جستجوی استاد راهنمای دانشگاهی

• Embedded Systems
• Fault Tolerance
• Precision Agriculture
• +۵ مورد دیگر

Maurizio Rebaudengo is a Full Professor at the Department of Control and Computer Science (DAUIN) of the Polytechnic University of Turin. He is a member of the PIC4SeR Interdepartmental Center for Service Robotics and holds expertise in embedded systems, fault tolerance, and sensor network applications. Research Interests: Embedded systems, fault tolerance, precision agriculture, RFID technology, wireless sensor networks, and system dependability Scientific Awards: Ramamoorthy Best Paper Award (1997) Leadership Roles: Program Chair for the 4th International EURASIP Workshop on RFID, Committee Member for Public Administration Agreements (2020-2024) Projects: Scientific Director for HONEY (Hybrid ONline tEchnologY for particle therapy), FDM (Food Digital Monitoring), IDEM (Internet of Data for Environmental Monitoring), and OPLON (Healthy Longevity Opportunities). Teaching: Course lecturer for Electronic Calculators, Computer Science, Systems Programming, and Cybersecurity at both undergraduate and postgraduate levels. His work spans IoT applications in agriculture and healthcare, focusing on low-cost sensor networks , RFID-based traceability , and cyber-physical system resilience . Recent publications emphasize particulate matter monitoring and secure communication protocols in wireless environments.

Dimitrios Soudrisمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Reconfigurable Architectures
• +۵ مورد دیگر

Dimitrios Soudris is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), leading the Microprocessor and Digital Systems Lab (MicroLab). Previously, he served as Lecturer, Assistant, and Associate Professor at Democritus University of Thrace from 1995 to 2008. Diploma in Electrical Engineering (1987), University of Patras PhD in Electrical Engineering (1992), University of Patras His research focuses on Embedded Systems , Reconfigurable Architectures (FPGAs) , Hardware Accelerators for data centers/space/cloud, Edge Computing , and Low Power VLSI Design . Recent publications highlight trends in: AI/ML acceleration for edge and space applications Secure FPGA architectures for 6G networks Energy-efficient heterogeneous memory systems Approximate computing techniques Transformer optimization for low-power contexts Scientific Awards: INTEL and IBM awards (project LPGD #25256) HiPEAC, DAC, and ISCA awards (2010–2024) XILINX Open Hardware Design Contest (2017, 2019, 2021) He has coordinated >70 R&D projects funded by the European Commission, ENIAC-JU, ESA, and industry partners. As Associate Editor of ACM TODAES and conference chair (PATMOS, VLSI-SOC), he contributes to academic leadership. His lab, MicroLab, specializes in hardware-software co-design for emerging computing paradigms.

• Cybersecurity
• Infocommunications
• Cloud Technologies
• +۵ مورد دیگر

Alexey Nikolaevich Nazarov is a Professor at the Department of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics (HSE), where he has worked since 2015. His academic and teaching experience spans 14 years, with a focus on cybersecurity, cloud technologies, and information confrontation in social media. Doctor of Technical Sciences (2000) Engineer-Mathematician, Moscow Geological Prospecting Institute (1980) UN and ITU-certified expert His research interests center on cybersecurity , information warfare , and mathematical modeling , particularly in IoT and social media contexts. Articles highlight trends in neural network intrusion detection , ferroelectric memory devices , and cloud resource optimization . He has supervised 5 postgraduate and 2 doctoral students, organized scientific seminars, and contributed to cloud technology curricula. His expertise includes international standards (ITU, VAK), Skolkovo grant #87 (2012), and leadership in cybersecurity research.

• Data Science
• Big Data
• Natural Language Query Processing
• +۴ مورد دیگر

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

• Physics
• Chemistry
• Machine Learning
• +۵ مورد دیگر

Giuseppe De Luca is a Research Fellow at the Department of Physics and Chemistry - Emilio Segrè, University of Palermo. His work spans interdisciplinary domains including theoretical physics, biophysics, machine learning, and social dynamics. Research Themes : Model averaging techniques, liquid-liquid phase separation, quantum complexity, holography, and social exclusion effects in education. Methodologies : Bayesian inference, multiscale analysis, and machine learning optimization. Recent publications focus on weighted-average least squares estimation, structural degradation in biomaterials, and virtual reality applications in psychological research. He contributes to the SHARE project with statistical expertise in survey weighting and imputation strategies.

