استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۶۷۶-۶۹۰ از ۶۹۹ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
دانشیار
Felix Dobslaw is a Senior Lecturer and Associate Professor at Mid Sweden University , affiliated with the Department of Communication, Quality Technology and Information Systems (KKI). He leads the cross-disciplinary Software Engineering and Education (SEE) research group, focusing on the intersection of technology and human use in software development, with a particular emphasis on Generative AI applications.
Natalia Seoane Iglesias serves as an Associate Professor in the Department of Applied Physics at the University of Santiago de Compostela's College of Physics. Her research integrates semiconductor device physics with high-performance computing to address challenges in next-generation electronics and energy conversion systems. B.Sc. in Physics, University of Santiago de Compostela (Spain) Ph.D. in Physics, University of Santiago de Compostela (2007) Postdoctoral research: University of Glasgow (2007-09), University of Edinburgh (2011), Swansea University (2013-15) Her research focuses on semiconductor device simulation , nanoscale variability analysis , and laser power conversion systems . She develops advanced computational tools combining 3D finite-element modeling with machine learning techniques to optimize device performance. Current projects target ultra-high efficiency (>80%) SiC-based laser converters for space applications and statistical variability studies in sub-10nm transistor architectures. Her publication portfolio reveals strong trends in machine learning-enhanced TCAD and high-concentration photovoltaics . Recent work demonstrates how vertical epitaxial heterostructures with SiC/GaN materials can overcome traditional efficiency barriers in wireless power transfer systems, while her nanoscale variability studies provide critical insights for future CMOS scaling. Key scientific contributions include: Development of MLFoMpy for semiconductor data post-processing Novel Pelgrom-based predictive models for device variability Breakthrough laser power converter architectures exceeding 80% efficiency Comprehensive studies of metal grain effects in nanosheet FETs She leads the rePowerSiC project (2024-2028) developing space-qualified laser power systems and contributes to multiple EU-funded initiatives in high-performance computing. Her team employs advanced simulation frameworks including VENDES and Silvaco Atlas for device characterization, with applications ranging from satellite power systems to refinery monitoring drones.
Geir Kjetil Ferkingstad Sandve is a Professor at the Biomedical Informatics Research Group within the Department of Informatics (UiO), University of Oslo, specializing in machine learning methodology for biomedical applications. His research focuses on Generalization in machine learning models Exploiting mechanistic/causal domain relations Software platforms for domain-adapted ML Multiple-instance learning techniques Historical work in statistical genome analysis and motif discovery His recent publications (2022-2025) demonstrate expertise in Adaptive immune receptor repertoire analysis Antibody-antigen interaction prediction Proteome scoring systems for celiac disease Causal modeling in biomarker generalization Multi-modal single-cell data integration Synthetic dataset generation for immunology He leads a computational research group prioritizing Reproducible codebases and software development Competence-building through strategic research Interdisciplinary collaboration with biomedical teams Training in algorithmic challenges and software design
Konstantin Korovin is a Reader at the Department of Computer Science, The University of Manchester. He has held various academic roles including Senior Lecturer (2015-2023), Royal Society University Research Fellow (2007-2015), and Research Associate (2004-2007). Current research focuses on automated theorem proving , machine learning integration , and verification of hardware/software . His work includes developing systems like iProver , iProver-ML , and SMLP , which combine formal methods with ML techniques. Key contributions span non-linear constraint solving , quantified Boolean logic , and DNA computing . He has won over 20 international awards, including SMT-COMP and CASC categories. Scientific Awards : Ackermann Award, Best Thesis Prize, Best Paper at FroCoS'19, CASC and SMT-COMP prizes. He supervises PhD and postdoc researchers, with alumni working at Intel, Google, and MathWorks. His tools are applied in industry, notably by Intel for hardware optimization.
