جست‌وجوی استاد و پژوهشگر

Şahin Serhat Şekerمشاهده پروفایل

استاد

• Electric Motor
• Induction Motor
• Vibration Signal
• +۵ مورد دیگر

Şahin Serhat Şeker is a Professor in the Department of Electrical Engineering at Istanbul Technical University (ITU), Istanbul, with continuous research activity since 1993. He maintains an active publication record through 2025, demonstrating ongoing academic engagement, and has accumulated 111 research outputs with 1084 citations and an h-index of 19. His research centers on power system analysis and electric motor diagnostics, specializing in vibration signal processing for fault detection in induction motors. He employs continuous wavelet transform for non-stationary signal analysis to identify bearing damage, aging processes, and fault signatures. Recent work integrates machine learning for renewable energy forecasting and power grid stability, reflecting a strategic shift toward AI-driven solutions for energy infrastructure challenges. Professor Şeker's 2023-2025 publications reveal a strong trend toward deep learning applications in transient instability prediction, solar irradiance forecasting, and photovoltaic system optimization. His work increasingly addresses real-world energy challenges in regions like Djibouti, focusing on grid stability and renewable integration through hybrid modeling approaches. He has supervised 13 students and secured research funding through two major projects: SENSOR VALIDATION AND FUSION FOR SYSTEM MONITORING (2018-2019): Developed sensor validation frameworks for industrial system monitoring Analysis of Ferroresonance in Power Systems Using Multiresolution Wavelet Analysis and Extraction of Nonlinear Properties (2013-2018): Investigated nonlinear phenomena in power grids through advanced signal processing

Rebecca Alexanderمشاهده پروفایل

استاد

• Biochemistry
• Molecular Biology
• RNA Biology
• +۷ مورد دیگر

Rebecca Alexander is a Professor in the Department of Chemistry at Wake Forest University and currently serves as the Associate Dean for Research and Community Engagement. Her research focuses on the molecular mechanisms of protein synthesis, particularly the role of tRNA-acting enzymes in establishing and maintaining the genetic code. Education: B.S., University of Delaware, 1990 (Honors) Ph.D., University of Pennsylvania, 1996 (Advisor: Barry S. Cooperman) Postdoctoral Research Associate, MIT, 1996–1997 (Advisor: Paul Schimmel) Postdoctoral Research Associate, The Scripps Research Institute, 1997–1999 (Advisor: Paul Schimmel) Her research interests lie at the intersection of biochemistry, molecular biology, and biophysics , with a focus on aminoacyl-tRNA synthetases, tRNA modification, translational fidelity, RNA dynamics, and molecular evolution . She employs both experimental and computational approaches to understand how these enzymes recognize their substrates and maintain accuracy in protein synthesis. Her work has implications for understanding fundamental biological processes, disease mechanisms, and the origins of life. The analysis of her recent publications reveals a consistent focus on tRNA and synthetase function, with increasing integration of computational methods such as molecular dynamics. Her work spans from mechanistic enzymology to evolutionary questions about the genetic code. Key themes include domain communication in synthetases, tRNA structural dynamics, and the role of RNA modifications in cellular adaptation. Scientific Awards: Robert P. and Debra Lee Faculty Fellowship (2008–2011) F. M. Kirby Family Faculty Fellowship (2014–2017) While specific names of her advisees are not listed in the provided text, her extensive publication record with multiple collaborators and first-author students suggests active mentorship in research. She has not mentioned external grant funding in the provided text, but her sustained research output and fellowships indicate strong institutional and likely external support. Her role as Associate Dean highlights leadership in research administration and community engagement. Her research is conducted within the Department of Chemistry at Wake Forest University, likely involving a dedicated laboratory team focused on RNA-protein interactions and enzyme mechanisms. She collaborates with computational and structural biologists, as evidenced by co-authored works involving molecular dynamics and biophysical analysis.