Anastasiia Minenkovaمشاهده پروفایل

استادیار

• Applied Mathematics
• Numerical Analysis
• Perturbation Theory
• +۴ مورد دیگر

Anastasiia Minenkova serves as Assistant Professor in the Department of Mathematics within the College of Arts and Sciences at the University of Hartford, joining the institution in 2023. Her academic home provides a foundation for interdisciplinary research bridging theoretical mathematics and computational applications. Her educational journey spans international institutions: BS from Donetsk National University (Ukraine), MS from the University of Mississippi, and PhD from the University of Connecticut. This diverse training informs her integrated approach to mathematical research and education. Professor Minenkova's research centers on applied mathematics with dual emphases: theoretical investigation of numerical algorithm stability through perturbation theory, and practical applications of numerical linear algebra in machine learning and quantum computing. Her work develops robust computational frameworks for eigenvalue problems and quantum state modeling, addressing critical reliability challenges in emerging technologies. This research trajectory demonstrates increasing sophistication in connecting classical mathematical theorems with contemporary computational paradigms. Her recent publications reveal a strategic focus on cross-disciplinary validation – applying numerical analysis to verify quantum computing models while using machine learning contexts to stress-test algorithmic stability. Both papers exhibit methodological rigor in perturbation analysis with direct implications for error mitigation in computational systems. Professor Minenkova actively mentors undergraduate researchers, supervising projects that translate theoretical concepts into computational experiments. She integrates this mentorship with her commitment to inclusive pedagogy, designing accessible research pathways for students from diverse mathematical backgrounds. Her service contributions extend to departmental initiatives promoting equitable participation in advanced mathematics courses.

Fernando Magno Quintão Pereiraمشاهده پروفایل

دانشیار

• Compilers
• Code Generation
• Compiler Optimizations
• +۳ مورد دیگر

Fernando Magno Quintão Pereira is an Associate Professor at the Federal University of Minas Gerais (UFMG), Brazil, specializing in compiler design and program analysis. His academic journey began with a Ph.D. from UCLA in 2008 under Jens Palsberg's supervision, establishing his foundation in compiler research. His research focuses on compilers , with core expertise in code generation , compiler optimizations , and static program analyses . Recent work explores quantum compilation, binary analysis, and security-aware compilation techniques. His publications reveal consistent contributions to major conferences including PLDI, CGO, and SPLASH, with emphasis on practical optimization frameworks and theoretical compiler advancements. Analysis of his 15 most recent publications (2020-2026) shows dominant themes in binary optimization (e.g., AnghaBench), security-aware compilation (e.g., Memory-Safe Elimination of Side Channels), and emerging architecture support (e.g., Quantum Computing Compilation). His work bridges theoretical compiler principles with real-world systems challenges. He actively contributes to the academic community through: Program committees for PLDI (2020-2025), CGO (2021-2026), and SPLASH conferences Leadership roles including CGO Finance Chair (2026) and PLDI Diversity & Inclusion Co-Chair (2023-2024) Organizing JENSFEST 2024 and serving on multiple conference steering committees Pereira maintains an active research group evidenced by continuous publication output and conference leadership, with his personal website ( homepages.dcc.ufmg.br/~fernando/ ) serving as a hub for his academic activities.

• network security
• network measurement and analysis
• cloud computing
• +۴ مورد دیگر

Dr. Kuai Xu serves as a Professor of Computer Science at Arizona State University's School of Mathematical and Natural Sciences within the New College of Interdisciplinary Arts and Sciences. He maintains affiliations with the Center for Cybersecurity and Trusted Foundations, focusing on securing critical network infrastructures. His academic credentials include: Ph.D. in Computer Science, University of Minnesota (2006) M.S. in Computer Science, Peking University, China (2001) B.S. in Computer Science, Peking University, China (1998) Dr. Xu's research centers on network security , network measurement , and IoT systems , with significant contributions to smart home security, network behavior analysis, and social media epidemiology. His work bridges theoretical modeling and practical implementations for real-world network challenges. Recent publications (2022-2025) reveal a concentrated focus on IoT security frameworks, machine learning-driven activity inference from device traffic, and pandemic surveillance through social media analysis. Key themes include DNS security hardening, attack graph modeling, and privacy-preserving spatial analytics in smart environments. Through courses like ACO 331 (Network Forensics Analysis) and ACO 361 (Secure Coding Concepts), Dr. Xu mentors students in cybersecurity fundamentals while supervising research via ACO 399 and individualized instruction (ACO 499). His teaching integrates cutting-edge research into classroom applications. As an active affiliate of ASU's Center for Cybersecurity and Trusted Foundations, Dr. Xu contributes to interdisciplinary initiatives addressing emerging threats in home networks, IoT ecosystems, and social media platforms.

