming language theory, with particular emphasis on type systems and their applications to security and verification. Pierce's work bridges theoretical foundations with practical implementations, most notably through his development of the Unison file synchronization tool and contributions to the Clowdr virtual conference platform. His research interests form a cohesive trajectory from foundational type theory to applied security and verification techniques. Pierce's scholarly output shows consistent focus on property-based testing, type systems, and formal verification methods. His recent publications demonstrate evolving interests in differential privacy verification, synchronization technologies, and the practical challenges of implementing formal methods in real-world systems. The progression of his work reflects both theoretical depth and practical relevance to software development challenges. Fellow of the ACM Author of influential textbooks Types and Programming Languages and Software Foundations Lead designer of the Unison file synchronizer Co-developer of the Clowdr virtual conference platform Former editorial leadership for multiple prominent programming languages journals As an educator and mentor, Pierce has contributed to the Programming Languages Mentoring Workshop (PLMW) and has served on numerous conference program committees. His academic service extends to SIGPLAN leadership roles including SIGPLAN Vice Chair and Steering Committee membership. His textbook Software Foundations has become a standard resource for teaching formal methods and proof assistants.
- Algorithms
- Text Mining
- Computational Complexity
- +۷ مورد دیگر
Jie Wang is a Professor of Computer Science at the University of Massachusetts Lowell's R. Miner School of Computer and Information Sciences. He joined UMass Lowell in 2001 as a Full Professor and chaired the department for 9 years from 2007 to 2016. He serves as Director for China Partnership of the US-based Consortium for Mathematics and Its Applications (COMAP) since 2011. Prior to UMass Lowell, he was Assistant Professor and then Associate Professor of Computer Science at the University of North Carolina. Professor Wang's research spans multiple areas including text mining algorithms and systems, data modeling, combinatorial optimizations, network security, wireless sensor networks, and computational complexity theory. His work has evolved from theoretical foundations in computational complexity (1980s-early 2000s) to practical applications in data analysis, intelligent text automation, and AI systems. His recent publications focus on AI-Oracle machines, LLMs, text mining, document engineering, and network security. His research portfolio demonstrates a clear evolution from theoretical computer science to applied research with practical impact. The publications show increasing focus on AI, text mining, and document engineering in recent years, while maintaining foundations in algorithm design and network security. His work bridges theoretical computer science with real-world applications across multiple domains. Honorary Advisor (2013) - NeoUnion Hong Kong Education Science Culture Organization MHE Scholar (2012) - Ministry of Higher Education, China PMYR Award for Major New Initiatives (2010) - University of Massachusetts Lowell Teaching Excellence Award (2002) - University of Massachusetts Lowell Nominee of Board of Governors' Teaching Excellence Award (2000) - University of North Carolina Professor Wang has graduated 18 PhD students and is currently directing 5 PhD students. His research has been funded by the National Science Foundation, IBM, Intel, and other companies totaling approximately $4.8 million. He is active in professional service, including chairing conference program committees, serving as journal editors, and as editor-in-chief of a book series on mathematical and interdisciplinary modeling. His laboratory work focuses on text mining systems, network security applications, and computational models for practical problems.
Pavel Zemcikمشاهده پروفایل
استاد مهمان
- Computer Graphics
- Computer Vision
- Machine Vision
- +۷ مورد دیگر
Pavel Zemcik serves as a Visiting Professor in the Computational Engineering department at the School of Engineering Sciences, Lappeenranta University of Technology (LUT). His academic work spans multiple domains within computer science with a strong emphasis on visual computing technologies. Dr. Zemcik's research interests encompass a broad spectrum of visual computing disciplines, including computer graphics, computer vision, machine vision, and image processing. His work particularly focuses on light field rendering techniques, 3D display technologies, and advanced wavelet transform applications. He has made significant contributions to GPU acceleration methods for real-time visual processing and has explored applications in both industrial settings and medical imaging. Analysis of his recent publication trends reveals a concentrated research trajectory in light field technologies and 3D display systems over the past five years. His work demonstrates increasing sophistication in handling visual quality metrics, focus management, and compression techniques specifically tailored for 3D displays. The research shows strong interdisciplinary connections between computer graphics, signal processing, and human perception studies. His scholarly output demonstrates consistent productivity across multiple high-impact venues in computer graphics and computer vision. While specific grant information isn't detailed in the available materials, his publication pattern suggests sustained research funding supporting his work in visual computing technologies.
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