James Davisمشاهده پروفایل

استاد

• Algebra
• Combinatorics
• Number Theory
• +۴ مورد دیگر

James Davis is a Professor in the Department of Mathematics at the University of Richmond, School of Arts and Sciences. He is actively engaged in research and teaching, with office located in Jepson Hall and contactable via email jdavis@richmond.edu and phone (804) 289-8094. Ph.D. in Mathematics, University of Virginia, 1987 M.S. in Mathematics, University of Virginia, 1985 B.S. in Mathematics, Lafayette College, 1983 His research focuses on algebraic combinatorics, particularly difference sets, bent functions, and error-correcting codes, with a goal of discovering new constructions with optimal or near-optimal structures. His work bridges theoretical mathematics and practical applications in coding and information theory. Although no explicit publications are listed in the provided text, his research interests suggest a strong output in combinatorial design theory and algebraic coding. His patents and industrial experience at Hewlett-Packard reflect applied impact. Dr. Davis has served as a testifying expert in patent litigation between Apple and Samsung and holds 15 U.S. patents in coding and memory systems. K.K. Smith, J. Jedwab, J.A. Davis, D. Banks and S.R. Wyatt, System for error correction coding and decoding, U.S. Patent No. 7418644, 26 Aug 2008. D. Banks, J. Davis, and J. Jedwab, Identifying uncorrectable codewords in a Reed-Solomon decoder for errors and erasures, US Patent No. 7278086, 2 October 2007. J. Davis, K. Eldredge, J. Jedwab, G. Seroussi, and K. Smith, MRAM with controller, US Patent No. 7266732, 4 September 2007. He advises students indirectly through academic materials and summer research programs, as suggested by files like krantz_phd_advice.pdf and directories such as summerwork/ and problemoftheweek/ . He is involved in institutional service, including the General Education Committee. His research has been supported through academic and industrial grants, though specific grant names are not listed. Dr. Davis maintains a personal academic website with teaching materials, research notes, and committee work, indicating active participation in both pedagogy and academic leadership.

Omar Fawziمشاهده پروفایل

پژوهشگر

• Quantum Information Theory
• Theoretical Computer Science
• Quantum Algorithms
• +۴ مورد دیگر

Omar Fawzi is a Research Director (Directeur de Recherche) at Inria, heading the QInfo team at École Normale Supérieure de Lyon. His primary roles include leading research in quantum information theory and theoretical computer science, with a focus on quantum algorithms, error-correcting codes, and quantum cryptography. He has held academic positions since 2011, including teaching at McGill University and ENS Lyon. His research interests span quantum information theory, theoretical computer science, and their applications. He has contributed to foundational work on quantum channel capacities, entropy accumulation theorems, and quantum error correction codes like quantum expander codes. His work bridges theoretical insights with practical implementations, including fault-tolerant quantum computing and device-independent cryptography. Fawzi has advised numerous PhD students and postdoctoral researchers, with students such as Aadil Oufkir (now at RWTH Aachen) and Antoine Grospellier (teaching in France). He has led major grants including the ERC Starting Grant AlgoQIP and the ANR-18-CE47-0011 ACOM project. His research team focuses on advancing quantum communication protocols, quantum algorithms, and the mathematical foundations of quantum mechanics. Key contributions include the entropy accumulation theorem, variational bounds on quantum divergences, and efficient simulation methods for quantum systems. His work emphasizes interdisciplinary approaches, combining tools from computer science, mathematics, and physics to tackle fundamental quantum information challenges.

• Differential Transform Method
• Boundary Value Problems
• Approximate Analytical Solution
• +۳ مورد دیگر

Muhammed Kurulay is a Professor in the Department of Mathematics Engineering at Istanbul Technical University with an active research profile showing 41 documented publications and an h-index of 12 with 512 citations according to Scopus data. His primary research fingerprint reveals deep expertise in: Differential Transform Method (100% relevance) Boundary Value Problems (100%) Approximate Analytical Solutions (89%) Homotopy Analysis Method (74%) Fractional Partial Differential Equations (67%) Professor Kurulay's recent work (2023-2025) demonstrates remarkable interdisciplinary reach, bridging pure mathematics with practical applications across diverse fields. His publication pattern shows consistent scholarly output with notable spikes in 2012 (10 publications) and recent years, indicating sustained research productivity throughout his career. His scientific impact spans multiple domains: His plant disease detection research has achieved 30 citations and 141 reader captures, indicating significant agricultural technology impact Quantum error correction work has garnered 9 citations with notable interdisciplinary interest Virus spread modeling publications show medical relevance with news mentions Professor Kurulay maintains an extensive international collaboration network with co-authors from Turkey, Algeria, Saudi Arabia, and other regions, reflecting his global research engagement across computer science, mathematics, and life science applications.