Fuyuki Ishikawaمشاهده پروفایل

دانشیار

• Software Engineering
• Testing
• Formal Methods
• +۴ مورد دیگر

Fuyuki Ishikawa is an Associate Professor at the Information Systems Architecture Science Research Division of the National Institute of Informatics (NII) in Tokyo, Japan, where he also serves as Director of the GRACE Center. Additionally, he holds positions as Associate Professor at Sokendai (The Graduate University for Advanced Studies) and Visiting Associate Professor at The University of Electro-Communications. His research program, which he describes as 'Trustworthy & Smart Software Engineering,' focuses on dependability in Cyber-Physical Systems and Machine Learning Systems through techniques of formal methods and automated test generation. He investigates verification, reasoning, optimization, automated test generation, and self-adaptation by utilizing various models for requirements, specifications, and designs. His group promotes international and industry-academia collaborations with members from different organizations. Ishikawa's recent work has increasingly concentrated on quantum program testing, autonomous driving systems verification, and quality assurance frameworks for AI systems. His publications demonstrate significant contributions to software engineering, particularly in applying formal methods to emerging domains like quantum computing and machine learning. His research often bridges theoretical foundations with practical applications in safety-critical contexts, particularly for autonomous systems. Awards for Science and Technology (Research Category) from the Minister of Education, Culture, Sports, Science and Technology (April 2024) IPSJ/IEEE Computer Society Young Researcher Award for 'Research on Intelligence-driven Engineering of Dependable Smart Systems' (March 2020) Multiple Best Paper Awards at conferences including ICFEM 2020, SEKE 2020, ICECCS 2019, and ISSRE 2019 Industry Track Best Artifact Award at ISSTA 2015 Ishikawa actively supervises research projects and interns through the ERATO-MMSD Project and MIRAI-eAI Project. He has served on numerous program committees for major software engineering conferences including ASE, ICSE, and ICST, and has organized workshops on machine learning systems engineering (SIG-MLSE) and quality assurance for AI systems (QA4AI). His laboratory provides two main research directions: generative AI for trustworthy software engineering and testing/trust exploration for AI systems, with specific focus on autonomous driving and deep learning applications.

Charles Louis Feffermanمشاهده پروفایل

استاد

• Mathematical Analysis
• Harmonic Analysis
• Partial Differential Equations
• +۷ مورد دیگر

Charles Louis Fefferman is the Herbert E. Jones, Jr. '43 University Professor of Mathematics at Princeton University, where he has held a faculty position since 1977. Previously, he served as a full professor at the University of Chicago from 1971 to 1977, becoming the youngest full professor in U.S. history at age 22. His academic journey began at the University of Maryland, College Park, where he earned his undergraduate degree at 17 before completing his PhD at Princeton under Elias Stein at age 20. Fefferman's research spans mathematical analysis with particular emphasis on harmonic analysis, partial differential equations, and complex analysis. His groundbreaking work on singular integrals, Hardy spaces, and the Bergman kernel revolutionized these fields, leading to his Fields Medal in 1978. More recently, he has made significant contributions to Whitney extension problems, fluid dynamics singularity formation, and mathematical aspects of topological materials. His publication record shows remarkable consistency over five decades, with recent work (2019-2023) focusing on manifold learning, smooth function interpolation, and quantum systems. These publications demonstrate both theoretical depth and increasing connections to data science applications, maintaining his position at the forefront of mathematical research. Fefferman's scientific honors form an exceptional constellation of recognition: Fields Medal (1978) Alan T. Waterman Award (1976, inaugural recipient) Salem Prize (1971) Bergman Prize (1992) Bôcher Memorial Prize (2008) Wolf Prize in Mathematics (2017) BBVA Foundation Frontiers of Knowledge Award (2021) As an advisor, Fefferman has mentored numerous doctoral students who have become leaders in their fields, including Matei Machedon, Luis Seco, and Michael Christ. His research group continues to explore fundamental questions in analysis while developing mathematical frameworks for emerging applications in data science and quantum physics. Fefferman remains actively engaged in research, with publications through 2023 demonstrating his continued intellectual vitality and mathematical creativity.