Dr. C. (Chrysa) Papagianni is an Assistant Professor at the Informatics Institute within the Faculty of Science, Mathematics and Computer Science at the University of Amsterdam . She holds a Ph.D. in Electrical and Computer Engineering from the National Technical University of Athens and has held academic and research positions at institutions including the University of Maryland, Hellenic Open University, and Nokia Bell Labs (Belgium). Ph.D., Electrical and Computer Engineering, NTUA (2009) M.Sc., Information Systems (2017-2019), Hellenic Open University Her research focuses on network softwarization , SDN , network slicing , and AI/ML-driven network intelligence for next-generation networks. Recent work explores machine learning for resource allocation , programmable data planes , and intent-based networking . Her publications span journals like IEEE Transactions on Network and Service Management and conferences including IEEE ICC and IEEE CoNEXT . Key projects include NSF FABRIC , H2020 FED4FIRE+ , and 5Growth platform development. She contributes to technical standards in edge computing and cloud-native networks via 5GPPP white papers.
عضو هیئت علمی
Lazar Krstic is an Assistant at the Institute of Mathematics and Informatics within the Faculty of Science and Mathematics at the University of Kragujevac. His research focuses on Artificial Intelligence and Machine Learning, operating in the Informatics field. He completed his Master's degree in 2017 and earned his Doctorate in 2019. His work integrates advanced computational techniques with applications in medical physics, education, and environmental modeling. Research Trends: Recent publications highlight expertise in radiation therapy optimization, physics-informed neural networks, and evolutionary algorithms. Articles demonstrate interdisciplinary applications spanning healthcare, education, and computational hydrology.
استادیار
Dan Fu is an Assistant Professor at the University of California, San Diego (UCSD) in the Computer Science and Engineering Department and a Distinguished Research Scientist at Together AI. He leads the SandyResearch Lab and is affiliated with the MLSys group, focusing on making machine learning models faster and more efficient through hardware-aware algorithms and subquadratic architectures. Research interests include: Efficient ML architectures (Chipmunk, Hyena, Monarch Mixer) Hardware-aware systems algorithms (ThunderKittens, FlashAttention) Long-sequence modeling and GPU optimization His recent work spans training-free Transformer acceleration (Chipmunk), convolutional language models (Hyena), and hardware-aware attention optimizations (FlashAttention). Projects are deployed in production at Together AI and integrated with frameworks like PyTorch. Scientific awards include: NDSEG Fellowship (2025) Best Paper at ICML Hardware Aware Workshop (2022) Best Student Paper Runner Up at UAI (2022) Best Poster at ENLSP Workshop (NeurIPS 2023) He contributes to open-source projects (Safari repository, FlyingSquid) and teaches courses on machine learning systems (Stanford CS 324/528, Harvard CS 61/152).
Prof. Dr. Virginijus Marcinkevičius is a Professor at Vilnius University , serving as the head of the Smart Technologies Research Group and the Artificial Intelligence Laboratory within the Institute of Data Science and Digital Technologies . He is also a Senior Researcher , Project Lead Researcher , and Group Leader . Based in Vilnius, Lithuania, he has been instrumental in advancing research in machine learning , artificial intelligence , cybersecurity , and natural language processing . Research Interests: Machine Learning & AI Cybersecurity & Threat Detection Natural Language Processing Hyperspectral Imaging & Remote Sensing Autonomous Systems & Robotics Big Data & Cloud Computing His work spans both theoretical and applied aspects, including IoT security , visual analytics , and intelligent decision support systems . Recent projects include the development of propaganda detection systems , hyperspectral unmixing algorithms , and autonomous driving agents . Doctoral Supervision: He has supervised 19+ PhD students and 5+ consultants , covering topics from machine learning in cybersecurity to neural machine translation and autonomous UAV navigation . Projects & Grants: He has led or contributed to 15+ national and EU-funded projects , including: CognitiveSTATS – COVID-19 data literacy platform Propaganda and Disinformation Research – ML-based detection DAMIS – Data mining system for national research Raštija 2 – Lithuanian language resource integration Publications: He has authored or co-authored 60+ peer-reviewed publications in journals like IEEE Access , Informatica , Frontiers in Psychology , and Machine Vision and Applications . Professional Memberships: He is a member of the Lithuanian Computer Society , Lithuanian Mathematical Society , and Lithuanian Operations Research Society .
Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.
دانشیار
Gang Tan is an Associate Professor at the Pennsylvania State University's College of Engineering, Department of Computer Science and Engineering. He also holds the James F. Will Career Development Professorship and is affiliated with the Institute for Computational and Data Sciences (ICDS). His research focuses on binary reverse engineering , cybersecurity , Internet of Things (IoT) security , machine learning fairness , and information flow security . He has led numerous NSF-funded projects, including work on precise binary analysis, IoT policy enforcement, and automated fairness repair in AI systems. Recent work trends include memory safety validation , pseudocode extraction , and control-flow integrity mechanisms. His 127+ research outputs reflect deep engagement with static program analysis , cache side-channel detection , and secure kernel-driver interfaces . Scientific Awards: James F. Will Career Development Professorship Gang Tan has secured multiple grants from the National Science Foundation (NSF) and U.S. Navy for projects like Sliver (information flow verification) and Semantics-Directed Binary Reverse Engineering . His work involves advising teams on IoT safety, and he has 19 active or completed grants since 2008.
Elisa Bertino is the Samuel D. Conte Professor of Computer Science at Purdue University, where she also directs the Purdue Cyberspace Security Lab (Cyber2Slab) and serves as Research Director at CERIAS. Since joining Purdue in January 2004, her work has integrated rigorous theory with practical solutions across information security, database systems, and emerging cyber-physical domains. Education Ph.D. in Computer Science, University of Pisa (1980) Research Interests Professor Bertino’s research portfolio is exceptionally broad, spanning information security and assurance , database and data mining technologies , and AI-driven cybersecurity . She pursues foundational advances in access-control models (RBAC, ABAC, trust negotiation), secure data publishing and broadcast protocols for XML and streaming data, privacy-preserving analytics through differential privacy and secure multi-party computation, and zero-trust architectures for 5G/6G and software-defined networks. Application domains include secure mobile and IoT ecosystems, medical informatics, and humanities data. Recent Publication Trends Her 2024–2025 publications reveal a strategic pivot toward next-generation network security (5G/6G, SDN/NFV), trustworthy AI (federated learning, transformer-based malware analysis), and privacy technologies (differential privacy, homomorphic encryption). A recurring theme is the rigorous integration of AI techniques with cryptographic and systems-level defenses to achieve scalable, privacy-preserving security solutions. Scientific Awards & Honors IEEE Fellow ACM Fellow IEEE Computer Society Technical Achievement Award (2002) IEEE Tsutomu Kanai Award (2005) ACM Athena Lecturer Award (2019) Kristian Beckman Award (2020) IEEE 2021 Innovation in Societal Infrastructure Award Advising & Grants Professor Bertino has mentored a large cohort of doctoral students and post-doctoral researchers. Recent externally funded projects include NSF “Privacy-Enhanced Secure Data Provenance” (2011-2016), NIST “Advancing Commercial Participation in the NSTIC Ecosystem” (2012-2013), and Sypris Electronics “Security Techniques for Smart Mobile Devices” (2012). Laboratories & Teams She leads the Cyber2Slab (Cyber Space Security Lab), a vibrant research group investigating insider-threat mitigation, IoT and drone security, digital identity management, cloud data protection, and AI-assisted defense mechanisms. The lab collaborates extensively with federal agencies, industry partners, and international research consortia such as the Data Analytics and Information Science International Technology Alliance (DAIS ITA).