Eugenia Chiappeمشاهده پروفایل

پژوهشگر

• Neuroscience
• Sensorimotor Integration
• Neural Circuits
• +۳ مورد دیگر

Eugenia Chiappe is a Principal Investigator at the Champalimaud Center for the Unknown , leading the Chiappe Lab . Her research focuses on the neural circuits involved in self-movement estimation and sensorimotor integration in Drosophila melanogaster , combining behavioral paradigms , neural activity recording , and genetic tools to uncover mechanistic explanations for brain function in adaptive behaviors. Research highlights : Investigation of HS and VS cells in flies as self-motion estimators . Development of head-fixed walking paradigms for reversible neural perturbation. Identification of nonvisual signals modulating visual responses during locomotion. Exploration of neural dynamics encoding combined visual and motor inputs. Scientific contributions : Marie Curie Career Integration Grant PCIG13-GA-2013-618854 Bial Foundation grant 191/12 Collaborative projects on state-dependent sensory processing (Journal of Neuroscience, 2017) Advising and Collaborations : Mentored Daniel Tendero (External PhD Student) and Miguel Paço (INDP PhD Student). Collaborations with teams at Yale University , MIT , and University College London .

• Algebra and Number Theory
• Information and Coding Theory
• Information Security and Cryptology
• +۲ مورد دیگر

Dr. Fatih Temiz serves as an Assistant Professor at Uskudar University, specializing in mathematical foundations of information security and coding theory. His work bridges abstract algebra with practical cryptographic applications, focusing on number-theoretic approaches to data protection. His academic journey includes: B.Sc. in Mathematics (English) from Dokuz Eylul University (2010) M.Sc. in Mathematics from Yildiz Technical University (2012) Ph.D. in Mathematics from Yildiz Technical University (2018) Research centers on algebraic structures for secure information systems, with emphasis on cellular automata applications in secret sharing and cryptographic protocol design. His investigations into cyclic codes and enumeration techniques advance error-correction methodologies for modern communication networks, demonstrating consistent focus on theoretical underpinnings of data security. His single documented publication in the Journal of Symbolic Computation (2023) reveals strong thematic alignment with algebraic coding theory, indicating specialized expertise in symbolic computation for cryptographic applications despite limited output volume. This work establishes foundational connections between finite field theory and practical code enumeration. Dr. Temiz actively supervises graduate research and serves as academic adviser for 151-200 students since 2021. He concurrently participates in institutional governance as a member of the Education Commission (Institute/Faculty level), contributing to curriculum development and academic policy since 2021.

• Machine Learning
• Multi-class Classification
• Neural Networks
• +۳ مورد دیگر

Rimantas Kybartas , Associate Professor at Vilnius University's Faculty of Mathematics and Informatics , specializes in machine learning and software systems architecture . His research focuses on multi-class classification methodologies, including pair-wise classifiers and fuzzy template systems. Current academic affiliation: Vilnius University Key research domains: Neural Networks, Ensemble Learning, Pattern Recognition Teaching focus: Software Systems Architecture and Design His publication record from 2010-2012 demonstrates expertise in solving multi-classification challenges through innovative ensemble architectures and similarity feature engineering. Notably, he has developed frameworks for mineral recognition and generalized multi-category neural network systems. Recent publications reveal emphasis on: Optimizing pair-wise classifier ensembles Addressing complexity in neural network design Domain adaptation techniques for classification tasks Statistical learning in multi-class contexts

• Computer Science
• Information Theory
• Machine Learning
• +۳ مورد دیگر

Daniel Cullina is an Assistant Professor in Electrical Engineering, specializing in theoretical computer science and machine learning. His research explores fundamental aspects of adversarial robustness, graph alignment, and information theory, with applications spanning cybersecurity and data science. Research Focus: Adversarial machine learning: Robustness guarantees, attack/defense strategies for classifiers Graph algorithms: Alignment and recovery in random graph models like Erdős-Rényi Information theory: Fundamental limits of database matching and Gaussian alignment Coding theory: Deletion error correction and converse bounds His publications (28+ with 575+ Scopus citations) demonstrate consistent contributions to understanding adversarial vulnerabilities in ML systems and combinatorial algorithms for graph/data matching. Recent work (2020-2023) focuses on theoretical characterization of optimal losses under attacks and database alignment frameworks. With an h-index of 12, his research output shows sustained productivity since 2012, peaking in 2016 (8 publications) and maintaining 3-5 annual publications in recent years.