Héctor Gibrán Ceballos-Cancinoمشاهده پروفایل

استاد

• Social Network Analysis
• Machine Learning
• Process Mining
• +۵ مورد دیگر

Héctor Gibrán Ceballos-Cancino serves as Director of the Living Lab & Data Hub of the Institute for the Future of Education (IFE) at Tecnologico de Monterrey and is a full-time faculty member of the Computer Science Graduate Program (DCC). Previously, he led the Scientometrics office at the Research Vice-Rectory of Tecnologico de Monterrey for 18 years, establishing himself as a key figure in research evaluation at the institution. His educational background includes: Computer Systems Engineer from Instituto Tecnológico De Veracruz Master of Science with specialization in Intelligent Systems from Tecnologico de Monterrey PhD in Information Technology and Communications from Tecnologico de Monterrey (2010) Dr. Ceballos-Cancino's research program bridges artificial intelligence with practical applications in education and organizational contexts. His work spans Social Network Analysis, Machine Learning, Process Mining and Agent theory, applied specifically to Research Analytics and Educational Data Mining. He has developed significant expertise in applying AI techniques to solve real-world problems across multiple domains, with particular focus on educational improvement and research evaluation. Analysis of his recent publications reveals a strong trajectory toward quantum machine learning applications for education, learning analytics in Latin American contexts, and sophisticated student success prediction models. His work demonstrates consistent innovation in applying advanced computational techniques to educational challenges, while also maintaining strong contributions to scientometrics and building technology applications. His scientific recognition includes: Premio Tecnos 2023 conferred by Gobierno de Nuevo León Mexican Researcher Certification - Level 1 Member of the Mexican National System of Researchers (SNI) Adherent member of the Mexican Academy on Computing (AMEXCOMP) Dr. Ceballos-Cancino has supervised thesis work as evidenced by his teaching activity in 'Design of Intelligent Agents Thesis II.' His research has been supported by institutional initiatives at Tecnologico de Monterrey, including predictive analytics platforms for public administration evaluation. He has also provided expert consulting services for banks and IT companies, and promoted Semantic Web technologies adoption across multiple sectors. As Director of the Living Lab & Data Hub, he leads initiatives focused on 'Engaging and Motivating Learning Models,' connecting his technical expertise with educational innovation. His work aligns with multiple UN Sustainable Development Goals, particularly Quality Education (SDG 4) and Industry, Innovation and Infrastructure (SDG 9), demonstrating his commitment to technology for social impact.

Luciano Baresiمشاهده پروفایل

استاد

• Software Engineering
• Self-Adaptive Systems
• Edge Computing
• +۵ مورد دیگر

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

• Cybersecurity
• Cloud/Edge Computing
• Mobile Computing
• +۳ مورد دیگر

Mengjun Xie serves as Professor and Head of the Department of Computer Science & Engineering at the University of Tennessee at Chattanooga (UTC), holding dual distinguished titles as Guerry Professor and UC Foundation Professor. He directs the UTC InfoSec Center, a hub for cybersecurity research and education, and teaches advanced courses including Algorithm Analysis, Network Security, and Software Engineering. His leadership spans departmental administration, curriculum development, and strategic initiatives in computer science education. Education: PhD in Computer Science, College of William and Mary, 2010 (Supporting Areas: Cybersecurity) Dr. Xie's research spans Cybersecurity, Cloud/Edge Computing, Mobile Computing, Social Network Analysis, Big Data Analytics, and Computer Education. His work focuses on practical security solutions for IoT systems through knowledge graph applications, remote live forensics for Android devices, and blockchain-based IoT platforms. He pioneers educational tools for hands-on cybersecurity training, emphasizing privacy technologies and cloud-based lab development. His publications demonstrate consistent innovation at the intersection of theoretical research and real-world implementation. Recent publications (2022-2024) reveal a strong trend toward knowledge graph applications in cybersecurity, particularly for IoT forensics and log anomaly detection. His research bridges cloud security challenges with educational outreach, evidenced by developments like ForensiQ for IoT forensics and hands-on lab materials for privacy education. This dual focus on cutting-edge security research and pedagogical innovation defines his scholarly impact. Dr. Xie actively mentors students through thesis and dissertation supervision and serves as Faculty Advisor for UTC MocSec Cyber Defense since 2018. His service portfolio includes: Faculty Member, UTC Quantum Initiative (2022-Present) Committee Member, UTC Faculty Senate (2020-2024) Project Supervisor, UTC Employee COVID-19 Self-Check Application (2020-2021) Committee Chair, UTC CAE-CD Re-designation Committee (2022-2023) Committee Member, CECS Outreach and Research Committee (2023-2025) As Director of the InfoSec Center , he leads a multidisciplinary team advancing cybersecurity research, education, and community engagement through the CAE-CD program, industry partnerships, and national collaborations including NSA-sponsored initiatives.