Professor Luca Fanucci is a Full Professor of Electronics at the Department of Information Engineering , University of Pisa. He serves as Rector's Delegate for Inclusion of Students with Disabilities and leads research in integrated circuits, embedded systems, and assistive technologies . Institutional roles include membership in the National University Conference of Delegates for Disability (CNUDD) and leadership in the PhD program in Information Engineering. Born: Montecatini Terme (1965) Education: Laurea in Electronic Engineering (1992), PhD in Information Engineering (1996), University of Pisa Professional Journey: ESA research (1992-1996), CNR researcher (1996-2004), University of Pisa faculty (2004-present) His research focuses on: System-level design of integrated circuits and embedded systems Hardware-software co-design for low power consumption Spacecraft and satellite communication systems Medical devices and telemonitoring platforms Assistive technologies for disabilities Recent publications highlight AI in space applications and telemedicine systems . Key projects include the Ingeniars spin-off for satellite communications and the AsTech National Laboratory for assistive technologies. Scientific Recognition : IEEE Fellow (2019) 40+ patents H-index 34 (5000+ citations) He coordinates international conferences (DATE, HiPEAC, Spacewire) and serves as Associate Editor for Technology and Disability and Microprocessors and Microsystems journals.
Faiza Allah Bukhsh is an Associate Professor specializing in Artificial Intelligence, Data Mining, Process Mining, Health Informatics, Cybersecurity, and Ethical AI. Her work bridges technical innovation with societal impact, particularly in healthcare systems analysis, telecommunications resilience, and ethical data governance. Digital Society Institute TechMed Centre Datamanagement & Biometrics Her research focuses on Explainable AI , Process Mining , and Privacy Assurance in healthcare systems, with recent work on AI music perception, sepsis treatment analysis, and privacy-utility trade-offs. Key article trends include: AI in music and creative domains Process Mining for healthcare insights Explainable Machine Learning workflows Privacy-preserving analytics Telcom infrastructure resilience
Dr. Shahin Mehdipour Ataee is an Assistant Professor at the State University of New York at Fredonia with teaching experience since 2007. His office hours are held Tuesdays (12:30-2:00 PM), Wednesdays (3:30-5:00 PM by appointment), and Thursdays (12:30-2:00 PM). Education: Ph.D. in Computer Engineering - Information Systems Track, Eastern Mediterranean University (2018) M.S. in Software Engineering, Qazvin Azad University (2010) B.S. in Computer Engineering - Software Track, Mazandaran University of Science and Technology (2007) His research focuses on Machine Learning , Deep Learning , and Semantic Web technologies, with applications in AI problem-solving, multimedia pattern recognition, and web service frameworks. Recent publications demonstrate expertise in logic-based systems and computational methods. Awards: Teaching Excellence Award 2024-2025 Teaching Excellence Award 2023-2024 Research Excellence Award 2022-2023 He teaches a broad spectrum of computer science courses including core subjects like Algorithms, Operating Systems, and specialized topics in AI/ML. While no active lab or grants are mentioned, his publication record indicates ongoing scholarly activity.
Dr. Lecturer İbrahim ŞANLIALP is a faculty member at the Faculty of Engineering and Architecture, Ahi Evran University, specializing in Computer Hardware and Software Engineering. He holds a Ph.D. (2022) and M.Sc. (2015) in Computer Engineering from Süleyman Demirel University, alongside a B.Sc. in Electronics and Computer Education (2010). Education: Ph.D. in Computer Engineering (Süleyman Demirel University, 2015-2022) M.Sc. in Computer Engineering (Süleyman Demirel University, 2012-2015) B.Sc. in Computer Engineering (Süleyman Demirel University, 2005-2010) Diploma in Electronics and Computer Education (Süleyman Demirel University, 2005-2010) His research focuses on Computer Software and Programming Languages , with recent studies analyzing energy efficiency in code refactoring, landslide prediction using statistical methods, and machine learning applications in public health. His work spans green computing , geospatial analysis , and AI-driven classification systems. Dr. Şanlıalp has contributed to 9 publications, including 2 journal articles (SCI-Exp, TR Dizin) and 7 conference papers. His projects include "3D Environment Path-Finding Algorithms for UAVs" (2025-2026) and "Grid-Based Path Planning Simulation" (2024-2025) as principal investigator. Metrics highlight 26 total citations (Google Scholar), h-index of 1, and active collaborations with researchers from Süleyman Demirel University, Ankara Yıldırım Beyazıt University, and Niğde Ömer Halisdemir University. His teaching portfolio includes courses like "Game Programming", "Computer Concepts", and "Cybercrime" since 2022.