• Quantum Computing
• Machine Learning
• Electrical Engineering
• +۵ مورد دیگر

Swaroop Ghosh is a Professor in Electrical Engineering, focusing on cutting-edge research at the intersection of Quantum Computing , Machine Learning , and Hardware Security . His work spans theoretical and applied domains, addressing challenges in quantum error correction, cybersecurity, and drug discovery. Research Trends: His recent publications emphasize Quantum Machine Learning (e.g., adversarial attacks, molecular modeling), Error Correction (surface codes, qubit quality optimization), and Security (cloud-based quantum systems, compiler vulnerabilities). Grants & Projects: He leads multiple National Science Foundation-funded initiatives, including Drug discovery using quantum machine learning and Securing Large-Scale Noisy-Intermediate Scale Quantum Computing . Collaborations: His projects involve cross-disciplinary partnerships with experts in physics, computer science, and cybersecurity. Though no explicit students or awards are listed in the provided text, his prolific research output and leadership in quantum technologies underscore his significant contributions to academia and industry.

Oliver T Johnsonمشاهده پروفایل

استاد

• Information Theory
• Probability Theory
• Statistics
• +۴ مورد دیگر

Professor Oliver T Johnson is Head of the School of Mathematics at the University of Bristol. His research spans probability theory, statistics, and information theory, with both theoretical and applied focuses. Develops theoretical frameworks for entropy properties in limit theorems (Central Limit Theorem, Poisson convergence) Works on group testing algorithms and their information-theoretic capacities Applies information theory to communication systems (interference alignment, spectrum sensing) His research outputs include foundational work on discrete analogues of entropy inequalities and converse bounds for inference problems. Current projects explore efficient algorithms for change-point detection and information geometry of graphs. He actively supervises PhD students and engages in public outreach through media appearances and educational talks. Recent collaborative projects include: Interdisciplinary work with Electrical Engineering and Computer Science Investigations into discrete transport equations and Shepp-Olkin conjectures Applications of group testing to spectrum sensing and epidemic modeling

John R. Barryمشاهده پروفایل

استاد

• Communication Theory
• Wireless Communications
• Optical Communications
• +۴ مورد دیگر

John R. Barry is a Professor at the School of Electrical and Computer Engineering, Georgia Institute of Technology. He holds a Ph.D. in Electrical Engineering from UC Berkeley (1992) and previously worked at Bell Communications Research, IBM, Hughes Aircraft, and General Dynamics. His research spans communication theory , wireless systems , optical communications , and magnetic recording , with a focus on modulation, coding, synchronization, and equalization . Education : B.S., State University of New York at Buffalo (1986) M.S., University of California at Berkeley (1987) Ph.D., University of California at Berkeley (1992) His recent journal and conference publications address multi-cell MIMO , polar codes for recording , two-dimensional magnetic recording , and joint radar-communication systems . Articles span IEEE Transactions on Magnetics , IEEE Transactions on Communications , and Globecom conferences, covering topics like timing recovery at low SNR , iterative equalization , and space-time coding . Scientific Awards : David J. Griep Memorial Prize (1992) Eliahu Jury Award (1993) NSF Research Initiation Award (1993) IBM Faculty Development Award (1993) Senior Member, IEEE He has advised 15 Ph.D. students and 2 M.S. students, including Hyuncheol Park , Chen-Chu Yeh , and Elnaz Sadeghian , all pursuing careers in academia and industry. His lab has received grants for iterative timing recovery , multi-input multi-output (MIMO) systems , and optical wireless communication . Professor Barry's research group has contributed to patents in timing recovery and MIMO detection, with applications in wireless and optical systems. He served as Guest Editor for the IEEE Journal on Selected Areas in Communications and Technical Program Chair for IEEE Globecom 2013 .

Håkan Grahnمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Systems
• GPU Computing
• +۷ مورد دیگر

Håkan Grahn is a Professor of Computer Engineering at the Department of Computer Science, School of Computing, Blekinge Institute of Technology (BTH) in Sweden. He has been a faculty member since 1996, becoming a full professor in 2007. His academic leadership includes serving as Head of Department (1999-2002) and Dean of Research (2011-2013) at BTH. He leads multiple significant research projects including GPAI (General Purpose AI Computing) and Green Clouds, with funding from ELLIIT, the Knowledge Foundation, and Vinnova. His educational background includes: M.Sc. in Computer Science and Engineering (1990) from Lund University Ph.D. in Computer Engineering (1995) from Lund University Håkan's research spans several interconnected domains in computer science and engineering, with a strong emphasis on practical applications. His work in computer architecture focuses on optimizing system performance through innovative cache coherence protocols and memory management techniques. In the realm of parallel computing , he investigates multicore systems, GPU computing, and thread-level speculation to enhance computational efficiency. His research in AI and machine learning addresses energy efficiency, data stream mining, and practical applications in areas like district heating systems and airborne networks. The integration of image processing with machine learning forms another significant strand of his work, particularly in historical document analysis and medical imaging applications. These research areas converge in his leadership of major initiatives like BigData@BTH and GPAI, where he bridges theoretical advances with real-world implementation challenges. Analysis of Håkan's recent publications reveals a clear trajectory toward increasingly applied research with strong industry connections. While maintaining foundational work in computer architecture, his output increasingly focuses on practical AI applications, energy efficiency in computing, and domain-specific implementations in sectors like telecommunications, energy systems, and defense. The interdisciplinary nature of his work is evident in collaborations spanning computer science, engineering, and domain-specific applications, with a growing emphasis on sustainability and resource optimization in computing systems. Håkan has successfully supervised numerous doctoral students, with ten graduates and six current Ph.D. candidates. His research has been supported by substantial funding from: The Knowledge Foundation (BigData@BTH, HINTS, Green Clouds) ELLIIT (GPAI project) Vinnova (FANET-MCA, Directed COM & EW) Industry partners including Ericsson, Saab, Telenor, and Fortnox He is actively involved in multiple research groups including DISL (Distributed and Intelligent Systems Lab), CCS-Lab (Communication and Computer Systems Research Lab), and previously PAARTS (Parallel Architectures and Applications for Real-Time Systems). His leadership extends to organizing academic events like the Nordic workshop on Multi-Core Computing and the Swedish Artificial Intelligence Society workshop.

Elahe Mehrabanمشاهده پروفایل

مدرس

• Number Theory
• Group Theory
• Cryptography
• +۱ مورد دیگر

Dr. Elahe Mehraban is a Lecturer and Researcher at Near East University's Department of Mathematics, where she has been employed since September 2023. She maintains additional academic ties as a Graduate Student Advisor at the University of Guilan in Iran. Education background: BS in Applied Mathematics, Payam Noor University, Iran (2006) MS in Mathematics (Group Theory), University of Guilan, Iran (2011) PhD in Mathematics (Algebra), University of Guilan, Iran (2021) Her mathematical research spans both theoretical and applied domains, with concentrated expertise in: Algebraic Structures : Group Theory and related abstract algebra foundations Mathematical Applications : Cryptographic systems and error-correcting codes through Coding Theory Advanced Number Theory : Investigating properties of integers and their extensions While specific publication details aren't provided, her collaborative works demonstrate consistent scholarly output in ISI-indexed mathematics journals, particularly exploring connections between abstract algebra and computational applications.

• Tax Law
• Public Law
• Criminal Tax Law
• +۳ مورد دیگر

Prof. Bekir Mahmut Khashoggi is a distinguished Professor at Istanbul University, Faculty of Law, Department of Public Law with specialization in Tax Law. He has served continuously at Istanbul University since 1987, progressing through academic ranks from Research Assistant to his current position as Professor (since 2017). His extensive career includes service on the University Executive Board (2009-2015) and decades of contributions to Turkish tax law scholarship. Prof. Khashoggi's educational background includes: Doctorate (1991-1999) - Istanbul University, Institute of Social Sciences, Public Law Postgraduate studies (1987-1991) - Istanbul University, Institute of Social Sciences, Public Law Undergraduate degree (1983-1987) - Istanbul University, Faculty of Law His research focuses on Tax Law with particular expertise in Tax Evasion, Tax Procedure Law, Customs Law, and Comparative Tax Systems between Turkey and France. Prof. Khashoggi is fluent in English and French at C1 Advanced level, enabling his comparative legal studies and international collaborations. His scholarly work bridges theoretical legal frameworks with practical tax administration challenges in Turkey. Prof. Khashoggi has supervised 13 graduate theses covering diverse aspects of Turkish tax law, including tax information sharing, taxpayer documentation obligations, capital market violations, and tax jurisdiction principles. His academic advising reflects his comprehensive approach to tax education across multiple dimensions of tax administration and compliance. His scholarly output demonstrates consistent engagement with evolving tax challenges, particularly in digital taxation, constitutional dimensions of tax rights, and comparative analyses of tax enforcement mechanisms. Prof. Khashoggi's work addresses both theoretical legal frameworks and practical implementation issues in Turkish tax administration. Among his notable contributions are publications on force majeure in tax law, digital service taxation in Turkey, and critical examinations of tax smuggling crimes under Turkish Penal Code. His scholarly impact extends through numerous peer-reviewed journal articles, books including "Vergi Ceza Hukuku" (Tax Criminal Law), and presentations at national conferences addressing contemporary tax challenges and legal reforms.